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Distribution	Loglikelihood	Deviance	$\theta (\mu )$	$b(\theta )$	$b'(\theta )$	$b''(\theta )$	Valid Links
Poisson	${\frac {1}{\phi }}(y\ln {(\mu )}-\mu -\ln {\Gamma (y+1)})$	$y\ln {\left({\frac {y}{\mu }}\right)}-(y-\mu )$	$\ln {(\mu )}$	$\exp {(\theta )}$	$\exp {\theta }$	$\exp {\theta }$	Id, ln, sqrt
Gaussian	${\frac {1}{\phi }}\left(y\mu -{\frac {\mu ^{2}}{2}}\right)^{2}$	$\left(y-\mu \right)^{2}$	$\mu$	${\frac {\theta ^{2}}{2}}$	$\theta$	$1$	Id, ln, power
Gamma	$\ln {\left({\frac {y}{\mu }}\right)}-{\frac {y}{\mu }}+\ln {(y)}$	$\left({\frac {y-\mu }{\mu }}\right)}-\ln {\left({\frac {y}{\mu }}\right)$	$-{\frac {1}{\mu }}$	$-\ln {(-\theta )}$	$-{\frac {1}{\theta }}$	${\frac {1}{\theta ^{2}}}$	$\text{[math]}$
Bernoulli	${\frac {1}{\phi }}(y\ln {\left({\frac {\mu }{1-\mu }}\right)}+\ln {(1-\mu )})$	$y\ln {\left({\frac {y}{\mu }}\right)}+(1-y)\ln {\left({\frac {1-y}{1-\mu }}\right)}$	$\ln {\left({\frac {\mu }{1-\mu }}\right)}$	$\ln {(1+\exp {\theta })}$	${\frac {\exp {(\theta })}{1+\exp {(\theta )}}}$	${\frac {\exp {(\theta })}{(1+\exp {(\theta )})^{2}}}$	$\text{[math]}$
Inverse Gaussian	${\frac {1}{\phi }}\left(y{\frac {1}{2\mu ^{2}}}-{\frac {1}{\mu }}+{\frac {1}{2y}}\right)$	${\frac {(y-\mu )^{2}}{y\mu ^{2}}}$	${\frac {1}{\mu ^{2}}}$	$-(-2\theta )^{\frac {1}{2}}$	${\frac {1}{{\sqrt {2}}{\sqrt {-\theta }}}}$	${\frac {1}{2{\sqrt {2}}(-x)^{\frac {3}{2}}}}$	$\text{[math]}$
Negative Binomial	$y\ln {\left({\frac {\mu }{k+\mu }}\right)}+k\ln {\left({\frac {k}{k+\mu }}\right)}+\ln {\Gamma (y+k)}$	$\text{[math]}$	$\ln {\frac {\mu }{k+\mu }}$	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$	$\text{[math]}$

Factor Analysis

Factor analytic techniques in the most general form seek to model p observed variables as a linear combination of k usually $\ll p$ latent or unobserved variables plus orthogonal error variance:

$x=z\Lambda +\epsilon ;\quad E[zz']=\Phi ;\quad E[\epsilon \epsilon ']=\Psi ={\text{diag}}(\psi _{1},\psi _{2}\dots \psi _{p});\quad E[xx']=\Lambda \Phi \Lambda '+\Psi$

This can be formulated as the modeling of the observed sample covariance, denoted $S$ with the covariance implied by the factor analytic model $\Sigma =\Lambda \Phi \Lambda '+\Psi$ . The model can be specified with a general class of fitting functions, the GLS functions of the general form $F_{GLS}=(s-\sigma )'V^{-1}(s-\sigma )$ for $\sigma ={\text{vech}}(\Sigma )$ and $s={\text{vech}}(S)$ which provided it provided V is a consistent estimator of the asymptotic covariance of the sample covariance(i.e. $E[X^{2}X^{2}]$ ), is asymptotically equivalent to estimation by maximum likelihood under normality assumptions, with the fitting function being

$F_{ML}=\ln \vert \Sigma \vert +{\text{tr}}\left(S\Sigma ^{-1}\right)$

A common constraint, usually imposed under the name of Exploratory Factor Analysis(EFA), is $\Phi =I_{k}\rightarrow \Sigma =\Lambda \Lambda '+\Psi$ The model without this constraint is usually called Confirmatory Factor Analysis(CFA)

Due to orthogonality constraints, EFA solutions are not unique, and $\Lambda$ is invariant under rotation by an orthogonal matrix. These rotations may be used to obtain a loadings matrices that has desirable properties for the given application(i.e. loadings forced towards zero or one).

Factor analysis is frequently confused with principal component analysis, but it differs as it is a probabilistic rather than a deterministic model, as PCA does not have an underlying generative model.

Fitting of the model by newton-raphson algorithms or MM algorithms(e.g. Expectation Maximization, or EMCM) usually require at least first order derivatives. These are, for a parameter vector $\theta =\left[{\text{vec}}(\Lambda ),{\text{vech}}(\Psi ),{\text{vech}}(\Psi )\right]$

${\frac {\partial F_{ML}(\theta )}{\partial \theta '}}=\Delta ={\begin{bmatrix}2{\text{vec}}\left(\Sigma ^{-1}(S-\Sigma )\Sigma ^{-1}\Lambda \Phi \right)\\{\text{vech}}\left(\Lambda '\Sigma ^{-1}(S-\Sigma )\Sigma ^{-1}\Lambda \right)\\2{\text{vech}}\left(\Sigma ^{-1}(S-\Sigma )\Sigma ^{-1}\right)\end{bmatrix}}$

${\frac {\partial ^{2}F_{ML}(\theta )}{(\partial \theta ')(\partial \theta )}}=\Delta '(\Sigma ^{-1}\otimes \Sigma ^{-1}(S-\Sigma )\Sigma ^{-1})\Delta$

The inverse of the second matrix represents the observed fisher information matrix, ${\mathcal {I}}_{obs}$ , while the expected information can be expressed as ${\mathcal {I}}_{exp}=\left[\Delta '(\Sigma ^{-1}\otimes \Sigma ^{-1})\Delta \right]^{-1}$

Popular methods of estimation include Lawley and Maxwell's maximum likelihood algorithm,^[1] Rubin and Thayer's Expectation Maximization Algorithm^[2], or generalized least squares.^[3]

Pathophysiology of Depression

Neuroimaging

Structural Neuroimaging

https://www.ncbi.nlm.nih.gov/pubmed/21727252

MDD > Ctrl : Ventricles, CSF
Ctrl > MDD : Thalamus, hippocampus, frontal lobe, orbitofrontal gyrus, basal ganglia, gyrus rectus
Current MDD <Remitted : Hippocampus

https://www.ncbi.nlm.nih.gov/pubmed/22691300

WM/FA MDD<Ctrl : Frontal lobe, right fusiform gyrus, right occipital lobe

https://www.ncbi.nlm.nih.gov/pubmed/25597656

ReHo MDD>Ctrl : mPFC

Functional Neuroimaging

https://www.ncbi.nlm.nih.gov/pubmed/23890584

Altered patterns of activity were observed in the prefrontal and limbic regions, in support of the cortico-limib model

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3384264/

Hyper-responsiveness in the amygdala, insula, and parahippocampal gyrus to negative facial stimuli
Inconsistent magnitude of difference in the dlPFC, OFC and Cingulate gyrus, but a difference from control was consistently observed.

https://www.ncbi.nlm.nih.gov/pubmed/25785575

Hypoconnectivity within the frontoparietal system, as well as between the frontoparietal system and the dorsal attention network.
Hyperconnectivity within the default network.

https://www.ncbi.nlm.nih.gov/pubmed/23206667

Depressed> Ctrl in response to negative stimuli, and Ctrl>Depressed in response to positive stimuli in Anterior Cingulate Cortex, Hippocampus, Amygdala, Striatum

https://www.ncbi.nlm.nih.gov/pubmed/22521254

Altered activations of the striatum, insula, anterior cingulate in response to emotional stimuli.

https://www.ncbi.nlm.nih.gov/pubmed/26332700

Hyperactivation in the anterior cingulate cortex, ventrolateral prefronta cortex, and hypoactivity in the caudate across all cognitive and emotional tasks

https://www.ncbi.nlm.nih.gov/pubmed/22535198

Increased activity in the pulivnar nuclei, amygdala, insular, and anterior cingulate cortex in response to negative stimuli
Decreased activity in the striatum in response to positive stimuli.

Molecular

Monoamines

https://www.ncbi.nlm.nih.gov/pubmed/27623971

Reduced 5-HT1A receptor binding in the mesiotemporal cortex, the raphe nucleus, the insula and the anterior cingulate cortex.

https://www.ncbi.nlm.nih.gov/pubmed/24802331

Reduced 5-HTT binding in the thalamus, amygdala, midbrain, striatum and brainstem.

https://www.ncbi.nlm.nih.gov/pubmed/25497470

No altered striatal DAT availability

https://www.ncbi.nlm.nih.gov/pubmed/22218074

5-HT1A: Most literature points towards reduced BP in the mesiotemporal cortex, possible related with elevated cortisol
5-HT2A: PET examinations have found decreased BP, but post-mortem research suggests elevation in some depressed populations.
5-HTT: Moderate evidence for elevated 5-HTT activity in depression
D1 receptor: Decreased in the striatum
D2 receptor: Unclear
Muscarinic Receptors: Evidence for elevation in depression

https://www.ncbi.nlm.nih.gov/pubmed/25625874

The 5-HT/5-HIAA ratio, antidepressant efficacy gap, and failure of tryptophan depletion to elicit depression in health persons suggests elevated synaptic concentrations of serotonin in at least some subtypes of depression.

Inflammation

https://www.ncbi.nlm.nih.gov/pubmed/24228900

Depression is associated with mild inflammation, and increased oxidative and nitrosative stress.

https://www.ncbi.nlm.nih.gov/pubmed/24417575

Psychosocial stress is thought to contribute to depression by elevating inflammatory factors such as IL-1B, and TNF-alpha.
These inflammatory factors alter processing in the anterior cingulate cortex, striatum, and cerebellum.

https://www.ncbi.nlm.nih.gov/pubmed/23995180

Cytokines concentrations are elevated in depression, and trials with cytokine based treatments so far have been successful.

https://www.ncbi.nlm.nih.gov/pubmed/22553073

A subset of depressed patients exhibit decreased cellular immunity, and elevated pro-inflammatory cytokines, which can alter neurotransmitter metabolis.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3002174/

The structural changes in the hippocampus and prefrontal cortex may be a result of chronic low grade inflammation, leading to altered kynurenine metabolism and the production of neurotoxic metabolites.

Genetics

https://www.ncbi.nlm.nih.gov/pubmed/25709105

Shortened Telomere lengths in depression

https://www.ncbi.nlm.nih.gov/pubmed/11007705

The heritability of depression is estimated at 31-42%.

https://www.ncbi.nlm.nih.gov/pubmed/24433458

The BDNF Val66Met allele modulates the relationship between stress and depression

https://www.ncbi.nlm.nih.gov/pubmed/21199959

The 5-HTT promotor region was associated with increased risk of depression, especially in association with stress and childhood maltreatment

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3837431/

A meta analysis observed no significant SNPs, probably due to the complex and heterogenous nature of depression

https://www.ncbi.nlm.nih.gov/pubmed/20038947/

Significant SNPs included Metabotropic glutamate receptor 7 and ATP6V1B2

https://www.ncbi.nlm.nih.gov/pubmed/21042317/

No significant SNPs

Endocrine

https://www.ncbi.nlm.nih.gov/pubmed/24495607

Dysregulated cortisol observed in the elderly with depression

https://www.ncbi.nlm.nih.gov/pubmed/21257974

Increased cortisol but no increase in CRH

https://www.ncbi.nlm.nih.gov/pubmed/20447770
Increased morning and evening cortisol in depression
https://www.ncbi.nlm.nih.gov/pubmed/15961250
Depressed patients exhibited elavated recovery levels of cortisol after psychological stressors.

Neurogenesis

https://www.ncbi.nlm.nih.gov/pubmed/23958957

Decreased peripheral measures of BDNF

https://www.ncbi.nlm.nih.gov/pubmed/25934030

The decreased hippocampal volume appears in post mortem studies to be a result of altered neuroplasticity.

https://www.ncbi.nlm.nih.gov/pubmed/22525700

Inflammatory factors in depression may lead to altered neuroplasticity through modulating neural stem cell survival, neurogenesis, dendritic branching and long term potentiation

https://www.ncbi.nlm.nih.gov/pubmed/22054117

Decreased BDNF and decreased neuroplasticity in depression.

Antidepressants

https://www.ncbi.nlm.nih.gov/pubmed/28178949

While antidepressants statistically significantly reduced hamilton depresson rating scale, this effect did not reach clinical significance, according to a predefined threshold set according to NICE standards. Overall, the risk outweighed the rewards.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3133866/

The monoaminergic activity of antidepressants correlated with long term risk of relapse, probably due to monoaminergic dys-homeostasis.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4172306/

The lack of a unified mechanism of antidepressants, and high placebo rates negate the small statistical difference between placebo and antidepressants. Supports the above studies finding that ADs may predispose to future relapse

http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(16)30385-3/abstract

In adolescence and children, only fluoxetine was statistically more effective than placebo.

http://www.cochrane.org/CD007954/DEPRESSN_antidepressants-versus-placebo-for-depression-in-primary-care

Depression is effectively treated by TCAs and SSRIs, but most studies were of short duration and funded by pharmaceutical companies.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3712503/

Antidepressants are only better than placebo for patients with very severe depression(Hamilton>23)

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4592645/

While antidepressants are statistically better than placebo, poor design quality and low effect sizes are characteristic of most studies.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3280655/

Moderate effect size for ADs, but over the years placebo efficacy has been increasing and response rates have been decreasing.

Pathophysiology of OCD

Cortico-Basal Ganglia-Thalamocortical circuits
Orbitofrontal cortex, anterior cingulate cortex
Anxiety/Impulse control disorder
Treated with

CBT
TCAs>SSRIs

Neuroimaging

Functional

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3079445/
Functional neuroimaging studies usually report abnormalities in the OFC and ACC, although the direction is inconsistent
https://www.ncbi.nlm.nih.gov/pubmed/15546704

Meta analysis observed significant elevation of activity in the OFC and head of the caudate

https://www.ncbi.nlm.nih.gov/pubmed/18787662
During provocation of symptoms, significant activation likelihood was observed in the dorsolateral prefrontal cortex, precuneus, orbitofrontal cortex and ACC
https://www.ncbi.nlm.nih.gov/pubmed/27276220

OCD and ADHD share reduced ventromedial PFC GMV and functionality

Structural

https://www.ncbi.nlm.nih.gov/pubmed/18718575

The left ACC and right OFC demonstrated decreased volume in OCD.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4023155/

Decreased FA observed in anterior midline tracts

https://www.ncbi.nlm.nih.gov/pubmed/26733823
OCD involve aberrant connectivity between the OFC and striatum
https://www.ncbi.nlm.nih.gov/pubmed/20603451

OCD demonstrates increased bilateral lenticular and caudate volumes relative to other anxiety disorders

Structural/Functional Reviews

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4681810/

Affective dysfunction appears to underly and link obsessions and compulsions.
Amygdala hyperactivity in response to fearful stimuli, and, along with the ventral striatum, hypoactivity in response to positive stimuli.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2889493/

Structural and functional imaging suggests the involvement of the OFC-Striatal circuit, which involved the OFC/ACC, and basolateral amygdala/hippocampus projecting to the ventral striatum, which in turn projects to the globus padillus and returns to the OFC/ACC/amygdala/hippocampus through the thalamus.

https://www.ncbi.nlm.nih.gov/pubmed/25766413

Structural alterations were observed in the cerebellar, frontal, striatal and thalamic regions, implicating dysfunction of the corticobasal ganglia thalamocortical loop in OCD. Furthermore, deficits in functionality localized to these regions such as inference, inhibition and task switching were observed.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3972003/

OCD appears to have five different subtypes, of which the hoarding subtype appears to have a different underlying neurobiological mechanism

https://www.ncbi.nlm.nih.gov/pubmed/26635696

The altered corticolimbic circuitry likely results in an excessive reliance in habit based learning and deficits in goal directed behavior

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4148591/

OCD and ADHD demonstrate similar deficits in executive function, and involve dysfunction in overlaping corticostriatal circuits

https://www.ncbi.nlm.nih.gov/pubmed/22678698
There is a significant association altered functionality cortico-basal ganglia-thalamocortical circuits and OCD, despite inconsistencies in the direction of changes
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4476073/

Theoretical SEC model suggests that various behavioral patterns represented in the cortex are executed with the help of motivational signals.
In OCD, the ACC generates aberrant SEC error signals, and anxiety arises from limbic systems. The basal ganglia then generates "compensatory" SECs in the form of compulsions.

https://www.ncbi.nlm.nih.gov/pubmed/16242823

OCD models defined by two neuropsychological facets, executive function and modulatory control. The first model involves dysfunction in the dlPFC, striatum, and thalamus, and the second of which involves dysfunction in the OFC and ACC

https://www.ncbi.nlm.nih.gov/pubmed/25150566

OCD involves inflexibility of internal cognition, and supporting this notion is the observation of altered DMN activity

Genetic

https://www.ncbi.nlm.nih.gov/pubmed/25896187

The 5-HTTPR S type was associated with OCD, but only in females

https://www.ncbi.nlm.nih.gov/pubmed/23606572

Moderate association between OCD and SNPs of the glutamate transporter

https://www.ncbi.nlm.nih.gov/pubmed/22665263

OCD was associated with 5-HTTPLR and 5-HT2A gene polymorphisms, and in males only, the COMT and MAO-A genes

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4218751/

OCD was associated with https://www.ncbi.nlm.nih.gov/gene/79154, https://www.ncbi.nlm.nih.gov/gene/140862, and DLGAP1

https://www.ncbi.nlm.nih.gov/pubmed/18186076

While no overall association between the 5-HTTPLR S or L gene with OCD, family based studies found an association between the L type and OCD in caucasians

https://www.ncbi.nlm.nih.gov/pubmed/24177038

Microstructure abnormalities were observed in the ACC and OFC pathways to the basal ganglia, and hyperconnectivity in the genu of the corpus callosum.

https://www.ncbi.nlm.nih.gov/pubmed/17264842

Association between OCD and COMT polymoprhism, but only in men.

https://www.ncbi.nlm.nih.gov/pubmed/14582147

No association between COMT and OCD

https://www.ncbi.nlm.nih.gov/pubmed/17291658

OCD was associated with 5-HTTPLR SS allele, but inversely with the LS allele

Neurochemical

https://www.ncbi.nlm.nih.gov/pubmed/24986387

5-Fold increase in risk of anti-basal ganglia antibodies in OCD, supporting the theory that a subset of OCD involve basal ganglia auto-immunity

https://www.ncbi.nlm.nih.gov/pubmed/22892718

Reduced N-Acetylaspartate in the medial PFC in OCD

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4681810/

While some evidence suggests "elevated" dopamine in OCD, however this is contradicted by much evidence, including the fact that antipsychotics can exacerbate OCD, and the observation of contradictory molecular imaging findings with regard to DAT

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3205262/

Elevated CSF glutamate
Inconsistent molecular imaging studies about glutamate, although abnormalities have been frequently observed
Significant association between glutamate regulating genes and OCD

Dopaminergic Modulation of the PFC

Phasic dopamine release in the prefrontal cortex is capable of mediating cognitive flexibility. Activation of D1 receptors on a neuron increases the influence of strong signals and inhibits the weaker signals, effectively strengthening the representation of information in the PFC. D2 receptors on the other hand increase sensitivity to external noise, resulting in increased cognitive flexibility. This dynamic enables a switch in focus due to a salient stimulus. Given that D2 receptors tend to be synaptic, and D1 receptors tend to be extrasynaptic, phasic transmission is characterized first by preferential D2 activation, followed by tonic D1 activation. A salient stimulis can then trigger a transition state, followed by restrengthening.^[4]

The role of DA in modulating the PFC is conceptualized by two regimes. The D1 dominated regime is characterized by a "robust", "high energy barrier" that facilitates the maintenance of information. The D2 dominated regime is in turn characterized by a low energy barrier that facilitates quick transition between representational states.^[5]

Bipolar Disorder

Etiology

The etiology of bipolar disorder is unknown. The overall heritability of bipolar is estimated at 79%-93%, and first degree relatives of bipolar probands have a relative risk of developing bipolar around 7-10. While the heritability is high, no specific genes have been conclusively associated with bipolar, and a number of hypothesis have been posited to explain this fact. The polygenic common rare variant hypothesis suggests that a large number of risk conferring genes are carried in a population, and that a disease manifests when a person has a sufficient number of these genes. The multiple rare variant model suggests that multiple genes that are rare in the population are capable of causing a disease, and that carrying one or a few can lead to disease.

A number of genome wide associations have been reported, including CACNA1C and ODZ4. Less consistently reported loci include ANK3 and NCAN, ITIH1, ITIH3 and NEK4. Significant overlaps with schizophrenia have been reported at CACNA1C, ITIH, ANK3, and ZNF804A. This overlap is congruent with the observation that relatives of probands with schizophrenia are at higher risk for bipolar disorder and vice versa. In light of associations between bipolar and circadian abnormalities(such as decreased need for sleep and increased sleep latency), polymorphisms in the CLOCK gene have been tested for association, although findings have been inconsistent.^[6] Other circadian genes associated with bipolar at relaxed significance thresholds include ARTNL, RORB, and DEC1.^[7]

Pathophysiology

Structural neuroimaging studies consistently report increased frequency of white matter hyperintensities in people with bipolar. However, whether or not the lesions play a causative role is unknown. It is possible that they are a result of secondary factors, such as in increased risk of cardiovascular disease in bipolar. On the other hand, the observation of reduced white matter integrity in frontal-subcortical regions makes it possible that these hyperintensities play a role dysfunction between limbic and cortical regions. Global brain volume and morphology are normal in bipolar. Regional deficits in volume have been reported in ventrolateral and dorsolateral prefrontal regions. Based on this, it has been suggested that reduced limbic regulation by prefrontal regions plays a role in bipolar. Findings related to the volume of the basal ganglia have been inconsistent.

Functional neuroimaging studies, contrary to structural studies, consistently find hyperactivation of the basal ganglia, amygdala, and thalamus. Prefrontal abnormalities are less consistently reported, although hyperactivation in the ventral prefrontal cortex is a fairly consistent finding.^[8] Mania is generally associated with frontal/ventral hypoactivation, while depression is generally associated with the opposite. A degree of lateralization with regard to abnormalities has been reported, with mania being associated with the right hemisphere, and depression the left. Trait abnormalities in euthymic patients have been observed, including hypoactivity in the ventral prefrontal cortex, and hyperactvity in the amygdala.^[9]

Cause

Precursors of bipolar disorders: a systematic literature review of prospective studies.

Juvenile onset; Mood lability.

COMT/DRD3/DRD2/BDNF polymorphisms distinguish BPI from BPII

Predominately manic BP==pre onset drug use and earlier dx

Genetics

ARTNL, RORB, and DEC1 all associated with BP

Environmental

Predominately manic BO associated with drug use and earlier diagnosis

Mechanism

The bipolarity of light and dark: A review on Bipolar Disorder and circadian cycles.

Variation in CLOCK genes and circadian rhythm associated with BPD

Investigating the mechanism(s) underlying switching between states in bipolar disorder.

Season variation in BPD symptoms, with manic switches occurring during long photoperiods and depression episodes occurring during periods with less light. Furthermore, photoperiod induced variations in tyrosine hydroxylase expression support a role of catecholamine dysregulation in BPD.

The catecholaminergic-cholinergic balance hypothesis of bipolar disorder revisited.

Inconsistent evidence across D2 type receptors and DAT in both post-mortem studies but D1Rs BP is decreased/prbly due to increase synaptic conc
Pharmacological models of mania are generally DA release agents, and mood stabilizers reduce DA synthesis and release
Reduced in vivo BP of muscarinic receptors in BPD Dep, and decreased receptor expression in post-mortem studies-likely due to excessive synaptic conc
Pharmacological support^^

Integrated neurobiology of bipolar disorder.

Decreased vlPFC activity in both euthymic and manic bipolar disorder
Decreased activity(likely indicative of increased DA stimulation) in the striatum in mania, and increased activity in euthymia
Elavated dorsal ACC activity in mania
Thinning of the vmPFC and well as enlargement of ventricles correlates with manic episodes
More cells in the LC, slightly fewer cells in the RN
Hyperresponsiveness of the HPA axis correlated with longer duration of affective episodes
Delayed sleep phase in common in BPD, sleep efficiency is decreased, higher density of REM sleep
Nighttime correlates with symptom presentation, exaggerated melatonin reduction in response to light, and elevated afternoon cortisol.
Most anti-manic drugs alter intracellular GSK-3 signaling.

Neurocircuits

A critical appraisal of neuroimaging studies of bipolar disorder: toward a new conceptualization of underlying neural circuitry and a road map for future research.
Dysfunction in emotional regulatory circuits characterized by reduced prefrontal-amygdala connectivity

Hypersensitivity to reward indicated by elevated ventral striatal, ventrolateral prefrontal, and orbitofrontal cortex response to positive emotional stimuli.
The functional neuroanatomy of bipolar disorder: a consensus model

Structural neuroimaging studies in the early stages of bipolar suggests that the progressive nature of bipolar disorder reflects dysregulated growth and connectivity in prefrontal-limbic circuits, especially in the amygdala

Neurobiological

GSK-3

IP

Psychosis

Models

Neurodevelopment model of schizophrenia: updated 2012

Risk factors: toxoplasmosis gondii, Obstetric complications, Low birth weight, Urban environment,Immigration Status,Childhood trauma
Neurexin SNPs associated with schizophrenia
Drawn out period of development, remodeling and instability lasts into late thirties

Schizophrenia and reelin: a model based on prenatal stress to study epigenetics, brain development and behavior.

Reduced mRNA of GAD67 in the PFC, reduced GABAergic cells, reduced inhibitory synapses
Prenatal stress increases risk of schizophrenia, and has been demonstrated to lead to delayed migration, and morphological changes in PFC cells.
Reelin has been demonstrated to be hypermethylated in schizophrenia, and therefore under expressed. Heterozygous knockout mouse demonstrate similar alterations in neurodevelopment, and a reduction in GABAergic neurons that are characteristic of schizophrenia. Furthermore, stress had been observed in animal models to lead to underexpression

Toward a Neurobiology of Delusions

Functional neuroimaging studies have found that aberrant prediction error signalling occurs in schizophrenics, and correlates with delusion severity
Schizophrenics are more susceptible to illusions produced by altering sensory experience
Schizophrenics demonstrate abnormalities with predicting the effects of their actions
The role of NMDA in schizophrenia and the ability for NMDA anagonists to mimic schizophrenia fit in with the model suggesting that delusions are a result of dysfunctional prediction systems

Cellular models to study schizophrenia: A systematic review.

Morphological differences in dendrites

On the neurobiology of hallucinations

Schizophrenics demonstrate deficits in the ability to correctly attribute stimuli, or link actions to sensory outcomes. Disruption of the thalamocortical gamma bands may play a role.

The neural basis of hallucinations and delusions.

Abnormal connectivity between the frontal and parietal lobes are suggested to give rise to a decreased sense of control, but an increased sense of agency

Cause

Genetics

Genome-wide association studies (GWAS) of schizophrenia: does bigger lead to better results?

Genes associated with SCZ include TCF4, NRGN, VRK2, MIR137, PCGEM1, CSMD1, MMP16, CNNM2, NT5C2, and CACNA1C
The most commonly associated genes are generally involved in MHC regions, synaptic plasticity, and neurodevelopment

Epigenetic aspects in schizophrenia etiology and pathogenesis

DGCR8 has been observed to be prone to deletion in schizophrenics, and is deferentially expressed.
DGCR8 being differential expressed leads to altered transcription of synaptic proteins.
Increased methylation of H3 histone R17 in the PFC of schizophrenics

Brain vs behavior: an effect size comparison of neuroimaging and cognitive studies of genetic risk for schizophrenia.

Most neuroimaging studies demonstrate a more significant effect size compared to cognitive studies. The greatest gene associated with neuroimaging alterations was DAOA

Genomic and network patterns of schizophrenia genetic variation in human evolutionary accelerated regions.

Most of the genes associated with recent primate evolution that are associated with schizophrenia are involved in synaptic regulation, and are expressed in the PFC in higher concentrations.

No historical candidate genes were found to be significant in the SZgene study, but four genes (DRD2, GRM3, NOTCH4, TNF) have been found to be significant in the PCG. Two have been found in the MHC.

The Role of Genetics in the Etiology of Schizophrenia

NRXN1 and MHC genes have been most frequently associated with schizophrenia(along with NOTCH4 and the aforementioned TNF)

Environment

The environment and susceptibility to schizophrenia

Infections during the prenatal period have been associated with schizophrenia. Some evidence points towards the effect of pro-inflammatory proteins on neurodevelopment(including IL-8 and TNF)
Famine and materna nutritional deficiencies have been associated with an increase risk of schizophrenia.
Advanced paternal age
Prenatal stress has been linked to schizophrenia, but not as strongly as low birth weight, which prenatal stress has been strongly linked with. CRH and inflammatory mechanisms may be involved
Cannabis use has been associated with schizophrenia when used in adolescence, and a dose response relationship has been suggested. This finding has been replicated a number of times, with a meta analysis reporting a pooled odds ratio of 2.3 in studies assessing cannabis use before schizophrenia onset, suggesting a causative role.

Gene-Environment Interactions in Schizophrenia: Review of Epidemiological Findings and Future Directions

While most genetic and environmental studies have reported small effects, assessing GxE associations has significantly increased effect.

Environmental risk factors for psychosis

Urbanization, childhood stress, migration, and adverse life events, and being born during the winter or spring have been associated with schizophrenia.

Schizophrenia: a consequence of gene-environment interactions?

Oxidative stress may cause NMDA receptor hypofunction due to genetic alterations in synaptic regulation combined with stress.

Fetal Environment and Schizophrenia

Along with the aforementioned associations with infections and maternal famine, maternal obesity has been associated with SCZ as well.

Mechanism

Neurochemical

Glutamate and dopamine in schizophrenia: an update for the 21st century.

Post mortem studies while few in number are associated with a reduction in post-synaptic dopamine receptors, and an increase in presynaptic D2 receptors, as well as an increase in TH, no change in DAT, elavation of D4 receptors, a reduction in D2 monomers, and a large increase in D2 dimers
In Vivo neurochemical imaging has demonstrated a large increase in presynaptic dopamine metabolism, but the post-synaptic dopaminergic protein results have been inconsistent
While genetic studies and the mimicry of SCZ symptoms by NMDA agonists implicates glutamate dysfunction in SCZ, a lack of consistency in post-mortem studies suggests that a deficit in NMDA receptor expression may be too simple a model, rather NMDA dysfunction may be characterized by dysfunctional regional expression and altered subunit composition.
While those with familial risk, first episode psychosis, or psychotic symptoms demonstrate increased glutamine levels using in vivo imaging, chronic schizophrenics demonstrate a reduction or normal levels. Furthermore, an increase in glutamine/gluamate ratio has been observed in some studies

Neurodevelopment, GABA, and Schizophrenia

Due to the lack of a definitive neurodegenerative course in the morphological changes associated with schizophrenia, the common finding of reduced cortical thickness and increase ventricle volume are thought to be a result of developmental processes.
Variable findings in interstitial white matter have been observed with regard to GABAergic neurons.
Both genetic and environmental risk factors for SCZ have been associated with altered GABAergic development

The nature of dopamine dysfunction in schizophrenia and what this means for treatment

Significant elavation in presynaptic dopamine metabolism, increase in D2/D3 receptor expression(post-synaptic) and no effect on DAT

Alterations in cortical and extrastriatal subcortical dopamine function in schizophrenia: systematic review and meta-analysis of imaging studies.

Altered expression of D1 in the ACC, uncus, and D2 in the PFC have been osberved, as well as DAT alterations in the thalamus

The role of extrastriatal dopamine D2 receptors in schizophrenia.

D2 receptor expression in the thalamus and ACC is reduced in schizophrenia

Pathway-Specific Dopamine Abnormalities in Schizophrenia.

Stimulant evoked DA release is elevated in the striatum, but reduced elsewhere

Neurobiological

The impact of NMDA receptor hypofunction on GABAergic neurons in the pathophysiology of schizophrenia

NMDARs are necessary for large network connectivity and heavily influence GABAergic development. Both genetic and post-mortem studies have demonstrated alterations in GABAergic and NMDAR proteins and regulating genes, and a widely replciated finding is a reduction in NMDA receptors globally, although this effect is particularly significant in GABAergic neurons, and is especially prominent in NR1 and NR2C subunits in the PFC.
Intact GABAergic signalling is necessary for gamma band activity

Glutamatergic postsynaptic density protein dysfunctions in synaptic plasticity and dendritic spines morphology: relevance to schizophrenia and other behavioral disorders pathophysiology, and implications for novel therapeutic approaches.

Younger SCZs demonstrate decreased expression of PSD-95 with concurrent increases in thalamic NR2B, while the elderly with SCZ demonstrate the inverse.

The impact of NMDA Receptor hypofunction on GABAergic interneurons in the pathophysiology of schizophrenia

Expression of NMDA subunits appears to be decreased in the prefrontal cortex, as well as in general. Combined with evidence from NMDA receptor antagonists, this data suggests that schizophrenia involves hypofunctioning of NMDA receptors. This may contribute to resting state abnormalities in connectivity, as well as dopaminergic abnormalities.

GABAergic interneuron origin of schizophrenia pathophysiology.

The involvement of N-methyl-D-aspartate receptor (NMDAR) subunit NR1 in the pathophysiology of schizophrenia.

Schizophrenia: Evidence implicating hippocampal GluN2B protein and REST epigenetics in psychosis pathophysiology.

Neurocircuitry

Synchrony in schizophrenia: a window into circuit-level pathophysiology.

Ehh not really a great review

Neuroimaging studies of social cognition in schizophrenia.

Paywalled

Meta-analysis of functional neuroimaging studies of emotion perception and experience in schizophrenia.

Ctrl>SCZ: dlPFC, Amygdala, Hippocampus, ACC, Thalamus, Caudate and Midbrain.
SCZ>Ctrl: Parietal Lobule, Precentral Gyrus and STG.
Variable Amygdala findings, possibly reflecting general reduced responsively to social stimuli.

Ctrl>SCZ: dlPFC, right vPFC, rACC and anterior ACC extending into the mPFC, right ventral PMC, putamen and thalamus.
SCZ>Ctrl: left dorsal and ventral PMC, dorsal ACC and amygdala.
Possible due to frontal top down dysfunction, leading to brain wide dysfunction

Beyond hypofrontality: a quantitative meta-analysis of functional neuroimaging studies of working memory in schizophrenia.

Although reduced dlPFC was observed, hyperactivity in the frontal pole and ACC were also found

Meta-Analysis of Functional Neuroimaging and Cognitive Control Studies in Schizophrenia: Preliminary Elucidation of a Core Dysfunctional Timing Network.

Ctrl>SCZ bilaterally in the IFG and SMA, and unilaterally in the basal ganglia, superior OCC, SMA, IPG, and cuneus.
SCZ>Ctrl in post-central gyrus and fusiform gyrus

Progressive loss of cortical gray matter in schizophrenia: a meta-analysis and meta-regression of longitudinal MRI studies.

Reduction in STG over course of SCZ

Meta-analysis of gray matter anomalies in schizophrenia: application of anatomic likelihood estimation and network analysis.

Reductions in GMV were seen in the insula and ACC,the left parahippocampus, left MFG, left postcentral gyrus, and left thalamus.

Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium.
Reduced hippocampus, amygdala, thalamus, NAcc, and GP volumes.
Structural correlates of auditory hallucinations in schizophrenia: a meta-analysis.

AVH correlated with Ctrl>SCZ in left ins, right STG

Neuroanatomical abnormalities in schizophrenia: a multimodal voxelwise meta-analysis and meta-regression analysis.

Ctrl>SCZ: FA/WM in inter hemispheric fibers, anterior thalamic radiations, inferior longitudinal occipital fascicule, cingulum, and fornix
Ctrl>SCZ: GM in Insula, Interior frontal cortex, STG, ACC, thalamus, left amygdala

BG

Schizophrenia cont

The symptoms of schizophrenia largely fall into three categories, reality distortion, negative symptoms and disorganization. The dichotomy, traced back to work by Kraepalin, whereby bipolar affective and psychotic disorders are treated as separate entities, is contradicted by continuity of affective and psychotic symptoms through genetic and neurobiological factors.^[10]
The heritability of Schizophrenia is estimated to be roughly 80%. The heritable component is evident in monozygotic and dizygotic concordance, 41-65% and 0-28% respectively, as well as the elevated risk of developing in schizophrenia in those with first degree relative with schizophrenia, or bipolar disorder. Schizophrenia has been associated in GWAS studies with.^[11]

MIR137
SDCCAG8-rs6703335
VRK2-rs2312147
ZNF804A-rs1344706
PCGEM1-rs17662626
ITIH3-rs2239547
Extended Major Histocompatibility Complex Regions
CSMD1-rs10503253
LSM1-rs16887244
MMP16-rs7004633
ANK3-rs10994359
CNNM2-rs7914558
NT5C2-rs11191580
AMBRA1-rs11819869
ODZ4-rs12576775
NRGN-rs12807809
CACNA1C-rs4765905
TCF4-rs9960767
CCDC68-rs12966547
NCAN-rs1064395

Significant overlap in GWAS studies of bipolar and schizophrenia have been observed at ANK3, CACNA1C, ZNF804A, and ITIH3-ITIH4. Studies of brain structure and function in Schizophrenia have included longitudinal, patient-control constrast, and at-risk study designs. Schizophrenia is associated with reduced volumes, usually of the magnitude 5-10%. Robust structural findings include 1)Ventricular enlargement, 2)Reduced whole brain volume, 3)Reduced GMV of the medial temporal lobe extending to the hippocampus and amygdala, 4)Reduced GMV in the STG. thirty-five out of forty-six studies report significant STG volume reduction, and eighteen out of thirty studies reported correlations between STG volume and severity of auditory hallucinations or though disorders. Thirteen studies have reported bilateral thalamic reductions, including studies on both first episodes and chronic patients. Grey matter reductions are particularly evident in the anterior ingulate, medial and inferior frontal lobes, and anterior insula. Bipolar parients and Schizophrenic pateints exhibit significant overlaps in the ACC, mPFC, lPFC, and bilateral insula, only differing in magnitude. The Cerebellum, Corpus Callosum, and Basal Ganglia are frequently observed to be structurally abnormal, although the findings as well as direction of changes is inconsistent. Longitudinal neuroimaging suggests that schizophrenia appears to be more of a neurodevelopment disorder, rather than a neurodegenerative disorder.^[12] Schizophrenics exhibit hypoactivity in the dlPFC during executive function tasks, frequently extending to the ACC, thalamus, and inferior and posterior parietal cortices. Hyperactivity during executive function tasks have been observed in midline cortical regions, premotor cortcices and the vlPFC. Reduced activation of the dlPFC during episodic memory tasks has been observed. Reality distortion has been assocaited with increased medial temporal blood flow, and decreased prefrontl boodflow in the other two symptom dimensions. Negtative symptoms in schizophrenia are characterized by more pronounced and widespread GMV losses. MTL generally differentiates affective disorders from less affective psychotic disorders. Longitutinally, Schizophrenia is associated with continual reductions in thalamic and prefrontal volumes and less consistend temporal lobe changes. Hippocampal reductions over time are more contested. In those at high risk for psychosis, inferior frontal and superior temporal reductions are associated with transition to psychosis. Magnetic resonance spectroscopy observe first episode increased in Gln and decreased in Glu, followed by accelerated age related decreased in Gln and Glu relative to controls--->Taken to mean first episode increased in Glu--->Increased in Gln+Decreaese in Glu---->Thereafter reductions in Gln+Glu.^[13]

While there is no argument that schizophrenia is associated with cognitive deficits which are in turn associated with abnormalities in the prefrontal cortex and medial temporal lobe, the fundemental cognitive processes that malfunctioning are yet to be determined. Endophenotypic and medication status-independent deficits in WM are observed in schizophrenia, regardless of the modality tested(i.e. verbal versus visual). The temporal characteristics of WM deficits in schizophrenia have led some authors to postulate that deficits are encoding related; in line with this, deficits are constant regardless of delay. However, maintence appears to be dysfunctional as well. While "hypofrontality" is a major principal in schizophenia theories, the relationship between WM task deficit appears to follow a biphasic curve, with increased dlPFC signals occuring earlier in schizophrenic patients, indicating less efficient systems of internal representation. Episodic memory also impaired, and evidence suggests that this is most prominently expressed in relational reasoning caused by hippocampal and prefrontal dysfunction. Hedonic, valenced, processing in schizophrenia appears to largely be intact, as ventral-striatal responses and subjective "liking" is not diminished, although loss-avoidance responsivity is impaired. Despite the aforementioned findings, increased anhedonia(negative symptoms) have also been reportedly associated with reduced striatal responsiveness to rewards. While basal ganglia dysfuncton is present in schizophrenia, simple reinforcement learning is not impaired. However, when reinforcement learning paradigms increase in complexity, impairment becomes apparent. While these impairments are fairly consistent, whether or the deficit is a result of online processing mechanisms(e.g. dlPFC mediated WM), associative processes, or both, is unknown. Monetary incentive delay tasks, assesing desire or "wanting" is frequently associated with reduced ventral striatal signaling in schizophrenia. This has been correlated with various symptoms and symptom dimensions, including apathy and negative symptoms in both medication naive and medicated patients. Reduced prediction errors have also been observed.

Overall, a failure of on line maintence and reward associativty is thought to lead to impairment in the generation of cognition and behavior recquired to obtain rewads, despite normal hedonic responses.^[14] From a longitudinal standpoint, deficits in verbal, nonverbal and mathematical, tests scores have been observed in people who go on to develop schizophrenia. Attentional deficits has the ability to identify which children of parents with schizophrenia will and wont go on to develop the disease themselves. One study examining the Iowa test found that deficits were only significant after the onset of puberty in those who went on to develop the disease in adulthood. ^[15]

Neuropathological studies suggest that volumetric deficits in the temporal lobes, prefrontal lobes, and subiculum of those with Schizophrenia can be better attributed to deficits in neuropil(neuronal extensions such as dendrites) rather than fewer cells. Postmortem gene expression studies have observed reductions in protein associated with myelin formation and regulation, which is congruent with the association between NRGN and DISC1 genetic polymorphisms and schizophrenia.^[16] Reductions in GAD67 mRNA levels in the prefrontal cortex and general reduction in parvalbumin mRNA have been observed in schizophrenia, being most significant but not limited to prefrontal regions. The reductions in GABAergic neuronal proteins may be linked to the observed reductions of TrKB and BDNF as the latter proteins are involved in the regulation of the former. The reduction may also be linked to reduced excitation of GABAergic neurons, possible due to hypofunction of NMDA receptors.^[17]

Symptom Dimension	Regional Morphological Differences	Regional Functional Differences
Negative Symptoms	Reduced left vmPFC Reduced Subgenual/Anterior Cingulate Reduced Perisylvian regions Reduced lateral prefrontal cortex Reduced insula	Reduced PFC bloodflow
Psychosis	Reduced left perisylvian region Reduced bilateral thalamic volume Reduced superior temporal gyrus Reduced supramarginal gyrus	Increased medial temporal lobe activity
Disorganization	Increased bilateral hippocampus Reduced amygdala Bilateral reduction in perisylvian regions Increased bilateral parahippocampus	Reduced PFC bloodflow

Resting state hypofrontality.^[18]
Rediced activation in the right middle frontal gyrus, right inferior parietal lobule, and right insula during attentional tasks. Increased activation in the left insula duting memory task.^[19]
Insula, inferior frontal cortex, superior temporal gyrus, anterior cingulate cortex, medial frontal cortex, thalamus and left amygdala volume reductions. Reduced fractional anisotropy or white matter in interhemispheric fibers, anterior thalamic radiations, inferior longitudinal fasiculus, inferior frontal occipital fasiculi, cingulum and fornix.^[20]

Biology of bipolar disorder

The biology of bipolar disorder is unknown. Various abnormalities in studies of brain structure and function have been demonstrated. Various theories have posited dysfunction in monoaminergic regulation, neural circuitry, and mitochondria function as underlying bipolar disorder.

Etiology

The etiology of bipolar disorder is unknown. The overall heritability of bipolar is estimated at 79%-93%, and first degree relatives of bipolar probands have a relative risk of developing bipolar around 7-10. While the heritability is high, no specific genes have been conclusively associated with bipolar, and a number of hypothesis have been posited to explain this fact. The polygenic common rare variant hypothesis suggests that a large number of risk conferring genes are carried in a population, and that a disease manifests when a person has a sufficient number of these genes. The multiple rare variant model suggests that multiple genes that are rare in the population are capable of causing a disease, and that carrying one or a few can lead to disease.

A number of genome wide associations have been reported, including CACNA1C and ODZ4. Less consistently reported loci include ANK3 and NCAN, ITIH1, ITIH3 and NEK4. Significant overlaps with schizophrenia have been reported at CACNA1C, ITIH, ANK3, and ZNF804A. This overlap is congruent with the observation that relatives of probands with schizophrenia are at higher risk for bipolar disorder and vice versa. In light of associations between bipolar and circadian abnormalities(such as decreased need for sleep and increased sleep latency), polymorphisms in the CLOCK gene have been tested for association, although findings have been inconsistent.^[21] Other circadian genes associated with bipolar at relaxed significance thresholds include ARTNL, RORB, and DEC1.^[22] One meta analysis reported a significant association of the short allele of the serotonin transporter, although the study was specific to european populations.^[23] Two polymorphisms in the tryptophan hydroxylase 2 gene have been associated with bipolar disorder.^[24]

No significant association exists for the BDNF Val66Met allele and bipolar disorder, except in a subgroup of bipolar II cases.^[25]

Pathophysiology

Neuroimaging

Structural neuroimaging studies consistently report increased frequency of white matter hyperintensities in people with bipolar. However, whether or not the lesions play a causative role is unknown. It is possible that they are a result of secondary factors, such as in increased risk of cardiovascular disease in bipolar. On the other hand, the observation of reduced white matter integrity in frontal-subcortical regions makes it possible that these hyperintensities play a role dysfunction between limbic and cortical regions. Global brain volume and morphology are normal in bipolar. Regional deficits in volume have been reported in ventrolateral and dorsolateral prefrontal regions. Based on this, it has been suggested that reduced limbic regulation by prefrontal regions plays a role in bipolar. Findings related to the volume of the basal ganglia have been inconsistent.

Functional neuroimaging studies, contrary to structural studies, consistently find hyperactivation of the basal ganglia, amygdala, and thalamus. Prefrontal abnormalities are less consistently reported, although hyperactivation in the ventral prefrontal cortex is a fairly consistent finding.^[26] Mania is generally associated with frontal/ventral hypoactivation, while depression is generally associated with the opposite. A degree of lateralization with regard to abnormalities has been reported, with mania being associated with the right hemisphere, and depression the left. Trait abnormalities in euthymic patients have been observed, including hypoactivity in the ventral prefrontal cortex, and hyperactvity in the amygdala.^[27] Hyperactivity in the amygdala and hypoactivity in the medial and ventral prefrontal cortex during exposure to emotional stimuli has been interpreted as reflecting dysfunction in emotional regulation circuits. Increased effective connectivity between the amygdala and orbitofrontal cortex, and elevated striatal responsiveness during reward tasks have been interpreted as hyper-responsiveness in positive emotion and reward circuitry. The abnormal activity in these circuits has been observed in non-emotional tasks, and is congruent with changes in grey and white matter in these circuits.^[28] Neural response during reward tasks differentiates unipolar depression from bipolar depression, with the former being associated with reduced neural response and the latter being associated with elevated neural response.^[29]

Meta analyses of structural neuroimaging studies have reported reductions in fronto-insula cortices, the anterior cingulate cortex,^[30] ventricular enlargement,^[31] increased volumes of the globus pallidus and increased amygdala volume relative to those with schizophrenia,^[32] and reduced grey matter in the claustrum and temporal cortex.^[33] A significant overlap of bipolar disorder with schizophrenia in grey matter volume reduction occurs in the anterior cingulate cortex, medial prefrontal cortex, lateral prefrontal cortex and bilateral insula.^[34]

Regardless of mood state, during response inhibition tasks, people with bipolar disorder underactivate the right inferior frontal gyrus. Changes specific on euthymia include hyperactivations in the left superior temporal gyrus and hypoactivations in the basal ganglia, and changes specific to mania include hyperactivation in the basal ganglia.^[35] A meta analysis of fMRI studies reported underactivations in the inferior frontal gyrus and putamen and hyperactivation of the parahippocampus, hippocampus, and amygdala. State specific abnormalities were reported for mania and euthymia. During mania, hypoactivation was significant in the inferior frontal gyrus, while euthymia was associated wit hypoactivation of the lingual gyrus and hyperactivation of the amygdala.^[36]

Neurometabolites

Increased combined glutamine and glutamate(Glx) have been observed globally, regardless of medication status.^[37] Increased Glx has been associated with reduced frontal mismatch negativity, interpreted as dysfunction in NMDA signaling.^[38] N-acetyl aspartate levels in the basal ganglia are reduced in bipolar disorder, and trends towards increased in the dorsolateral prefrontal cortex. NAA to creatine ratios are reduced in the hippocampus.^[39]

Neurochemistry

Various hypotheses related to monoamines have been proposed. The biogenic amine hypothesis posits general dysregulation of monoamines underlies bipolar and affective disorders. The cholinergic aminergic balance hypothesis posits that an increased ratio of cholingeric activity relative to adrenergic signaling underlies depression, while increased adrenergic signaling relative to cholinergic signaling underlies mania. The permissive hypothesis suggests that serotonin is necessary but not sufficient for affective symptoms, and that reduced serotonergic tone is common to both depression and mania.^[40]

Studies of the binding potential of Dopamine receptor D2 and dopamine transporter have been inconsistent but Dopamine receptor D1's binding potential has been observed to be decreased, interpreted as reflecting increased synaptic dopamine in mania. Drugs that release dopamine produce effects similar to mania, further supporting the hypothesis of increase catecholaminergic activity in mania. The binding potential of muscarinic receptors are reduced in vivo during depression, as well as in post mortem studies, supporting the cholinergic aminergic balance hypothesis.^[41]

Further evidence for monoamine dysfunction in bipolar comes on studies of neurotransmitter metabolites. Reduced concentration of homovallinic acid, the primary metabolite of dopamine, in the cerebrospinal fluid(CSF) of people with depression is consistently reported. This finding is related to psychomotor retardation and anhedonia. Furthermore, parkinson's disease is associated with high rates of depression, and one case study has reported the abolishment of parkinson's symptoms during manic episodes. The binding potential of VMAT2 is also elevated in bipolar I patients with a history of psychosis, although this finding is inconsistent with finding that valproate increases VMAT2 expression in rodents.^[42]

Studies of serotonin's primary metabolite 5-HIAA have been inconsistent,^[43] although limited evidence points towards reduced central serotonin signaling in a subgroup of aggressive or suicidal patients.^[42] Studies assessing the binding potential of the serotonin transporter or serotonin receptors have also been inconsistent, but generally point towards abnormal serotonin signalling.^[44]

Bipolar disorder is associated with elevated basal and dexamethasone elicited cortisol and Adrenocorticotropic hormone(ACTH). These abnormalities are particularly prominent in mania, and are inversely associated with antipsychotic use.^[45]

Intracellular Signaling

The levels of G_αs but not other g proteins is increased in the frontal, temporal and occipital cortices. The binding of serotonin receptors to g proteins is also elevated globally. Leukocyte and platelet levels of G_αs and G_αi is also elavated in those with bipolar disorder. Downstream targets of g protein signaling is also altered in bipolar disorder. Increased levels of adenylyl cyclase, protein kinase A(PKA), and cyclic adenosine monophosphate induced PKA activity is also elevated. Phosphoinositide signaling is also altered, with elevated levels of phospholipase C, protein kinase C, and G_αq being reported in bipolar.^[46]

Glycogen synthase kinase 3 has been implicated in bipolar disorder, as bipolar medications lithium and valproate have been shown to increase ints phosphorylation, thereby inhibiting it. However, postmortem studies hvae not shown any differences in GSK-3 levels or the levels of a downstream target β-catenin.^[47]

Mitochondrial Dysfunction

Some researchers have suggested bipolar disorder is a mitochondrial disease. Some cases of familial chronic progressive external ophthalmoplegia demonstrate increased rates of bipolar disorder before the onset of CPEO, and the higher rate of maternal inheritance patterns support this hypothesis. Further support this hypothesis is the abnormal findings in magnetic resonance spectroscopy studies of brain metabolites in people with bipolar.^[48]

Neuropathology

A number of abnormalities in GABAergic neurons have been reported in people with bipolar disorder. People with bipolar demonstrate reduced expression of GAD67 in CA3/CA2 subregion of the hippocampus. More extensive reductions of other indicators of GABA function have been reported in in the CA4 and CA1. Abnormal expression of kainate receptors on GABAergic cells have been reported, with reductions in GRIK1 and GRIK2 mRNA in the CA2/CA3 being found in people with bipolar. Decreased levels of HCN channels have also been reported, which, along with abnormal glutamate signaling, could contribute to reduced GABAergic tone in the hippocampus.^[49]

The observation of increased Glx in the prefrontal cortex is congruent with the observation of reduced glial cell counts and prefrontal cortex volume, as glia play an important role in glutamate homeostasis.^[50]

Immune Dysfunction

Elevated levels of IL-6, C-reactive protein(CRP) and TNFα have been reported in bipolar. Levels of some(IL-6 and CRP) but not all (TNFα) may be reduced by treatment. Increases in IL-6 have been reported in mood episodes, regardless of polarity.^[51]

Sources to Integrate

Reviews

Bipolar

Obsessive-compulsive disorder

Bipolar I and II Disorders; A Systematic Review and Meta-Analysis on Differences in Comorbid Obsessive-Compulsive Disorder.

Meta analyses

Bipolar

Obsessive-compulsive disorder

Biological markers for anxiety disorders, OCD and PTSD: A consensus statement. Part II: Neurochemistry, neurophysiology and neurocognition

MDD

Table 1
Link	Decreases(-)	Increases(+)	Measure
Kühn 2013 Resting-state brain activity in schizophrenia and major depression: a quantitative meta-analysis.	Left postcentral gyrus Left insula Left Fusiform gyrus	Left ventral striatum Bilateral ventromedial prefrontal cortex Left Thalamus	Resting activity
Miller 2015 https://www.ncbi.nlm.nih.gov/pubmed/26332700	Caudate Dorsal anterior cingulate cortex Dorsal anterior insula	Ventrolateral prefrontal cortex Subgenual cingulate cortex Dorsolateral prefrontal cortex Superior temporal gyrus Thalamus Parahippocampus	Functional activity
Wang 2016 Serotonin-1A receptor alterations in depression: a meta-analysis of molecular imaging studies	Anterior cingulate cortex Hippocampus Insula Mesiotemporal cortex		5-HT1A receptor BP
Du 2012 Voxelwise meta-analysis of gray matter reduction in major depressive disorder	Bilateral anterior cingulate cortex Right middel frontal gyrus Right inferior frontal gyrus Right hippocampus Left thalamus		Grey matter volume
Hamilton 2012 Functional neuroimaging of major depressive disorder: a meta-analysis and new integration of base line activation and neural response data	In response to negative stimuli: Dorsal striatum, dlPFC	Pulvinar Nucleus In response to negative stimuli: Amygdala, insula, dACC	Functional activity
Zhang 2016 Brain gray matter alterations in first episodes of depression: A meta-analysis of whole-brain studies	Right SMA Right MTG Left insula		Grey matter volume
Bora 2012 Gray matter abnormalities in Major Depressive Disorder: a meta-analysis of voxel based morphometry studies	rACC dmPFC Amygdala Parahippocampus		Grey matter
Lai 2013 Gray matter volume in major depressive disorder: a meta-analysis of voxel-based morphometry studies	Bilateral ACC		Grey matter
Sacher 2012 Mapping the depressed brain: a meta-analysis of structural and functional alterations in major depressive disorder	Right dmPFC Right paracingulate cortex Bilateral Amygdala	Right sgACC Right pgACC	Decreases(GMV), increases(Glucose metebolism)
Gryglewski 2014 Meta-analysis of molecular imaging of serotonin transporters in major depression	Midbrain Amygdala		5-HTT Binding potential

Various Shenanigans

Title	Year	Disorder	Papers(n)	Subjects	Pubmed Link	Full Text Link	Full Text Link	DOI	Increased Activity, GM, WM or BP	Reduced Activity, GM, WM OR BP	Average Subjects
Functional Decoding and Meta-analytic Connectivity Modeling in Adult Attention-Deficit/Hyperactivity Disorder.	2016	ADHD	24	-	https://www.ncbi.nlm.nih.gov/pubmed/27569542	sci-hub.tw/10.1016/j.biopsych.2016.06.014.	sci-hub.la/10.1016/j.biopsych.2016.06.014.	10.1016/j.biopsych.2016.06.014.		L PUT, L IFG, R CAUD	-
A systematic review and meta-analysis of tract-based spatial statistics studies regarding attention-deficit/hyperactivity disorder	2016	ADHD	10	947	https://www.ncbi.nlm.nih.gov/pubmed/27450582	sci-hub.tw/10.1016/j.neubiorev.2016.07.022	sci-hub.la/10.1016/j.neubiorev.2016.07.022	10.1016/j.neubiorev.2016.07.022	-	SS, Sagital Stratum	94.7
Striatal dopamine transporter alterations in ADHD: pathophysiology or adaptation to psychostimulants? A meta-analysis.	2012	ADHD	9	169	https://www.ncbi.nlm.nih.gov/pubmed/22294258	sci-hub.tw/10.1176/appi.ajp.2011.11060940.	sci-hub.la/10.1176/appi.ajp.2011.11060940.	10.1176/appi.ajp.2011.11060940.	Striatal DAT	-	18.8
Toward systems neuroscience of ADHD: a meta-analysis of 55 fMRI studies.	2012	ADHD	55	1542	https://www.ncbi.nlm.nih.gov/pubmed/22983386	sci-hub.tw/10.1176/appi.ajp.2012.11101521.	sci-hub.la/10.1176/appi.ajp.2012.11101521.	10.1176/appi.ajp.2012.11101521.	MCC, Angular	R vlPFC, L INS, L PUT	28.0
Diffusion tensor imaging in attention deficit/hyperactivity disorder: a systematic review and meta-analysis	2012	ADHD	9	342	https://www.ncbi.nlm.nih.gov/pubmed/22305957	sci-hub.tw/10.1016/j.neubiorev.2012.01.003	sci-hub.la/10.1016/j.neubiorev.2012.01.003	10.1016/j.neubiorev.2012.01.003			38.0
Meta-analysis of structural MRI studies in children and adults with attention deficit hyperactivitydisorder indicates treatment effects	2012	ADHD	11	608	https://www.ncbi.nlm.nih.gov/pubmed/22118249	sci-hub.tw/10.1111/j.1600-0447.2011.01786.x	sci-hub.la/10.1111/j.1600-0447.2011.01786.x	10.1111/j.1600-0447.2011.01786.x			55.3
Gray matter volume abnormalities in ADHD: voxel-based meta-analysis exploring the effects of age and stimulant medication	2011	ADHD	14	622	https://www.ncbi.nlm.nih.gov/pubmed/21865529	sci-hub.tw/10.1176/appi.ajp.2011.11020281	sci-hub.la/10.1176/appi.ajp.2011.11020281	10.1176/appi.ajp.2011.11020281			44.4
Structural Neuroimaging of Anorexia Nervosa: Future Directions in the Quest for Mechanisms Underlying Dynamic Alterations.	2018	AN	-	-	https://www.ncbi.nlm.nih.gov/pubmed/28967386	sci-hub.tw/10.1016/j.biopsych.2017.08.011	sci-hub.la/10.1016/j.biopsych.2017.08.011	10.1016/j.biopsych.2017.08.011			-
Behind binge eating: A review of food-specific adaptations of neurocognitive and neuroimaging tasks.	2017	AN	-	-	https://www.ncbi.nlm.nih.gov/pubmed/28363840	sci-hub.tw/10.1016/j.physbeh.2017.03.037	sci-hub.la/10.1016/j.physbeh.2017.03.037	10.1016/j.physbeh.2017.03.037			-
Functional brain alterations in anorexia nervosa: a scoping review.	2016	AN	49	-	https://www.ncbi.nlm.nih.gov/pubmed/27933159	sci-hub.tw/10.1186/s40337-016-0118-y	sci-hub.la/10.1186/s40337-016-0118-y	10.1186/s40337-016-0118-y			-
A systematic review of resting-state functional-MRI studies in anorexia nervosa: Evidence for functional connectivity impairment in cognitive control and visuospatial and body-signal integration.	2016	AN	15	294	https://www.ncbi.nlm.nih.gov/pubmed/27725172	sci-hub.tw/10.1016/j.neubiorev.2016.09.032	sci-hub.la/10.1016/j.neubiorev.2016.09.032	10.1016/j.neubiorev.2016.09.032			19.6
Brain morphological changes in adolescent and adult patients with anorexia nervosa.	2016	AN	29	273	https://www.ncbi.nlm.nih.gov/pubmed/27188331	sci-hub.tw/10.1007/s00702-016-1567-9	sci-hub.la/10.1007/s00702-016-1567-9	10.1007/s00702-016-1567-9			9.4
Morphological changes in the brain of acutely ill and weight-recovered patients with anorexia nervosa. A meta-analysis and qualitative review.	2014	AN	-	177	https://www.ncbi.nlm.nih.gov/pubmed/24365959	sci-hub.tw/10.1024/1422-4917/a000265	sci-hub.la/10.1024/1422-4917/a000265	10.1024/1422-4917/a000265			-
Anorexia nervosa is linked to reduced brain structure in reward and somatosensory regions: a meta-analysis of VBM studies.	2013	AN	9	228	https://www.ncbi.nlm.nih.gov/pubmed/23570420	sci-hub.tw/10.1186/1471-244X-13-110	sci-hub.la/10.1186/1471-244X-13-110	10.1186/1471-244X-13-110			25.3
Neural basis of a multidimensional model of body image distortion in anorexia nervosa.	2012	AN	-	-	https://www.ncbi.nlm.nih.gov/pubmed/22613629	sci-hub.tw/10.1016/j.neubiorev.2012.05.003	sci-hub.la/10.1016/j.neubiorev.2012.05.003	10.1016/j.neubiorev.2012.05.003			-
Processing of food, body and emotional stimuli in anorexia nervosa: a systematic review and meta-analysis of functional magnetic resonance imaging studies.	2012	AN	15	-	https://www.ncbi.nlm.nih.gov/pubmed/22945872	sci-hub.tw/10.1002/erv.2197	sci-hub.la/10.1002/erv.2197	10.1002/erv.2197			-
The common traits of the ACC and PFC in anxiety disorders in the DSM-5: meta-analysis of voxel-based morphometry studies.	2014	ANX	24	1264	https://www.ncbi.nlm.nih.gov/pubmed/24676455	sci-hub.tw/10.1371/journal.pone.0093432	sci-hub.la/10.1371/journal.pone.0093432	10.1371/journal.pone.0093432			52.7
Functional neuroimaging of anxiety: a meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia.	2007	ANX	19	1674	https://www.ncbi.nlm.nih.gov/pubmed/17898336	sci-hub.tw/10.1176/appi.ajp.2007.07030504	sci-hub.la/10.1176/appi.ajp.2007.07030504	10.1176/appi.ajp.2007.07030504			88.1
Prefrontal Structural and Functional Brain Imaging findings in Antisocial, Violent, and Psychopathic Individuals: A Meta-Analysis	2009	ASPD	43	1262	https://www.ncbi.nlm.nih.gov/pubmed/19833485	sci-hub.tw/10.1016/j.pscychresns.2009.03.012	sci-hub.la/10.1016/j.pscychresns.2009.03.012	10.1016/j.pscychresns.2009.03.012			29.3
Neuroimaging in bulimia nervosa and binge eating disorder: a systematic review.	2018	BN	32	-	https://www.ncbi.nlm.nih.gov/pubmed/29468065	sci-hub.tw/10.1186/s40337-018-0187-1	sci-hub.la/10.1186/s40337-018-0187-1	10.1186/s40337-018-0187-1			-
Inhibitory control in obesity and binge eating disorder: A systematic review and meta-analysis of neurocognitive and neuroimaging studies.	2016	BN	8	150	https://www.ncbi.nlm.nih.gov/pubmed/27381956	sci-hub.tw/10.1016/j.neubiorev.2016.06.041	sci-hub.la/10.1016/j.neubiorev.2016.06.041	10.1016/j.neubiorev.2016.06.041			18.8
Common and distinct patterns of grey-matter volume alteration in major depression and bipolar disorder: evidence from voxel-based meta-analysis.	2017	BP	86	6058	https://www.ncbi.nlm.nih.gov/pubmed/27217146	sci-hub.tw/10.1038/mp.2016.72	sci-hub.la/10.1038/mp.2016.72	10.1038/mp.2016.72	S dmPFC	pgACC, sgACC, INS	70.4
Meta-analysis of functional magnetic resonance imaging studies of timing and cognitive control in schizophrenia and bipolar disorder: Evidence of a primary time deficit.	2017	BP	22	772	https://www.ncbi.nlm.nih.gov/pubmed/28169089	sci-hub.tw/10.1016/j.schres.2017.01.039	sci-hub.la/10.1016/j.schres.2017.01.039	10.1016/j.schres.2017.01.039			35.1
Brain functional effects of psychopharmacological treatments in bipolar disorder.	2016	BP	140	-	https://www.ncbi.nlm.nih.gov/pubmed/27617780	sci-hub.tw/10.1016/j.euroneuro.2016.06.006	sci-hub.la/10.1016/j.euroneuro.2016.06.006	10.1016/j.euroneuro.2016.06.006			-
Subcortical volumetric abnormalities in bipolar disorder.	2016	BP	1	4304	https://www.ncbi.nlm.nih.gov/pubmed/26857596	sci-hub.tw/10.1038/mp.2015.227	sci-hub.la/10.1038/mp.2015.227	10.1038/mp.2015.227			4304.0
Voxel-Based Meta-Analytical Evidence of Structural Disconnectivity in Major Depression and Bipolar Disorder.	2016	BP	40	2429	https://www.ncbi.nlm.nih.gov/pubmed/25891219	sci-hub.tw/10.1016/j.biopsych.2015.03.004	sci-hub.la/10.1016/j.biopsych.2015.03.004	10.1016/j.biopsych.2015.03.004			60.7
A critical appraisal of neuroimaging studies of bipolar disorder: toward a new conceptualization of underlying neural circuitry and a road map for future research.	2014	BP	-	-	https://www.ncbi.nlm.nih.gov/pubmed/24626773	sci-hub.tw/10.1176/appi.ajp.2014.13081008	sci-hub.la/10.1176/appi.ajp.2014.13081008	10.1176/appi.ajp.2014.13081008			-
Elevated reward-related neural activation as a unique biological marker of bipolar disorder: assessment and treatment implications.	2014	BP	-	-	https://www.ncbi.nlm.nih.gov/pubmed/25241675	sci-hub.tw/10.1016/j.brat.2014.08.011	sci-hub.la/10.1016/j.brat.2014.08.011	10.1016/j.brat.2014.08.011			-
Developmental meta-analyses of the functional neural correlates of bipolar disorder.	2014	BP	94	3896	https://www.ncbi.nlm.nih.gov/pubmed/25100166	sci-hub.tw/10.1001/jamapsychiatry.2014.660	sci-hub.la/10.1001/jamapsychiatry.2014.660	10.1001/jamapsychiatry.2014.660			41.4
Could glutamate spectroscopy differentiate bipolar depression from unipolar?	2014	BP	11	431	https://www.ncbi.nlm.nih.gov/pubmed/25082118	sci-hub.tw/10.1016/j.jad.2014.05.019	sci-hub.la/10.1016/j.jad.2014.05.019	10.1016/j.jad.2014.05.019			39.2
Gray matter abnormalities as brain structural vulnerability factors for bipolar disorder: A review of neuroimaging studies of individuals at high genetic risk for bipolar disorder.	2013	BP	-	-	https://www.ncbi.nlm.nih.gov/pubmed/23864160	sci-hub.tw/10.1177/0004867413496482	sci-hub.la/10.1177/0004867413496482	10.1177/0004867413496482			-
Functional neuroanatomy of response inhibition in bipolar disorders--combined voxel based and cognitive performance meta-analysis.	2013	BP	30	1302	https://www.ncbi.nlm.nih.gov/pubmed/24070910	sci-hub.tw/10.1016/j.jpsychires.2013.08.015	sci-hub.la/10.1016/j.jpsychires.2013.08.015	10.1016/j.jpsychires.2013.08.015			43.4
Evidence of diagnostic specificity in the neural correlates of facial affect processing in bipolar disorder and schizophrenia: a meta-analysis of functional imaging studies.	2013	BP	29	750	https://www.ncbi.nlm.nih.gov/pubmed/22874625	sci-hub.tw/10.1017/S0033291712001432	sci-hub.la/10.1017/S0033291712001432	10.1017/S0033291712001432			25.9
A systematic review and meta-analysis of proton magnetic resonance spectroscopy and mismatch negativity in bipolar disorder.	2013	BP	15	368	https://www.ncbi.nlm.nih.gov/pubmed/23968965	sci-hub.tw/10.1016/j.euroneuro.2013.07.007	sci-hub.la/10.1016/j.euroneuro.2013.07.007	10.1016/j.euroneuro.2013.07.007			24.5
Mapping vulnerability to bipolar disorder: a systematic review and meta-analysis of neuroimaging studies.	2012	BP	37	2254	https://www.ncbi.nlm.nih.gov/pubmed/22297067	sci-hub.tw/10.1503/jpn.110061	sci-hub.la/10.1503/jpn.110061	10.1503/jpn.110061			60.9
Neurometabolites in schizophrenia and bipolar disorder - a systematic review and meta-analysis.	2012	BP	43	1461	https://www.ncbi.nlm.nih.gov/pubmed/22981426	sci-hub.tw/10.1016/j.pscychresns.2012.02.003	sci-hub.la/10.1016/j.pscychresns.2012.02.003	10.1016/j.pscychresns.2012.02.003			34.0
Common and distinct neural correlates of emotional processing in Bipolar Disorder and Major Depressive Disorder: a voxel-based meta-analysis of functional magnetic resonance imaging studies.	2012	BP	20	701	https://www.ncbi.nlm.nih.gov/pubmed/21820878	sci-hub.tw/10.1016/j.euroneuro.2011.07.003	sci-hub.la/10.1016/j.euroneuro.2011.07.003	10.1016/j.euroneuro.2011.07.003			35.1
Grey matter differences in bipolar disorder: a meta-analysis of voxel-based morphometry studies.	2012	BP	8	520	https://www.ncbi.nlm.nih.gov/pubmed/22420589	sci-hub.tw/10.1111/j.1399-5618.2012.01000.x	sci-hub.la/10.1111/j.1399-5618.2012.01000.x	10.1111/j.1399-5618.2012.01000.x			65.0
A quantitative meta-analysis of fMRI studies in bipolar disorder.	2011	BP	65	2114	https://www.ncbi.nlm.nih.gov/pubmed/21320248	sci-hub.tw/10.1111/j.1399-5618.2011.00893.x	sci-hub.la/10.1111/j.1399-5618.2011.00893.x	10.1111/j.1399-5618.2011.00893.x			32.5
Toward a functional neuroanatomical signature of bipolar disorder: quantitative evidence from the neuroimaging literature.	2011	BP	55	1584	https://www.ncbi.nlm.nih.gov/pubmed/21676596	sci-hub.tw/10.1016/j.pscychresns.2011.02.011	sci-hub.la/10.1016/j.pscychresns.2011.02.011	10.1016/j.pscychresns.2011.02.011			28.8
Neuroimaging-based markers of bipolar disorder: evidence from two meta-analyses.	2011	BP	28	1561	https://www.ncbi.nlm.nih.gov/pubmed/21470688	sci-hub.tw/10.1016/j.jad.2011.03.016	sci-hub.la/10.1016/j.jad.2011.03.016	10.1016/j.jad.2011.03.016			55.8
Voxelwise meta-analysis of gray matter abnormalities in bipolar disorder.	2010	BP	21	1430	https://www.ncbi.nlm.nih.gov/pubmed/20303066	sci-hub.tw/10.1016/j.biopsych.2010.01.020	sci-hub.la/10.1016/j.biopsych.2010.01.020	10.1016/j.biopsych.2010.01.020			68.1
Correlation between amygdala volume and age in bipolar disorder - a systematic review and meta-analysis of structural MRI studies.	2010	BP	13	877	https://www.ncbi.nlm.nih.gov/pubmed/20226638	sci-hub.tw/10.1016/j.pscychresns.2009.09.004	sci-hub.la/10.1016/j.pscychresns.2009.09.004	10.1016/j.pscychresns.2009.09.004			67.5
Magnetic resonance imaging studies in bipolar disorder and schizophrenia: meta-analysis.	2009	BP	65	1384	https://www.ncbi.nlm.nih.gov/pubmed/19721106	sci-hub.tw/10.1192/bjp.bp.108.059717	sci-hub.la/10.1192/bjp.bp.108.059717	10.1192/bjp.bp.108.059717			21.3
Meta-analysis, database, and meta-regression of 98 structural imaging studies in bipolar disorder.	2008	BP	98	8196	https://www.ncbi.nlm.nih.gov/pubmed/18762588	sci-hub.tw/10.1001/archpsyc.65.9.1017	sci-hub.la/10.1001/archpsyc.65.9.1017	10.1001/archpsyc.65.9.1017			83.6
Meta-analysis of amygdala volumes in children and adolescents with bipolar disorder.	2008	BP	11	553	https://www.ncbi.nlm.nih.gov/pubmed/18827720	sci-hub.tw/10.1097/CHI.0b013e318185d299	sci-hub.la/10.1097/CHI.0b013e318185d299	10.1097/CHI.0b013e318185d299			50.3
Neurochemical alterations of the brain in bipolar disorder and their implications for pathophysiology: a systematic review of the in vivo proton magnetic resonance spectroscopy findings.	2006	BP	-	-	https://www.ncbi.nlm.nih.gov/pubmed/16677749	sci-hub.tw/10.1016/j.pnpbp.2006.03.012	sci-hub.la/10.1016/j.pnpbp.2006.03.012	10.1016/j.pnpbp.2006.03.012			-
Neural Correlates of Disturbed Emotion Processing in Borderline Personality Disorder: A Multimodal Meta-Analysis.	2016	BPD	29	1115	https://www.ncbi.nlm.nih.gov/pubmed/25935068	sci-hub.tw/10.1016/j.biopsych.2015.03.027	sci-hub.la/10.1016/j.biopsych.2015.03.027	10.1016/j.biopsych.2015.03.027			38.4
Mapping the brain correlates of borderline personality disorder: A functional neuroimaging meta-analysis of resting state studies.	2016	BPD	7	299	https://www.ncbi.nlm.nih.gov/pubmed/27552444	sci-hub.tw/10.1016/j.jad.2016.07.025	sci-hub.la/10.1016/j.jad.2016.07.025	10.1016/j.jad.2016.07.025			42.7
Meta-analysis of molecular imaging of serotonin transporters in ecstasy/polydrug users.	2016	DA	7	305	https://www.ncbi.nlm.nih.gov/pubmed/26855234	sci-hub.tw/10.1016/j.neubiorev.2016.02.003	sci-hub.la/10.1016/j.neubiorev.2016.02.003	10.1016/j.neubiorev.2016.02.003			43.6
A Systematic Review and Meta-analysis of Neuroimaging in Oppositional Defiant Disorder (ODD) and Conduct Disorder (CD) Taking Attention-Deficit Hyperactivity Disorder (ADHD) Into Account.	2016	DBD	29	838	https://www.ncbi.nlm.nih.gov/pubmed/26846227	sci-hub.tw/10.1007/s11065-015-9315-8	sci-hub.la/10.1007/s11065-015-9315-8	10.1007/s11065-015-9315-8			28.9
Meta-Analysis of fMRI Studies of Disruptive Behavior Disorders.	2016	DBD	24	636	https://www.ncbi.nlm.nih.gov/pubmed/27523497	sci-hub.tw/10.1176/appi.ajp.2016.15081089	sci-hub.la/10.1176/appi.ajp.2016.15081089	10.1176/appi.ajp.2016.15081089			26.5
Alterations in emotion generation and regulation neurocircuitry in depression and eating disorders: A comparative review of structural and functional neuroimaging studies.	2016	ED	-	-	https://www.ncbi.nlm.nih.gov/pubmed/27422451	sci-hub.tw/10.1016/j.neubiorev.2016.07.011	sci-hub.la/10.1016/j.neubiorev.2016.07.011	10.1016/j.neubiorev.2016.07.011			-
A systematic review of temporal discounting in eating disorders and obesity: Behavioural and neuroimaging findings.	2016	ED	31	4546	https://www.ncbi.nlm.nih.gov/pubmed/27693228	sci-hub.tw/10.1016/j.neubiorev.2016.09.024	sci-hub.la/10.1016/j.neubiorev.2016.09.024	10.1016/j.neubiorev.2016.09.024			146.6
Neuroimaging and neuromodulation approaches to study eating behavior and prevent and treat eating disorders and obesity.	2015	ED	-	-	https://www.ncbi.nlm.nih.gov/pubmed/26110109	sci-hub.tw/10.1016/j.nicl.2015.03.016	sci-hub.la/10.1016/j.nicl.2015.03.016	10.1016/j.nicl.2015.03.016			-
Positron emission tomography studies in eating disorders: multireceptor brain imaging, correlates with behavior and implications for pharmacotherapy.	2005	ED	-	-	https://www.ncbi.nlm.nih.gov/pubmed/16243652	sci-hub.tw/10.1016/j.nucmedbio.2005.06.011	sci-hub.la/10.1016/j.nucmedbio.2005.06.011	10.1016/j.nucmedbio.2005.06.011			-
Melancholy, anhedonia, apathy: the search for separable behaviors and neural circuits in depression.	2018	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/29529482	sci-hub.tw/10.1016/j.conb.2018.02.018	sci-hub.la/10.1016/j.conb.2018.02.018	10.1016/j.conb.2018.02.018			-
Circuit-based frameworks of depressive behaviors: The role of reward circuitry and beyond.	2018	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/29309799	sci-hub.tw/10.1016/j.pbb.2017.12.010	sci-hub.la/10.1016/j.pbb.2017.12.010	10.1016/j.pbb.2017.12.010			-
Treatment resistant depression: A multi-scale, systems biology approach.	2018	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/28859997	sci-hub.tw/10.1016/j.neubiorev.2017.08.019	sci-hub.la/10.1016/j.neubiorev.2017.08.019	10.1016/j.neubiorev.2017.08.019			-
Dopamine System Dysregulation in Major Depressive Disorders.	2018	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/29106542	sci-hub.tw/10.1093/ijnp/pyx056	sci-hub.la/10.1093/ijnp/pyx056	10.1093/ijnp/pyx056			-
Progress in understanding mood disorders: optogenetic dissection of neural circuits.	2018	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/23682971	sci-hub.tw/10.1111/gbb.12049	sci-hub.la/10.1111/gbb.12049	10.1111/gbb.12049			-
Presentation and Neurobiology of Anhedonia in Mood Disorders: Commonalities and Distinctions.	2018	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/29520717	sci-hub.tw/10.1007/s11920-018-0877-z	sci-hub.la/10.1007/s11920-018-0877-z	10.1007/s11920-018-0877-z			-
Lateral habenula in the pathophysiology of depression.	2018	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/29175713	sci-hub.tw/10.1016/j.conb.2017.10.024	sci-hub.la/10.1016/j.conb.2017.10.024	10.1016/j.conb.2017.10.024			-
Neuroimaging genomic studies in major depressive disorder: A systematic review.	2018	MDD	64	-	https://www.ncbi.nlm.nih.gov/pubmed/29476595	sci-hub.tw/10.1111/cns.12829	sci-hub.la/10.1111/cns.12829	10.1111/cns.12829			-
Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group.	2017	MDD	20	10105	https://www.ncbi.nlm.nih.gov/pubmed/27137745	sci-hub.tw/10.1038/mp.2016.60	sci-hub.la/10.1038/mp.2016.60	10.1038/mp.2016.60			505.3
Subcortical brain structure and suicidal behaviour in major depressive disorder: a meta-analysis from the ENIGMA-MDD working group.	2017	MDD	1	3097	https://www.ncbi.nlm.nih.gov/pubmed/28463239	sci-hub.tw/10.1038/tp.2017.84	sci-hub.la/10.1038/tp.2017.84	10.1038/tp.2017.84			3097.0
Disorganization of white matter architecture in major depressive disorder: a meta-analysis of diffusion tensor imaging with tract-based spatial statistics	2017	MDD	18	1122	https://www.ncbi.nlm.nih.gov/pubmed/26906716	sci-hub.tw/10.1038/srep21825	sci-hub.la/10.1038/srep21825	10.1038/srep21825			62.3
Computational meta-analysis of statistical parametric maps in major depression	2017	MDD	12	1052	https://www.ncbi.nlm.nih.gov/pubmed/26854015	sci-hub.tw/10.1002/hbm.23108	sci-hub.la/10.1002/hbm.23108	10.1002/hbm.23108			87.7
Altered Brain Activity in Unipolar Depression Revisited: Meta-analyses of Neuroimaging Studies.	2017	MDD	57	2116	https://www.ncbi.nlm.nih.gov/pubmed/27829086	sci-hub.tw/10.1001/jamapsychiatry.2016.2783	sci-hub.la/10.1001/jamapsychiatry.2016.2783	10.1001/jamapsychiatry.2016.2783			37.1
Characterization of brain blood flow and the amplitude of low-frequency fluctuations in major depressive disorder: A multimodal meta-analysis.	2017	MDD	16	972	https://www.ncbi.nlm.nih.gov/pubmed/28068619	sci-hub.tw/10.1016/j.jad.2016.12.032	sci-hub.la/10.1016/j.jad.2016.12.032	10.1016/j.jad.2016.12.032			60.8
Intrinsic cerebral activity at resting state in adults with major depressive disorder: A meta-analysis.	2017	MDD	15	859	https://www.ncbi.nlm.nih.gov/pubmed/28174129	sci-hub.tw/10.1016/j.pnpbp.2017.02.001	sci-hub.la/10.1016/j.pnpbp.2017.02.001	10.1016/j.pnpbp.2017.02.001			57.3
Microstructural brain abnormalities in medication-free patients with major depressive disorder: a systematic review and meta-analysis of diffusion tensor imaging	2017	MDD	15	864	https://www.ncbi.nlm.nih.gov/pubmed/27780031	sci-hub.tw/10.1503/jpn.150341	sci-hub.la/10.1503/jpn.150341	10.1503/jpn.150341			57.6
Corticostriatal circuitry in regulating diseases characterized by intrusive thinking.	2016	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/27069381	https://www.ncbi.nlm.nih.gov/pubmed/27069381	https://www.ncbi.nlm.nih.gov/pubmed/27069381	https://www.ncbi.nlm.nih.gov/pubmed/27069381			-
Reward processing by the lateral habenula in normal and depressive behaviors.	2016	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/25157511	sci-hub.tw/10.1038/nn.3779	sci-hub.la/10.1038/nn.3779	10.1038/nn.3779			-
Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group.	2016	MDD	1	8927	https://www.ncbi.nlm.nih.gov/pubmed/26122586	sci-hub.tw/10.1038/mp.2015.69	sci-hub.la/10.1038/mp.2015.69	10.1038/mp.2015.69			8927.0
Shared white matter alterations across emotional disorders: A voxel-based meta-analysis of fractional anisotropy.	2016	MDD	37	1954	https://www.ncbi.nlm.nih.gov/pubmed/27995068	sci-hub.tw/10.1016/j.nicl.2016.09.001	sci-hub.la/10.1016/j.nicl.2016.09.001	10.1016/j.nicl.2016.09.001			52.8
Functional alterations of fronto-limbic circuit and default mode network systems in first-episode, drug-naïve patients with major depressive disorder: A meta-analysis of resting-state fMRI data.	2016	MDD	31	908	https://www.ncbi.nlm.nih.gov/pubmed/27639862	sci-hub.tw/10.1016/j.jad.2016.09.005	sci-hub.la/10.1016/j.jad.2016.09.005	10.1016/j.jad.2016.09.005			29.3
Essential brain structural alterations in major depressive disorder: A voxel-wise meta-analysis on first episode, medication-naive patients.	2016	MDD	10	669	https://www.ncbi.nlm.nih.gov/pubmed/27100056	sci-hub.tw/10.1016/j.jad.2016.04.001	sci-hub.la/10.1016/j.jad.2016.04.001	10.1016/j.jad.2016.04.001			66.9
Serotonin-1A receptor alterations in depression: a meta-analysis of molecular imaging studies.	2016	MDD	10	479	https://www.ncbi.nlm.nih.gov/pubmed/27623971	sci-hub.tw/10.1186/s12888-016-1025-0	sci-hub.la/10.1186/s12888-016-1025-0	10.1186/s12888-016-1025-0			47.9
Abnormal reward functioning across substance use disorders and major depressive disorder: Considering reward as a transdiagnostic mechanism.	2015	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/25655926	sci-hub.tw/10.1016/j.ijpsycho.2015.01.011	sci-hub.la/10.1016/j.ijpsycho.2015.01.011	10.1016/j.ijpsycho.2015.01.011			-
Reinforcement learning in depression: A review of computational research.	2015	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/25979140	sci-hub.tw/10.1016/j.neubiorev.2015.05.005	sci-hub.la/10.1016/j.neubiorev.2015.05.005	10.1016/j.neubiorev.2015.05.005			-
Role of the Brain's Reward Circuitry in Depression: Transcriptional Mechanisms.	2015	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/26472529	sci-hub.tw/10.1016/bs.irn.2015.07.003	sci-hub.la/10.1016/bs.irn.2015.07.003	10.1016/bs.irn.2015.07.003			-
The serotonin transporter in depression: Meta-analysis of in vivo and post mortem findings and implications for understanding and treating depression.	2015	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/26281039	sci-hub.tw/10.1016/j.jad.2015.07.034	sci-hub.la/10.1016/j.jad.2015.07.034	10.1016/j.jad.2015.07.034			-
The Brain's Response to Reward Anticipation and Depression in Adolescence: Dimensionality, Specificity, and Longitudinal Predictions in a Community-Based Sample.	2015	MDD	1	1576	https://www.ncbi.nlm.nih.gov/pubmed/26085042	sci-hub.tw/10.1176/appi.ajp.2015.14101298	sci-hub.la/10.1176/appi.ajp.2015.14101298	10.1176/appi.ajp.2015.14101298			1576.0
Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity.	2015	MDD	27	1074	https://www.ncbi.nlm.nih.gov/pubmed/25785575	sci-hub.tw/10.1001/jamapsychiatry.2015.0071	sci-hub.la/10.1001/jamapsychiatry.2015.0071	10.1001/jamapsychiatry.2015.0071			39.8
Voxel-wise meta-analyses of brain blood flow and local synchrony abnormalities in medication-free patients with major depressive disorder.	2015	MDD	23	1068	https://www.ncbi.nlm.nih.gov/pubmed/25853283	sci-hub.tw/10.1503/jpn.140119	sci-hub.la/10.1503/jpn.140119	10.1503/jpn.140119			46.4
Neuropsychological mechanism underlying antidepressant effect: a systematic meta-analysis	2015	MDD	50	1569	https://www.ncbi.nlm.nih.gov/pubmed/24662929	sci-hub.tw/10.1038/mp.2014.24	sci-hub.la/10.1038/mp.2014.24	10.1038/mp.2014.24			31.4
Molecular imaging of striatal dopamine transporters in major depression--a meta-analysis.	2015	MDD	12	523	https://www.ncbi.nlm.nih.gov/pubmed/25497470	sci-hub.tw/10.1016/j.jad.2014.11.045	sci-hub.la/10.1016/j.jad.2014.11.045	10.1016/j.jad.2014.11.045			43.6
Meta-analysis of Functional Neuroimaging of Major Depressive Disorder in Youth.	2015	MDD	14	520	https://www.ncbi.nlm.nih.gov/pubmed/26332700	sci-hub.tw/10.1001/jamapsychiatry.2015.1376	sci-hub.la/10.1001/jamapsychiatry.2015.1376	10.1001/jamapsychiatry.2015.1376			37.1
Optogenetics to study the circuits of fear- and depression-like behaviors: a critical analysis.	2014	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/24727401	sci-hub.tw/10.1016/j.pbb.2014.04.002	sci-hub.la/10.1016/j.pbb.2014.04.002	10.1016/j.pbb.2014.04.002			-
Deep brain stimulation of the human reward system for major depression--rationale, outcomes and outlook.	2014	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/24513970	sci-hub.tw/10.1038/npp.2014.28	sci-hub.la/10.1038/npp.2014.28	10.1038/npp.2014.28			-
Meta-analysis of molecular imaging of serotonin transporters in major depression.	2014	MDD	18	736	https://www.ncbi.nlm.nih.gov/pubmed/24802331	sci-hub.tw/10.1038/jcbfm.2014.82	sci-hub.la/10.1038/jcbfm.2014.82	10.1038/jcbfm.2014.82			40.9
Optogenetic dissection of neural circuits underlying emotional valence and motivated behaviors.	2013	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/23142759	sci-hub.tw/10.1016/j.brainres.2012.11.001	sci-hub.la/10.1016/j.brainres.2012.11.001	10.1016/j.brainres.2012.11.001			-
The brain reward circuitry in mood disorders.	2013	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/23942470	sci-hub.tw/10.1038/nrn3381	sci-hub.la/10.1038/nrn3381	10.1038/nrn3381			-
Neuroreceptor imaging in depression.	2013	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/22691454	sci-hub.tw/10.1016/j.nbd.2012.06.001	sci-hub.la/10.1016/j.nbd.2012.06.001	10.1016/j.nbd.2012.06.001			-
Gray matter volume in major depressive disorder: a meta-analysis of voxel-based morphometry studies.	2013	MDD	20	1594	https://www.ncbi.nlm.nih.gov/pubmed/23146253	sci-hub.tw/10.1016/j.pscychresns.2012.06.006	sci-hub.la/10.1016/j.pscychresns.2012.06.006	10.1016/j.pscychresns.2012.06.006			79.7
Meta-analytic evidence for neuroimaging models of depression: state or trait?	2013	MDD	40	1270	https://www.ncbi.nlm.nih.gov/pubmed/23890584	sci-hub.tw/10.1016/j.jad.2013.07.002	sci-hub.la/10.1016/j.jad.2013.07.002	10.1016/j.jad.2013.07.002			31.8
Resting-state brain activity in schizophrenia and major depression: a quantitative meta-analysis.	2013	MDD	11	470	https://www.ncbi.nlm.nih.gov/pubmed/22080493	sci-hub.tw/10.1093/schbul/sbr151	sci-hub.la/10.1093/schbul/sbr151	10.1093/schbul/sbr151			42.7
A meta-analysis of neurofunctional imaging studies of emotion and cognition in major depression.	2012	MDD	40	1127	https://www.ncbi.nlm.nih.gov/pubmed/22521254	sci-hub.tw/10.1016/j.neuroimage.2012.04.005	sci-hub.la/10.1016/j.neuroimage.2012.04.005	10.1016/j.neuroimage.2012.04.005			28.2
Functional neuroimaging of major depressive disorder: a meta-analysis and new integration of base line activation and neural response data.	2012	MDD	14	599	https://www.ncbi.nlm.nih.gov/pubmed/22535198	sci-hub.tw/10.1176/appi.ajp.2012.11071105	sci-hub.la/10.1176/appi.ajp.2012.11071105	10.1176/appi.ajp.2012.11071105			42.8
Structural and functional neuroimaging studies of the suicidal brain.	2011	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/21216267	sci-hub.tw/10.1016/j.pnpbp.2010.12.026	sci-hub.la/10.1016/j.pnpbp.2010.12.026	10.1016/j.pnpbp.2010.12.026			-
Structural neuroimaging studies in major depressive disorder. Meta-analysis and comparison with bipolar disorder.	2011	MDD	143	18379	https://www.ncbi.nlm.nih.gov/pubmed/21727252	sci-hub.tw/10.1001/archgenpsychiatry.2011.60	sci-hub.la/10.1001/archgenpsychiatry.2011.60	10.1001/archgenpsychiatry.2011.60			128.5
Functional neuroimaging of reward processing and decision-making: a review of aberrant motivational and affective processing in addiction and mood disorders.	2008	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/18675846	sci-hub.tw/10.1016/j.brainresrev.2008.07.004	sci-hub.la/10.1016/j.brainresrev.2008.07.004	10.1016/j.brainresrev.2008.07.004			-
Dynamics of the dopaminergic system as a key component to the understanding of depression.	2008	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/18772037	sci-hub.tw/10.1016/S0079-6123(08)00913-8	sci-hub.la/10.1016/S0079-6123(08)00913-8	10.1016/S0079-6123(08)00913-8			-
The mesolimbic dopamine reward circuit in depression.	2006	MDD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/16566899	sci-hub.tw/10.1016/j.biopsych.2005.09.018	sci-hub.la/10.1016/j.biopsych.2005.09.018	10.1016/j.biopsych.2005.09.018			-
Review of 1H magnetic resonance spectroscopy findings in major depressive disorder: a meta-analysis.	2006	MDD	42	1688	https://www.ncbi.nlm.nih.gov/pubmed/16806850	sci-hub.tw/10.1016/j.pscychresns.2005.12.004	sci-hub.la/10.1016/j.pscychresns.2005.12.004	10.1016/j.pscychresns.2005.12.004			40.2
Distinct Subcortical Volume Alterations in Pediatric and Adult OCD: A Worldwide Meta- and Mega-Analysis.	2017	OCD	1	3589	https://www.ncbi.nlm.nih.gov/pubmed/27609241	sci-hub.tw/10.1176/appi.ajp.2016.16020201	sci-hub.la/10.1176/appi.ajp.2016.16020201	10.1176/appi.ajp.2016.16020201			3589.0
Neural correlates of affective and non-affective cognition in obsessive compulsive disorder: A meta-analysis of functional imaging studies.	2017	OCD	54	2345	https://www.ncbi.nlm.nih.gov/pubmed/28992533	sci-hub.tw/10.1016/j.eurpsy.2017.08.001	sci-hub.la/10.1016/j.eurpsy.2017.08.001	10.1016/j.eurpsy.2017.08.001			43.4
Meta-analytic investigations of common and distinct grey matter alterations in youths and adults with obsessive-compulsive disorder.	2017	OCD	25	1575	https://www.ncbi.nlm.nih.gov/pubmed/28442404	sci-hub.tw/10.1016/j.neubiorev.2017.04.012	sci-hub.la/10.1016/j.neubiorev.2017.04.012	10.1016/j.neubiorev.2017.04.012			63.0
Structural and Functional Brain Abnormalities in Attention-Deficit/Hyperactivity Disorder and Obsessive-Compulsive Disorder: A Comparative Meta-analysis.	2016	OCD	108	5274	https://www.ncbi.nlm.nih.gov/pubmed/27276220	sci-hub.tw/10.1001/jamapsychiatry.2016.0700	sci-hub.la/10.1001/jamapsychiatry.2016.0700	10.1001/jamapsychiatry.2016.0700			48.8
Meta-analytic investigations of structural grey matter, executive domain-related functional activations, and white matter diffusivity in obsessive compulsive disorder: an integrative review.	2015	OCD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/25766413	sci-hub.tw/10.1016/j.neubiorev.2015.03.002	sci-hub.la/10.1016/j.neubiorev.2015.03.002	10.1016/j.neubiorev.2015.03.002			-
Multimodal voxel-based meta-analysis of white matter abnormalities in obsessive-compulsive disorder.	2015	OCD	22	1112	https://www.ncbi.nlm.nih.gov/pubmed/24407265	sci-hub.tw/10.1038/npp.2014.5	sci-hub.la/10.1038/npp.2014.5	10.1038/npp.2014.5			50.5
Brain circuitries of obsessive compulsive disorder: a systematic review and meta-analysis of diffusion tensor imaging studies.	2013	OCD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/24177038	sci-hub.tw/10.1016/j.neubiorev.2013.10.008	sci-hub.la/10.1016/j.neubiorev.2013.10.008	10.1016/j.neubiorev.2013.10.008			-
Neuroimaging of cognitive brain function in paediatric obsessive compulsive disorder: a review of literature and preliminary meta-analysis.	2012	OCD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/22678698	sci-hub.tw/10.1007/s00702-012-0813-z	sci-hub.la/10.1007/s00702-012-0813-z	10.1007/s00702-012-0813-z			-
Reduction of N-acetylaspartate in the medial prefrontal cortex correlated with symptom severity in obsessive-compulsive disorder: meta-analyses of (1)H-MRS studies.	2012	OCD	16	458	https://www.ncbi.nlm.nih.gov/pubmed/22892718	sci-hub.tw/10.1038/tp.2012.78	sci-hub.la/10.1038/tp.2012.78	10.1038/tp.2012.78			28.6
Gray matter alterations in obsessive-compulsive disorder: an anatomic likelihood estimation meta-analysis.	2010	OCD	10	661	https://www.ncbi.nlm.nih.gov/pubmed/19890260	sci-hub.tw/10.1038/npp.2009.175	sci-hub.la/10.1038/npp.2009.175	10.1038/npp.2009.175			66.1
Meta-analysis of brain volume changes in obsessive-compulsive disorder.	2009	OCD	14	778	https://www.ncbi.nlm.nih.gov/pubmed/18718575	sci-hub.tw/10.1016/j.biopsych.2008.06.019	sci-hub.la/10.1016/j.biopsych.2008.06.019	10.1016/j.biopsych.2008.06.019			55.6
Voxel-wise meta-analysis of grey matter changes in obsessive-compulsive disorder.	2009	OCD	12	777	https://www.ncbi.nlm.nih.gov/pubmed/19880927	sci-hub.tw/10.1192/bjp.bp.108.055046	sci-hub.la/10.1192/bjp.bp.108.055046	10.1192/bjp.bp.108.055046			64.8
Integrating evidence from neuroimaging and neuropsychological studies of obsessive-compulsive disorder: the orbitofronto-striatal model revisited.	2008	OCD	-	-	https://www.ncbi.nlm.nih.gov/pubmed/18061263	sci-hub.tw/10.1016/j.neubiorev.2007.09.005	sci-hub.la/10.1016/j.neubiorev.2007.09.005	10.1016/j.neubiorev.2007.09.005			-
Provocation of obsessive–compulsive symptoms: a quantitative voxel-based meta-analysis of functional neuroimaging studies	2008	OCD	8	94	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2527721/	sci-hub.tw/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2527721/	sci-hub.la/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2527721/	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2527721/			11.8
A meta-analysis of functional neuroimaging in obsessive-compulsive disorder.	2004	OCD	13	40	https://www.ncbi.nlm.nih.gov/pubmed/15546704	sci-hub.tw/10.1016/j.pscychresns.2004.07.001	sci-hub.la/10.1016/j.pscychresns.2004.07.001	10.1016/j.pscychresns.2004.07.001			3.1
Different patterns of 5-HT receptor and transporter dysfunction in neuropsychiatric disorders--a comparative analysis of in vivo imaging findings.	2016	PSY	136	-	https://www.ncbi.nlm.nih.gov/pubmed/26376220	sci-hub.tw/10.1515/revneuro-2015-0014	sci-hub.la/10.1515/revneuro-2015-0014	10.1515/revneuro-2015-0014			-
Mapping anhedonia-specific dysfunction in a transdiagnostic approach: an ALE meta-analysis.	2016	PSY	89	3272	https://www.ncbi.nlm.nih.gov/pubmed/26487590	sci-hub.tw/10.1007/s11682-015-9457-6	sci-hub.la/10.1007/s11682-015-9457-6	10.1007/s11682-015-9457-6			36.8
The serotonin transporter in psychiatric disorders: insights from PET imaging.	2015	PSY	-	-	https://www.ncbi.nlm.nih.gov/pubmed/26249305	sci-hub.tw/10.1016/S2215-0366(15)00232-1	sci-hub.la/10.1016/S2215-0366(15)00232-1	10.1016/S2215-0366(15)00232-1			-
Mechanisms Underlying Motivational Deficits in Psychopathology: Similarities and Differences in Depression and Schizophrenia.	2014	PSY	-	-	https://www.ncbi.nlm.nih.gov/pubmed/26026289	sci-hub.tw/10.1007/7854_2015_376	sci-hub.la/10.1007/7854_2015_376	10.1007/7854_2015_376			-
Brain imaging findings in children and adolescents with mental disorders: a cross-sectional review	2010	PSY	274	-	https://www.ncbi.nlm.nih.gov/pubmed/20620025	sci-hub.tw/10.1016/j.eurpsy.2010.04.010	sci-hub.la/10.1016/j.eurpsy.2010.04.010	10.1016/j.eurpsy.2010.04.010			-
The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology.	2010	PSY	-	-	https://www.ncbi.nlm.nih.gov/pubmed/21102431	sci-hub.tw/10.1038/mp.2010.4	sci-hub.la/10.1038/mp.2010.4	10.1038/mp.2010.4			-
Aberrant Resting-State Brain Activity in Post-Traumatic Stress Disorder: A Meta-Analysis And Systematic Review.	2016	PTSD	23	663	https://www.ncbi.nlm.nih.gov/pubmed/26918313	sci-hub.tw/10.1002/da.22478	sci-hub.la/10.1002/da.22478	10.1002/da.22478			28.8
In search of the trauma memory: a meta-analysis of functional neuroimaging studies of symptom provocation in posttraumatic stress disorder (PTSD).	2013	PTSD	14	66	https://www.ncbi.nlm.nih.gov/pubmed/23536785	sci-hub.tw/10.1371/journal.pone.0058150	sci-hub.la/10.1371/journal.pone.0058150	10.1371/journal.pone.0058150			4.7
Common and distinct neural correlates of facial emotion processing in social anxiety disorder and Williams syndrome: A systematic review and voxel-based meta-analysis of functional resonance imaging studies.	2014	SAD	17	575	https://www.ncbi.nlm.nih.gov/pubmed/25194208	sci-hub.tw/10.1016/j.neuropsychologia.2014.08.027	sci-hub.la/10.1016/j.neuropsychologia.2014.08.027	10.1016/j.neuropsychologia.2014.08.027			33.8
Neuroimaging in social anxiety disorder—a meta-analytic review resulting in a new neurofunctional model.	2014	SAD	19	409	https://www.ncbi.nlm.nih.gov/pubmed/25124509	sci-hub.tw/10.1016/j.neubiorev.2014.08.003	sci-hub.la/10.1016/j.neubiorev.2014.08.003	10.1016/j.neubiorev.2014.08.003			21.5
Schizophrenia symptomatic associations with diffusion tensor imaging measured fractional anisotropy of brain: a meta-analysis.	2017	SCZ	33	2242	https://www.ncbi.nlm.nih.gov/pubmed/28550466	sci-hub.tw/10.1007/s00234-017-1844-9	sci-hub.la/10.1007/s00234-017-1844-9	10.1007/s00234-017-1844-9			67.9
Positive symptoms associate with cortical thinning in the superior temporal gyrus via the ENIGMA Schizophrenia consortium.	2017	SCZ	1	1987	https://www.ncbi.nlm.nih.gov/pubmed/28369804	sci-hub.tw/10.1111/acps.12718	sci-hub.la/10.1111/acps.12718	10.1111/acps.12718			1987.0
Patients with schizophrenia show aberrant patterns of basal ganglia activation: Evidence from ALE meta-analysis.	2017	SCZ	42	1290	https://www.ncbi.nlm.nih.gov/pubmed/28275545	sci-hub.tw/10.1016/j.nicl.2017.01.034	sci-hub.la/10.1016/j.nicl.2017.01.034	10.1016/j.nicl.2017.01.034			30.7
Brain-Wide Analysis of Functional Connectivity in First-Episode and Chronic Stages of Schizophrenia.	2017	SCZ	6	789	https://www.ncbi.nlm.nih.gov/pubmed/27445261	sci-hub.tw/10.1093/schbul/sbw099	sci-hub.la/10.1093/schbul/sbw099	10.1093/schbul/sbw099			131.5
Altered Hub Functioning and Compensatory Activations in the Connectome: A Meta-Analysis of Functional Neuroimaging Studies in Schizophrenia.	2016	SCZ	314	10000	https://www.ncbi.nlm.nih.gov/pubmed/26472684	sci-hub.tw/10.1093/schbul/sbv146	sci-hub.la/10.1093/schbul/sbv146	10.1093/schbul/sbv146			31.8
The neural mechanisms of hallucinations: A quantitative meta-analysis of neuroimaging studies.	2016	SCZ	17	211	https://www.ncbi.nlm.nih.gov/pubmed/27473935	sci-hub.tw/10.1016/j.neubiorev.2016.05.037	sci-hub.la/10.1016/j.neubiorev.2016.05.037	10.1016/j.neubiorev.2016.05.037			12.4
Rostral medial prefrontal dysfunctions and consummatory pleasure in schizophrenia: a meta-analysis of functional imaging studies.	2015	SCZ	19	615	https://www.ncbi.nlm.nih.gov/pubmed/25637357	sci-hub.tw/10.1016/j.pscychresns.2015.01.001	sci-hub.la/10.1016/j.pscychresns.2015.01.001	10.1016/j.pscychresns.2015.01.001			32.4
Alterations in the serotonin system in schizophrenia: a systematic review and meta-analysis of postmortem and molecular imaging studies.	2014	SCZ	-	-	https://www.ncbi.nlm.nih.gov/pubmed/24971825	sci-hub.tw/10.1016/j.neubiorev.2014.06.005	sci-hub.la/10.1016/j.neubiorev.2014.06.005	10.1016/j.neubiorev.2014.06.005			-
Alterations in cortical and extrastriatal subcortical dopamine function in schizophrenia: systematic review and meta-analysis of imaging studies.	2014	SCZ	23	543	https://www.ncbi.nlm.nih.gov/pubmed/25029687	sci-hub.tw/10.1192/bjp.bp.113.132308	sci-hub.la/10.1192/bjp.bp.113.132308	10.1192/bjp.bp.113.132308			23.6
Brain vs behavior: an effect size comparison of neuroimaging and cognitive studies of genetic risk for schizophrenia.	2013	SCZ	-	-	https://www.ncbi.nlm.nih.gov/pubmed/22499782	sci-hub.tw/10.1093/schbul/sbs056	sci-hub.la/10.1093/schbul/sbs056	10.1093/schbul/sbs056			-
Striatal presynaptic dopamine in schizophrenia, Part I: meta-analysis of dopamine active transporter (DAT) density.	2013	SCZ	13	349	https://www.ncbi.nlm.nih.gov/pubmed/22282456	sci-hub.tw/10.1093/schbul/sbr111	sci-hub.la/10.1093/schbul/sbr111	10.1093/schbul/sbr111			26.8
Striatal presynaptic dopamine in schizophrenia, part II: meta-analysis of [(18)F/(11)C]-DOPA PET studies.	2013	SCZ	11	244	https://www.ncbi.nlm.nih.gov/pubmed/22282454	sci-hub.tw/10.1093/schbul/sbr180	sci-hub.la/10.1093/schbul/sbr180	10.1093/schbul/sbr180			22.2
Meta-analysis of functional neuroimaging studies of emotion perception and experience in schizophrenia.	2012	SCZ	26	872	https://www.ncbi.nlm.nih.gov/pubmed/21993193	sci-hub.tw/10.1016/j.biopsych.2011.09.007	sci-hub.la/10.1016/j.biopsych.2011.09.007	10.1016/j.biopsych.2011.09.007			33.5
Quantitative meta-analysis on state and trait aspects of auditory verbal hallucinations in schizophrenia.	2012	SCZ	15	274	https://www.ncbi.nlm.nih.gov/pubmed/21177743	sci-hub.tw/10.1093/schbul/sbq152	sci-hub.la/10.1093/schbul/sbq152	10.1093/schbul/sbq152			18.3
The "paradoxical" engagement of the primary auditory cortex in patients with auditory verbal hallucinations: a meta-analysis of functional neuroimaging studies.	2011	SCZ	12	477	https://www.ncbi.nlm.nih.gov/pubmed/21872614	sci-hub.tw/10.1016/j.neuropsychologia.2011.08.010	sci-hub.la/10.1016/j.neuropsychologia.2011.08.010	10.1016/j.neuropsychologia.2011.08.010			39.8
Facial emotion processing in schizophrenia: a meta-analysis of functional neuroimaging data.	2010	SCZ	17	-	https://www.ncbi.nlm.nih.gov/pubmed/19336391	sci-hub.tw/10.1093/schbul/sbn190	sci-hub.la/10.1093/schbul/sbn190	10.1093/schbul/sbn190			-
Meta-analysis of 41 functional neuroimaging studies of executive function in schizophrenia.	2009	SCZ	41	-	https://www.ncbi.nlm.nih.gov/pubmed/19652121	sci-hub.tw/10.1001/archgenpsychiatry.2009.91	sci-hub.la/10.1001/archgenpsychiatry.2009.91	10.1001/archgenpsychiatry.2009.91			-
Neural correlates of somatoform disorders from a meta-analytic perspective on neuroimaging studies.	2016	SD	10	447	https://www.ncbi.nlm.nih.gov/pubmed/27182487	sci-hub.tw/10.1016/j.nicl.2016.04.001	sci-hub.la/10.1016/j.nicl.2016.04.001	10.1016/j.nicl.2016.04.001			44.7
Neural correlates of conversion disorder: overview and meta-analysis of neuroimaging studies on motor conversion disorder.	2016	SD	12	335	https://www.ncbi.nlm.nih.gov/pubmed/27283002	sci-hub.tw/10.1186/s12888-016-0890-x	sci-hub.la/10.1186/s12888-016-0890-x	10.1186/s12888-016-0890-x			27.9

MDD studies
Title	Summary	Pubmed
Melancholy, anhedonia, apathy: the search for separable behaviors and neural circuits in depression.	In rodent models of depression, activity in the mPFC as a whole relates to reduced sucrose preference and sociability, with are thought to be analogous to anhedonia and social withdrawal, respectively, in humans. Inhibiting this region via stimulating it at a supraphysiological frequency appears to attenuate these deficits.	https://www.ncbi.nlm.nih.gov/pubmed/29529482
Circuit-based frameworks of depressive behaviors: The role of reward circuitry and beyond.	Evidence related to the role of the VTA in producing depression-like behaviors in animals is complex.	https://www.ncbi.nlm.nih.gov/pubmed/29309799
Treatment resistant depression: A multi-scale, systems biology approach.		https://www.ncbi.nlm.nih.gov/pubmed/28859997
Dopamine System Dysregulation in Major Depressive Disorders.	A	https://www.ncbi.nlm.nih.gov/pubmed/29106542
Progress in understanding mood disorders: optogenetic dissection of neural circuits.	A	https://www.ncbi.nlm.nih.gov/pubmed/23682971
Presentation and Neurobiology of Anhedonia in Mood Disorders: Commonalities and Distinctions.	A	https://www.ncbi.nlm.nih.gov/pubmed/29520717
Neuroimaging genomic studies in major depressive disorder: A systematic review.	A	https://www.ncbi.nlm.nih.gov/pubmed/29476595
Lateral habenula in the pathophysiology of depression.	A	https://www.ncbi.nlm.nih.gov/pubmed/29175713
Intrinsic cerebral activity at resting state in adults with major depressive disorder: A meta-analysis.	A	https://www.ncbi.nlm.nih.gov/pubmed/28174129
Characterization of brain blood flow and the amplitude of low-frequency fluctuations in major depressive disorder: A multimodal meta-analysis.	A	https://www.ncbi.nlm.nih.gov/pubmed/28068619
Altered Brain Activity in Unipolar Depression Revisited: Meta-analyses of Neuroimaging Studies.	A	https://www.ncbi.nlm.nih.gov/pubmed/27829086

Pathophysiology of PTSD

Name/Link	Article Type	Finding(s)
Post-traumatic stress influences the brain even in the absence of symptoms: A systematic, quantitative meta-analysis of neuroimaging studies	MA	-
Neural, psychophysiological, and behavioral markers of fear processing in PTSD: a review of the literature	R	-
From Pavlov to PTSD: the extinction of conditioned fear in rodents, humans, and anxiety disorders	R	-
Neurocircuitry models of posttraumatic stress disorder and extinction: human neuroimaging research--past, present, and future	R	-
Is posttraumatic stress disorder a stress-induced fear circuitry disorder?	R	-
A causal model of post-traumatic stress disorder: disentangling predisposed from acquired neural abnormalities	R	-
Epigenetic mechanisms in fear conditioning: implications for treating post-traumatic stress disorder	R	-
Post-traumatic stress disorder: the neurobiological impact of psychological trauma	R	-
Neurobiology of posttraumatic stress disorder	R	-
Biological studies of post-traumatic stress disorder	R	-
Circuits and systems in stress. II. Applications to neurobiology and treatment in posttraumatic stress disorder	R	-
A Network-Based Neurobiological Model of PTSD: Evidence From Structural and Functional Neuroimaging Studies	MA	-
Neurocircuitry models of posttraumatic stress disorder and beyond: a meta-analysis of functional neuroimaging studies	MA	-
A systematic review and meta-analysis of magnetic resonance imaging measurement of structural volumes in posttraumatic stress disorder	MA	-
A coordinate-based meta-analytic model of trauma processing in posttraumatic stress disorder	MA	-
In search of the trauma memory: a meta-analysis of functional neuroimaging studies of symptom provocation in posttraumatic stress disorder (PTSD)	MA	-
Neuroimaging in anxiety disorders	R	-

Anhedonia

Name/Link	Finding(s)
Reconceptualizing anhedonia: novel perspectives on balancing the pleasure networks in the human brain	-
Reconsidering anhedonia in depression: lessons from translational neuroscience	-
The brain reward circuitry in mood disorders	-
The neurobiology of anhedonia and other reward-related deficits	-
Neural Basis of Anhedonia and Amotivation in Patients with Schizophrenia: The Role of Reward System	-
Measuring anhedonia: impaired ability to pursue, experience, and learn about reward	-
Depression, stress, and anhedonia: toward a synthesis and integrated model	-
Assessing anhedonia in depression: Potentials and pitfalls	-

VTA

Medial VTA DA neurons project to the medial shell of the NAc as well as the NAc core, the mPFC and the BLA. Lateral VTA DA neurons project to the lateral NAc shell, and likely represent the population of neurons that were historically assessed in studies that identified DA neurons by waveform and electrophysiological characteristics.
The LDTg projects to the lVTA, driving the lVTA to release DA in the lateral NAc shell, producing CPP. The LHb, on the other hand, excites RMTg neurons that inhibit lVTA-Lateral NAc shell projections, but also excites NAc-->mPFC projections, inducing CPA.

The VTA receives input from the lOFC, ventral striatum, BNST, ventral pallidum, lateral hypothalamus, PPTg, and central nucleus of the amygdala. These areas encode a distributed representation of reward expectation and outcome with a high degree of redundancy.

Viral vector strategies for investigating midbrain dopamine circuits underlying motivated behaviors

The DR sends glutaminergic projections to the VTA which drive incentive salience; the LHb sends glutaminergic projections to the RMTg and mVTA to mPFC projections, the former of which provides general inhibition to DAergic neurons, and the latter of which excited mVTA to mPFC projections producing aversion; the BNST sends glutaminergic projections to GABAergic neurons, driving aversion, and GABAergic projections to GABAergic neurons, driving reward; the LH sends a pattern of projections similar to the BNST; the LDTg sends reward driving glutaminergic and GABAergic/glutaminergic coreleasing inputs to the VTA.

Ventral tegmental area: cellular heterogeneity, connectivity and behaviour

Supports similar results as with (Cardozo Pinto and Lammel), but also includes VTA--->NAc PV---|NAc producing aversion

OFC inputs to the VTA are necessary for error signals.

Neuronal Reward and Decision Signals: From Theories to Data

Recent

Schizophrenia

The cause and mechanism of schizophrenia is unknown. Evidence from phenomenology, pharmacology, neuroimaging, post mortem studies, genetics, and animal models implicate a number of possible and likely interrelated mechanisms, such as abnormalities in dopaminergic signalling, glutaminergic neurotransmission, and neurodevelopment. Many frameworks have been hypothesized to link these biological abnormalities to symptoms, including psychological and computational theories.^[52]^[53]

Abnormal dopamine signalling has been implicated in schizophrenia by the efficacy of D₂ receptor antagonists, as well as the consistent observation in positron emission tomography of elevated dopamine synthesis^[54] and release during acute psychosis.^[55] Abnormalities in dopaminergic symptoms have been hypothesized to underlie delusions via dysfunctional signalling of salience.^[56]^[57]^[58] Dopaminergic predictions errors, which mediate learning when expectancies are violated, are abnormal in schizophrenia, and these abnormalities correlate with the severity of delusions. Furthermore, impaired learning, putatively reflecting the functionality of the dopaminergic system, is present in schizophrenia and correlates with delusion severity.^[59] Dysfunctional prediction errors may be related to hyperactive input from the hippocampus, which is observed to be metabolically overactive in schizophrenia,^[56] and in turn may be related to abnormalities in NMDA receptor functioning on hippocampal interneurons.^[60] Hypoactivation of D₁ receptors in the prefrontal cortex may also be responsible for deficits in working memory and cognition,^[61]^[62]^[63]^[64] although direct evidence from neuroreceptor imaging studies is inconsistent.^[65]

Reduced NMDA receptor signalling is suggested by multiple lines of evidence. Post-mortem studies demonstrate reduced NMDA receptor expression and NMDA receptor antagonists mimic both schizophrenia symptoms and the electrophysiological abnormalities associated with schizophrenia (notable reduced mismatch negativity and P300).^[66]^[67]^[68] Two meta analyses of magnetic resonance spectroscopy studies have found evidence interpreted to be consistent with abnormal glutaminergic signalling, possible involving NMDA receptor abnormalities, despite contradictory findings (i.e. reduced versus elevated glutamate/glutamine ratios).^[69]^[70] This deficit in NMDA signalling may be related to the abnormalities observed in parvalbumin interneurons that express NMDA receptors.^[71] Post mortem studies consistently find that a subset of these neurons fail to express GAD67 in addition to abnormalities in morphology,^[72] although neuroimaging studies examining indicators of GABAergic signalling do not consistently report abnormalities.^[73]^[74] The subsets of interneurons that are abnormal in schizophrenia are responsible for the synchronizing of neural ensembles that is necessary during working memory tasks, a process that is electrophysiologically reflected in gamma frequency (30-80 Hz) oscillations. Both working memory tasks and gamma oscillations are impaired in schizophrenia, which may reflect abnormal interneuron functionality.^[75]^[76]^[77]^[78]^[79]

Multiple lines of evidence suggest that schizophrenia has a neurodevelopmental component.^[80] Schizophrenia is associated with premorbid impairments in cognition, social functioning, and motor skills,^[81] and prenatal insults such as maternal infection,^[82]^[83] maternal malnutrition and obsteric complications all increase risk for schizophrenia.^[84] Animal models of these insults demonstrate patterns of cellular and molecular abnormalities similar to those in schizophrenia, such as increased RELN methylation and abnormal GABAergic cell development.^[85] Schizophrenia usually emerges symptomatically during late adolescence, 18-25, an age period that overlaps with certain stages of neurodevelopment that are implicated in schizophrenia, such as abnormal synaptic pruning and myelination.^[86]

Deficits in executive functions, such as planning, inhibition, and working memory, are pervasive in schizophrenia. Although these functions are dissociable, their dysfunction in schizophrenia may reflect an underlying deficit in the ability to represent goal related information in working memory, and to utilize this to direct cognition and behavior.^[87]^[88] These impairments have been linked to a number of neuroimaging and neuropathological abnormalities. For example, functional neuroimaging studies report evidence of reduced neural processing efficiency, whereby the dorsolateral prefrontal cortex is activated to a greater degree to achieve a certain level of performance relative to controls on working memory tasks. These abnormalities may be linked to the consistent post-mortem finding of reduced neuropil, evidenced by increased pyramidal cell density, reduced dentritic spine density,^[89] and reduced expression mRNA associated with synapses.^[90] These cellular and functional abnormalities may also be reflected in structural neuroimaging studies that find reduced grey matter volume in association with deficits in working memory tasks.^[89]

Different symptoms have been linked to specific neuroanatomical abnormalities or neurobiological models. For example, positive and negative symptoms have been linked to reduced cortical thickness in the superior temporal lobe,^[91] and orbitofrontal cortex, respectively.^[92] Auditory hallucinations, a prominent component of positive symptoms, are also reflected in functional hyperactivity of auditory cortices, and in the predictive coding framework are hypothesized to reflect impaired feedforward cancellation of internally generated speech.^[93] Anhedonia, traditionally defined as a reduced capacity to experience pleasure, is frequently reported in schizophrenia. However, a large body of evidence suggests that hedonic responses are intact in schizophrenia,^[94] and that what is reported to be anhedonia is a reflection of dysfunction in other processes related to reward.^[95] Overall, a failure of online maintenance and reward associativity is thought to lead to impairment in the generation of cognition and behavior required to obtain rewards, despite normal hedonic responses.^[96] A meta analysis of neuroimaging studies examining reward related paradigms reported results consistent with impairments in the neural substrates that mediate learning, but not in the experience of reward.^[97]

Bayesian models of brain function have been utilized to link abnormalities in cellular functioning to symptoms. These models propose that the brain generates predictive models in order to explain sensory information, and that predictive units are organized in a hierarchical fashion that reflect a more abstract model of causes for sensory information higher in the hierarchy. The essential computation performed by these models involves the minimization of error generated by discrepancies in top down predictions by modifying synaptic weights with the least precision.^[98]^[99] Improper specification of precision in bayesian models is largely consistent with the behavioral, neuroimaging, and electrophysiological abnormalities associated with schizophrenia. Both a failure to attenuate sensory precision and excessive weighting or priors have been proposed as potential and not necessarily conflicting explanations.^[100] In canonical models of circuits that mediate predictive coding, hypoactive NMDA receptor activation and abnormalities in dopaminergic signalling,^[101] similar to that seen in schizophrenia, could theoretically result in classic symptoms of schizophrenia such as delusions and hallucinations.^[102]^[59]^[103]

Images

OLS Regression Results                            
==============================================================================
Dep. Variable:               g factor   R-squared:                       0.202
Model:                            OLS   Adj. R-squared:                  0.201
Method:                 Least Squares   F-statistic:                     293.2
Date:                Thu, 02 Aug 2018   Prob (F-statistic):          1.11e-169
Time:                        12:11:03   Log-Likelihood:                -4542.1
No. Observations:                3478   AIC:                             9090.
Df Residuals:                    3475   BIC:                             9109.
Df Model:                           3                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
RELIMP        -0.0836      0.016     -5.372      0.000      -0.114      -0.053
AGE           -0.4253      0.015    -27.717      0.000      -0.455      -0.395
GENDER        -0.0582      0.015     -3.782      0.000      -0.088      -0.028
==============================================================================
Omnibus:                       12.035   Durbin-Watson:                   1.977
Prob(Omnibus):                  0.002   Jarque-Bera (JB):                9.991
Skew:                           0.057   Prob(JB):                      0.00677
Kurtosis:                       2.763   Cond. No.                         1.26
==============================================================================

 OLS Regression Results                            
==============================================================================
Dep. Variable:               g factor   R-squared:                       0.199
Model:                            OLS   Adj. R-squared:                  0.198
Method:                 Least Squares   F-statistic:                     288.0
Date:                Thu, 02 Aug 2018   Prob (F-statistic):          5.32e-167
Time:                        12:11:03   Log-Likelihood:                -4566.8
No. Observations:                3491   AIC:                             9140.
Df Residuals:                    3488   BIC:                             9158.
Df Model:                           3                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
PRAY          -0.0586      0.016     -3.718      0.000      -0.090      -0.028
AGE           -0.4313      0.015    -28.260      0.000      -0.461      -0.401
GENDER        -0.0600      0.016     -3.820      0.000      -0.091      -0.029
==============================================================================
Omnibus:                       11.895   Durbin-Watson:                   1.976
Prob(Omnibus):                  0.003   Jarque-Bera (JB):                9.650
Skew:                           0.043   Prob(JB):                      0.00803
Kurtosis:                       2.757   Cond. No.                         1.33
==============================================================================

OLS Regression Results                            
==============================================================================
Dep. Variable:               g factor   R-squared:                       0.201
Model:                            OLS   Adj. R-squared:                  0.201
Method:                 Least Squares   F-statistic:                     293.0
Date:                Thu, 02 Aug 2018   Prob (F-statistic):          1.27e-169
Time:                        12:11:03   Log-Likelihood:                -4555.1
No. Observations:                3487   AIC:                             9116.
Df Residuals:                    3484   BIC:                             9135.
Df Model:                           3                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
REL           -0.0780      0.015     -5.050      0.000      -0.108      -0.048
AGE           -0.4275      0.015    -27.954      0.000      -0.457      -0.398
GENDER        -0.0622      0.015     -4.062      0.000      -0.092      -0.032
==============================================================================
Omnibus:                       11.538   Durbin-Watson:                   1.967
Prob(Omnibus):                  0.003   Jarque-Bera (JB):                9.534
Skew:                           0.050   Prob(JB):                      0.00851
Kurtosis:                       2.764   Cond. No.                         1.22
==============================================================================

REL: Response to "How religious are you?", on a scale of 1-4, with 1 being the least religious and 4 being the most religious RELIMP: Response to "How important is religion to you?", on a scale of 1-4, with 1 being the least important and 4 being the most important PRAY: Response to "How often do you pray?", on a scale of 1-6, with 1 being never and 6 being once or more a day

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[68] Pratt, J; Dawson, N; Morris, BJ; Grent-'t-Jong, T; Roux, F; Uhlhaas, PJ (February 2017). "Thalamo-cortical communication, glutamatergic neurotransmission and neural oscillations: A unique window into the origins of ScZ?". Schizophrenia research. 180: 4–12. doi:10.1016/j.schres.2016.05.013. PMID 27317361.

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[70] Marsman, A; van den Heuvel, MP; Klomp, DW; Kahn, RS; Luijten, PR; Hulshoff Pol, HE (January 2013). "Glutamate in schizophrenia: a focused review and meta-analysis of ¹H-MRS studies". Schizophrenia bulletin. 39 (1): 120–9. doi:10.1093/schbul/sbr069. PMC 3523901. PMID 21746807.

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[72] Marín, O (18 January 2012). "Interneuron dysfunction in psychiatric disorders". Nature reviews. Neuroscience. 13 (2): 107–20. doi:10.1038/nrn3155. PMID 22251963.

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[75] Marín, O (18 January 2012). "Interneuron dysfunction in psychiatric disorders". Nature reviews. Neuroscience. 13 (2): 107–20. doi:10.1038/nrn3155. PMID 22251963.

[76] Lewis, DA; Hashimoto, T; Volk, DW (April 2005). "Cortical inhibitory neurons and schizophrenia". Nature reviews. Neuroscience. 6 (4): 312–24. doi:10.1038/nrn1648. PMID 15803162.

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[82] Khandaker, GM; Zimbron, J; Lewis, G; Jones, PB (February 2013). "Prenatal maternal infection, neurodevelopment and adult schizophrenia: a systematic review of population-based studies". Psychological medicine. 43 (2): 239–57. doi:10.1017/S0033291712000736. PMC 3479084. PMID 22717193.

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[84] Brown, AS (January 2011). "The environment and susceptibility to schizophrenia". Progress in neurobiology. 93 (1): 23–58. doi:10.1016/j.pneurobio.2010.09.003. PMC 3521525. PMID 20955757.

[85] Negrón-Oyarzo, I; Lara-Vásquez, A; Palacios-García, I; Fuentealba, P; Aboitiz, F (11 March 2016). "Schizophrenia and reelin: a model based on prenatal stress to study epigenetics, brain development and behavior". Biological research. 49: 16. doi:10.1186/s40659-016-0076-5. PMC 4787713. PMID 26968981.{{cite journal}}: CS1 maint: unflagged free DOI (link)

[86] Cannon, TD (December 2015). "How Schizophrenia Develops: Cognitive and Brain Mechanisms Underlying Onset of Psychosis". Trends in cognitive sciences. 19 (12): 744–756. doi:10.1016/j.tics.2015.09.009. PMC 4673025. PMID 26493362.

[87] Lesh, TA; Niendam, TA; Minzenberg, MJ; Carter, CS (January 2011). "Cognitive control deficits in schizophrenia: mechanisms and meaning". Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology. 36 (1): 316–38. doi:10.1038/npp.2010.156. PMC 3052853. PMID 20844478.

[88] Barch, DM; Ceaser, A (January 2012). "Cognition in schizophrenia: core psychological and neural mechanisms". Trends in cognitive sciences. 16 (1): 27–34. doi:10.1016/j.tics.2011.11.015. PMC 3860986. PMID 22169777.

[Eisenberg-89] Eisenberg, DP; Berman, KF (January 2010). "Executive function, neural circuitry, and genetic mechanisms in schizophrenia". Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology. 35 (1): 258–77. doi:10.1038/npp.2009.111. PMC 2794926. PMID 19693005.

[90] Osimo, EF; Beck, K; Reis Marques, T; Howes, OD (6 March 2018). "Synaptic loss in schizophrenia: a meta-analysis and systematic review of synaptic protein and mRNA measures". Molecular psychiatry. doi:10.1038/s41380-018-0041-5. PMC 6004314. PMID 29511299.

[91] Walton, E; Hibar, DP; van Erp, TG; Potkin, SG; Roiz-Santiañez, R; Crespo-Facorro, B; Suarez-Pinilla, P; Van Haren, NE; de Zwarte, SM; Kahn, RS; Cahn, W; Doan, NT; Jørgensen, KN; Gurholt, TP; Agartz, I; Andreassen, OA; Westlye, LT; Melle, I; Berg, AO; Mørch-Johnsen, L; Faerden, A; Flyckt, L; Fatouros-Bergman, H; Karolinska Schizophrenia Project Consortium, (KaSP).; Jönsson, EG; Hashimoto, R; Yamamori, H; Fukunaga, M; Preda, A; De Rossi, P; Piras, F; Banaj, N; Ciullo, V; Spalletta, G; Gur, RE; Gur, RC; Wolf, DH; Satterthwaite, TD; Beard, LM; Sommer, IE; Koops, S; Gruber, O; Richter, A; Krämer, B; Kelly, S; Donohoe, G; McDonald, C; Cannon, DM; Corvin, A; Gill, M; Di Giorgio, A; Bertolino, A; Lawrie, S; Nickson, T; Whalley, HC; Neilson, E; Calhoun, VD; Thompson, PM; Turner, JA; Ehrlich, S (May 2017). "Positive symptoms associate with cortical thinning in the superior temporal gyrus via the ENIGMA Schizophrenia consortium". Acta psychiatrica Scandinavica. 135 (5): 439–447. doi:10.1111/acps.12718. PMC 5399182. PMID 28369804.

[92] Walton, E; Hibar, DP; van Erp, TGM; Potkin, SG; Roiz-Santiañez, R; Crespo-Facorro, B; Suarez-Pinilla, P; van Haren, NEM; de Zwarte, SMC; Kahn, RS; Cahn, W; Doan, NT; Jørgensen, KN; Gurholt, TP; Agartz, I; Andreassen, OA; Westlye, LT; Melle, I; Berg, AO; Morch-Johnsen, L; Færden, A; Flyckt, L; Fatouros-Bergman, H; Karolinska Schizophrenia Project Consortium, (KaSP).; Jönsson, EG; Hashimoto, R; Yamamori, H; Fukunaga, M; Jahanshad, N; De Rossi, P; Piras, F; Banaj, N; Spalletta, G; Gur, RE; Gur, RC; Wolf, DH; Satterthwaite, TD; Beard, LM; Sommer, IE; Koops, S; Gruber, O; Richter, A; Krämer, B; Kelly, S; Donohoe, G; McDonald, C; Cannon, DM; Corvin, A; Gill, M; Di Giorgio, A; Bertolino, A; Lawrie, S; Nickson, T; Whalley, HC; Neilson, E; Calhoun, VD; Thompson, PM; Turner, JA; Ehrlich, S (January 2018). "Prefrontal cortical thinning links to negative symptoms in schizophrenia via the ENIGMA consortium". Psychological medicine. 48 (1): 82–94. doi:10.1017/S0033291717001283. PMC 5826665. PMID 28545597.

[93] Javitt, DC; Sweet, RA (September 2015). "Auditory dysfunction in schizophrenia: integrating clinical and basic features". Nature reviews. Neuroscience. 16 (9): 535–50. doi:10.1038/nrn4002. PMC 4692466. PMID 26289573.

[94] Cohen, AS; Minor, KS (January 2010). "Emotional experience in patients with schizophrenia revisited: meta-analysis of laboratory studies". Schizophrenia bulletin. 36 (1): 143–50. doi:10.1093/schbul/sbn061. PMC 2800132. PMID 18562345.

[95] Strauss, GP; Gold, JM (April 2012). "A new perspective on anhedonia in schizophrenia". The American journal of psychiatry. 169 (4): 364–73. doi:10.1176/appi.ajp.2011.11030447. PMC 3732829. PMID 22407079.

[96] Young, Jared; Anticevic, Alan; Barch, Deanna (2018). "Cognitive and Motivational Neuroscience of Psychotic Disorders". In Charney, Dennis; Buxbaum, Joseph; Sklar, Pamela; Nestler, Eric (eds.). Charney & Nestler's Neurobiology of Mental Illness (5th ed.). New York: Oxford University Press. pp. 215, 217. ISBN 9780190681425. Several recent reviews (e.g., Cohen and Minor, 2010) have found that individuals with schizophrenia show relatively intact self-reported emotional responses to affect-eliciting stimuli as well as other indicators of intact response(215)...Taken together, the literature increasingly suggests that there may be a deficit in putatively DA-mediated reward learning and/ or reward prediction functions in schizophrenia. Such findings suggest that impairment in striatal reward prediction mechanisms may influence "wanting" in schizophrenia in a way that reduces the ability of individuals with schizophrenia to use anticipated rewards to drive motivated behavior.(217)

[97] Chase, HW; Loriemi, P; Wensing, T; Eickhoff, SB; Nickl-Jockschat, T (July 2018). "Meta-analytic evidence for altered mesolimbic responses to reward in schizophrenia". Human brain mapping. 39 (7): 2917–2928. doi:10.1002/hbm.24049. PMID 29573046. Specifically, a reduced anticipatory response might result from a reduction in the learning rate, and reflect a slower transfer from outcome to reward-related responses. This account would predict intact or enhanced outcome-locked activation, with reduced anticipatory activation. This account is also consistent with behavioral evidence of reinforcement learning deficits in SCZ (Waltz et al., 2007) and outcome-locked null results (Culbreth, West- brook, Xu, Barch, & Waltz, 2016b).

[98] Fletcher, PC; Frith, CD (January 2009). "Perceiving is believing: a Bayesian approach to explaining the positive symptoms of schizophrenia". Nature reviews. Neuroscience. 10 (1): 48–58. doi:10.1038/nrn2536. PMID 19050712.

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[101] Friston, K; Brown, HR; Siemerkus, J; Stephan, KE (October 2016). "The dysconnection hypothesis (2016)". Schizophrenia research. 176 (2–3): 83–94. doi:10.1016/j.schres.2016.07.014. PMC 5147460. PMID 27450778.

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