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The polygenic architecture of schizophrenia implicates several molecular pathways involved in synaptic function. However, it is unclear how polygenic risk funnels through these pathways to translate into syndromic illness. Using tensor decomposition, we analyze gene co-expression in the caudate nucleus, hippocampus, and dorsolateral prefrontal cortex of post-mortem brain samples from 358 individuals. We identify a set of genes predominantly expressed in the caudate nucleus and associated with both clinical state and genetic risk for schizophrenia that shows dopaminergic selectivity. A higher polygenic risk score for schizophrenia parsed by this set of genes predicts greater dopamine synthesis in the striatum and greater striatal activation during reward anticipation. These results translate dopamine-linked genetic risk variation into in vivo neurochemical and hemodynamic phenotypes in the striatum that have long been implicated in the pathophysiology of schizophrenia., (© 2024. The Author(s).)

Alterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals. To capture individual brain features more accurately than single-session fMRI, we studied an average of three fMRI scans per individual. To identify potential familial risk-related FNC changes, we compared age-related FNC in first-degree relatives of SCZ patients mostly including unaffected siblings (SIB) with neurotypical controls (NC) at the same age stage. Then, we examined how polygenic risk scores for SCZ influenced risk-related FNC patterns. Finally, we investigated the same risk-related FNC patterns in adult SCZ patients (oSCZ) and young individuals with subclinical psychotic symptoms (PSY). Age-sensitive risk-related FNC patterns emerge during adolescence and early adulthood, but not before. Young SIB always followed older NC patterns, with decreased FNC in a cerebellar-occipitoparietal circuit and increased FNC in two prefrontal-sensorimotor circuits when compared to young NC. Two of these FNC alterations were also found in oSCZ, with one exhibiting reversed pattern. All were linked to polygenic risk for SCZ in unrelated individuals (R 2 varied from 0.02 to 0.05). Young PSY showed FNC alterations in the same direction as SIB when compared to NC. These results suggest that age-related neurotypical FNC correlates with genetic risk for SCZ and is detectable with MRI in young participants.

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d = -0.42, p = 3 × 10 -5 ), with weak evidence of IQ reductions among BD-FDRs (d = -0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

We propose a novel optimization framework that integrates imaging and genetics data for simultaneous biomarker identification and disease classification. The generative component of our model uses a dictionary learning framework to project the imaging and genetic data into a shared low dimensional space. We have coupled both the data modalities by tying the linear projection coefficients to the same latent space. The discriminative component of our model uses logistic regression on the projection vectors for disease diagnosis. This prediction task implicitly guides our framework to find interpretable biomarkers that are substantially different between a healthy and disease population. We exploit the interconnectedness of different brain regions by incorporating a graph regularization penalty into the joint objective function. We also use a group sparsity penalty to find a representative set of genetic basis vectors that span a low dimensional space where subjects are easily separable between patients and controls. We have evaluated our model on a population study of schizophrenia that includes two task fMRI paradigms and single nucleotide polymorphism (SNP) data. Using ten-fold cross validation, we compare our generative-discriminative framework with canonical correlation analysis (CCA) of imaging and genetics data, parallel independent component analysis (pICA) of imaging and genetics data, random forest (RF) classification, and a linear support vector machine (SVM). We also quantify the reproducibility of the imaging and genetics biomarkers via subsampling. Our framework achieves higher class prediction accuracy and identifies robust biomarkers. Moreover, the implicated brain regions and genetic variants underlie the well documented deficits in schizophrenia., (Copyright © 2021. Published by Elsevier Inc.)

Background: Schizophrenia and bipolar disorder share genetic liability, and some structural brain abnormalities are common to both conditions. First-degree relatives of patients with schizophrenia (FDRs-SZ) show similar brain abnormalities to patients, albeit with smaller effect sizes. Imaging findings in first-degree relatives of patients with bipolar disorder (FDRs-BD) have been inconsistent in the past, but recent studies report regionally greater volumes compared with control subjects., Methods: We performed a meta-analysis of global and subcortical brain measures of 6008 individuals (1228 FDRs-SZ, 852 FDRs-BD, 2246 control subjects, 1016 patients with schizophrenia, 666 patients with bipolar disorder) from 34 schizophrenia and/or bipolar disorder family cohorts with standardized methods. Analyses were repeated with a correction for intracranial volume (ICV) and for the presence of any psychopathology in the relatives and control subjects., Results: FDRs-BD had significantly larger ICV (d = +0.16, q < .05 corrected), whereas FDRs-SZ showed smaller thalamic volumes than control subjects (d = -0.12, q < .05 corrected). ICV explained the enlargements in the brain measures in FDRs-BD. In FDRs-SZ, after correction for ICV, total brain, cortical gray matter, cerebral white matter, cerebellar gray and white matter, and thalamus volumes were significantly smaller; the cortex was thinner (d < -0.09, q < .05 corrected); and third ventricle was larger (d = +0.15, q < .05 corrected). The findings were not explained by psychopathology in the relatives or control subjects., Conclusions: Despite shared genetic liability, FDRs-SZ and FDRs-BD show a differential pattern of structural brain abnormalities, specifically a divergent effect in ICV. This may imply that the neurodevelopmental trajectories leading to brain anomalies in schizophrenia or bipolar disorder are distinct., (Copyright © 2019 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

The purpose of this study was to examine measures of anatomical connectivity between the thalamus and lateral prefrontal cortex (LPFC) in schizophrenia and to assess their functional implications. We measured thalamocortical connectivity with diffusion tensor imaging (DTI) and probabilistic tractography in 15 patients with schizophrenia and 22 age- and sex-matched controls. The relationship between thalamocortical connectivity and prefrontal cortical blood-oxygenation-level-dependent (BOLD) functional activity as well as behavioral performance during working memory was examined in a subsample of 9 patients and 18 controls. Compared with controls, schizophrenia patients showed reduced total connectivity of the thalamus to only one of six cortical regions, the LPFC. The size of the thalamic region with at least 25% of model fibers reaching the LPFC was also reduced in patients compared with controls. The total thalamocortical connectivity to the LPFC predicted working memory task performance and also correlated with LPFC BOLD activation. Notably, the correlation with BOLD activation was accentuated in patients as compared with controls in the ventral LPFC. These results suggest that thalamocortical connectivity to the LPFC is altered in schizophrenia with functional consequences on working memory processing in LPFC.

Context: Schizophrenia is a brain disorder with predominantly genetic risk factors, and previous research has identified heritable cortical and subcortical reductions in local brain volume. To our knowledge, cortical thickness, a measure of particular interest in schizophrenia, has not previously been evaluated in terms of its heritability in relationship to risk for schizophrenia., Objective: To quantify the distribution and heritability of cortical thickness changes in schizophrenia., Design: We analyzed a large sample of normal controls, affected patients, and unaffected siblings using a surface-based approach. Cortical thickness was compared between diagnosis groups on a surfacewide node-by-node basis. Heritability related to disease risk was assessed in regions derived from an automated cortical parcellation algorithm by calculating the Risch lambda., Setting: Research hospital., Participants: One hundred ninety-six normal controls, 115 affected patients with schizophrenia, and 192 unaffected siblings., Main Outcome Measure: Regional cortical thickness., Results: Node-by-node mapping statistics revealed widespread thickness reductions in the patient group, most pronouncedly in the frontal lobe and temporal cortex. Unaffected siblings did not significantly differ from normal controls at the chosen conservative threshold. Risch lambda analysis revealed widespread evidence for heritability for cortical thickness reductions throughout the brain., Conclusions: To our knowledge, the present study provides the first evidence of broadly distributed and heritable reductions of cortical thickness alterations in schizophrenia. However, since only trend-level reductions of thickness were observed in siblings, cortical thickness per se (at least as measured by this approach) is not a strong intermediate phenotype for schizophrenia.

Background: Schizophrenia is a devastating psychiatric disorder with a strong genetic component that has been related to a number of structural brain alterations. Currently available data on the heritability of these structural changes are inconsistent., Methods: To examine heritability of morphological alterations in a large sample, we used a novel and validated fully-automated whole brain segmentation technique to study disease-related variability and heritability in anatomically defined regions of interest in 221 healthy control subjects, 169 patients with schizophrenia, and 183 unaffected siblings., Results: Compared with healthy control subjects, patients showed a bilateral decrease in hippocampal and cortical gray matter volume and increases in bilateral dorsal striatum and right lateral ventricle. No significant volumetric differences were found in unaffected siblings compared with normal control subjects in any structure. Post hoc analysis of the dorsal striatum showed the volumetric increase to be widespread, including caudate, putamen, and globus pallidus. With Risch's lambda (lambda(s)), we found strong evidence for heritability of reduced cortical volume and moderate evidence for hippocampal volume, whereas abnormal striatal and ventricle volumes showed no sign of heritability. Additional exploratory analyses were performed on amygdala, thalamus, nucleus accumbens, ventral diencephalon, and cerebral and cerebellar cortex and white matter. Of these regions, patients showed increased volume in ventral diencephalon and cerebellum., Conclusions: These findings support evidence of genetic control of brain volume even in adults, particularly of hippocampal and neocortical volume and of cortical volumetric reductions being familial, but do not support measures of subcortical volumes per se as representing intermediate biologic phenotypes.