dopamine hypothesis

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The Dopamine Hypothesis of Schizophrenia – Advances in Neurobiology and Clinical Application

The dopamine hypothesis stems from early research carried out in the 1960’s and 1970’s when studies involved the use of amphetamine (increases dopamine levels) which increased psychotic symptoms while reserpine which depletes dopamine levels reduced psychotic symptoms.

The original dopamine hypothesis was put forward by Van Rossum in 1967 that stated that there was hyperactivity of dopamine transmission, which resulted in symptoms of schizophrenia and drugs that blocked dopamine reduced psychotic symptoms. [1]

DOPAMINE PRODUCTION AND METABOLISM

Dopamine is synthesised from the amino acid tyrosine. Tyrosine is converted into DOPA by the enzyme tyrosine hydroxylase.

DOPA is converted into dopamine (DA) by the enzyme DOPA decarboxylase (DOPADC).

This dopamine is packed and stored into synaptic vesicles via the vesicular monoamine transporter (VMAT2) and stored until its release into the synapse.

Dopamine Receptors:

When dopamine is released during neurotransmission, it acts on 5 types of postsynaptic receptors (D1-D5).

A negative feedback mechanism exists through the presynaptic D2 receptor which regulates the release of dopamine from the presynaptic neuron.

Dopamine Breakdown

Any excess dopamine is also ‘mopped up’ from the synapse by Dopamine transporter (DAT) and stored in the vesicles via VMAT2.

Dopamine is broken down by monoamine oxidase A (MAO-A), MAO-B and catechol-o-methyltransferase (COMT).

Learning points:

• Tyrosine hydroxylase is the rate-limiting step in the production of dopamine. Its expression is significantly increased in the substantia nigra of schizophrenia patients when compared to normal healthy subjects. [2]
• Carbidopa is a peripheral DOPA-decarboxylase inhibitor co-administered with levodopa. Carbidopa prevents the conversion of levodopa to dopamine in the periphery, thus allowing more levodopa to pass the blood-brain barrier to be converted into dopamine for its therapeutic effect.
• Methamphetamine increases extracellular dopamine by interacting at vesicular monoamine transporter-2 (VMAT2) to inhibit dopamine uptake and promote dopamine release from synaptic vesicles, increasing cytosolic dopamine available for reverse transport by the dopamine transporter (DAT).
• Valbenazine a highly selective VMAT2 inhibitor has been approved by the FDA for the treatment of tardive dyskinesia.
• There is compelling evidence that presynaptic dopamine dysfunction results in increased availability and release of dopamine and this has been shown to be associated with prodromal symptoms of schizophrenia. Furthermore, dopamine synthesis capacity has also been shown to steadily increase with the onset of severe psychotic symptoms. [3] , [Howes & Shatalina, 2022]

• Dopaminergic transmission in the prefrontal cortex is mainly mediated by D1 receptors , and D1 dysfunction has been linked to cognitive impairment and negative symptoms of schizophrenia . [4]

THE 4 DOPAMINE PATHWAYS IN THE BRAIN

1.The Mesolimbic Pathway

• The pathway projects from the ventral tegmental area (VTA) to the nucleus accumbens in the limbic system.
• Hyperactivity of dopamine in the mesolimbic pathway mediates positive psychotic symptoms. The pathway may also mediate aggression.
• The mesolimbic pathway is also the site of the rewards pathway and mediates pleasure and reward. Antipsychotics can block D2 receptors in this pathway reducing pleasure effects. This may be one explanation as to why individuals with schizophrenia have a higher incidence of smoking as nicotine enhances dopamine in the reward pathway (self-medication hypothesis)
• Antagonism of D2 receptors in the mesolimbic pathway treats positive psychotic symptoms.
• There is an occupancy requirement with the minimum threshold at 65% occupancy for treatment to be effective. Observations support this relationship between D2-receptor occupancy and clinical response that 80% of responders have D2-receptor occupancy above this threshold after treatment. [5]

2.The Mesocortical Pathway

• Projects from the VTA to the prefrontal cortex.
• Projections to the dorsolateral prefrontal cortex regulate cognition and executive functioning.
• Projections into the ventromedial prefrontal cortex regulate emotions and affect.
• Decreased dopamine in the mesocortical projection to the dorsolateral prefrontal cortex is postulated to be responsible for negative and depressive symptoms of schizophrenia.
• Nicotine releases dopamine in the mesocortical pathways alleviating negative symptoms (self-medication hypothesis).

3.The Nigrostriatal Pathway

• Projects from the dopaminergic neurons in the substantia nigra to the basal ganglia or striatum.
• The nigrostriatal pathway mediates motor movements.
• Blockade of dopamine D2 receptors in this pathway can lead to dystonia, parkinsonian symptoms and akathisia.
• Hyperactivity of dopamine in the nigrostriatal pathway is the postulated mechanism in hyperkinetic movement disorders such as chorea, tics and dyskinesias.
• Long-standing D2 blockade in the nigrostriatal pathway can lead to tardive dyskinesia. 

4.The Tuberoinfundibular (TI) Pathway

• Projects from the hypothalamus to the anterior pituitary.
• The TI pathway inhibits prolactin release.
• Blockade of D2 receptors in this pathway can lead to hyperprolactinemia which clinically manifests as amenorrhoea, galactorrhoea and sexual dysfunction.
• Long-term hyperprolactinemia can be associated with osteoporosis.

Conceptualisation of Schizophrenia

Based on the above understanding, schizophrenia is best conceptualised as a complex entity which involves multiple pathways.

In clinical practice, there can be a disproportionate focus on positive psychotic symptoms.

It is however, important to recognise that affective (e.g depressive), negative and cognitive symptoms are a core part of schizophrenia and should be taken into account in treatment.

The aim of treatment, thus, is to modulate treatment creating a balance between effectiveness and reduction of side effects.

The balance is achieved by optimal dopamine blockade in the mesolimbic pathway while preserving (or enhancing) dopamine transmission in the other pathways.

DOPAMINE AND SCHIZOPHRENIA

The dopamine hypothesis of schizophrenia has moved from the dopamine receptor hypothesis (increased dopamine transmission at the postsynaptic receptors) to a focus on presynaptic striatal hyperdopaminergia.

According to Howes and Kapur-

This hypothesis accounts for the multiple environmental and genetic risk factors for schizophrenia and proposes that these interact to funnel through one final common pathway of presynaptic striatal hyperdopaminergia. In addition to funneling through dopamine dysregulation, the multiple environmental and genetic risk factors influence diagnosis by affecting other aspects of brain function that underlie negative and cognitive symptoms. Schizophrenia is thus dopamine dysregulation in the context of a compromised brain. [6]

Read more on the molecular imaging of dopamine abnormalities in schizophrenia. 

Clinical Implications

The hypothesis that the final common pathway is presynaptic dopamine dysregulation has some important clinical implications. Firstly, it implies that current antipsychotic drugs are not treating the primary abnormality and are acting downstream. While antipsychotic drugs block the effect of inappropriate dopamine release, they may paradoxically worsen the primary abnormality by blocking presynaptic D2 autoreceptors, resulting in a compensatory increase in dopamine synthesis. This may explain why patients relapse rapidly on stopping their medication, and if the drugs may even worsen the primary abnormality, it also accounts for more severe relapse after discontinuing treatment. This suggests that drug development needs to focus on modulating presynaptic striatal dopamine function, either directly or through upstream effects. [6]

Concept of Salience

Usually, dopamine’s role is to mediate motivational salience and thereby gives a person the ability to determine what stimulus grabs their attention and drives the subsequent behaviour.

The salience network consists of the Anterior Cingulate Cortex (ACC), insula and the amygdala.

Schizophrenia is associated with an aberrant attribution of salience due to dysregulated striatal dopamine transmission.

Dysregulation of the dopamine system ultimately leads to irrelevant stimuli becoming more prominent which provides a basis for psychotic phenomena such as ideas of reference, where everyday occurrences may be layered with a with a heightened sense of bizarre significance.  Furthermore, this misattribution of salience can lead to paranoid behaviour and persecutory delusions. [7]

A stimulus, even if initially lacking inherent salience, once paired with dopaminergic activity, maintains the ability to evoke dopaminergic activity over time. This suggests that in psychosis, once an environmental stimulus has been highlighted by aberrant dopamine signalling, it may maintain its ability to trigger dopaminergic activity, potentially cementing its position in a delusional framework, even if the system subsequently returns to normal function. [McCutcheon, et al, 2019]

LIMITATIONS OF THE DOPAMINE HYPOTHESIS OF SCHIZOPHRENIA

Current research shows that one-third of individuals with schizophrenia do not respond to non-clozapine antipsychotics despite high levels of D2-receptor occupancy.

Furthermore, a study using tetrabenazine (used as augmentation) which depletes presynaptic dopamine was not found to be effective in augmenting a clinical response in schizophrenia. [8]

Therefore, for a significant number of patients with schizophrenia, the basis of their symptoms is either unrelated to dopaminergic dysfunction or is associated with something more than just dopamine excess.

Alternatively, this could also mean that for some patients with schizophrenia there might be a non-dopaminergic sub-type of schizophrenia.

The current dopamine hypothesis of schizophrenia does not adequately explain the cognitive and negative symptoms. Current treatments which modulate dopamine transmission have only modest effects in improving these symptoms.

It has taken two decades for the dopamine hypothesis to evolve and reach its current state. More recent evidence shows another neurotransmitter, glutamate playing an essential role in schizophrenia.

The future likely holds a lot more secrets about schizophrenia which should unravel with the advances in understanding the brain.

Learn more:

Simplified Guide to Mechanisms of Action of Oral Antipsychotics

RECOMMENDED BOOKS

Howes O, et al . Midbrain dopamine function in schizophrenia and depression: a post-mortem and positron emission tomographic imaging study. Brain . 2013

Howes OD, Shatalina E. Integrating the Neurodevelopmental and Dopamine Hypotheses of Schizophrenia and the Role of Cortical Excitation-Inhibition Balance. Biol Psychiatry. 2022 Sep 15;92(6):501-513.

Howes, O., McCutcheon, R., & Stone, J. (2015). Glutamate and dopamine in schizophrenia: an update for the 21st century. Journal of psychopharmacology , 29 (2), 97-115.

Kapur S, et al . Relationship between dopamine D(2) occupancy, clinical response, and side effects: a double-blind PET study of first-episode schizophrenia. American Journal of Psychiatry . 2000

Howes O, Murray R. Schizophrenia: an integrated sociodevelopmental-cognitive model. Lancet . 2014

McCutcheon, R. A., Abi-Dargham, A., & Howes, O. D. (2019). Schizophrenia, dopamine and the striatum: from biology to symptoms.  Trends in neurosciences ,  42 (3), 205-220

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Does the dopamine hypothesis explain schizophrenia?

Chi-Ieong David Lau is a Consultant Neurologist whose research interests focus on the cognitive neuroscience underpinning neurological diseases. His recent work includes the investigation of the visual system in migraine, as well as the modulation of slow-wave-sleep-related memory consolidation using a variety of methods, including EEG, neuroimaging, brain stimulation, and genetics. He completed his medical degree and neurology training in Taiwan and postgraduate studies at the University College London and the University of Oxford, supported by the British Chevening Scholarship.

Han-Cheng Wang is a Consultant Neurologist at Shin Kong Wu Ho-Su Memorial Hospital, with specialist clinics for Parkinson’s disease and movement disorders. He is Assistant Professor of Neurology at the College of Medicine, National Taiwan University. He is the former President and present Standing Member of the Executive Board of Taiwan Movement Disorder Society. His research interests include understanding basic neurophysiology underlying human movements and movement disorders. He is interested in linking clinical features with functional connectivity of the brain, reflected in his recent works correlating regional cerebral blood flow (CBF) changes and tract-specific abnormalities with severity of Parkinsonism.

Jung-Lung Hsu is a Clinical Neurologist. He is interested in behavioral/cognitive neuroscience. His main study is focused on brain structural change and human behavior. He is also participating in the event-related potential (ERP) study (P50 and MMN) of schizophrenia patients.

Mu-En Liu’s research interests include biological psychiatry and geriatric psychiatry. Some of the study topics are novel in the genetic study of cognitive ageing. Recently, he examined genetic effects on age-related morphologic changes in the brain. His researches may clarify the underlying molecular mechanisms of brain aging.

The dopamine hypothesis has been the cornerstone in the research and clinical practice of schizophrenia. With the initial emphasis on the role of excessive dopamine, the hypothesis has evolved to a concept of combining prefrontal hypodopaminergia and striatal hyperdopaminergia, and subsequently to the present aberrant salience hypothesis. This article provides a brief overview of the development and evidence of the dopamine hypothesis. It will argue that the current model of aberrant salience explains psychosis in schizophrenia and provides a plausible linkage between the pharmacological and cognitive aspects of the disease. Despite the privileged role of dopamine hypothesis in psychosis, its pathophysiological rather than etiological basis, its limitations in defining symptoms other than psychosis, as well as the evidence of other neurotransmitters such as glutamate and adenosine, prompt us to a wider perspective of the disease. Finally, dopamine does explain the pathophysiology of schizophrenia, but not necessarily the cause per se. Rather, dopamine acts as the common final pathway of a wide variety of predisposing factors, either environmental, genetic, or both, that lead to the disease. Other neurotransmitters, such as glutamate and adenosine, may also collaborate with dopamine to give rise to the entire picture of schizophrenia.

About the authors

The authors would like to thank Miss Frankie Wing See Tam for her valuable comments on the manuscript.

Conflicts of interest: The authors have no conflicts of interest relevant to this article.

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The role of dopamine in schizophrenia from a neurobiological and evolutionary perspective: old fashioned, but still in vogue.

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Corrigendum: The Role of Dopamine in Schizophrenia from a Neurobiological and Evolutionary Perspective: Old Fashioned, but Still in Vogue

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• 1 Department of Forensic Medicine, Medical University of Gdańsk, Gdańsk, Poland
• 2 School of Medical Sciences, The University of Adelaide, Adelaide, SA, Australia
• 3 Centre for Evolutionary Medicine, University of Zurich, Zurich, Switzerland
• 4 Department of Psychiatry and Psychotherapy, Ruhr University Bochum, Bochum, Germany
• 5 Department of Psychiatry, Otto-von-Guericke-University of Magdeburg, Magdeburg, Germany
• 6 Department of Zoology, Institute of Biology, Otto-von-Guericke-University of Magdeburg, Magdeburg, Germany
• 7 Biological Anthropology and Comparative Anatomy Research Unit, School of Biomedical Sciences, The University of Adelaide, Adelaide, SA, Australia

Dopamine is an inhibitory neurotransmitter involved in the pathology of schizophrenia. The revised dopamine hypothesis states that dopamine abnormalities in the mesolimbic and prefrontal brain regions exist in schizophrenia. However, recent research has indicated that glutamate, GABA, acetylcholine, and serotonin alterations are also involved in the pathology of schizophrenia. This review provides an in-depth analysis of dopamine in animal models of schizophrenia and also focuses on dopamine and cognition. Furthermore, this review provides not only an overview of dopamine receptors and the antipsychotic effects of treatments targeting them but also an outline of dopamine and its interaction with other neurochemical models of schizophrenia. The roles of dopamine in the evolution of the human brain and human mental abilities, which are affected in schizophrenia patients, are also discussed.

Brief History of Dopamine Hypothesis in Schizophrenia

Dopamine, adrenaline, and noradrenaline are neurotransmitters that belong to the catecholamine family. Dopamine is produced in the substantia nigra and ventral tegmental regions of the brain, and dopamine alterations are related to schizophrenia ( 1 , 2 ). Dopaminergic projections are divided into the nigrostriatal, mesolimbic, and mesocortical systems. Impairments in the dopamine system result from dopamine dysfunctions in the substantia nigra, ventral tegmental region, striatum, prefrontal cortex, and hippocampus ( 3 – 5 ). The “original dopamine hypothesis” states that hyperactive dopamine transmission results in schizophrenic symptoms. This hypothesis was formed upon the discovery of dopamine as a neurotransmitter in the brain by Arvid Carlsson ( 6 – 12 ). Dopamine receptor blockade by chlorpromazine and haloperidol, proposed in 1963 by Arvid Carlsson and Margit Lindqvist, was a cornerstone in psychiatry ( 13 ). However, the association between schizophrenic symptoms and dopamine over-activity has already been questioned ( 14 ). The positive symptoms of schizophrenia include hallucinations and delusions as a result of increased subcortical release of dopamine, which augments D 2 receptor activation ( 15 ), and are thought to be due to a disturbed cortical pathway through the nucleus accumbens ( 16 ). The negative symptoms of schizophrenia include anhedonia, lack of motivation, and poverty of speech, which result from reduced D 1 receptor activation ( 15 ) in the prefrontal cortex and decreased activity of the nucleus caudatus ( 16 ). Alterations in D (3)-receptors might also be involved in the negative symptoms of schizophrenia ( 17 ). Furthermore, dopaminergic and serotonergic deviations are known to contribute significantly to both the positive and negative symptoms of schizophrenia [review by Davis et al. ( 18 ); Castner and Goldman-Rakic ( 19 ); Carlsson et al. ( 20 )].

The “revised dopamine hypothesis” proposes hyperactive dopamine transmission in the mesolimbic areas and hypoactive dopamine transmission in the prefrontal cortex in schizophrenia patients ( 21 – 23 ). In addition to the mesolimbic brain areas, dopamine dysregulation is also observed in brain regions including the amygdala and prefrontal cortex, which are important for emotional processing ( 24 ). PET-studies (positron emission tomography) have identified differences in dopamine contents in the prefrontal cortex, cingulate cortex, and hippocampus between schizophrenia patients and neuropsychiatric healthy control subjects ( 25 ). In particular, the dopamine system in the hippocampus is overactive in schizophrenia patients [review by Grace ( 26 )].

Recent Animal Models Implicating Dopamine in Schizophrenia

The prepulse inhibition (PPI) of the acoustic SR (ASR) is a neurophysiologic measurement of sensorimotor gating and a marker for information-processing deficits in neuropsychiatric disorders such as schizophrenia ( 27 – 30 ). PPI refers to a reduced startle response to a strong sensory stimulus when the stimulus is preceded by a barely detectable stimulus (i.e., the prepulse). PPI is similar in human and experimental animal models. Deficits in PPI can be produced in rodents by administering psychotomimetics such as dopaminergic and serotonergic agonists and glutamatergic antagonists ( 31 – 34 ). Furthermore, dopaminergic stabilizers have been shown to restore social behavior in a rat model of schizophrenia ( 35 ), and regulatory feedback loops exist among serotonergic, GABAergic, and dopaminergic neurotransmitters ( 36 ). For example, interactions between accumbal dopamine and various non-dopamine receptors, such as N -methyl- d -aspartate (NMDA)-, AMPA-, GABA (A)-, and nicotinic-receptors were reported in a rodent model of schizophrenia ( 37 ). NMDA- and D (1)-receptors in the nucleus accumbens interact with other, and this interaction is controlled by PPI ( 38 ). GABA is also involved in the pathophysiology of PPI. Pregnenolone, a neurosteroid in the central nervous system (CNS), works by improving cognitive deficits through GABA, and pregnenolone improves PPI deficits in dopamine transporter knockout mice ( 39 ). Activation of GABA-receptors in the rat brain results in various receptor interactions with glutamate ( 40 , 41 ), and modulation of GABA (A) a5 receptors improves cognitive deficits in rats ( 42 ). In a rat model of schizophrenia, the increase in dopamine is caused by hyperactivity of the ventral hippocampus ( 43 ). Changes in dopamine receptors (D-2) caused by antipsychotic drugs, such as quinpirole, have been demonstrated in a validated rodent model of schizophrenia ( 44 , 45 ). However, recent work by Bay-Richter et al. ( 46 ) indicates that antipsychotic drugs, such as AMP, clozapine, and haloperidol, cause behavioral changes independent of D (2)-receptors in a mouse model. An up-regulation of D2-high receptors is a consistent feature in animal models of schizophrenia ( 47 ). However, alterations in D (3) dopamine receptors caused by novel antipsychotic drugs, such as cariprazine, decrease cognitive deficits in knockout mice ( 48 ). Therefore, D (3)-receptor antagonists are recommended as a new pharmacological strategy to improve cognitive function in schizophrenia [review by Nakajima et al. ( 49 )]. Social isolation rearing in rats, which is a valid neurodevelopmental model of schizophrenia, reduces dopamine levels in the frontal cortex ( 50 ).

Cognition in Schizophrenia

Cognitive deficits in schizophrenia affect working memory, language and executive function, episodic memory, processing speed, attention inhibition, and sensory processing ( 51 ). The prefrontal region is affected in cognitive discrepancies connected with working memory [see the systematic review by Smieskova et al. ( 52 )], which consists of visual, verbal, central executive, episodic components, and working memory disturbances in schizophrenia are primarily due to altered dorsolateral prefrontal cortex (DLPFC) function ( 51 ). Episodic memory discrepancies in schizophrenia involve the medial temporal cortex, particularly the hippocampus, and the prefrontal cortex, particularly the ventral and dorsolateral prefrontal regions ( 51 ). Additionally, auditory processing involving memory procedures is impaired in the working memory of schizophrenia patients ( 53 ). Cognitive deficits correlate with a decline in dopamine in the prefrontal cortex, primarily at the level of D (1)-receptors ( 54 – 59 ) but also due an imbalance of D (1) and D (2)-receptors in the prefrontal cortex [review by Durstewitz and Seamans ( 60 ); Takahashi ( 61 )]. Several studies have proposed that an inverted U-shaped relation between working memory and activation of the prefrontal cortex exists in schizophrenia patients ( 62 ). There is ongoing discussion regarding the involvement of D (1)- and D (2)-receptors in cognition in schizophrenia patients ( 63 – 66 ). Cognitive discrepancies and working memory deficits in the prefrontal cortex are associated with an increase in dopamine and D (1)-receptors in the prefrontal cortex in schizophrenia patients ( 67 , 68 ). Atypical antipsychotics such as clozapine block D (2)-receptors in the striatum and 5-HT 1A -receptors in the prefrontal cortex, which results in increased dopamine activity ( 69 , 70 ). By blocking D (2)-receptors through antipsychotics, the apoptotic mechanisms in the brain regions involved in cognition are impaired ( 71 ). The disturbed activity of working memory in the DLPFC in schizophrenia patients is influenced by the release of dopamine in the midbrain in schizophrenia patients, which is regulated by a deficit in glutamatergic projection from the DLPFC to midbrain dopamine neurons ( 72 ). Extrastriatal dopamine transmission is necessary for attention and working memory, and these deficits in the fronto–striato–thalamic pathway are involved in cognition in schizophrenia ( 73 ). Newer antipsychotic drugs such as olanzapine and clozapine, which have a better affinity for dopamine receptors and blocking 5-HT 2A receptors, decrease the hyperactivity of the mesolimbic dopaminergic pathway and improve the activity of D (1)-receptors in the prefrontal cortex ( 74 ). Furthermore, nicotine improves cognition in schizophrenia patients ( 75 ).

The COMT-Val-allele leads to a deficit in cognitive abilities. Interactions between dopaminergic and methylation mechanisms may result in cognitive deficits in schizophrenia patients. The COMT Met-allele results in lower COMT-activity, leading to greater production of dopamine and increased D (1)-receptor activity in the prefrontal cortex and, subsequently, better cognitive abilities in carriers of the Met-allele ( 76 – 82 ). A link between Met-carriers and smoking has been recently reviewed ( 83 ), and an association between COMT and cognitive dysfunction in bipolar disorder has also been discussed ( 84 ). The COMT-alleles are composed of two different alleles that result in varied activity levels: the low-activity COMT-allele (L-COMT) and the high-activity COMT-allele (H-COMT) ( 85 ). The L-COMT allele has the Met-/Met-genotype, and the H-COMT allele has the Val-/Val-genotype ( 86 ). Middle-aged healthy women with H-COMT who carry the Val158 allele show better cognitive abilities, including executive processing and cognitive flexibility, than carriers of the Met allele ( 87 ).

Dopamine Receptors and Antipsychotic Effects in Schizophrenia

Dopamine receptors are G-protein-coupled receptors and can be divided into D (1), D (2), D (3), and D (4)-receptors ( 88 ). D (1) receptors in the prefrontal cortex are decreased in schizophrenia patients and are unaffected by chronic treatment of typical neuroleptics [review by Friedmann et al. ( 89 )]. In contrast, D (1)-receptors are increased in the parieto-temporal cortex in schizophrenia patients ( 90 ). Increased D2 mRNA has been found in the frontal cortex in schizophrenia patients when compared with neuropsychiatric healthy control subjects ( 91 ). Both the classic and -current antipsychotic drugs act primarily by increasing high-affinity D (2)-receptor expression ( 92 – 98 ). Haloperidol has been shown to increase high-affinity D (2)-receptors in dopamine-sensitive rats in an animal of schizophrenia ( 99 ). Dopamine agonists bind to D (2)-high and D (2)-low-receptors ( 93 , 100 ). This D (2) two-state model is still controversial, although discussions tend to doubt its validity, as demonstrated by in vitro binding experiments ( 101 ). The action of dopamine agonists is related to dopamine hyperactivity in psychosis ( 102 ). Dopamine antagonists and, to a lesser extent, dopamine agonists increase the D (2)-high-receptors ( 103 ). This increase in D (2)-high-receptors is a necessary basic requirement for the development of a psychosis that correlates with dopamine supersensitivity ( 104 ). This specific increase in D (2)-receptors and dopamine supersensitivity might result in antipsychotic treatment failure ( 105 , 106 ). Although D (2)-receptor antagonists induce dopamine activity ( 107 ), the mechanisms underlying the action of dopamine D (2)-receptor antagonists are not entirely clear. The low therapeutic advantage of dopamine D (2)-receptor antagonists and their high pharmacological selectivity require future research ( 108 ). Antipsychotic drugs block D (2) receptors and increase the release of glutamate in the striatum ( 109 ), particularly on the right side of the striatum, which is a brain region involved in cognition and reward motivation ( 110 ). Glutamate agonists have an effect on D (2) high-receptors in schizophrenia ( 111 , 112 ). For example, alterations in D (2)-receptor function caused by antipsychotic medication in a rodent model of schizophrenia ( 44 ) or by the application of an amphetamine in schizophrenia patients ( 113 ) have been recently demonstrated. A D (2)-receptor occupancy of 80% is considered essential for the positive effects of antipsychotic medication ( 114 , 115 ), whereas continuous high D (2)-receptor occupancy is not required [review by Kapur and Seeman ( 116 ); Remington and Kapur ( 117 ), systematic review by Uchida et al. ( 118 ); Seeman ( 119 )]. The atypical antipsychotic clozapine results in a lower D (2) receptor occupancy than 80% but still has positive effects [review by Nord and Farde ( 120 )]. Schizophrenia patients with extrapyramidal syndromes (EPSs) show an increased D (2)-receptor occupancy (above 80%) in comparison with schizophrenia patients with a good clinical response and no EPSs (i.e., receptor occupancy of 65–80%) [review by Nord and Farde ( 120 )]. Lower doses of antipsychotics such as risperidone are effective and do not induce EPSs ( 121 , 122 ). This specific D (2)-receptor occupancy in the striatum in schizophrenia patients interacts with the antagonistic effects of 5-HT 2A receptors [review by Pani et al. ( 123 )]. D (1)-receptors and NMDA-receptors cooperate with each other ( 124 ). Furthermore, the intensification of D (2)-receptor antagonists by D (1)-receptor agonists results in better NMDA transmission, exemplified by the action of clozapine as a partial D (1)-receptor agonist ( 109 ). NMDA and D (1) dopamine receptor interaction occurs through signal transduction and phosphorylation and dephosphorylation mechanisms ( 125 ). D (1)-receptors are present in GABAergic interneurons ( 54 ). For example, valproic acid affects GABA and, subsequently, dopamine ( 126 ).

A slightly increased density of D (2)-receptors in basal condition and a significant increase in D (2)-receptors in the striatum of schizophrenia patients has been found ( 127 ). This increase of striatal dopamine D (2)-receptors in schizophrenia has also been demonstrated in neuroimaging and molecular imaging studies ( 128 , 129 ). Specific neurotransmitter pathways such as those of glutamate, GABA, and acetylcholine lead to a high-affinity of the D (2)-receptor ( 130 ). Dopamine receptors such as the D (2)-receptor contain receptor mosaics (i.e., RM; dimeric or high-order receptor oligomers). These D 2 /NMDA receptor mosaics have also been found in the ventral striato-pallidal GABA neurons. Decreased D (2)-receptors in the thalamus and anterior cingulate cortex in schizophrenia might suggest that they are involved in abnormalities in dopamine transmission from the thalamus to the prefrontal cortex ( 131 ).

Low doses of D (2)-receptor antagonists and signaling enhancers of NMDA-receptors are recommended as new treatments in schizophrenia [review by Fuxe et al. ( 132 )]. In the associative striatum, an increased D (2)-receptor availability has been found in schizophrenia patients ( 127 ). Increased dopamine release in the striatum is linked to substance dependence, such as amphetamine dependency, in schizophrenia ( 133 ). For example, stimulation of NMDA/AMPA and kainate receptors by direct application of glutamate or glutamate agonists increases the dopaminergic cell-firing rate ( 133 ). However, the role of dopamine in the dysfunction of the striatum in schizophrenia patients requires future research ( 134 ).

It can be summarized that, to date, the mechanism of every effective antipsychotic medication in schizophrenia involves dopamine and its interaction with other neurochemical pathways such as those of glutamate, GABA, serotonin, and acetylcholine.

Alternate Neurochemical Models in Schizophrenia and Their Interactions with Dopamine

Deviations in dopamine and glutamate have been reported in the prefrontal cortex of schizophrenia patients ( 135 ). NMDA-receptors are involved in releasing dopamine into the striatum and frontal cortex in schizophrenia patients [Ref. ( 136 , 137 ), review by Castner and Williams ( 138 ); Javitt ( 139 ); Balla et al. ( 140 ); Laruelle ( 141 )] and in rats in an animal model of schizophrenia ( 142 ). These interactions are accompanied by calcium-dependent changes ( 143 ) and exchanges between DAT and G72 in various brain regions ( 144 ). In contrast to dopamine receptors, glutamate receptors are found in the subcortical and cortical brain regions ( 145 ). The activity of dopamine is regulated by GABA and glutamate. For example, corticostriatal glutamatergic pathways interact with dopamine terminals ( 146 , 147 ), and specific glutamate receptors in the striatum, such as mGlu2, are sensitive to dopamine ( 112 ). High glutamate levels have been found in the dorsal caudate nucleus of schizophrenia patients ( 148 ). Adenosine interacts with glutamate, NMDA-receptors, and dopamine [review by Burnstock et al. ( 149 )]. It can be summarized that NMDA-receptors and D (1)-receptors in cortical brain areas such as the prefrontal cortex and an excess of D (2)-receptors in subcortical brain areas such as the striatum are interconnected with each other through a positive feedback mechanism ( 150 ). However, through its presynaptic action, dopamine reduces the release of glutamate in the pyramidal neurons of layer V in the prefrontal cortex ( 151 ). Dopamine dysregulation in the basal ganglia of schizophrenia patients is an important intrinsic feature in the pathology of schizophrenia and not a medication side effect [review by Perez-Costas et al. ( 3 )].

The finding by Brisch et al. ( 152 ) that astrocyte density is increased in the frontal cortex in schizophrenia suggests a disturbance in the dopamine–glutamate function. Furthermore, Sokoloff et al. ( 153 ) demonstrated that D (3)-receptors either act directly on NMDA-receptors at glutamate synapses on the terminals of pyramidal cells in the nucleus accumbens or act indirectly through dopamine at the presynaptic junction to regulate pyramidal cells in the prefrontal cortex. Indeed, injection of NMDA-antagonists such as MK801 increases glutamate concentration in the frontal, retrosplenial, and cingulate cortices ( 154 ). Glutamate dysfunction in the prefrontal cortex and hippocampus causes the release of dopamine in the striatum ( 155 ). A new focus on glutamatergic signaling mediated by NMDA and metabotropic receptors may benefit new drug developments [review by Field et al. ( 156 ); Javitt ( 139 ); Matosin and Newell ( 157 ); Moghadam and Krystal ( 158 ); Noetzel et al. ( 159 )]. The review by Bernstein et al. ( 160 ) attributes the disturbed function of astrocytes in schizophrenia to diminished glutamate metabolism. The enzyme glutamine synthetase, which degrades glutamate into glutamine, is located in glial cells and is decreased in schizophrenia patients ( 161 ). Additionally, the glutamate transporter for astrocytes, GLT-1, is increased in schizophrenia patients ( 162 ). Although Arai et al. ( 163 ) reported no association between glutamine synthetase and schizophrenia, the enzyme glutamine synthetase displays gender-specific differences in schizophrenia ( 164 ) and is involved in suicidal behavior ( 165 , 166 ). Moreover, the atypical antipsychotic agent risperidone increases glutamine synthetase levels ( 167 ).

Through NMDA-stimulated GABA-release and GABA B -receptor activity, glycine reduces the release of dopamine by modulating DAT-type transporters in the prefrontal cortex and striatum ( 168 ). The GABA B -receptor inhibits the release of glutamate in the ventral tegmental area ( 169 ). A synergistic interaction of adenosine and glutamate affecting the ventral striato-pallidal GABA pathway has been demonstrated in a rat model ( 170 ). The interactions of pyramidal neurons with dopamine receptors on their dendrites and pyramidal cells with glutamate on their spines, and GABAergic interneurons in the prefrontal cortex in schizophrenia patients might offer new insights into receptor-targeted therapies [Ref. ( 53 ); review by Wassef et al. ( 171 ); Lisman et al. ( 172 )]. An increased number of GABA-cells expressing D (1)-receptors exists in the rat prefrontal cortex ( 173 ). In the nucleus accumbens, neurotensin (NT) inhibits dopamine discharge, which increases glutamate release and activates the ventral striato-pallidal GABA pathway, leading to a subsequent increase in glutamate transport from the mediodorsal thalamus to the prefrontal cortex ( 174 ). Another interaction between dopamine in the prefrontal cortex and glutamate in the mediodorsal thalamus might be responsible for the effects of zotepine, which increases the extracellular levels of noradrenaline, dopamine, glutamate, and GABA ( 175 ). GABA interacts with acetylcholine by constraining its excitatory contribution to cholinergic interneurons, which are decreased in the striatum of schizophrenia patients, resulting in prefrontal deviations in schizophrenia ( 176 ). Dopamine also interacts with acetylcholine, which increases with smoking frequency in schizophrenia patients ( 177 ). Acute nicotine administration might have positive effects on cognition in schizophrenia patients [Ref. ( 178 , 179 ), review by Mackowick et al. ( 180 )].

Dopamine neurons in the midbrain release serotonin, which is important during combined drug treatment with serotonin to prevent the so-called serotonin syndrome, a surplus of serotonin in some brain regions ( 181 ). Atypical antipsychotics involving serotonin receptors include 5-HT 1A receptor agonists or antagonists, 5-HT 2A receptor antagonists, 5-HT 2c receptor inverse or partial agonists or neutral antagonists, 5-HT 6 receptor antagonists, and 5-HT 7 receptor antagonists ( 182 ).

Antipsychotics (such as clozapine and aripiprazole) possessing 5-HT- 1A agonist properties induce hippocampal neurogenesis and increase dopamine in the prefrontal cortex [review by Schreiber and Newman-Tancredi ( 183 )]. It can be summarized that various serotonin–dopamine interactions, which include both direct and indirect feedback mechanisms, contribute to the pathology of schizophrenia [Ref. ( 151 , 184 – 189 ), review by Arranz and de Leon ( 190 ); Alex and Pehek ( 191 ); McCreary et al. ( 192 ); Bhattacharyya et al. ( 193 ); Meltzer et al. ( 194 ); McMahon and Cunningham ( 195 ); Gao et al. ( 196 )].

Novel antipsychotic drugs, such as asenapine, increase dopamine and glutamate levels in various subcortical and cortical areas ( 197 ). New antipsychotic drugs with novel mechanisms induce alterations in both dopamine and glutamate [review by Paz et al. ( 198 ); Seeman et al. ( 99 ); Stone ( 199 ); Leroy et al. ( 200 ); Coyle et al. ( 201 )]. For example, metabotropic glutamate and NMDA-receptors are future targets for new drugs ( 202 , 203 ). A range of dopamine/serotonin, glutamate/serotonin, and acetylcholine/serotonin interactions activate receptors and signaling molecules in response to antipsychotic drugs and have been observed in various brain regions, including the prefrontal cortex and limbic regions, in schizophrenia ( 20 , 98 , 176 , 182 , 194 , 204 – 211 ). Future drug development should target signaling molecules involved in dopamine, glutamate, and serotonin neurotransmission such as Akt and glycogen synthase kinase-3 ( 98 , 212 , 213 ) as well as the control of presynaptic dopamine synthesis and release ( 114 ). Stress in schizophrenia patients causes an increased release of dopamine in the prefrontal cortex, which cannot be counteracted by reduced GABA A receptor complex activity, as well as dendritic spine loss in the prefrontal cortex ( 214 , 215 ). When used in schizophrenic patients, cannabis induces hyperdopaminergic and hypoglutamatergic activities with both positive and negative symptoms ( 216 ). In particular, cannabis increases dopamine transmission in the nucleus accumbens, which might cause or aggravate psychoses ( 217 ). A high-low activity polymorphism in COMT interacts with adolescent cannabis abuse, increasing the risk for schizophrenia ( 218 ). Further, genes such as disrupted-in-schizophrenia-1 (DISC1) play a role in stress pathways and the metabolism of dopamine in schizophrenia [review by Hains and Arnsten ( 219 ); Lipina et al. ( 220 )].

Dopamine and Human Evolution

The role of dopamine in human evolution has hitherto received little theoretical attention. It is still unclear to what extent dopaminergic expansion in hominin evolution was due to genetic adaptations or epigenetic factors. Dopamine has expanded throughout primate and hominin evolution and that dopamine is especially concentrated in the prefrontal cortex, which is involved in higher order functioning. The dopaminergic hypothesis contends that climatic changes occurring in sub-Saharan Africa during the Pliocene and Pleistocene periods, which resulted in increases of the Savannah belt expanded hominin locomotory range. It is also speculated that some human groups ventured to the more habitable African southern coast leading to dietary changes (i.e., increasing amounts of fish/shellfish) that aided dopaminergic expansion ( 221 – 223 ). Dopamine increase may have also been linked with a concomitant elevation in thyroid hormone production. Higher T4 found in Homo may have represented an early endocrinological difference between humans and other primates ( 224 ). In humans, T4 concentration is associated with tyrosine conversion to dopa (a precursor to dopamine); deficiencies of T4 concentrations are linked with various neurological impairments ( 224 ).

Recent research suggests that from Homo erectus onward, humans became persistence hunters, requiring various morphological and thermo-regulatory modifications (i.e., vascular reactivity to temperature, large body surface area, plantar arch), which provides approximately 20% energy return during running, elastic tendons, short toes, more pronounced gluteus maximus muscle, long legs, CNS coordination of metabolic, and cardio-vascular responses to sustained running ( 225 ). From Homo erectus onward there is an evident increase in stride size, which also optimized ergonomic requirements of bipedalism while diminishing energy requirements. Greater mass of slow twitch muscles would have also assisted long distance locomotion. Long distance locomotion in conjunction with greater hunting activities in ancestral hominins incorporated all aspects of the CNS such as retention and memory recall of large geographic areas, which maximized resource acquisition. The locomotion/behavior interplay, which was mediated by nerve cells and the dopaminergic system may have evolutionary expanded cortical regions and neuro-hormonal organization in ancestral hominins ( 225 ).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

AMPA, (S)-2-amino-3-(3-hydroxy-5-methyl-4-isoxazolyl)-propionic acid; COMT, catechol- O -methyltransferase; CNS, central nervous system; D (1), dopamine D 1 receptor; D (2), dopamine D 2 receptor; D (3), dopamine D 3 receptor; DAT, dopamine transporter; DAOA or G72, d -amino acid oxidase activator; DLPFC, dorsolateral prefrontal cortex; GABA, γ-aminobutyric acid; H-COMT, high-activity catechol- O -methyltransferase; L-COMT, low-activity catechol- O -methyltransferase; Met, methionine; mRNA, messenger ribonucleic acid; NMDA-receptor, N -methyl- d -aspartate receptor; PET, positron emission tomography; PPI, prepulse inhibition; RNA, ribonucleic acid; SR, startle reflex; Val, valine.

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Keywords: dopamine, schizophrenia, cognition, glutamate, dopamine receptors, cannabis, animal models of schizophrenia, evolution of the human brain

Citation: Brisch R, Saniotis A, Wolf R, Bielau H, Bernstein H-G, Steiner J, Bogerts B, Braun K, Jankowski Z, Kumaratilake J, Henneberg M and Gos T (2014) The role of dopamine in schizophrenia from a neurobiological and evolutionary perspective: old fashioned, but still in vogue. Front. Psychiatry 5 :47. doi: 10.3389/fpsyt.2014.00047

Received: 20 January 2014; Accepted: 23 April 2014; Published online: 19 May 2014.

Reviewed by:

Copyright: © 2014 Brisch, Saniotis, Wolf, Bielau, Bernstein, Steiner, Bogerts, Braun, Jankowski, Kumaratilake, Henneberg and Gos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ralf Brisch, Department of Forensic Medicine, Medical University of Gdańsk, ul. Debowa 23, Gdańsk PL-80-204, Poland e-mail: ralfbrisch@hotmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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The Relationship Between Schizophrenia and Dopamine

Arlin Cuncic, MA, is the author of The Anxiety Workbook and founder of the website About Social Anxiety. She has a Master's degree in clinical psychology.

Alla Bielikova / Getty Images

• Dopamine and Schizophrenia Symptoms
• Implications for Treatment

What Does This Mean for Patients?

• Causes of Schizophrenia
• High vs. Low Dopamine
• Implications

Serotonin and Schizophrenia

Experts do not fully understand what causes schizophrenia, but evidence suggests that dopamine abnormalities may play a role. High and low levels of dopamine in certain regions of the brain can also affect different symptoms of schizophrenia.

Schizophrenia is a debilitating mental disorder with a multitude of symptoms. These can range from disorganized speech and behavior to delusions and hallucinations. Some cases are far more disabling than others, but in most cases, people with this disorder require lifelong treatment and care.

Current research suggests that schizophrenia is a neurodevelopmental disorder with an important dopamine component. Four decades of research have focused on the role of dopamine in schizophrenia, and it seems clear that excesses or deficiencies in dopamine can lead to schizophrenic symptoms.

At a Glance

While other factors also play a role in the development of schizophrenia, dopamine imbalances have been identified as a key factor affecting symptoms. Too much dopamine in key areas of the brain results in delusions and hallucinations (positive symptoms) or cognitive deficits and reduced social/emotional activity (negative symptoms). Understanding the factors that contribute to dopamine symptoms can help doctors treat the condition more effectively.

What Is the Dopamine Hypothesis of Schizophrenia?

The dopamine hypothesis of schizophrenia was one of the first neurobiological theories for this disease.

Dopamine Hypothesis

This theory suggests that an imbalance of dopamine is responsible for schizophrenic symptoms. In other words, dopamine plays a role in controlling our sense of reality, and too much or too little can cause delusions and hallucinations.

The evidence for this theory comes from many sources, including post-mortem studies that have imbalances of dopamine as well as its metabolites in schizophrenic patients. In addition, drugs that block the receptors for dopamine can help control schizophrenic symptoms.

How Does Dopamine Cause Schizophrenic Symptoms?

There are two types of schizophrenia symptoms that an excess of dopamine may cause: positive and negative . Positive symptoms include delusions and hallucinations. Negative symptoms include a decrease in social activity, emotional range, and cognitive function.

Positive Symptoms

Positive symptoms are those that appear to come from outside the person. These can include delusions, hallucinations, or thought disorders.

Dopamine contributes to the development of positive symptoms through its effects on subtype-3A dopamine receptors (D3) of cortical neurons. The subtype-3A receptor is found in the prefrontal cortex, which controls planning, thinking, and other cortical areas.

When these receptors are activated by dopamine, they overstimulate neurons. This can lead to all three types of positive symptoms. Evidence for this idea comes from studies that show that patients with schizophrenia have significantly lower levels of the D3 receptor than healthy people.

Negative Symptoms

While positive symptoms appear to come from outside, negative symptoms appear to be internal. These include decreased social activity and emotional range, as well as cognitive deficits like poor problem-solving or memory deficit.

The mechanisms contributing to negative symptoms are linked to dopamine levels in the limbic system . Dopamine excess leads to an increase in the activity of dopamine receptors, creating overstimulation similar to that seen in positive symptoms.

Some researchers suggest that this overactivity decreases neuronal inhibition , leading to decreased social behavior and cognitive deficits.

Treatment Implications of the Dopamine Hypothesis

The dopamine hypothesis has important treatment implications. The vast majority of current antipsychotic medications target dopamine, and this makes sense, given that these drugs were discovered through serendipitous observations of their effect on schizophrenia.

The most important dopamine-affecting medications are the typical antipsychotics, which increase post-synaptic receptor stimulation by blocking dopamine receptors.

Unfortunately, these medications produce a number of debilitating side effects, most notably extrapyramidal symptoms (EPS) like tardive dyskinesia . Newer second-generation antipsychotics have fewer side effects, but none are perfect.

Treatment with dopamine agonists is a third possibility suggested by the dopamine hypothesis. Dopamine agonists stimulate post-synaptic dopamine receptors directly, and as such, they can be used to treat schizophrenia without producing EPS.

Being diagnosed with schizophrenia can be extremely hard on patients and their families. It's important that doctors and researchers continually investigate new treatments that could improve the lives of people living with this disorder.

However, it's also important to remember that schizophrenia is a complex disorder, and there are many ways the disease can manifest. Dopamine hyperactivity may not be the primary cause of schizophrenia in all patients. Furthermore, even if dopamine hyperactivity is the primary cause it still doesn't explain why some patients respond more strongly than others to the same treatment.

The best way for patients and their loved ones to navigate these issues is by staying informed and asking questions about any new or experimental treatments. They should also work with doctors to develop a personalized treatment plan that's appropriate for their own needs.

Does Too Much Dopamine Cause Schizophrenia?

Increased activity of the mesolimbic pathway is related to positive symptoms of schizophrenia (delusions, hallucinations, etc.). This means that increasing the activity of dopamine receptors in this brain system could theoretically reduce delusions and hallucinations.

A closely related idea is that by blocking post-synaptic dopamine receptors, scientists can reduce the psychotic symptoms of schizophrenia.

As mentioned previously, this is what most modern medications do: they block post-synaptic dopamine receptors in order to reduce psychotic symptoms. Unfortunately, when scientists block all available dopamine receptors they also produce a number of debilitating side effects such as extrapyramidal symptoms (EPS) and tardive dyskinesia.

Is Dopamine High or Low in Schizophrenia?

The most common theory about the cause of schizophrenia is that there are too many dopamine receptors in certain parts of the brain, specifically the mesolimbic pathway. This causes an increase in mesolimbic activity which results in delusions, hallucinations, and other psychotic symptoms.

Other research suggests that schizophrenia might be caused by a lack of dopamine activity in other parts of the brain. For example, scientists have discovered that the hippocampus is overactive in schizophrenia.

Schizophrenia might also be characterized by low dopamine in the prefrontal cortex, but again the evidence is inconclusive. Some studies have found that schizophrenics have elevated levels of dopamine in this region, while others suggest that there are too few dopamine receptors.

Implications of the Dopamine Hypothesis

It's important to note that schizophrenia is a complex disorder. Even if dopamine hyperactivity is the primary cause, certain types of schizophrenia might be characterized by increased activity in certain brain areas while others are characterized by reduced activity in certain brain areas.

Furthermore, it's also possible that different patients will respond to treatment differently based on how their disease manifests.

It's important for healthcare providers and researchers to continue investigating how schizophrenia works in the brain. This will help them develop better treatments for this complex disorder.

Research also implicates serotonin as a regulator of dopamine release. Antipsychotic medications, including olanzapine and clozapine , reduce serotonin activity and increase dopamine activity.

For example, olanzapine-induced reductions in serotonin metabolism were associated with significant improvements in negative and positive symptoms, but not cognitive deficits.

Schizophrenia is a severe mental disorder that can be treated. If you or someone you know was recently diagnosed with schizophrenia, you might be wondering what the future holds. Healthcare professionals can help you manage your symptoms and chart a course for the best possible outcome.

Sometimes, there may be periods of remission that allow you to live a productive life even when coping with schizophrenia. As new treatments are continually being developed, we can look forward to better options for people who experience this disorder in the future.

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Brisch R, Saniotis A, Wolf R, et al. The role of dopamine in schizophrenia from a neurobiological and evolutionary perspective: old fashioned, but still in vogue [published correction appears in Front Psychiatry. 2014;5:110. Braun, Anna Katharina [corrected to Braun, Katharina]; Kumaritlake, Jaliya [corrected to Kumaratilake, Jaliya]].  Front Psychiatry . 2014;5:47. Published 2014 May 19. doi:10.3389/fpsyt.2014.00047

Purves-Tyson TD, Owens SJ, Rothmond DA, et al. Putative presynaptic dopamine dysregulation in schizophrenia is supported by molecular evidence from post-mortem human midbrain .  Transl Psychiatry . 2017;7(1):e1003. Published 2017 Jan 17. doi:10.1038/tp.2016.257

Ceraso A, Lin JJ, Schneider-Thoma J, et al. Maintenance treatment with antipsychotic drugs for schizophrenia .  Cochrane Database Syst Rev . 2020;8:CD008016. doi:10.1002/14651858.CD008016.pub3

Guma E, Rocchetti J, Devenyi GA, et al. Role of D3 dopamine receptors in modulating neuroanatomical changes in response to antipsychotic administration .  Sci Rep . 2019;9(1):7850. doi:10.1038/s41598-019-43955-4

Maia TV, Frank MJ. An Integrative Perspective on the Role of Dopamine in Schizophrenia .  Biol Psychiatry . 2017;81(1):52-66. doi:10.1016/j.biopsych.2016.05.021

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By Arlin Cuncic, MA Arlin Cuncic, MA, is the author of The Anxiety Workbook and founder of the website About Social Anxiety. She has a Master's degree in clinical psychology.

The dopamine hypothesis: an overview of studies with schizophrenic patients

• PMID: 6127808
• DOI: 10.1093/schbul/8.3.438

For the past decade, the dopamine hypothesis of schizophrenia has been the predominant biochemical theory of schizophrenia. Despite the extensive study of tissue samples obtained from schizophrenics, indirect pharmacological evidence still provides the major support for the hypothesis. Direct support is either uncompelling or has not been widely replicated. The dopamine hypothesis is limited in theoretical scope and in the range of schizophrenic patients to which it applies. No comprehensive biological scheme has yet been proposed to draw together the genetic, environmental, and clinical features of schizophrenia. Recent refinements of the dopamine hypothesis may aid in the delineation of biologically homogeneous subgroups. Positive symptoms (e.g., hallucinations, delusions) and negative symptomatology (e.g., affective flattening, social withdrawal) may result from different pathophysiological processes. Schizophrenia research might benefit from an increased attention to neurophysiological adaptations.

• Adenylyl Cyclases / metabolism
• Brain / enzymology
• Catechol O-Methyltransferase / metabolism
• Dextroamphetamine / therapeutic use
• Dopa Decarboxylase / metabolism
• Dopamine / metabolism*
• Dopamine beta-Hydroxylase / metabolism
• Homovanillic Acid / metabolism
• Levodopa / therapeutic use
• Monoamine Oxidase / blood
• Monoamine Oxidase Inhibitors / therapeutic use
• Receptors, Dopamine / metabolism
• Schizophrenia / drug therapy
• Schizophrenia / enzymology*
• Synapses / enzymology
• Tyrosine 3-Monooxygenase / metabolism
• Monoamine Oxidase Inhibitors
• Receptors, Dopamine
• Tyrosine 3-Monooxygenase
• Dopamine beta-Hydroxylase
• Monoamine Oxidase
• Catechol O-Methyltransferase
• Dopa Decarboxylase
• Adenylyl Cyclases
• Dextroamphetamine
• Homovanillic Acid

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• Published: 30 April 2024

Dopamine signaling enriched striatal gene set predicts striatal dopamine synthesis and physiological activity in vivo

• Leonardo Sportelli   ORCID: orcid.org/0000-0002-3028-3262 1 , 2   na1 ,
• Daniel P. Eisenberg   ORCID: orcid.org/0000-0003-4306-9342 3   na1 ,
• Roberta Passiatore 2 ,
• Enrico D’Ambrosio   ORCID: orcid.org/0000-0001-9774-3860 2 , 4 ,
• Linda A. Antonucci 2 ,
• Jasmine S. Bettina 3 ,
• Qiang Chen 1 ,
• Aaron L. Goldman 1 ,
• Michael D. Gregory   ORCID: orcid.org/0000-0003-3749-8343 3 ,
• Kira Griffiths 4 , 5 ,
• Thomas M. Hyde   ORCID: orcid.org/0000-0002-8746-3037 1 , 6 , 7 ,
• Joel E. Kleinman   ORCID: orcid.org/0000-0002-4210-6052 1 , 7 ,
• Antonio F. Pardiñas   ORCID: orcid.org/0000-0001-6845-7590 8 ,
• Madhur Parihar   ORCID: orcid.org/0000-0001-7747-0922 1 ,
• Teresa Popolizio 9 ,
• Antonio Rampino 2 , 10 ,
• Joo Heon Shin   ORCID: orcid.org/0000-0002-5563-8605 1 ,
• Mattia Veronese   ORCID: orcid.org/0000-0003-3562-0683 11 , 12 ,
• William S. Ulrich 1 ,
• Caroline F. Zink 13 ,
• Alessandro Bertolino 2 , 10 ,
• Oliver D. Howes   ORCID: orcid.org/0000-0002-2928-1972 4 ,
• Karen F. Berman 3 ,
• Daniel R. Weinberger   ORCID: orcid.org/0000-0003-2409-2969 1 , 6 , 7 , 14 , 15 &
• Giulio Pergola   ORCID: orcid.org/0000-0002-9193-1841 1 , 2 , 7  

Nature Communications volume  15 , Article number:  3342 ( 2024 ) Cite this article

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• Gene expression profiling
• Genetics of the nervous system
• Schizophrenia

The polygenic architecture of schizophrenia implicates several molecular pathways involved in synaptic function. However, it is unclear how polygenic risk funnels through these pathways to translate into syndromic illness. Using tensor decomposition, we analyze gene co-expression in the caudate nucleus, hippocampus, and dorsolateral prefrontal cortex of post-mortem brain samples from 358 individuals. We identify a set of genes predominantly expressed in the caudate nucleus and associated with both clinical state and genetic risk for schizophrenia that shows dopaminergic selectivity. A higher polygenic risk score for schizophrenia parsed by this set of genes predicts greater dopamine synthesis in the striatum and greater striatal activation during reward anticipation. These results translate dopamine-linked genetic risk variation into in vivo neurochemical and hemodynamic phenotypes in the striatum that have long been implicated in the pathophysiology of schizophrenia.

Introduction

Schizophrenia (SCZ) is a mental illness with complex heritability and polygenic architecture 1 . The largest genome-wide association study (GWAS) to date has identified an extensive set of potential SCZ risk genes converging on the synaptic biology of central nervous system neurons 2 . To the extent that the downstream consequences of diverse risk alleles might affect shared biological functions, genetic risk for SCZ is likely best understood in the context of molecular ensembles, rather than at a single gene level. This perspective puts gene co-expression at the forefront of investigating genetic risk convergence as an instrumental approach to model the effect of many variants on interconnected genetic systems and, ultimately, downstream neurochemical and neural functioning 3 , 4 , 5 .

A large body of evidence implicates synaptic dysfunction and neurotransmission across several key brain circuits that bridge the striatum, the dorsolateral prefrontal cortex (DLPFC), and the hippocampus (HP) as key pathological mechanisms in SCZ 6 , 7 , 8 , 9 . As such, understanding gene co-expression across multiple brain regions may reveal how broad genetic variation translates into an increased risk of illness 1 . This translation is especially important as polygenic approaches usually lack biological characterization.

There is also a large body of evidence for dopamine involvement in SCZ, including the emergence of psychotic symptoms (e.g., hallucinations and delusions) following the administration of pro-dopaminergic agents and therapeutic antipsychotic effects elicited by dopamine-blocking drugs targeting D 2 receptors 10 . In the D 2 -rich striatum where illness-related dysfunction has been observed, positron emission tomography (PET) studies have found an array of dopamine-system disturbances in SCZ suggesting increased dopaminergic drive from mesencephalic synaptic terminals, including elevated presynaptic dopamine synthesis 7 , 11 , 12 , 13 , 14 . There is also evidence that individuals at clinical risk for SCZ, e.g., with subthreshold psychotic symptoms, as well as first-degree relatives, show a similar pattern of elevated striatal presynaptic dopamine synthesis capacity 15 , 16 , which may be enhanced with progression to frank illness 17 . Importantly, striatal dopamine synthesis shows heterogeneity across patients with SCZ 18 , particularly in treatment-resistant individuals, who have demonstrated synthesis capacity decreases 19 . Different mechanisms may be at play in treatment-resistant patients 20 . Recent evidence from post-mortem human caudate nucleus (CN) has revealed that decreased expression of the short (predominantly presynaptic autoreceptor) isoform of the D 2 dopamine receptor gene DRD2 —and not the long (predominantly postsynaptic) isoform—may be a causative mechanism for association of the SCZ GWAS risk allele mapped to the DRD2 locus 19 . By identifying diminished expression of the inhibitory D 2 presynaptic autoreceptor as one potential mechanism of SCZ risk, this work further implicates exaggerated presynaptic dopamine activity in pathogenesis 21 , consistent with earlier work associating a single-nucleotide polymorphism (SNP) with differential DRD2 splicing, striatal dopamine D2 signaling, and prefrontal and striatal activity during working memory 22 , 23 .

Functional magnetic resonance imaging (fMRI) studies have reported altered brain activity in patients with SCZ while performing dopamine-dependent reward processing tasks, possibly arising from synaptic dysfunction and neurotransmission dysregulation 24 , 25 . Moreover, anticipatory striatal activation during reward task performance has been shown to be a heritable trait 26 ( h 2  = 0.20–0.73). Therefore, genetic investigations may help better define important connections between this phenotype and dopamine-relevant SCZ risk molecular factors. In light of this and because dopamine dysfunction in SCZ generally appears to have at least in part a genetic basis 21 , 27 , 28 , 29 , we hypothesized that a SCZ-related genetically driven increase of striatal presynaptic dopamine synthesis might be reflected functionally in an increase of striatal fMRI activation during reward anticipation at least in neurotypical individuals.

While most of the same genes are expressed across brain regions, mRNA expression patterns vary consistently with differing functions subserved at a system level. A widely used approach to analyze gene co-expression patterns is a combination of graph theory and clustering, such as in the popular weighted gene co-expression network analysis 30 , which has been extensively applied to transcriptomics of SCZ patients as well as neurotypical controls 3 , 27 , 28 , 31 , 32 , 33 , 34 . This approach, however, has important limitations in its handling of higher-dimensional data, particularly in accounting for the multiplicity of co-expression contexts across brain regions and cell types. These aspects are crucial to capture the biological reality in which different tissues, cells, and molecular pathways share common genes. Another class of co-expression detection methods called sparse decomposition of arrays (SDA) circumvents these limitations 35 . SDA is based on singular value decomposition, a family of techniques that includes independent component analysis (ICA) and principal component analysis (PCA). SDA is able to effectively identify relationships between genes in multi-tissue experiments 35 . By decomposing a 3D Array (also called a “Tensor”) with dimensions representing individuals, genes, and tissues, respectively, into several latent components (or factors), SDA captures major directions of variation in the dataset. This approach identifies components that uncover functional biology 35 , 36 and outperforms other co-expression detection strategies in the identification of functionally related and co-regulated groups of genes 37 .

Using SDA and two independent post-mortem brain samples, we investigated human RNA sequencing data from three brain regions prominently implicated in SCZ, i.e., CN, HP, and DLPFC (Fig.  1 ). We sought to identify gene sets enriched both for genes differentially expressed in SCZ and for genes associated with SCZ genetic risk. Focusing on gene sets with convergence of illness state and illness risk in neurotypical brain avoids epiphenomena related to drug treatment in patient samples and the same directionality of effects supports genetic risk inferences.

Graphic summary of the study design.

We identified a co-expression component in the SDA data that meets these criteria and is especially enriched for dopamine function genes. We then aimed to evaluate whether this component specifically translates into SCZ-relevant brain functional correlates in vivo. To that end, we studied striatal dopamine synthesis capacity determined via PET in both neurotypical controls (NC) and patients with SCZ and obtained corroborative evidence in an independent replication dataset. We then measured brain physiological activation during reward anticipation with fMRI in two independent neurotypical cohorts performing different reward tasks. We sought to translate dopamine-linked gene sets in the post-mortem brain involved in manifest illness and illness risk into neurochemical and neurofunctional outcomes in the living human brain concordant with known SCZ-associated phenotypes.

Gene co-expression analysis

From the SDA of post-mortem CN, HP, and DLPFC tissue from our discovery cohort (Table  1 ) we obtained 69 robust components not associated with confounding variables. Supplementary Data  1 and 2 report the output of SDA as well as the association with biological covariates and technical confounders and summary information regarding the number of genes included in each component.

When comparing samples from NC and individuals with SCZ, two of 69 filtered components (C80: 2,497 genes; C109: 1,211 genes; see Supplementary Data  2 for component gene membership) were associated with diagnosis (C80: F [1,210]  = 11.4, p  = 0.0009, p [FDR]  = 0.038, η 2  = 0.05; C109: F [1,210]  = 10.9, p  = 0.001, p [FDR]  = 0.039, η 2  = 0.03) (Fig.  2a ). To identify SCZ-associated components more likely linked to pathogenic biology rather than treatment history or other factors, we additionally tested these components across samples for association with SCZ genetic risk before proceeding with further analyses. Only the SDA component C80 was also significantly associated with SCZ polygenic risk score (PRS), a measure of overall cumulative risk burden, in a diagnosis-consistent direction (see Methods for PRS computation; C80: t [93]  = 1.67, one-tailed p  = 0.048, partial R 2  = 0.03; C109: t [93]  = −1.2, one-tailed p  = 0.11, partial R 2  = 0.015) (Fig.  2a ). Patients with SCZ had greater C80 scores and, consistently, healthy controls with greater SCZ PRS had relatively greater C80 scores.

a Notched box plots show SDA component C80 and C109 scores for post-mortem data samples in SCZ and NC groups ( n   =  229 individuals; 145 NC and 84 SCZ). These were the only components showing a significant group effect. Group medians (horizontal line), 95% confidence intervals (notches), interquartile range (box edges), and whiskers (25 th /75 th percentiles or extrema) are shown. The scatter plot demonstrates SDA component C80 and C109 scores as a function of polygenic risk for schizophrenia and includes a regression fit line with mean fitted values and related shaded 95% confidence interval shown ( n  = 103 individuals; 64 NC and 39 SCZ). C80 is the only one with a significant PRS association consistent with diagnosis direction. Source data are provided as a Source Data file. b Gene enrichment analysis results are shown for the C80 component. From the bottom, the first (GWAS), second (MAGMA), and third orange grids (H-MAGMA) show enrichment results for schizophrenia risk genes, other psychiatric illness risk genes, and immune condition risk genes. Enrichment testing results are shown for differentially expressed genes, differentially methylated genes, and loss of function variant intolerant genes in the green grid. The final light-blue grid shows C80 tissue specificity as determined by the tissue scores generated during the SDA process and reflects the relative contribution of component gene networks within each of the sampled regions to the overall component. Adjusted p -values shown are empirical p -values obtained from permutation tests (overrepresentation analysis: one-sided Fisher exact test). c Venn diagram shows the intersection between C80 genes and SCZ, MDD, and ADHD GWAS risk genes. Blank regions indicate no common genes. In the case of a single gene result, that gene is listed. d Cell-type specificity of C80 component using human (left) and mouse (right) single-cell atlases. Mean-rank Gene Set Test in the limma R package 115 was used to obtain the enrichment p -values shown. y -axes show FDR-adjusted p -values after correcting for multiple comparisons across components ( N  = 69) and cell types (human atlas = 10; mouse atlas = 24). Red dashed lines represent α [FDR]  = 0.05. Individual data points are shown using overlaid dot plots. Barplots demonstrate a higher specificity for GABAergic, medium spiny, and dopaminergic neurons. Source data are provided as a Source Data file. ADHD attention deficit hyperactivity disorder, ASC astrocytes, ASD autism spectrum disorder, BD bipolar disorder, CD Crohn’s disease, CN Caudate Nucleus, DEGs differentially expressed genes, DLPFC dorsolateral prefrontal cortex, DMGs differentially methylated genes, END endothelial cells, HP hippocampus, exCA pyramidal neurons from the hippocampal CA region, exDG granule neurons from the hippocampal dentate gyrus, exPFC pyramidal neurons from the prefrontal cortex, GABA GABAergic interneurons, LoF loss of function intolerant genes, MDD major depressive disorder, MG microglia, NC Neurotypical controls, NSC neuronal stem cells, OCD obsessive-compulsive disorder, ODC oligodendrocytes, OPC oligodendrocyte precursor cells, PRS polygenic risk score as reported by the third wave (primary) analyses of the Psychiatric Genetics Consortium 2 ; PTSD posttraumatic stress disorder, SA suicide attempt, SCZ Patients with schizophrenia, UC ulcerative colitis.

Biological characterization of this component showed enrichment for SCZ, major depressive disorder (MDD) and attention deficit hyperactivity disorder (ADHD) risk genes (Fig.  2b, c ) as well as SCZ differentially expressed genes (DEGs) previously observed in the CN 21 and in the DLPFC, differentially methylated genes (DMG; i.e., genes proximal to regions enriched in CpG islands differentially methylated in SCZ compared to healthy controls) and loss of function intolerant genes (all empirical p  < 0.05; Fig.  2b ). Moreover, we used Multi-marker Analysis of GenoMic Annotation (MAGMA) 38 and H-MAGMA 39 , which leverages chromatin accessibility datasets, to perform a gene-set enrichment analysis for pathology-specific GWAS variants and found that the association with SCZ risk of the variants falling within or regulating (based on chromatin interactions) C80 genes was greater than that in the remaining sets ( p [FDR]  < 0.05; Fig.  2b ). Interestingly, this technique showed greater specificity to SCZ, as there was no consistency across MAGMA, H-MAGMA, and GWAS variant analyses with MDD and ADHD results (Fig.  2b ). Further exploratory genetic risk association and biological characterization analyses of the other identified components are reported in the Supplementary Methods and Supplementary Figs.  1a , 6 . The broader landscape of co-expression components highlights further potential pathways of interest linked to SCZ risk.

To determine which tissue contributed more to the inter-individual variation within a given component, we evaluated the tissue score matrix obtained by SDA, which represents the covariance between the overall gene expression derived from one tissue and the component identified. Using a threshold of |0.5| (as previously reported by SDA developers 35 ) in the tissue loading matrix, we found the C80 component to be most active in the CN (Fig.  2b ). Accordingly, cell specificity analysis suggested a highly significant preponderance of medium spiny neurons (MSNs) and dopaminergic terminals ( p [FDR]  = 1.9 × 10 −57 ; Fig.  2d ), consistent with CN localization. Gene ontology analysis (Fig.  3a , Supplementary Fig.  1b, c and Supplementary Data  3 ) characterized C80 as a predominantly synaptic component (133 genes, fold enrichment = 2.3, p [FDR]  = 1 × 10 −18 ) with both pre (128 genes, fold enrichment = 1.9, p [FDR]  = 2.2 × 10 −12 ) and postsynaptic specializations (103 genes, fold enrichment = 2, p [FDR]  = 8 × 10 −12 , Fig.  3a ). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed enrichment for dopaminergic, GABAergic, glutamatergic, and cholinergic synapses, all characteristic of the CN (Fig.  3b and Supplementary Data  3 ).

a , b Gene ontology (cellular compartment) and KEGG enrichment of C80 for both pre and post-synaptic compartments as well as dopaminergic, GABAergic, and glutamatergic synapses. Overrepresentation analysis was performed using the clusterProfiler R 116 package and FDR-adjusted p -values are reported. Diamonds represent fold enrichment ( x -axis) for each Gene ontology category ( y -axis) and are colored based on the respective adjusted p -value. c Venn Diagram shows the intersection between C80 genes and genes expressed in subpopulations of D1- and D2-expressing MSNs in the nucleus accumbens as defined by Tran, Maynard 40 . A larger intersection is found with D2-MSN than D1-MSN. d Overlap between SDA components generated from the LIBD and GTEx datasets that are significantly replicated (one-sided permutation test; empirical p -value < 0.001) using JI or gene loading correlation. Discovery C80 and replication C18 are one of the 4 pairs of components consistent with both JI and gene loading. JI jaccard index, MSN medium spiny neurons.

To follow up on these network-level findings suggesting a role for this component in dopaminergic neurotransmission, we investigated the membership of C80 for dopamine receptor and synthesis genes. C80 included the dopamine D 2 receptor gene DRD2 but not DRD1 , along with tyrosine hydroxylase ( TH ) and DOPA decarboxylase ( DDC ) genes, necessary for presynaptic dopamine biosynthesis. We found that C80 was positively correlated to DDC expression ( t [201]  = 5.3, p  = 2.9 × 10 −7 , partial R 2  = 0.12) and negatively correlated to DRD2 expression ( t [201]  = −3.01, p  = 0.003, partial R 2  = 0.04) in the CN after covarying for biological and technical confounders (see Methods for details). Because DDC catalyzes the last committed step of dopamine synthesis and D 2 receptor signaling inhibits dopamine synthesis, these results are consistent with greater dopamine synthesis capacity in individuals with greater C80, who also have a higher polygenic risk for SCZ. Greater dopamine synthesis may thus be expected to be associated with decreased DRD2 expression in this context.

Interestingly, when restricting the analysis to only healthy individuals, we also found that C80 negatively correlated with DRD2 expression in the DLPFC ( t [218]  = −2.1; p  = 0.04; partial R 2  = 0.02). We further leveraged transcript-level information to disentangle to which extent the DRD2 expression variance was related to the short or long isoform in the CN (see Methods for details), and found a significant association with the long isoform expression in a consistent direction with previous gene-level analyses ( DRD2 short isoform transcript: t [183]  = −1.65, p  = 0.1, partial R 2  = 0.014; DRD2 long isoform transcript: t [183]  = −2.2, p  = 0.029, partial R 2  = 0.025). An even stronger transcript divergence was found with only healthy individuals ( DRD2 short isoform transcript: t [116]  = −1.38, p  = 0.16, partial R 2  = 0.016; DRD2 long isoform transcript: t [116]  = −2.8, p  = 0.006, partial R 2  = 0.06). Due to the MSN enrichment and selective presence of DRD2 compared with DRD1 in this component, we also examined this component’s membership for the 29 most preferentially expressed genes in each MSN class identified by Tran, Maynard 40 in the nucleus accumbens, with specific focus to the D1_A and D2_A clusters as they represented the largest D1-MSN (67%) and D2-MSN (87%) subclasses, respectively. We assessed the statistical significance of these intersections via permutation tests (see Methods for details). Interestingly, 17 out of 29 genes were shared between C80 and D2_A (including PENK , enkephalin typically expressed by indirect pathway MSNs 41 ; empirical p  < 1 × 10 −4 ) and only 8 out 29 were shared with D1_A (with the exclusion of DRD1 and PDYN typically expressed by direct pathway MSNs; empirical p  = 0.09), suggesting that the D 2 -expressing neuronal population may contribute more to the clustering observed in C80 (Fig.  3c ).

Finally, to replicate our findings, we applied SDA to the Genotype-Tissue Expression (GTEx) dataset ( https://gtexportal.org/home/ ) 42 . RNA-seq data were available for 120 NC across CN, DLPFC, and HP (see Table  1 for demographics). This replication analysis yielded 84 components which we used to assess the replication of the 69 LIBD components (see Supplementary Data  1 for SDA output). We assessed the Jaccard Index (JI), representing the overlap between gene sets, and the correlation of component-specific gene loadings as replication measures (see Methods for details). The former revealed more statistically significant replicated components (JI:62 vs. gene loading:34; empirical p  < 0.001; Fig.  3d and Supplementary Fig.  2a ). Indeed, most filtered components were replicated in GTEx (90%; Fig.  3d ) with a median JI = 0.13 in the framework of a gene universe overlap between the LIBD and GTEx dataset of JI = 0.67. Interestingly, C80 was among the only four replicated components out of 69 in which the associated GTEx component (C18) was consistently found using both JI and gene loading (JI = 0.19; gene loading R 2  = 0.19; empirical p  < 0.001; Fig.  3d and Supplementary Fig.  2a ). Moreover, the GTEx C18 component was again mainly active in the CN with a similar neurobiological profile by cell specificity, gene ontology, and KEGG pathway analyses and a similar enrichment for dopaminergic synapse (33 genes; fold enrichment = 1.7; p [FDR]  = 0.01) (Supplementary Fig.  2b–d and Supplementary Data  3 ). Accordingly, we replicated the association between C18 component loadings and DRD2 expression in the CN with effect directions consistent with our discovery C80 component ( t [94]  = −2.6; p  = 0.01). We did not find a significant association with either DRD2 long or short isoform transcripts ( DRD2 short isoform: t [116]  = −1.9, p  = 0.056, partial R 2  = 0.03; DRD2 long isoform: t [116]  = −1, p  = 0.3, partial R 2  = 0.008; see Methods for details). A transcript-level tensor decomposition might be best suited to capture the variance at this fine-grained biological resolution.

In summary, we identified replicable co-expression patterns relative to the dopaminergic neurotransmission in a completely independent dataset of neurotypical individuals. The list of genes within discovery C80 and replication C18 components is reported in Supplementary Data  3 .

Brain functional association analysis

Based on these results in support of C80’s role in SCZ, SCZ risk, and dopaminergic function, we computed a PRS stratified for genes within C80 (C80-PRS) to examine the relationships between C80-specific SCZ genetic risk burden and neurochemical and neurofunctional parameters in the living human brain (see Methods for details about PRS computation, p -value thresholds used and neuroimaging association analyses). C80-PRS was positively associated with greater striatal dopamine synthesis capacity as measured by [ 18 F]-FDOPA specific uptake in NC and patients with SCZ (C80-PRS1: Fisher’s z r[99.5% CI] : 0.33 [0.01, 0.65] ; p  = 0.0037; p [Bonferroni]  = 0.037; R 2  = 0.10) in the whole-striatum ROI analyzed in our discovery cohorts (Table  1 ; Fig.  4a ). In the same ROI, we also found a significant association for PRS2 (C80-PRS2: Fisher’s z r[99.5% CI] : 0.34 [0.03, 0.66] ; p  = 0.0024; p [Bonferroni]  = 0.024; R 2  = 0.11) (Supplementary Fig.  3a ). Furthermore, both the C80-PRS1 and C80-PRS2 were significantly and more strongly associated with [ 18 F]-DOPA PET uptake in the associative striatum (C80-PRS1: Fisher’s z r[99.5% CI] : 0.38 [0.07, 0.70] ; p  = 0.0006; p [Bonferroni]  = 0.006; R 2  = 0.13 C80-PRS2: Fisher’s z r[99.5% CI] : 0.35 [0.03, 0.66] ; p  = 0.002; p [Bonferroni]  = 0.02; R 2  = 0.11), (Supplementary Fig.  3b, c ). There was no significant correlation with limbic or sensorimotor striatum when correcting for multiple comparisons. Interestingly, these results remained consistent even across different genetic ancestry definitions (Supplementary Fig.  4a, b ; see Supplementary Methods for details).

a Associations between C80-PRS and both PET cohorts are shown. First row (PET discovery; n  = 84 individuals; 64 NC and 20 SCZ): on the left whole-striatum region of interest (ROI) coverage (red) is shown overlaid on a grayscale standardized [ 18 F]-FDOPA PET activity map; on the right graphs shows standardized individual mean K i values for this ROI plotted against C80-PRS for the neurotypical control and SCZ subjects (upper) as well as the forest plot of the metanalysis (lower). Second row (PET replication; n  = 150 NC): Region of a positive association between C80-PRS and presynaptic dopamine synthesis capacity ([ 18 F]-FDOPA K i ) is shown as a statistic parametric map (color indicates t -statistic value) overlaid on a grayscale standardized [ 18 F]-FDOPA PET activity map ( p  < 0.005, uncorrected for display). The scatter plot shows standardized individual mean K i values for a 2 mm sphere around the peak voxel plotted against C80-PRS. Mean fitted values and related shaded 95% confidence interval are shown in the scatterplots. Fisher’s r-to-z transformed correlation coefficients and related 99.5% confidence interval are shown in the forest plot. Source data are provided as a Source Data file. b Associations between C80-PRS and both fMRI cohorts are shown. First (fMRI discovery; n  = 86 NC) and second (fMRI replication; n  = 55 NC) rows: Regions of positive association between C80-PRS and fMRI BOLD response during reward anticipation are shown as statistic parametric maps (color indicates the threshold-free cluster enhancement (TFCE) statistics expressed in the –log10 scale). All results meet thresholds of p [TFCE-FDR]  < 0.05 and cluster extent >20 voxels. Scatter plots show standardized individual MID-related fMRI BOLD contrasts plotted against C80-PRS with mean fitted values and related shaded 95% confidence interval shown. Source data are provided as a Source Data file.

In our independent replication cohort (Table  1 ), C80-PRS was also positively associated with greater striatal [ 18 F]-FDOPA specific uptake, albeit at a different PRS threshold (C80-PRS6: t [149]  = 3.95; k  = 16; p [FWE]  < .05; R 2  = 0.10). This association was predominantly observed in the sensorimotor region extending into the associative striatum at p  < 0.005 (Fig.  4a ).

Finally, we investigated the association of C80-PRS with striatal functioning using fMRI in participants who performed a reward processing task (see Methods and Supplementary Methods for details). We found that the C80-PRS1 was positively correlated with differential anticipatory activation in the right associative striatum during high vs low motivation assessed in a discovery sample of 86 NC (Table  1 ; C80-PRS1: p [TFCE-FDR]  = 0.04; Z  = 3.54; x  = 17; y  = 15; z  = −5; 60 voxels; partial R 2  = 0.13). Specifically, participants with higher C80-PRS, and thus higher predicted striatal dopamine synthesis, showed greater striatal activation when they expected a reward during the task (Fig.  4b ). We consistently found this association in an additional independent sample of 55 NC (Table  1 ; C80-PRS1: p [TFCE-FDR]  = 0.03; Z  = 3.75; x  = 18; y  = 17; z  = 5; 34 voxels; partial R 2  = 0.25) in a cluster located once again in the associative striatum (voxel-wise discovery-replication overlap volume: 162 mm 3 ; see Supplementary Methods and Supplementary Fig.  7 ). It is worth mentioning that we confirmed the C80-PRS effects also on the BOLD signal extracted from both the right and left-associative striatum ROIs (see Supplementary Methods for details). Importantly, the neuroimaging associations we identified with C80-PRS were not significant for the other components associated with genetic risk for SCZ (see Supplementary Methods and Supplementary Fig.  6d ).

We also computed a measure of cumulative SCZ risk burden based on GWAS risk genes not in C80 (C80-PRS-complementary) and did not find any significant association in any of the PET and fMRI samples ( p  > 0.05; Supplementary Fig.  5a, b ). It is also worth mentioning that the number of SNPs included at each threshold for the C80-PRS was always lower than for the C80-PRS-complementary (Table  2 ), indicating the SNPs mapped to C80 genes represent a minority more closely involved in dopaminergic processes than the rest. Supplementary Data  4 includes SNPs mapping to C80 genes used to compute the C80-PRSs.

The genetic architecture of SCZ is complex and spans the genome 2 . Despite evidence for aggregation of implicated genes into certain clusters 43 , characterizing the functional biology of SCZ risk genetics has been a challenge. We applied a tensor decomposition method, i.e., SDA, to post-mortem brain gene expression data from three brain regions, i.e., CN, HP, and DLPFC. We identified cohesive biological pathways that are implicated in SCZ illness and risk. Such pathways delineate plausible routes from SCZ genetic risk variation to specific neural circuit functions perturbed in this condition. We discovered a CN-dominant co-expression gene set (C80) that is enriched for genes differentially co-expressed in individuals with SCZ relative to NC and is associated with individual genetic risk for SCZ, features that suggest a role in SCZ pathogenesis. Expanding long-held hypotheses of dopaminergic involvement in psychosis in general and in SCZ more specifically, this gene set showed enrichment for dopamine-system genes and embedded SCZ risk variation that specifically tracked in vivo neurochemical and neurofunctional dopamine- and illness-related phenotypes.

The SDA algorithm provided an efficient technique to uncover sparse gene networks that were not only statistically robust but also biologically coherent. The C80 component captured gene expression covariance showing biological specialization for striatal dopaminergic circuitry implicated in SCZ. A key component of this circuitry, the C80 member gene DRD2 , is expressed both at the presynaptic and postsynaptic terminals. The gene product is the D 2 dopamine receptor, acting as an autoreceptor regulating dopamine synthesis and release in the presynaptic terminal, and driving the indirect striatal pathway activity in the postsynaptic terminal of MSNs 44 . In line with DRD2 ’s autoreceptor role, SDA segregates DRD2 together with the genes for the primary dopamine biosynthesis enzymes, TH and DDC , in a single, SCZ-associated component, although isoform transcript analyses did not confirm an isoform preference replicated across LIBD and GTEx data. This is not surprising in principle, as greater dopamine biosynthesis should have downstream effects on both receptor isoforms. However, at the total gene expression level, greater C80 (higher SCZ risk) correlated with higher striatal expression of DDC , translating into an increase of dopamine synthesis, and as expected, lower expression of DRD2 , with both results replicated in an independent NC cohort. These findings, along with the neurofunctional results, highlight the control of presynaptic dopamine synthesis and release as a mechanism of dopamine-associated pathogenesis 21 . Importantly, while previous work on translating genetic risk into gene expression association has highlighted the presynaptic short isoform as the molecular mechanism of risk, here we are looking at co-expression in a broader biological context than genetic risk alone at a single locus.

It is notable that follow-up analyses of C80 gene membership additionally identified a preference for genes expressed in the indirect pathway, i.e., D 2 dopamine receptor bearing MSNs (e.g., DRD2 , PENK ) over those expressed in direct pathway associated, i.e., D 1 dopamine receptor bearing MSNs (e.g., DRD1 , PDYN ) 41 . As KEGG pathway analysis revealed a collection of dopaminergic, GABAergic, glutamatergic, and cholinergic pathway-related genes belonging to this component, it is interesting that among the genes segregated by SDA in both C80 and C18 components is the one encoding for the M4 muscarinic receptor ( CHRM4 ). This receptor was previously associated with the regulation of cholinergic and dopaminergic neurotransmission in SCZ 45 , 46 , 47 , 48 and was recently highlighted as a potential therapeutic target for this disorder 49 , 50 , 51 . Besides the relevance of C80 to presynaptic dopaminergic mechanisms, these observations point to a wider biological interpretation of the genes co-expressed within this SCZ-associated component, including cortico- and nigro-striatal terminals closely tethered to the indirect pathway.

Following the identification of these links between C80 and dopaminergic systems, we conducted in vivo neuroimaging investigations and found that SCZ genetic risk variation that is mapped to the C80 gene set—and not cumulative risk outside of it—is specifically associated with elevated presynaptic dopamine synthesis in the striatum, which we observed in both NC and SCZ cohorts across three independent PET samples totaling 235 participants. This is consistent with the [ 18 F]-FDOPA-associated phenotype in SCZ 52 and further supports the notion that C80, also expressed to a greater degree in SCZ, plays an important role in presynaptic dopamine dynamics. Thus, the present results may provide a molecular mechanism for the risk signature of a central phenotypic pillar of the modern dopamine hypotheses of SCZ 52 .

The specificity of these findings sheds light on the elusive source of heterogeneity in SCZ and its pathobiology. These data align with the notion that some routes to clinical illness (e.g., those within the C80 pathway) preferentially perturb presynaptic dopamine systems over others. This model provides a possible molecular basis for the observation that not all individuals with SCZ show excessive presynaptic dopamine synthesis capacity and not all patients respond well to antidopaminergic medications 18 , 19 . More generally, the approach we employed may be promising for stratifying patients based on their pathway-specific genetic liability to illness, which, if confirmed to be clinically informative, could provide new avenues for personalized medicine.

The association between these pathway-specific variants and heightened striatal dopamine synthesis, as evidenced by post-mortem and PET data, aligns with findings from a dual PET-fMRI study that demonstrated positive correlations between reward anticipation-related activation and striatal dopamine release in healthy individuals 53 . Accordingly, relative dopamine depletion attenuates striatal activation during the same task in healthy subjects 54 . We found convergent positive associations between SCZ risk within the dopamine-system-enriched C80 gene set and anticipatory activation in both our discovery and replication cohorts. The activation clusters localized to the head of the caudate, the same region used in the postmortem study. Moreover, our results are consistent with past findings of positive correlations between polygenic risk for SCZ and striatal activation during the MID task in a large sample of healthy adolescents 55 . Importantly and again, cumulative risk outside of this filter (i.e., variants not included in C80) did not show a significant relationship with this anticipatory BOLD response; along with the similar pattern observed in our PET results, this specificity suggests that parsing the PRS into co-expression pathways can provide a biologically accurate physiological modeling to translate genetic risk into brain mechanisms 43 .

The positive correlations between C80-specific SCZ risk burden and reward anticipation BOLD response in neurotypical individuals deviate in direction from prior findings of blunted striatal response to reward anticipation in patients with SCZ 24 , 25 , 56 , 57 , 58 , 59 , 60 . The difference between genetic and clinical findings may have multiple sources, including illness characteristics and pharmacological treatment. Patients with SCZ display abnormal salience attribution patterns 61 , which could lead to reduced contrast between anticipation cues and baseline, ultimately resulting in poorer motivational performance and BOLD contrast during reward anticipation 62 , 63 , 64 . Secondly, the activity of different brain regions involved in the reward system can be affected by the disorder 65 , while being preserved in our fMRI samples only including NC. Third, SCZ patients exhibit elevated striatal dopamine synthesis and release as measured by PET 7 , 11 , 12 , 13 , 14 , suggesting that higher steady-state dopamine levels may cause an apparent blunted response by elevating baseline activation. Consistent with this hypothesis, Knutson et al. 66 reported that amphetamine administration, which blunts task-based dopamine release while enhancing steady-state availability in the striatum 67 , leads to decreased peak activation but prolonged activation duration during reward anticipation in healthy individuals. Taken together, we suggest that the blunted response to stimuli in a saliency-modulating task observed in SCZ may arise at least in part from both reward devaluation as well as enhanced steady-state baseline activity.

A further paramount consideration in studies of reward circuity in patients with SCZ is the impact of neuroleptic medications on the sensitivity of the brain’s reward system 68 , 69 . Previous studies indicate that antipsychotic drugs can blunt reward-related anticipatory striatal activation in individuals with SCZ 56 , 57 . This effect may be associated with the blockade of D 2 dopamine receptors 56 , 57 or the suppression of dopaminergic neuron firing 70 , 71 , 72 , known as inactivation block. In fact, the recent report by Benjamin et al. 21 highlights a significant downregulation of the TH gene in CN of SCZ patients receiving antipsychotic drugs. Acute depletion of dopamine has been associated with reduced striatal activation during reward anticipation in both patients 73 and controls 54 . Notably, atypical antipsychotics, which exhibit a lower level of D 2 receptor affinity, were found to enhance striatal fMRI BOLD signal during reward anticipation relative to first-generation, high-affinity D 2 blocking agents 57 , 74 , 75 . Nonetheless, even atypical antipsychotic medications may increase baseline striatal activity in a dopamine-dependent fashion 76 . Thus, the effects of illness and pharmacological stimulation are not necessarily aligned with the relationship between illness genetic risk and striatal physiological activation in the neurotypical state. In this regard, examining the effects of polygenic risk for SCZ in samples of healthy controls provided an important perspective on risk biology while avoiding important illness-associated confounders, such as treatment with D 2 dopamine receptor antagonists.

Limitations of this study include the relatively small sample size used in the gene co-expression analysis, which is pivotal for decomposition approaches 37 . To obtain the 3D tensor used as input for SDA, we had to exclude samples without available data in all three brain regions analyzed. This filter was especially limiting for the GTEx replication dataset. It is also important to acknowledge unavoidable dataset discrepancies that might have hindered replications in the DRD2 transcript-level analysis. Table  1 delineates the demographic heterogeneity of LIBD samples in contrast with the more homogenous GTEx cohort, especially in age distributions (GTEx: 59.2 ± 9.6 years; LIBD: 46.2 ± 16.9 years; Wilcoxon rank sum test: one-tailed p < 2.2 × 10–16). Additionally, the substantial difference in sequencing depth (LIBD: mean of 125.2 million reads per sample 21 , 77 ; GTEx: mean of 50 million 42 ) suggests a different resolution across the two datasets in detecting fine-grained transcript-level variations. Despite these distinctions in demographic and technical aspects, at the total gene level, our tensor decomposition analysis successfully identified consistent co-expression patterns related to dopaminergic neurotransmission across both datasets. These results suggest that the finer biological resolution required for DRD2 isoform association might be more sensitive to the variations inherent in the datasets. Moreover, while including SCZ post-mortem data in our analyses provided important insights into illness associations of identified SDA components, antipsychotic treatment, and other illness-associated epiphenomena may have introduced confounding effects for the SDA analysis. We tried to address this issue by performing SDA on the same three tissues using an independent dataset consisting of only healthy controls (GTEx) to assess the rate of replication and generalizability of these results. Indeed, we found that 90% of the components identified were replicated, and the one we studied was very well reflected in GTEx. Furthermore, while the total sample size for [ 18 F]-FDOPA PET genetic studies in this work is unparalleled, the within-cohort sample sizes are limited, which may explain minor differences in peak findings across different PRS SNP p -value thresholds or anatomically within the striatum. Nonetheless, the convergent positive association identified between C80 and striatal presynaptic dopamine synthesis in all cohorts studied despite independent, multi-site sampling and diverse methods bolsters confidence in the PET results. Additionally, while the non-invasive reference region graphical linearization method used here for [ 18 F]-FDOPA quantification has been well validated, it is possible that alternative kinetic modeling in future studies using arterial data may allow for a more comprehensive view of observed effects. Finally, it is possible that sample size limitations in the fMRI dataset coupled with the substantial variability in the degree and direction of laterality across individuals might have affected the localization of the effects and prevented the identification of weaker but important bilateral activations at the voxel-wise level, although the consistency of findings across independent cohorts and at the ROI level mitigates this concern.

In conclusion, these results highlight a dopamine-related striatal gene set that characterizes the illness state in SCZ, is implicated in SCZ genetic risk, and is involved in dopamine synthesis and striatal physiological activity in vivo, suggesting that genetic risk within this pathway differentially affects SCZ-relevant striatal function. These observations provide evidence that polygenic risk for SCZ can be effectively parsed into pathways important for specific systems-level functions that are measurable even in the absence of illness 43 . Furthermore, they suggest a molecular basis of how genetic risk within the C80 pathway might affect illness-relevant striatal neurochemistry and neurofunction and may open new possible avenues for studying clinical heterogeneity 18 , 43 and drug treatment response 43 .

The research described herein complies with all relevant ethical regulations. Postmortem human brain tissue was obtained as previously described 77 , 78 . Briefly, tissues were primarily obtained by autopsy from the Offices of the Chief Medical Examiner of the District of Columbia and of the Commonwealth of Virginia, Northern District, all with informed consent from the legal next of kin (protocol 90-M-0142 approved by the National Institute of Mental Health (NIMH)/National Institutes of Health (NIH) Institutional Review Board). The National Institute of Child Health and Human Development Brain and Tissue Bank for Developmental Disorders ( https://medschool.umaryland.edu/BTBank ) provided infant, child, and adolescent brain tissue samples under the NO1-HD-43368 and NO1-HD-4-3383 contracts. Additionally, donations of postmortem human brain tissue from patients with SCZ were provided with informed consent by next of kin from the Office of the Chief Medical Examiner for the State of Maryland under protocol number 12–24 from the State of Maryland Department of Health and Mental Hygiene and from the Office of the Medical Examiner, Department of Pathology, Homer Stryker, Maryland School of Medicine under protocol number 20111080 from the Western Institute Review Board. The Institutional Review Board of the University of Maryland at Baltimore and the State of Maryland approved the study protocol. The Lieber Institute for Brain Development (LIBD) received the tissues by donation under the terms of a material transfer agreement.

The discovery cohort of participants in the PET study was obtained under ethical permission given by the Administration of Radioactive Substances Advisory Committee (ARSAC), the South London and Maudsley/Institute of Psychiatry NHS Trust, the London Bentham Research Ethics Committee, and the Hammersmith Research Ethics Committee. All participants provided written, informed consent per King’s College London (KCL) IRB-approved protocols.

The replication cohort of participants in the PET study was obtained under ethical permission given by the National Institute of Mental Health Institutional Review Board and the National Institutes of Health (NIH) Radiation Safety Committee. All participants provided written, informed consent per NIH IRB-approved protocols.

The discovery cohort of participants in the fMRI experiments had no history of any psychiatric or neurological disorders and gave written, informed consent for a protocol approved by the NIH Combined Neurosciences IRB. Participants were told that they would be monetarily compensated based on earnings in the task.

The replication cohort of participants in the fMRI experiments had no history of any psychiatric or neurological disorders and gave informed consent for a protocol approved by the institutional ethics committee of the University of Bari Aldo Moro (UNIBA). Participants were told that they would be compensated with one gift gadget (pen, t-shirt, pin, bag, pouch, notebook) when they earned at least 1700 points, and the chance of choosing between two or three gifts of their choice (when reaching 1900 and 2300 points, respectively) and encouraged to respond as quickly as possible.

The study design and conduct complied with all relevant regulations regarding the use of human study participants and was conducted in accordance with the criteria set by the Declaration of Helsinki.

Lieber Institute for Brain Development (LIBD) post-mortem data—discovery cohort

We used post-mortem human brain tissue from the LIBD Human Brain Repository. Patients with SCZ were collected from the Office of the Chief Medical Examiner for the State of Maryland under the State of Maryland Department of Health and Mental Hygiene Protocol 12–24 and from the Kalamazoo County Michigan Medical Examiners’ Office under Western Institutional Review Board Protocol 20111080. All included NC subjects had minimal age-associated neuropathology (determined from postmortem histopathological examination) and no substance or drug use from toxicology and were free from any psychiatric or neurological disorder from clinical histories. Postmortem clinical information was gathered by conducting family interviews with the next of kin. After psychiatric record reviews and postmortem family interviews were completed, brief psychiatric narratives were prepared on each case, summarizing the demographic, clinical, medical, and death information obtained from as many sources as possible (i.e., multiple psychiatric records, police reports, neuropathology reports, medical examiner’s information, toxicology screen, and postmortem family interview). Each case was then independently reviewed by two board-certified psychiatrists, who arrived at consensus DSM-IV Axis I lifetime diagnoses or consulted with a third reviewer to reach a final diagnosis. All samples were collected and processed using a standardized protocol specifically developed to minimize sample heterogeneity and technical artifacts 77 , 78 .

The CN samples were derived from the anterior ‘head’ portion, a subregion tightly connected with the prefrontal cortex; HP samples from the mid-hippocampus proper (all dissections included the dentate gyrus, CA3, CA2, and CA1) plus the subicular complex; and DLPFC samples from Brodmann Area 9/46 at the level of the rostrum of corpus callosum 79 .

For all tissues, RNA sequencing was performed via the Illumina Ribozero Kit as previously described 77 . Briefly, RNA was extracted using the QIAGEN AllPrep DNA/RNA Mini kit, which concurrently extracted DNA and total RNA. Following RNA extraction, sequencing libraries were prepared from 300 ng of total RNA using the TruSeq Stranded Total RNA Library Preparation kit with RiboZero Gold rRNA depletion. For quality control, synthetic External RNA Controls Consortium (ERCC) RNA Mix 1 was spiked into each sample. These paired-end, strand-specific libraries were sequenced on an Illumina HiSeq 3000 at the LIBD Sequencing Facility across multiple lanes. FASTQ files were generated using the Illumina Real-Time Analysis module by performing image analysis, base calling, and the BCL Converter (CASAVA v1.8.2). The reads were aligned to the hg38/GRCh38 human genome (GENCODE release 25, GRCh38.p7, chromosome only) using HISAT2 (v2.0.4)48 and Salmon (v0.7.2)49 using the reference transcriptome to initially guide alignment based on annotated transcripts. The synthetic ERCC transcripts were quantified with Kallisto (v0.43.0)50. Counts were generated as previously described 77 , 78 . Briefly, sorted BAM files from HISAT2 alignments were generated and indexed using SAMtools (v1.6; HTSlib v1.6). Alignment quality was assessed using RSeQC (v2.6.4)51. Gene-level mRNA expression was quantified as Reads Per Kilobase per Million mapped reads (RPKMs) and annotated as total gene expression separately for each brain region using GENCODE release 25, GRCh38.p7.

We included NC and SCZ samples with European or African American ancestry, all with RNA Integrity Number (RIN) ≥ 6. We used inter-array distance to identify tissue-specific outlier subjects deviating more than three standard deviations from the mean 32 (CN = 4; HP = 7; DLPFC = 5). We then focused our analyses on mRNA expression measurements that were available for common samples ( N  = 238) and genes ( N  = 58,037) across all three tissues. The sex of participants was determined based on self-report and used as a covariate in the following analyses. The demographic data are summarized in Table  1 .

The Genotype-Tissue Expression (GTEx) post-mortem data—replication cohort

We used the recount3 R 80 package to download already processed GTEx v8 RPKMs for CN, HP, and DLPFC (Frontal Cortex BA9). Data available for all three tissues consisted of 120 samples and 54,892 genes (Table  1 ). Sex of participants was determined based on self-report and used as a covariate in the following analyses.

King’s College London (KCL) PET data—discovery cohorts

Two cohorts, one with 92 NC and one with 47 individuals with SCZ, (see Supplementary Table  1 for demographics) underwent [ 18 F]-FDOPA positron emission tomography (PET) scans to measure dopamine synthesis capacity (indexed as the influx rate constant K i ) in the striatum as previously described 81 , 82 , 83 , 84 , 85 , 86 . In short, after pretreatment one hour before the scan with fixed doses of carbidopa (150 mg) and entacapone (400 mg) to reduce peripheral tracer metabolism, and immediately following intravenous injection of [ 18 F]-FDOPA, a series of dynamically binned emission frames were acquired over 95 minutes. Computed tomography (CT) imaging was performed for attenuation correction. Scans were obtained on one of the following PET scanners: an ECAT HR + 962 (CTI/Siemens, Knoxville, Tennessee), and an ECAT HR + 966 (CTI/Siemens, Knoxville, Tennessee), and two Siemens Biograph HiRez XVI PET-CT scanners (Siemens Healthcare, Erlangen, Germany). Reconstructed, attenuation-corrected emission scans were realigned to correct for interframe head motion. An atlas defining the regions of interest (striatum, its subdivisions, and the reference region (cerebellum) as described in Howes et al. 15 was co-registered to a tracer-specific template and transformed to each subject’s PET data series using SPM 12 software (UCL, London, UK). Time-activity curves were extracted for the regions of interest and entered into standard Patlak-Gjedde graphical linear models using the reference region to adjust for non-specific uptake to obtain the influx rate constant K i , a measure of specific tracer uptake 87 . The primary analyses focused on the whole striatum. For post-hoc exploratory analyses, the striatum was subdivided into limbic, associative (AST), and sensorimotor (SMST) subdivisions based on the functional topography of the striatum and its connectivity as previously described 88 . In short, measurements of the AST were derived as the spatially weighted average of the precommissural dorsal caudate, precommissural dorsal putamen, and post-commissural caudate. In addition, at the most detailed anatomic level, a significant regional overlap exists between the associative and limbic circuits. Thus, the classification used here identifies these functional circuits only in a probabilistic sense: these regions correspond mostly, but not exclusively, to the various functional subdivisions of the striatum.

The sex of participants was determined based on self-report and used as a covariate in the following analyses.

National Institute of Mental Health (NIMH) PET data—replication cohort

[ 18 F]-FDOPA PET scans were acquired for a total of 150 healthy subjects (demographics in Table  1 ) as previously described 89 . In short, after a required 6-hour fast to prevent competition for tracer transport to the brain, a 4-hour caffeine/nicotine restriction, and pretreatment with fixed doses of carbidopa (200 mg) to reduce peripheral tracer metabolism, and immediately following intravenous injection of [ 18 F]-FDOPA, a series of dynamically binned emission frames were acquired over 90 minutes. A transmission scan was performed in the same session for attenuation correction. All scans were obtained on a GE Advance PET tomograph operating in 3D mode with a thermoplastic mask applied to help restrict head movement. Reconstructed, attenuation-corrected emission scans were realigned to correct for interframe head motion. Spatial warping of PET data was performed with ANTs (v2.5.0) software to an MNI space tracer-specific template. A 10 mm Gaussian kernel smoothing was applied to improve voxel-wise signal-to-noise ratios. Using PMOD (v4.4) software ( https://www.pmod.com/ ), time-activity curves from voxels within the striatum were subjected to standard Patlak-Gjedde graphical linear modeling using cerebellar reference region time-activity data as an input function to yield K i as above 87 .

LIBD fMRI data—discovery cohort

An independent sample of 86 NC (demographics in Table  1 ) participated in an fMRI experiment in which participants performed a modified version of the Monetary Incentive Delay (MID) task 90 based on the expectancy theory of motivation 91 . Participants had no history of any psychiatric or neurological disorders and gave written, informed consent for a protocol approved by the NIH Combined Neurosciences IRB. Participants were told that they would be monetarily compensated based on earnings in the task. Details about the task layout are reported in the Supplementary Methods.

fMRI scans were acquired through a 3 T GE Signa scanner. Gradient-recall echo-planar imaging was used with the following parameters: TR = 2000 ms; TE = 28 ms; flip angle = 90; 64 × 64 matrix; FOV = 240 mm; and 35 3.5 mm slices acquired with an interleaved order of slice acquisition and first five frames discarded to allow steady-state magnetization. Slice timing correction, six-parameter co-registration to adjust for movement, mean functional-image driven spatial normalization to MNI space, and spatial smoothing with an 8 mm Gaussian kernel were applied and yielded time-series data with 3 mm isotropic resolution through SPM12 as implemented in MATLAB (v2023a; https://www.mathworks.com/products/matlab.html ). A separate general linear model (GLM) was specified for each participant, modeling time-locked BOLD responses to high reward vs low reward, i.e., low expectation vs. high expectation of reward event onsets, i.e., high motivation vs low motivation, by convolving the onset vectors with a synthetic hemodynamic response function as implemented by SPM12. At the model estimation stage, the data were high-pass filtered with a cutoff of 128 s to remove low-frequency drifts. Global scaling was not applied to the data.

UNIBA fMRI data—replication cohort

A cohort of 55 NC (demographics in Table  1 ) participated in an fMRI experiment in which participants performed an alternative version of the MID task 90 to the one described before. For details about the task layout see Supplementary Methods.

fMRI scans were acquired through a 3 T Philips Ingenia scanner. Gradient-recall echo-planar imaging was used with the following parameters: TR = 2000 ms; TE = 38 ms; flip angle = 90; 64 × 64 matrices; FOV = 240 mm; and 38 3.6 mm slices acquired with an interleaved order of slice acquisition. Slice timing correction, six-parameter co-registration to adjust for movement, mean functional-image driven spatial normalization to MNI space, and spatial smoothing with a 9 mm Gaussian kernel were applied and yielded time-series data with 3 mm isotropic resolution through SPM12 as implemented in MATLAB (v2023a; https://www.mathworks.com/products/matlab.html ). A separate GLM was specified for each participant, modeling time-locked BOLD responses to high reward vs low reward event onsets, i.e., high motivation vs low motivation, by convolving the onset vectors with a synthetic hemodynamic response function as implemented by SPM12. At the model estimation stage, the data were high-pass filtered with a cutoff of 128 s to remove low-frequency drifts. Global scaling was not applied to the data.

Genotype data processing and polygenic risk score (PRS) calculation

Genotype data acquisition, imputation, and processing as well as calculation of genomic eigenvariates (GEs) for population stratification were performed in each cohort separately. See Supplementary Methods for further details.

We indexed the whole-genome genetic risk for SCZ by computing the PRS for each sample using the PRSice-2 (v2.3.3) software 92 . To obtain a highly informative SNP set with as little statistical noise as possible, we excluded uncommon SNPs (MAF < 1%), low-quality variants (imputation INFO < 0.9), indels, and SNPs in the extended MHC region (chr6:25–34 Mb). We used PGC (wave 3; primary autosomal analysis) GWAS 2 summary statistics that did not include any of the LIBD discovery samples (leave-sample-out) to weight SNPs by the effect size of association with SCZ. We used European samples from the 1000 Genomes Project 93 (1000 G) as an external reference panel to improve the linkage disequilibrium (LD) estimation for clumping. Both PGC3 leave-LIBD-out and the reference panel were in reference to human genome Build 37.

To stratify SCZ genetic risk for genes within a specific component we first mapped European 1000 G SNPs at 100kbp up- and downstream of each component-specific gene using the MAGMA tool (v1.09b), we then matched component-specific SNPs with PGC3 leave-LIBD-out summary statistics and finally computed the scores for the KCL, NIMH, LIBD- and UNIBA-fMRI cohort separately using PRSset 94 and again the European 1000 G as LD reference panel. As negative control, we computed complementary scores including all PGC3 leave-LIBD-out SNPs not mapping to any of the component-specific genes.

We used PRSs based on 10 SNP sets corresponding to GWAS SNP association p -values of p  = 5 × 10 −8 (PRS1), p  = 1 × 10 −6 (PRS2), p  = 1 × 10 −4 (PRS3), p  = 0.001 (PRS4), p  = 0.01 (PRS5), p  = 0.05 (PRS6), p  = 0.1 (PRS7), p  = 0.2 (PRS8), p  = 0.5 (PRS9), and p  = 1 (PRS10) 2 . Table  2 shows the number of SNPs used for each cohort for each PRS threshold.

RNA data processing

To analyze LIBD postmortem data with SDA, we first removed mitochondrial genes and genes with RPKM expression median lower than 0.1 or deviating more than 3 standard deviations from the mean in each tissue. We then removed genes with more than 20% zeroes in all three tissues as previously done 35 . We log-transformed RPKM values with an offset of 1, i.e., log2(RPKM + 1). After performing quantile normalization to normalize samples based on their gene expression, we rank-normalized gene expression using Blom formula 27 , 28 , 95 to limit the impact of deviations from normality in expression data. We performed all normalization steps for each tissue separately. The final tensor of 22,356 mRNA expression measurements in 238 samples and across CN, HP, and DLPFC was used as input for SDA (see Table  1 ).

Sparse decomposition of arrays (SDA) computation

The SDA algorithm is developed in a Bayesian framework and uses a sparse ‘spike and slab’ 96 prior to allow the gene loadings of each component to have a unique level of sparsity. This allows us to shrink gene effects to zero so that we can infer more clearly which genes are involved in gene networks. We iterated the algorithm 10 times, and for each run, we obtained latent components defined by three bidimensional matrices: (i) the individual score matrix, which represented the magnitude of the effect of each component across individuals and was used to compute the association with diagnosis; (ii) the tissue score matrix, which indicated the activity of the component for each tissue and was used to identify the contribution of each tissue to the components; (iii) the gene loading matrix, which indicated the contribution of each gene to components and served to identify genes specific to tissues or shared between them (see Supplementary Methods for further details about parameters used).

We thus obtained robust components found consistently across multiple iterations, whereas others only occur in one or a few of them. We clustered similar components across different iterations following published procedures 35 (see Supplementary Methods). We obtained 126 large clusters containing components from multiple different iterations and combined components within each cluster by taking the mean of the individual scores, tissue scores, and gene loadings. We finally used the resulting 126 combined clusters as the basis for further analyses.

Tissue activity evaluation

We evaluated the tissue loadings across components by column-wise scaling the tissue score matrix (components as columns and tissues as rows) obtained by the SDA decomposition so that the largest score was equal to 1 and the lowest to −1 using a threshold of |0.5| (as previously reported by SDA developers 35 ) to infer the tissue specificity of each component and how many components are shared across tissues (Fig.  2b and Supplementary Fig.  1a ).

Confounder analysis

Since SDA identifies non-sparse components that might be expected to arise from confounding effects, we expected singular value decomposition to reveal latent confounders. Singular value decomposition in its principal component analysis implementation has been used often for this aim 35 , 97 , 98 , 99 . To identify components most likely to represent confounding effects, we computed a series of multiple linear regressions using as dependent variable individual scores from the individual score matrix for each of the 126 SDA components and as predictors both biological confounders (age, sex, diagnosis, first 10 GEs) and technical confounders (postmortem interval (PMI), RIN, mitochondrial mapping rate, rRNA rate, gene mapping rate).

We adopted a confounder detection approach consistent with the reference paper 35 by using the same confounder effect size of 0.274 ( p  = 10 −10 ; sample size = 845). We found that the same effect size corresponded to p  < 5 × 10 −4 in our sample of 238 individuals (observed power = 80%) and removed the 57 components associated at this threshold with at least one of the technical confounders or GEs. We focused our further analyses on the remaining 69 components.

Diagnosis and PRS association

To investigate whether the 69 components were differentially co-expressed between NC and SCZ, we tested the association of the component-specific individual scores with diagnosis via ANCOVA while covarying for biological (age, sex, and ancestry) and tissue-specific technical confounders (PMI, RIN, mitochondrial mapping rate, rRNA rate, gene mapping rate), taking into account the component tissue activity. Samples with age >17 were included ( n  = 229) as this was the minimum age in the SCZ sample.

We further evaluated the association of the differentially co-expressed components with PRS via multiple linear regression again covarying for age, sex, diagnosis, and tissue-specific confounders and including only samples with European ancestry ( n  = 103) since the summary statistics used are mainly based on the European population. For this analysis one-tailed tests were used because of the constraint on effect directionality, i.e., we discarded potentially significant results in the opposite direction of diagnosis association. We focused on PGC3 variants with a SCZ association p -value < 0.05, since this PRS has been shown to have the highest prediction accuracy for diagnostic status in multiple independent samples 2 . We used Benjamini-Hochberg false discovery rate (FDR) correction to correct for multiple comparisons across SDA components and set α FDR  < 0.05.

Biological and functional enrichment analysis

We explored the functional and biological significance of these components through enrichment analyses for multiple psychiatric disorders and immune disorders’ top risk loci genes, i.e., putative causal genes identified by setting a fixed distance around each index GWAS-significant SNP and subsequently integrating genomic functions or chromatin interactions 100 , 101 (ADHD—attention deficit hyperactivity disorder 102 ; ASD—autism spectrum disorder 102 ; BD—bipolar disorder 102 ; MDD—major depressive disorder 103 ; OCD—obsessive-compulsive disorder 103 ; OCD—obsessive-compulsive disorder 103 ; SA—suicide attempt 104 ; SCZ—schizophrenia 2 ; CD and UC—Crohn’s disease and ulcerative colitis 105 ).

We also computed enrichment for differentially expressed genes (DEGs) obtained from CN 21 , HP, and DLPFC 77 ; genes proximal to differentially methylated CpG islands (DMGs) in PFC and blood 78 , 106 , 107 , 108 , 109 , 110 and loss of function intolerant genes 111 . For DEGs, we performed a brain region-specific enrichment using the appropriate gene list of each tissue. Moreover, for DLPFC DEGs and DMGs enrichment, we computed a meta-analysis of the papers from which we retrieved target genes to obtain module-wise enrichment p -values (sum-log Fisher’s method). Considering the overlap between the SDA components obtained, we computed permutation statistics to control for multiple comparisons by first creating for each component a null distribution of 10,000 sets of randomly sampled genes using the 22,356 genes as the universe and then comparing the enrichment hits to the null distribution created from the permuted components (overrepresentation analysis: one-sided Fisher exact test; α  = 0.05).

MAGMA analysis

We used the MAGMA tool v1.09b, pathology-specific summary statistics as SNP p -value data, and 1000 G European as the reference data file for a European ancestry population to estimate LD between SNPs. We took the following steps: (i) we mapped 1000 G SNPs to genes encompassed in each component (a window of 100 kb upstream and downstream of each gene; for H-MAGMA we used Adult brain Hi-C annotation files already computed in the H-MAGMA publication 39 ), (ii) we calculated gene-wide association statistics based on summary statistic SNPs p -values (MAGMA “mean” method), (iii) we performed “competitive” gene-set enrichment analysis where the association statistic for genes in the components is compared to those of all other genes with at least one SNP mapped (universe used consisted of 22,356 genes used for SDA). FDR correction served to control for multiple comparisons ( α [FDR]  = 0.05).

Cell-type specificity analysis

We further asked whether SDA components mapped onto specific brain cell types. We used marker genes already identified by Skene et al. including cell-type specificity indices 112 . They computed specificity indices for each gene ranging between 1 (high specificity for a given cell type) and 0 (low specificity). We used specificity indices derived from single-nuclei RNA-seq of human brains 113 , which discriminated ten different cell types (neuron and glia); and from single-cell RNA-seq of mouse brains 112 which encompasses 24 different cell types. We used the Mean-rank Gene Set Test in the limma (v3.46) R package 114 , 115 to evaluate the enrichment of our components for the specificity indices of each cell type. This algorithm performs a competitive test comparing the specificity index rank of the co-expressed genes with the remaining genes. FDR correction served to control for multiple comparisons across components ( n  = 69) and cell types (human atlas = 10; mouse atlas = 24) tested ( α [FDR]  = 0.05).

Gene ontology analysis

Finally, we explored the gene ontology of components via enrichment analysis through the clusterProfiler (v3.18) R 116 package using the Gene Ontology Database (PANTHER, http://pantherdb.org ) 117 and the Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.genome.jp/kegg/ ) database and setting the 22,356 genes gave as input to SDA as the background set (overrepresentation analysis: one-sided Fisher exact test). FDR correction was applied to control for multiple comparisons ( α [FDR]  = 0.05).

Association with DRD2, DDC, and TH total gene expression

We evaluated the C80 association with DRD2 , DDC , and TH gene expression via a multiple linear regression analysis using C80 individual scores as the dependent variable and DRD2 , DDC , and TH expression across CN, DLPFC, and HP as independent variables. We also included age, sex, diagnosis, postmortem interval (PMI), tissue-specific RIN, mitochondrial mapping rate, rRNA rate, and gene mapping rate as covariates. Finally, we added to this model the interaction between diagnosis and each gene expression as well as the interaction between diagnosis and age to control for spurious association driven by postnatal samples (Eq.  1 ). The gene expression values used were the ones given as input to SDA for all 238 samples (quantile and rank-normalized matrices). We also performed this analysis using only healthy individuals ( N  = 154; Eq.  2 ).

DRD2 transcript-level association analysis

To evaluate the contribution of short and long isoforms to the DRD2 expression variance, we substituted the DRD2 terms in the previous gene-level models (Eqs.  1 and 2 ) with the long and short isoform transcript expression values (Eq.  3 ).

Transcript counts were preprocessed and normalized to transcripts per million (TPM) estimates as previously described 21 , 77 and were available for 222 out of the 238 samples previously used. After mapping 138,933 transcripts to the 22,356 genes used for previous analyses, we log-transformed TPM values with an offset of 1, i.e., log2(TPM + 1), and kept transcripts with a median higher than zero. We then performed quantile and rank normalization in each tissue separately as previously done. DRD2 short isoform survived filters for all tissues while the long isoform had a median higher than zero only in CN. This analysis was also performed using only healthy individuals ( N  = 143).

MSN pathways enrichment

We downloaded the 40 most preferentially expressed genes in each MSN class identified by Tran MN et al. 40 in the nucleus accumbens and focused on D 1 _A and D 2 _A clusters as they represented the largest D 1 -MSN (67%) and D 2 -MSN (87%) subclasses, respectively. As 11 genes shared expression for both D 1 _A and D 2 _A clusters, we considered the intersection without these genes for a total of 29 genes in each class. We permuted 10,000 gene sets matching both C80 component size and GC content, gene length, and average expression distributions of C80 genes. The universe from which random genes were pooled consisted of the protein-coding genes given as input to SDA for which this info was available (22,282 genes).

We then computed empirical p -values by comparing the enrichment hits with each MSN cluster to the null distribution created from the permuted gene sets ( α  = 0.05).

GTEx replication analysis

To replicate gene co-expression sets obtained with the LIBD data, we applied SDA on CN, HP, and DLPFC GTEx RNA-seq data using the exact same pipeline previously described. The input matrix for SDA is described in Table  1 .

Two replication measures were assessed: correlation between LIBD and GTEx component-specific gene loadings and Jaccard Index (JI) as the intersection/union of the LIBD and GTEx component-specific genes. To identify the LIBD-GTEx pair of replicated components we took for each of the LIBD components the GTEx component with the highest replication measure assessed and iteratively discarded that component to have unique LIBD-GTEx pairs. We then permuted the LIBD components 10,000 times and compared the replication measure previously assessed to the null distribution created from the permuted components to obtain a replication empirical p -value for each pair identified ( α  = 0.05).

Replication of total gene and transcript-level expression in GTEx

To replicate results obtained in the discovery sample, we assessed C18 association with DRD2 total gene expression via a multiple linear regression analysis using C18 individual scores as dependent variable and DRD2 expression across CN, DLPFC, and HP as independent variables as previously done in the discovery analysis (Eq.  2 ).

DRD2 long and short isoform transcript association was performed as previously done in the discovery analysis (Eq.  3 ).

We downloaded GTEx v8 transcript TPM values from the GTEx portal ( https://gtexportal.org/home/datasets ) and after mapping 133,788 transcripts to the 20,475 genes used we performed normalization steps as previously done in the discovery analysis.

Ancestry stratification

As the summary statistics used are mainly based on the European population and PRS association with other ancestry groups might lead to biases, we evaluated the individual ancestry based on the genotype data rather than only considering the self-reported ancestry for all cohorts included for the brain function association analysis. To this purpose, we used a procedure developed by the ENIGMA consortium that consists of performing a PCA on target data merged with the HapMap 118 phase 3 reference dataset ( https://enigma.ini.usc.edu/wp-content/uploads/2012/07/ENIGMA2_1KGP_cookbook_v3.pdf ). For this analysis we included all samples whose genotype information was available (KCL: 168; NIMH: 169; LIBD: 86; UNIBA: 2178; see Supplementary Table  1 ). We then computed an individual ancestry score based on the GE obtained from the PCA analysis. We trained a generalized linear model using the glmnet R package; we used the first 20 GE obtained as predictors and the ethnicity (European = 1; Others = 0) as a response variable and considered only samples in the reference dataset. Then, we used the trained model to predict the ethnicity of our samples using the first 20 GE. Finally, for each subject we obtained a European ancestry score and considered a threshold of 90% prediction probability to remove individuals with a non-European ancestry (KCL: 59; NIMH: 3; UNIBA: 213). The remaining samples whose genotype and PET/fMRI data were available were used in further analyses (demographics in Table  1 ).

Since the KCL discovery cohort was the most heterogeneous in terms of ethnicities included we decided to also evaluate different ancestry subdivisions based on the visualization of the first two PCA dimensions, i.e., top axes of variation (see details in Supplementary Methods).

Parsed-PRS association with PET data

Considering the sample heterogeneity in the KCL discovery cohort in terms of both ethnicity and scanners used and population type at the diagnostic level, we conducted the association analysis separately in the NC and SCZ samples. A multiple linear regression served to associate the C80 stratified PRS (C80-PRS) as well as its complementary score (C80-PRS-complementary) with [ 18 F]-FDOPA uptake in the striatum indexed by Ki using age, gender, cannabis use, scanner and first three GEs as covariates. We then combined the effect of the individual studies with a fixed-effect model meta-analysis, as random-effect models require data to be randomly extracted from equivalent populations, an assumption that does not hold for clinical and control cohorts. We converted t-statistics from the regression model into correlation coefficients using the following formula:

where DF is a number of the degrees of freedom for the t-statistic. We finally used Fisher’s r-to-z transformed correlation coefficients as outcome measures in the metafor 119 R package.

We focused our analysis on dopamine synthesis in an ROI encompassing the whole striatum and to obtain a more granular view of the relationship between risk and phenotypes corrected for multiple comparisons using the Bonferroni method ( α  = 0.05/10). We then explored this association also in different striatum subdivisions as well as across different ancestry definitions (see Supplementary Methods).

As in the KCL analyses described above, we performed separate multiple linear regression analyses for C80-PRS and C80-PRS-complementary predictors in the independent NIMH NC cohort, in each case using the same covariates as described above in the KCL analysis (i.e., age, sex, and the first three GEs; the ‘scanner’ variable was omitted, as all scans were obtained on a GE Advance PET scanner). Comparisons were conducted voxel-wise across the whole striatum, using SPM software at a height threshold of p  < 0.05, voxel-wise family-wise error (FWE) corrected for multiple comparisons.

Parsed-PRS association with fMRI data

Finally, we associated C80-PRS and C80-PRS-complementary to reward anticipation-related fMRI activation in the independent sample of 86 NC from LIBD. We used the data of 55 NC from UNIBA participants to replicate the results.

BOLD responses to events of interest were modeled separately and time-locked to event onset by convolving the onset vectors coinciding with the onset of events (including cues by type, button press, successful/unsuccessful outcomes, and error trials) with a synthetic hemodynamic response function as implemented by SPM12. For all analyses, the primary outcome measure was the contrast in the BOLD signal of rewarded relative to control cue events, which best reflects reward anticipation responses in this task. Participants additionally completed cognitive testing outside of the scanning environment that assessed full-scale intellective quotient (IQ), which was included as a nuisance covariate in analyses.

Age, sex, IQ, and first three GEs were used as covariates, consistently with previous analyses, whereas MID-related BOLD signal (cue-related anticipatory response during reward versus control trials) was the dependent variable. Cue-related individual contrasts of the 86 NC from LIBD were entered into a group-level analysis to identify voxels with a significant effect of C80-PRS and C80-PRS-complementary on reward anticipation through separate multiple regression performed with SPM12. We considered the threshold-free cluster enhancement correction 120 , 121 p [TFCE-FDR]  < 0.05 accounting for multiple comparisons as the number of voxels within the task-related activity mask derived by the one-sample t -test on cue-related individual contrasts ( p [FWE]  = 0.05). Next, the cue-related individual contrasts in the 55 NC from UNIBA were associated with C80-PRS, using age, sex, IQ and first three GEs as covariates. We considered significance at p [TFCE-FDR]  < 0.05 accounting for multiple comparisons as the number of voxels within the task-related activity mask derived by the one-sample t -test on cue-related individual contrasts ( p [FWE]  = 0.05).

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The LIBD post-mortem raw RNA-Seq FASTQ files for CN, DLPFC, and HP are available through the database of Genotypes and Phenotypes (dbGap) and Globus collections (CN: phs003495.v1.p1 [ https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs003495.v1.p1 ]; DLPFC: jhpce#bsp2-dlpfc [ http://research.libd.org/globus/jhpce_bsp2-dlpfc/index.html ]; HP: jhpce#bsp2-hippo [ http://research.libd.org/globus/jhpce_bsp2-hippo/index.html ]). The LIBD post-mortem raw genotype data are available through dbGap under accession code phs000979.v3.p2 [ https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000979.v3.p2 ]). The LIBD post-mortem processed RNA-seq data and accession codes to raw RNA-Seq FASTQ files and genotypes used in this study are also publicly available at: https://eqtl.brainseq.org/phase2/ and at: https://erwinpaquolalab.libd.org/caudate_eqtl/ . The GTEx post-mortem raw RNA-Seq FASTQ files for CN (GTEx tissue name: Brain-Caudate (basal ganglia)), DLPFC (GTEx tissue name: Brain—Frontal Cortex (BA9)) and HP (GTEx tissue name: Brain-Hippocampus) are available through dbGap with accession code phs000424.v8.p2 [ https://doi.org/10.1126/science.1262110 ]. The GTEx post-mortem processed RNA-seq data used in this study are publicly available at: https://gtexportal.org/home/downloads/adult-gtex/bulk_tissue_expression . The individual-level raw data for the discovery of PET cohorts from KCL supporting the findings of this study are available from The NeurOimaging DatabasE (NODE) institutional repository (Institute of Psychiatry, Psychology & Neuroscience, King’s College London) and can be accessed from co-author O.D.H. ([email protected]) upon request. The authors will allow analysis of data with restricted access within one year from the request, according to extant regulations. The individual-level raw data for the replication NIMH PET cohort, the LIBD fMRI discovery cohort, and the UNIBA fMRI replication cohort are not currently publicly available because the regulation at the time of consent acquisition required participants to explicitly consent to sharing data with select institutions and impedes data sharing unbeknown to participants. The SCZ GWAS summary statistics used in this study are publicly available at: https://figshare.com/articles/dataset/scz2022/19426775 . GRCh38 human genome reference genome is available here: https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_25/GRCh38.p7.genome.fa.gz . The GENCODE release 25, GRCh38.p7 annotation file used in this study is available at: https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_25/gencode.v25.basic.annotation.gtf.gz . SDA output data are available in the Supplementary Data  1 . Components summary information is available in the Supplementary Data  2 . GO enrichment results are available in Supplementary Data  3 . The aggregated deidentified PET and fMRI data along with PRSs and technical covariates for all discovery and replication cohorts used in this study are available in Supplementary Data  4 . To ensure the replicability of the results reported in this work, all processed and aggregated data (Supplementary Data  1 – 4 ), SDA input data, and SNPs used to compute C80-PRSs are also available at: https://doi.org/10.5281/zenodo.10699265 .  Source data are provided in this paper.

Code availability

SDA software is publicly available at: https://jmarchini.org/software/#sda and no customization of the source code was applied. The scripts used for the analyses conducted in this study are available in the Supplementary Software file.

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Acknowledgements

This research was supported by the Intramural Research Program of the National Institute of Health through project ZIAMH002942; NCT00004571/ NCT00024622/ NCT00001486. This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 798181 awarded to G.P. (PI), and A.B., D.R.W. NIH grant 5R21MH117432-02, awarded to D.R.W. (PI), G.P. and A.B., partially supported M.P. This research has been partially supported by the project “Dopamine-dysbindin genetic interaction: a multidisciplinary approach to characterize cognitive phenotypes of schizophrenia and develop personalized treatments” (PRIN: Progetti di Ricerca di Rilevante Interesse Nazionale–Bando 2017 Prot. 2017K2NEF4) awarded to G.P., the funding initiative Horizon Europe Seeds 2021 (Next Generation EU-MUR D.M. 737/2021) for the project S68 CUP H99J21017550006 to G.P., from the European Union funding within the MUR PNRR Extended Partnership initiative on Neuroscience and Neuropharmacology (Project no. PE00000006 CUP H93C22000660006 “MNESYS, A multiscale integrated approach to the study of the nervous system in health and disease”) to AB, AR, and GP, and the Apulian regional government for the project: “Early Identification of Psychosis Risk” to A.B. The LIBD supported tissue collection and maintenance, analysis, infrastructure, and personnel. We are grateful for the contributions of the Office of the Chief Medical Examiner of the State of Maryland, Office of the Chief Medical Examiner of Kalamazoo County Michigan, Office of the Chief Medical Examiner University of North Dakota School of Medicine, Gift of Life of Michigan, Office of the Chief Medical Examiner of Santa Clara County California, and Medical University of Sofia, Bulgaria in assisting the Lieber Institute for Brain Development in the acquisition and curation of brain tissue donations for this study. All research at the Lieber Institute for Brain Development is made possible by generous gifts from the families of Steve and Connie Lieber and Milton and Tamar Maltz. We would like to thank all the family members of the donors for their exceptional contribution. We would like to acknowledge Dr. Peter Herscovitch and the staff of the NIH PET Center for their data acquisition support. We are appreciative of all the neuroimaging research volunteers for their generous participation. We are in debt to A. Jaffe who has contributed to this work by offering data and software. We are grateful to Dr. Christopher J. Borcuk, Dr. Pasquale Di Carlo, Dr. Piergiuseppe Di Palo, Andrea Gaudio, Gianluca C. Kikidis, Ciro Mazza, Dr. Marco Papalino, and Prof. Paolo Taurisano for data collection, exploratory analyses and exchanges of ideas that have influenced this work.

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These authors contributed equally: Leonardo Sportelli, Daniel P. Eisenberg.

Authors and Affiliations

Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA

Leonardo Sportelli, Qiang Chen, Aaron L. Goldman, Thomas M. Hyde, Joel E. Kleinman, Madhur Parihar, Joo Heon Shin, William S. Ulrich, Daniel R. Weinberger & Giulio Pergola

Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy

Leonardo Sportelli, Roberta Passiatore, Enrico D’Ambrosio, Linda A. Antonucci, Antonio Rampino, Alessandro Bertolino & Giulio Pergola

Clinical and Translational Neuroscience Branch, National Institute of Mental Health, Intramural Research Program, NIH, DHHS, Bethesda, MD, USA

Daniel P. Eisenberg, Jasmine S. Bettina, Michael D. Gregory & Karen F. Berman

Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK

Enrico D’Ambrosio, Kira Griffiths & Oliver D. Howes

Holmusk Technologies, New York, NY, USA

Kira Griffiths

Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Thomas M. Hyde & Daniel R. Weinberger

Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Thomas M. Hyde, Joel E. Kleinman, Daniel R. Weinberger & Giulio Pergola

MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK

Antonio F. Pardiñas

Radiology Department, IRCCS Ospedale Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy

Teresa Popolizio

Azienda Ospedaliero Universitaria Consorziale Policlinico, Bari, Italy

Antonio Rampino & Alessandro Bertolino

Department of Information Engineering, University of Padua, Padua, Italy

Mattia Veronese

Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK

Baltimore Research and Education Foundation, Baltimore, MD, USA

Caroline F. Zink

Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Daniel R. Weinberger

Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

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Contributions

Conceptualization: L.S., G.P., and D.R.W. Data curation: L.S., D.E., R.P., E.D., L.A.A., J.S.B., C.F.Z., J.H.S., O.D.H. Formal analysis: L.S., D.E., R.P., G.P. Funding acquisition: G.P., D.R.W., and A.B. Investigation: L.S., D.E., G.P., C.F.Z., A.B., K.B., D.R.W. Methodology: L.S., D.E., R.P., L.A.A., J.H.S., M.P., Q.C. Project administration: G.P. Resources: J.S.B., A.G., L.A.A., M.D.G., K.G., T.H., J.K., A.F.P., T.P., A.R., M.V., W.U. Software: L.S., D.E. and R.P. Supervision: G.P. and D.R.W. Visualization: L.S., D.E. and R.P. Writing (original draft): L.S, D.E., R.P., G.P., and D.R.W. Writing (review and editing): all authors.

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Correspondence to Daniel R. Weinberger or Giulio Pergola .

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Competing interests.

E.D. and G.P. received lecture fees from Lundbeck. A.R. received travel fees from Lundbeck. A.B. received consulting fees from Biogen and lecture fees from Otsuka, Janssen, and Lundbeck. O.D.H. has received investigator-initiated research funding from and/or participated in advisory/speaker meetings organized by Angelini, Autifony, Biogen, Boehringer Ingelheim, Eli Lilly, Heptares, Global Medical Education, Invicro, Janssen, Lundbeck, Neurocrine, Otsuka, Sunovion, Recordati, Roche and Viatris/Mylan and was a part-time employee of H Lundbeck A/s. O.D.H. and M.V. have a patent for the use of dopaminergic imaging. D.R.W. serves on the Scientific Advisory Boards of Sage Therapeutics and Pasithea Therapeutics. The remaining authors declare no competing interests.

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Sportelli, L., Eisenberg, D.P., Passiatore, R. et al. Dopamine signaling enriched striatal gene set predicts striatal dopamine synthesis and physiological activity in vivo. Nat Commun 15 , 3342 (2024). https://doi.org/10.1038/s41467-024-47456-5

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Schizophrenia A-level Revisions Notes

Bruce Johnson

A-level Psychology Teacher

B.A., Educational Psychology, University of Exeter

Bruce Johnson is an A-level psychology teacher, and head of the sixth form at Caterham High School.

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On This Page:

What do the examiners look for?

• Accurate and detailed knowledge
• Clear, coherent, and focused answers
• Effective use of terminology (use the “technical terms”)

In application questions, examiners look for “effective application to the scenario” which means that you need to describe the theory and explain the scenario using the theory making the links between the two very clear. If there is more than one individual in the scenario you must mention all of the characters to get to the top band.

Difference between AS and A level answers

The descriptions follow the same criteria; however you have to use the issues and debates effectively in your answers. “Effectively” means that it needs to be clearly linked and explained in the context of the answer.

Read the model answers to get a clearer idea of what is needed.

Exam Advice

You MUST revise everything – because the exam board could choose any question, however, it does make sense to spend more time on those topics which have not appeared for a while.

With these particular questions there is a sizeable risk that people don’t understand the difference between the questions, and then write about the wrong thing.

Make sure you know which is which, for example do you understand the difference between “genetic explanation” and “neural correlates explanation”, and do you have a model essay for each?

Schizophrenia is a severe mental illness where contact with reality and insight are impaired, an example of psychosis.

Section 1: Diagnosis and Classification of Schizophrenia

Classification is the process of organising symptoms into categories based on which symptoms cluster together in sufferers. Psychologists use the DSM and ICD to diagnose a patient with schizophrenia.

Diagnosis refers to the assigning of a label of a disorder to a patient. The ICD-10 (only negative symptoms need to be present) is used worldwide and the DSM-5 (only positive symptoms need to be present) is used in America.

In order to diagnose Schizophrenia the Mental Health Profession developed the DSM (Diagnostic and Statistical Manual) still used today as a method of classifying mental disorders (particularly in the USA).

It is also used as a basis for the ICD (International Classification of Diseases) used by the World Health Organisation in classifying all disorders (mental and physical).

Note: you may come across the terms DSM-IV and ICD-10. These refer to the latest editions of the two classification systems.

Positive Symptoms

an excess or distortion of normal functions: including hallucinations and delusions.

Positive symptoms are an excess or distortion of normal functions, for example hallucinations, delusions and thought disturbances such as thought insertion.

• Hallucinations are usually auditory or visual perceptions of things that are not present. Imagined stimuli could involve any of the senses. Voices are usually heard coming from outside the person’s head giving instructions on how to behave. • Delusions are false beliefs. Usually the person has convinced him/herself that he/she is someone powerful or important, such as Jesus Christ, the Queen (e.g. Delusions of Grandeur). There are also delusions of being paranoid, worrying that people are out to get them. • Psychomotor Disturbances: Stereotypyical – Rocking backwards and forwards, twitches, & repetitive behaviors. Catatonia- staying in position for hours/days on end, cut off from the world.

Negative Symptoms

where normal functions are limited: including speech poverty and avolition.

Negative symptoms are a diminution or loss of normal functions such as psychomotor disturbances, avolition (the reduction of goal-directed behavior), disturbances of mood and thought disorders.

• Thought disorder in which there are breaks in the train of thought and the person appears to make illogical jumps from one topic to another (loose association). Words may become confused and sentences incoherent (so called ‘word salad). Broadcasting is a thought disorder whereby a person believes their thoughts are being broadcast to others, for example over the radio or through TV. Alogia – aka speech poverty – is a thought disorder were correct words are used but with little meaning. • Avolition: Lack of volition (i.e. desire): in which a person becomes totally apathetic and sits around waiting for things to happen. They engage in no self motivated behavior. Their get up and go has got up and gone!

Classification

Slater & Roth (1969) say that hallucinations are the least important of all the symptoms, as they are not exclusive to schizophrenic people.

Classification and diagnosis does have advantages as it allows doctors to communicate more effectively about a patient and use similar terminology when discussing them. In addition, they can then predict the outcome of the disorder and suggest related treatment to help the patient.

Scheff (1966) points out that diagnosis classification labels the individual, and this can have many adverse effects, such as a self-fulfilling prophecy (patients may begin to act how they are expected to act), and lower self-esteem.

Ethics – do the benefits of classification (care, treatment, safety) outweigh the costs (possible misdiagnosis, mistreatment, loss of rights and responsibility, prejudice due to labelling).

Reliability and Validity in Diagnosis and Classification of Schizophrenia

with reference to co-morbidity, culture and gender bias and symptom overlap.

Reliability

For the classification system to be reliable, differfent clinicians using the same system (e.g. DSM) should arrive at the same diagnosis for the same individual.

Reliability is the level of agreement on the diagnosis by different psychiatrists across time and cultures; stability of diagnosis over time given no change in symptoms.

Diagnosis of schizophrenia is difficult as the practitioner has no physical signs but only symptoms (what the patient reports) to make a decision on.

Jakobsen et al. (2005) tested the reliability of the ICD-10 classification system in diagnosing schizophrenia. A hundred Danish patients with a history of psychosis were assessed using operational criteria, and a concordance rate of 98% was obtained. This demonstrates the high reliability of the clinical diagnosis of schizophrenia using up-to-date classification.

Comorbidity describes people who suffer from two or more mental disorders. For example, schizophrenia and depression are often found together. This makes it more difficult to confidently diagnose schizophrenia. Comorbidity occurs because the symptoms of different disorders overlap. For example, major depression and schizophrenia both involve very low levels of motivation. This creates problems of reliability. Does the low motivation reflect depression or schizophrenia, or both?

Gender bias: Loring and Powell (1988) found that some behavior which was regarded as psychotic in males was not regarded as psychotic in females.

Validity – the extent to which schizophrenia is a unique syndrome with characteristics, signs and symptoms.

For the classification system to be valid it should be meaningful and classify a real pattern of symptoms, which result from a real underlying cause.

The validity of schizophrenia as a single disorder is questioned by many. This is a useful point to emphasise in any essay on the disorder. There is no such thing as a ‘normal’ schizophrenic exhibiting the usual symptoms.

Since their are problems with the validity of diagnois classification, unsuitable treatment may be administered, sometimes on an involuntary basis. This raises practical and ethical issues when selecting different types of tretment.

Problems of validity: Are we really testing what we think we are testing? In the USA only 20% of psychiatric patients were classed as having schizophrenia in the 1930s but this rose to 80% in the 1950s . In London the rate remained at 20%, suggesting neither group had a valid definition of schizophrenia.

Neuropsychologist Michael Foster Green suggests that neurocognitive deficits in basic functions such as memory, attention, central executive and problem solving skills may combine to have an outcome which we are labelling “Schizophrenia” as if it was the cause when in fact it is simply an umbrella term for a set of effects.

Predictive validity. If diagnosis leads to successful treatment, the diagnosis can be seen as valid. But in fact some Schizophrenics are successfully treated whereas others are not. Heather (1976) there is only a 50% chance of predicting what treatment a patient will receive based on diagnosis, suggesting that diagnosis is not valid.

Aetiological validity – for a diagnosis to be valid, all patients diagnosed as schizophrenic should have the same cause for their disorder. This is not the case with schizophrenia: The causes may be one of biological or psychological or both.

David Rosenhan (1973) famous experiment involving Pseudopatients led to 8 normal people being kept in hospital despite behaving normally. This suggests the doctors had no valid method for detecting schizophrenia. They assumed the bogus patients were schizophrenic with no real evidence. In a follow up study they rejected genuine patients whom they assumed were part of the deception.

Culture – One of the biggest controversies in relation to classification and diagnosis is to do with cultural relativism and variations in diagnosis. For example in some Asian countries people are not expected to show emotional expression, whereas in certain Arabic cultures public emotion is encouraged and understood. Without this knowledge a person displaying overt emotional behavior in a Western culture might be regarded as abnormal. Cochrane (1977) reported that the incidence of schizophrenia in the West Indies and the UK is 1 %, but that people of Afro-Caribbean origin are seven times more likely to be diagnosed as schizophrenic when living in the UK.

Cultural bias – African Americans and those of Afro-carribean descent are more likely to be diagnosed than their white counterparts but diagnostic rates in Africa and the West Indies is low – Western over diagnosis is a result of cultural norms and the diagnosis lacks validity.

Section 2: Biological Explanations for Schizophrenia

Family studies find individuals who have schizophrenia and determine whether their biological relatives are similarly affected more often than non-biological relatives.

There are two types of twins – identical (monozygotic) and fraternal (dizygotic). To form identical twins, one fertilised egg (ovum) splits and develops two babies with exactly the same genetic information.

• Gottesman (1991) found that MZ twins have a 48% risk of getting schizophrenia whereas DZ twins have a 17% risk rate. This is evidence that the higher the degree of genetic relativeness, the higher the risk of getting schizophrenia. • Benzel et al. (2007) three genes: COMT, DRD4, AKT1 – have all been associated with excess dopamine in specific D2 receptors, leading to acute episodes, positive symptoms which include delusions, hallucinations, strange attitudes. • Research by Miyakawa et al. (2003) studied DNA from human families affected by schizophrenia and found that those with the disease were more likely to have a defective version of a gene, called PPP3CC which is associated with the production of calcineurin which regulates the immune system. Also, research by Sherrington et al. (1988) has found a gene located on chromosome 5 which has been linked in a small number of extended families where they have the disorder. • Evidence suggests that the closer the biological relationship, the greater the risk of developing schizophrenia. Kendler (1985) has shown that first-degree relatives of those with schizophrenia are 18 times more at risk than the general population. Gottesman (1991) has found that schizophrenia is more common in the biological relatives of a schizophrenic, and that the closer the degree of genetic relatedness, the greater the risk.

Very important to note genetics are only partly responsible, otherwise identical twins would have 100% concordance rates.

One weakness of the genetic explanation of schizophrenia is that there are methodological problems. Family, twin and adoption studies must be considered cautiously because they are retrospective, and diagnosis may be biased by knowledge that other family members who may have been diagnosed. This suggests that there may be problems of demand characteristics.

A second weakness is the problem of nature-v-Nurture. It is very difficult to separate out the influence of nature-v-nurture. The fact that the concordance rates are not 100% means that schizophrenia cannot wholly be explained by genes and it could be that the individual has a pre-disposition to schizophrenia and simply makes the individual more at risk of developing the disorder. This suggests that the biological account cannot give a full explanation of the disorder.

A final weakness of the genetic explanation of schizophrenia is that it is biologically reductionist. The Genome Project has increased understanding of the complexity of the gene. Given that a much lower number of genes exist than anticipated, it is now recognised that genes have multiple functions and that many genes behavior.

Schizophrenia is a multi-factorial trait as it is the result of multiple genes and environmental factors. This suggests that the research into gene mapping is oversimplistic as schizophrenia is not due to a single gene.

The Dopamine Hypothesis

• Dopamine is a neurotransmitter. It is one of the chemicals in the brain which causes neurons to fire. The original dopamine hypothesis stated that schizophrenia suffered from an excessive amount of dopamine. This causes the neurons that use dopamine to fire too often and transmit too many messages. • High dopamine activity leads to acute episodes, and positive symptoms which include: delusions, hallucinations, confused thinking. • Evidence for this comes from that fact that amphetamines increase the amounts of dopamine . Large doses of amphetamine given to people with no history of psychological disorders produce behavior which is very similar to paranoid schizophrenia. Small doses given to people already suffering from schizophrenia tend to worsen their symptoms. • A second explanation developed, which suggests that it is not excessive dopamine but that fact that there are more dopamine receptors. More receptors lead to more firing and an over production of messages. Autopsies have found that there are generally a large number of dopamine receptors (Owen et al., 1987) and there was an increase in the amount of dopamine in the left amygdale (falkai et al. 1988) and increased dopamine in the caudate nucleus and putamen (Owen et al, 1978).

One criticism of the dopamine hypothesis is there is a problem with the chicken and egg. Is the raised dopamine levels the cause of the schizophrenia, or is it the raised dopamine level the result of schizophrenia?

It is not clear which comes first. This suggests that one needs to be careful when establishing cause and effect relationships in schizophrenic patients.

One of the biggest criticisms of the dopamine hypothesis came when Farde et al found no difference between schizophrenics’ levels of dopamine compared with ‘healthy’ individuals in 1990.

Noll (2009) also argues around one third of patients do not respond to drugs which block dopamine so other neurotransmitters may be involved.

A final weakness of the dopamine hypothesis is that it is biologically deterministic. The reason for this is because if the individual does have excessive amounts of dopamine then does it really mean that thy ey will develop schizophrenia? This suggests that the dopamine hypothesis does not account for freewill.

Neural Correlates

• Neural correlates are patterns of structure or activity in the brain that occur in conjunction with schizophrenia • People with schizophrenia have abnormally large ventricles in the brain . Ventricles are fluid filled cavities (i.e. holes) in the brain that supply nutrients and remove waste. This means that the brains of schizophrenics are lighter than normal. The ventricles of a person with schizophrenia are on average about 15% bigger than normal (Torrey, 2002).

A strength is that the research into enlarged ventricles and neurotransmitter levels have high reliability. The reason for this is because the research is carried out in highly controlled environments, which specialist, high tech equipment such as MRI and PET scans.

These machines take accurate readings of brain regions such as the frontal and pre-frontal cortex, the basil ganglia, the hippocampus and the amygdale. This suggests that if this research was tested and re-tested the same results would be achieved.

Supporting evidence for the brain structure explanation comes from further empirical support from Suddath et al. (1990). He used MRI (magnetic resonance imaging) to obtain pictures of the brain structure of MZ twins in which one twin was schizophrenic.

The schizophrenic twin generally had more enlarged ventricles and a reduced anterior hypothalamus. The differences were so large the schizophrenic twins could be easily identified from the brain images in 12 out of 15 pairs.

This suggests that there is wider academic credibility for enlarged ventricles determining the likelihood of schizophrenia developing.

A second weakness of the neuroanatomical explanations is that it is biologically deterministic. The reason for this is because if the individual does have large ventricles then does it really mean that they will develop schizophrenia? This suggests that the dopamine hypothesis does not account for freewill.

Section 3: Psychological Explanations for Schizophrenia

Family dysfunction.

Family Dysfunction refers to any forms of abnormal processes within a family such as conflict, communication problems, cold parenting, criticism, control and high levels of expressed emotions. These may be risk factors for the development and maintenance of schizophrenia.

• Laing and others rejected the medical / biological explanation of mental disorders. They did not believe that schizophrenia was a disease. They believed that schizophrenia was a result of social pressures from life. Laing believed that schizophrenia was a result of the interactions between people, especially in families. • Bateson et al. (1956) suggested the double bind theory, which suggests that children who frequently receive contradictory messages from their parents are more likely to develop schizophrenia. For example parents who say they care whilst appearing critical or who express love whilst appearing angry. They did not believe that schizophrenia was a disease. They believed that schizophrenia was a result of social pressures from life. • Prolonged exposure to such interactions prevents the development of an internally coherent construction of reality; in the long run, this manifests itself as typically schizophrenic symptoms such as flattening affect, delusions and hallucinations, incoherent thinking and speaking, and in some cases paranoia. • Another family variable associated with schizophrenia is a negative emotional climate, or more generally a high degree of expressed emotion (EE). EE is a family communication style that involves criticism, hostility and emotional over-involvement. The researchers concluded that this is more important in maintaining schizophrenia than in causing it in the first place, (Brown et al 1958). Schizophrenics returning to such a family were more likely to relapse into the disorder than those returning to a family low in EE. The rate of relapse was particularly high if returning to a high EE family was coupled with no medication.

One strength of the double bind explanation comes from further empirical support provided by Berger (1965). They found that schizophrenics reported a higher recall of double bind statements by their mothers than non-schizophrenics.

However, evidence may not be reliable as patient’s recall may be affected by their schizophrenia. This suggests that there is wider academic credibility for the idea of contradictory messages causing schizophrenia.

A second strength of the research into expressed emotion (EE) is that it has practical applications. For example Hogarty (1991) produced a type of therapy session, which reduced social conflicts between parents and their children which reduced EE and thus relapse rates.

This suggests that gaining an insight into family relationships allows psychiatric professionals to help improve the quality of patient’s lives.

Individual differences – EE is associated with relapse but not all patients who live in high EE families relapse and not all patients in low EE families avoid relapse – Family dysfunction is an incomplete explanation for schizophrenia.

A weakness of the family relationsships appraoch is that there is a problem of cause and effect. Mischler & Waxler (1968) found significant differences in the way mothers spoke to their schizophrenic daughters compared to their normal daughters, which suggests that dysfunctional communication may be a result of living with the schizophrenic rather than the cause of the disorder.

This suggests that there is a problem of the chicken and egg scenario in relation to expressed emotion causing schizophrenia.

A second weakness of the double bind theory is that there are ethical issues. There are serious ethical concerns in blaming the family, particularly as there is little evidence upon which to base this.

Gender bias is also an issue as the mother tends to be blamed the most, which means such research is highly socially sensitive. This suggests that the research therefore does not protect individuals from harm.

Cause and effect – It remains unclear whether cognitive factors cause schizophrenia or if schizophrenia causes these cognitions – Family dysfunction may not be a valid explanation for schizophrenia.

Cognitive explanations

including dysfunctional thought processing.

Cognitive approaches examine how people think, how they process information. Researchers have focused on two factors which appear to be related to some of the experiences and behaviors of people diagnosed with schizophrenia.

First, cognitive deficits which are impairments in thought processes such as perception, memory and attention. Second, cognitive biases are present when people notice, pay attention to, or remember certain types of information better than other.

Cognitive Deficits

• There is evidence that people diagnosed as schizophrenic have difficulties in processing various types of information, for example visual and auditory information. Research indicates their attention skills may be deficient – they often appear easily distracted. • A number of researchers have suggested that difficulties in understanding other people’s behavior might explain some of the experiences of those diagnosed as schizophrenic. Social behavior depends, in part, on using other people’s actions as clues for understanding what they might be thinking. Some people who have been diagnosed as schizophrenic appear to have difficulties with this skill. • Cognitive deficits have been suggested as possible explanations for a range of behaviors associated with schizophrenia. These include reduced levels of emotional expression, disorganised speech and delusions.

Cognitive Biases

• Cognitive biases refer to selective attention. The idea of cognitive biases has been used to explain some of the behaviors which have been traditionally regarded as ‘symptoms’ of ‘schizophrenia’. • Delusions: The most common delusion that people diagnosed with schizophrenia report is that others are trying to harm or kill them – delusions of persecution. Research suggests that these delusions are associated with specific biases in reasoning about and explaining social situations. Many people who experience feelings of persecution have a general tendency to assume that other people cause the things that go wrong with their lives.

A strength of the cognitive explanation is that it has practical applications. Yellowless et al. (2002) developed a machine that produced virtual hallucinations, such as hearing the television telling you to kill yourself or one person’s face morphing into another’s.

The intention is to show schizophrenics that their hallucinations are not real. This suggests that understanding the effects of cognitive deficits allows psychologists to create new initiatives for schizophrenics and improve the quality of their lives.

A final strength is that it takes on board the nurture approach to the development of schizophrenia. For example, it suggests that schizophrenic behavior is the cause of environmental factors such as cognitive factors.

One weakness of the cognitive explanation is that there are problems with cause and effect. Cognitive approaches do not explain the causes of cognitive deficits – where they come from in the first place.

Is it the cognitive deficits which causes the schizophrenic behavior or is the schizophrenia that causes the cognitive deficits? This suggests that there are problems with the chicken and egg problem.

A second weakness of the cognitive model is that it is reductionist. The reason for this is because the approach does not consider other factors such as genes.

It could be that the problems caused by low neurotransmitters creates the cognitive deficits. This suggests that the cognitive approach is oversimplistic when consider the explanation of schizophrenia.

Section 4: Drug Therapy: typical and atypical antipsychotics

Drug therapy is a biological treatment for schizophrenia. Antipsychotic drugs are used to reduce the intensity of symptoms (particularly positive symptoms).

Typical Antipsychotics

• First generation Antipsychotics are called “Typical Antipsychotics” Eg. Chlorpromazine and Haloperidol. • Typical antipsychotic drugs are used to reduce the intensity of positive symptoms, blocking dopamine receptors in the synapses of the brain and thus reducing the action of dopamine. • They arrest dopamine production by blocking the D2 receptors in synapses that absorb dopamine, in the mesolimbic pathway thus reducing positive symptoms, such as auditory hallucinations. • But they tended to block ALL types of dopamine activity, (in other parts of the brain as well) and this caused side effects and may have been harmful.

Atypical Antipsychotics

• Newer drugs, called “atypical antipsychotics” attempt to target D2 dopamine activity in the limbic system but not D3 receptors in other parts of the brain. • Atypical antipsychotics such as Clozapine bind to dopamine, serotonin and glutamate receptors. • Atypical antipsychotic drugs work on negative symptoms, improving mood, cognitive functions and reducing depression and anxiety. • They also have some effect on other neurotransmitters such as serotonin . They generally have fewer side effects eg. less effect on movement Eg. Clozapine, Olazapine and Risperidone.

Since the mid-1950s antipsychotic medications have greatly improved treatment. Medications reduce positive symptoms particularly hallucinations and delusions; and usually allow the patient to function more effectively and appropriately.

Antipsychotic drugs are highly effective as they are relatively cheap to produce, easy to administer and have a positive effect on many sufferers. However they do not “cure” schizophrenia, rather they dampen symptoms down so that patients can live fairly normal lives in the community.

Kahn et al. (2008) found that antipsychotics are generally effective for at least one year, but second- generation drugs were no more effective than first-generation ones.

Some sufferers only take a course of antipsychotics once, while others have to take a regular dose in order to prevent symptoms from reappearing.

There is a sizeable minority who do not respond to drug treatment. Pills are not as helpful with other symptoms, especially emotional problems.

Older antipsychotics like haloperidol or chlorpromazine may produce side effects Sometimes when people with schizophrenia become depressed, so it is common to prescribe anti-depressants at the same time as the anti-psychotics.

All patients are in danger of relapsing but without medication the relapses are more common and more severe which suggests the drugs are effective.

Clozapine targets multiple neurotransmitters, not just dopamine, and has been shown to be more effective than other antipsychotics, although the possibility of severe side effects – in particular, loss of the white blood cells that fight infection.

Even newer antipsychotic drugs, such as risperidone and olanzapine are safer, and they also may be better tolerated. They may or may not treat the illness as well as clozapine, however.

Meta–analysis by Crossley Et Al (2010) suggested that Atypical antipsychotics are no more effective, but do have less side effects.

Recovery may be due to psychological factors – The placebo effect is when patients’ symptoms are reduced because they believe that it should.

However, Thornley et al carried out a meta-analysis comparing the effects of Chlorpromazine to placebo conditions and found Chlorpromazine to be associated with better overall functioning – Drug therapy is an effective treatment for SZ.

RWA – Offering drugs can lead to an enhanced quality of life as patients are given independence – Positive impact on the economy as patients can return to work and no longer need to be provided with institutional care.

Ethical issues – Antipsychotics have been used in hospitals to calm patients and make them easier for staff to work with rather than for the patients’ benefit – Can lead to the abuse of the Human Rights Act (no one should be subject to degrading treatment).

Severe side effects – Long term use can result in tardive dyskinesia which manifests as involuntary facial movements such as blinking and lip smacking – While they may be effective, the severity of the side effects mean the costs outweigh the benefits therefore they are not an appropriate treatment.

In most cases the original “typical antipsychotics” have more side effects, so if the exam paper asks for two biological therapies you can write about typical anti-psychotics and emphasise the side effects, then you can write about the atypical antipsychotics and give them credit for having less side effects.

Section 5: Psychological Therapies for Schizophrenia

Family therapy.

Family therapy is a form of therapy carried out with members of the family with the aim of improving their communication and reducing the stress of living as a family.

Family Therapy aims to reduce levels of expressed emotion, and reduced the likelihood of relapse.

Aims of Family Therapy

• To educate relatives about schizophrenia. • To stabilize the social authority of the doctor and the family. • To improve how the family communicated and handled the situation. • To teach patients and carers more effective stress management techniques.

Methods used in Family Therapy

• Pharoah identified examples of how family therapy works: It helps family members achieve a balance between caring for the individual and maintaining their own lives, it reduces anger and guilt, it improves their ability to anticipate and solve problems and forms a therapeutic alliance. • Families taught to have weekly family meetings solving problems on family and individual goals, resolve conflict between members, and pinpoint stressors. • Preliminary analysis: Through interviews and observation the therapist identifies strengths and weaknesses of family members and identifies problem behaviors. • Information transfer – teaching the patient and the family the actual facts about the illness, it’s causes, the influence of drug abuse, and the effect of stress and guilt. • Communication skills training – teach family to listen, to express emotions and to discuss things. Additional communication skills are taught, such as “compromise and negotiation,” and “requesting a time out” . This is mainly aimed at lowering expressed emotion.

A study by Anderson et al. (1991) found a relapse rate of almost 40% when patients had drugs only, compared to only 20 % when Family Therapy or Social Skills training were used and the relapse rate was less than 5% when both were used together with the medication.

Pharaoh et al. (2003) meta – analysis found family interventions help the patient to understand their illness and to live with it, developing emotional strength and coping skills, thus reducing rates of relapse.

Pharoah identified examples of how family therapy works: It helps family members achieve a balance between caring for the individual and maintaining their own lives, it reduces anger and guilt, it improves their ability to anticipate and solve problems and forms a therapeutic alliance.

Economic Benefits: Family therapy is highly cost effective because it reduces relapse rates, so the patients are less likely to take up hospital beds and resources. The NICE review of family therapy studies demonstrated that it was associated with significant cost savings when offered to patients alongside the standard care – Relapse rates are also lower which suggests the savings could be even higher.

Lobban (2013) reports that other family members felt they were able to cope better thanks to family therapy. In more extreme cases the patient might be unable to cope with the pressures of having to discuss their ideas and feelings and could become stressed by the therapy, or over-fixated with the details of their illness.

Token Economy

• Token economies aim to manage schizophrenia rather than treat it. • They are a form of behavioral therapy where desirable behaviors are encouraged by the use of selective reinforcement and is based on operant conditioning. • When desired behavior is displayed eg. Getting dressed, tokens (in the form of coloured discs) are given immediately as secondary reinforcers which can be exchanged for rewards eg. Sweets and cigarettes. • This manages schizophrenia because it maintains desirable behavior and no longer reinforces undesirable behavior. • The focus of a token economy is on shaping and positively reinforcing desired behaviors and NOT on punishing undesirable behaviors. The technique alleviates negative symptoms such as poor motivation, and nurses subsequently view patients more positively, which raises staff morale and has beneficial outcomes for patients. • It can also reduce positive symptoms by not rewarding them, but rewarding desirable behavior instead. Desirable behavior includes self-care, taking medication, work skills, and treatment participation.

Paul and Lentz (1977) Token economy led to better overall patient functioning and less behavioral disturbance, More cost-effective (lower hospital costs)

Upper and Newton (1971) found that the weight gain associated with taking antipsychotics was addressed with token economy regimes. Chronic schizophrenics achieved 3lbs of weight loss a week.

McMonagle and Sultana (2000) reviewed token economy regimes over a 15-year period, finding that they did reduce negative symptoms, though it was unclear if behavioral changes were maintained beyond the treatment programme.

It is difficult to keep this treatment going once the patients are back at home in the community. Kazdin et al. Found that changes in behavior achieved through token economies do not remain when tokens are with¬drawn, suggesting that such treatments address effects of schizophrenia rather than causes. It is not a cure.

There have also been ethical concerns as such a process is seen to be dehumanising, subjecting the patient to a regime which takes away their right to make choices.

In the 1950s and 60s nurses often “rewarded” patients with cigarettes. Due to the pivotal role of dopamine in schizophrenia this led to a culture of heavy smoking an nicotine addiction in psychiatric hospitals of the era.

Ethical issues – Severely ill patients can’t get privileges because they are less able to comply with desirable behaviors than moderately ill patients – They may suffer from discrimination

Cognitive Behavioral Therapy

In CBT, patients may be taught to recognise examples of dysfunctional or delusional thinking, then may receive help on how to avoid acting on these thoughts. This will not get rid of the symptoms of schizophrenia but it can make patients better able to cope with them.

Central idea: Patients problems are based on incorrect beliefs and expectations. CBT aims to identify and alter irrational thinking including regarding:

• General beliefs.
• Self image.
• Beliefs about what others think.
• Expectations of how others will act.
• Methods of coping with problems.

In theory, when the misunderstandings have been swept away, emotional attitudes will also improve.

Assessment : The therapist encourages the patient to explain their concerns.

• describing delusions • reflecting on relationships • laying out what they hope to achieve through the therapy.

Engagement :

The therapist wins the trust of the patient, so they can work together. This requires honesty, patience and unconditional acceptance. The therapist needs to accept that the illusions may seem real to the patient at the time and should be dealt with accordingly.

ABC : Get the patients to understand what is really happening in their life:

A: Antecedent – what is triggering your problem ? B: behavior – how do you react in these situations ? C: Consequences – what impact does that have on your relationships with others?

Normalisation :

Help the patient realise it is normal to have negative thoughts in certain situations. Therefore there is no need to feel stressed or ashamed about them.

Critical Collaborative Analysis :

Carrying on a logical discussion till the patient begins to see where their ideas are going wrong and why they developed. Work out ways to recognise negative thoughts and test faulty beliefs when they arise, and then challenge and re-think them.

Developing Alternative Explanations :

Helping the patient to find logical reasons for the things which trouble them Let the patient develop their own alternatives to their previous maladaptive behavior by looking at coping strategies and alternative explanations.

Another form of CBT: Coping Strategy Enhancement (CSE)

• Tarrier (1987) used detailed interview techniques, and found that people with schizophrenia can often identify triggers to the onset of their psychotic symptoms, and then develop their own methods of coping with the distress caused. These might include things as simple as turning up the TV to drown out the voices they were hearing! • At least 73% of his sample reported that these strategies were successful in managing their symptoms. • CSE aims to teach individuals to develop and apply effective coping strategies which will reduce the frequency, intensity and duration of psychotic symptoms and alleviate the accompanying distress. There are two components: 1. Education and rapport training: therapist and client work together to improve the effectiveness of the client’s own coping strategies and develop new ones. 2. Symptom targeting: a specific symptom is selected for which a particular coping strategy can be devised Strategies are practised within a session and the client is helped through any problems in applying it. They are then given homework tasks to practice, and keep a record of how it worked.

CBT does seem to reduce relapses and readmissions to hospital (NICE 2014). However, the fact that these people were on medication and having regular meetings with doctors would be expected to have that effect anyway.

Turkington et al. (2006) CBT is highly effective and should be used as a mainstream treatment for schizophrenia wherever possible.

Tarrier (2005) reviewed trials of CBT, finding evidence of reduced symptoms, especially positive ones, and lower relapse rates.

Requires self-awareness and willingness to engage – Held back by the symptoms schizophrenics encounter – It is an ineffective treatment likely to lead to disengagement.

Lengthy – It takes months compared to drug therapy that takes weeks which leads to disengaged treatment as they don’t see immediate effects – A patient who is very distressed and perhaps suicidal may benefit better in the short term from antipsychotics.

Addington and Addington (2005) claim that CBT is of little use in the early stages of an acute schizophrenic episode, but perhaps more useful when the patient is more calm and beginning to worry about how life will be after they recover. In other words, it doesn’t cure schizophrenia, it just helps people get over it.

Research in Hampshire, by Kingdon and Kirschen (2006) found that CBT is not suitable for all patients, especially those who are too thought disorientated or agitated, who refuse medication, or who are too paranoid to form trusting alliances with practitioners.

As there is strong evidence that relapse is related to stress and expressed emotion within the family, it seems likely that CBT should be employed alongside family therapy in order to reduce the pressures on the individual patient.

Section 6: Interactionist Approach

The Interactionist approach acknowledges that there are a range of factors (including biological and psychological) which are involved in the development of schizophrenia.

The Diathesis-stress Model

• The diathesis-stress model states that both a vulnerability to SZ and a stress trigger are necessary to develop the condition. • Zubin and Spring suggest that a person may be born with a predisposition towards schizophrenia which is then triggered by stress in everyday life. But if they have a supportive environment and/or good coping skills the illness may not develop. • Concordance rates are never 100% which suggests that environmental factors must also play a role in the development of SZ. MZ twins may have the same genetic vulnerability but can be triggered by different stressors. • Tienari Et. A. (2004): Adopted children from families with schizophrenia had more chance of developing the illness than children from normal families. This supports a genetic link. However, those children from families schizophrenia were less likely to develop the illness if placed in a “good” family with kind relationships, empathy, security, etc. So environment does play a part in triggering the illness.

Holistic – Identifies that patients have different triggers, genes etc. – Patients can receive different treatments for their SZ which will be more effective.

Falloon et al (1996) stress – such as divorce or bereavement, causes the brain to be flooded with neurotransmitters which brings on the acute episode.

Brown and Birley (1968) 50% people who had an acute schizophrenic episode had experienced a major life event in 3 weeks prior.

Substance abuse: Amphetamine and Cannabis and other drugs have also been identified as triggers as they affect serotonin and glutamate levels.

Vasos (2012) Found the risk of schizophrenia was 2.37 times greater in cities than it was in the countryside, probably due to stress levels. Hickling (1999) the stress of urban living made African-Carribean immigrants in Britain 8 to 10 times more likely to experience schizophrenia.

Faris and Dunham (1939) found clear pattern of correlation between inner city environments and levels of psychosis. Pederson and Mortensen (Denmark 2001) found Scandanavian villages have very LOW levels of psychosis, but 15 years of living in a city increased risk.

Fox (1990): It is more likely that factors associated with living in poorer conditions (e.g. stress) may trigger the onset of schizophrenia, rather than individuals with schizophrenia moving down in social status.

Bentall’s meta-analysis (2012) shows that stress arising from abuse in childhood increases the risk of developing schizophrenia.

Toyokawa, Et. Al (2011) suggest many aspects of urban living – ranging from life stressors to the use of drugs, can have an effect on human epigenetics. So the stressors of modern living could cause increased schizophrenia in future generations.