INTRODUCTION: The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE: To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS: We identified 369 nominally genome-wide significant loci ( P < 5 × 10 ⁻⁸ ) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 360 loci for which replication data were available, 241 loci influencing surface area and 66 influencing thickness remained significant after replication, with 237 loci passing multiple testing correction ( P < 8.3 × 10 ⁻¹⁰ ; 187 influencing surface area and 50 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation ( r _G = −0.32, SE = 0.05, P = 6.5 × 10 ⁻¹² ), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 46 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION: This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function. Identifying genetic influences on human cortical structure.: ( A ) Measurement of cortical surface area and thickness from MRI. ( B ) Genomic locations of common genetic variants that influence global and regional cortical structure. ( C ) Our results support the radial unit hypothesis that the expansion of cortical surface area is driven by proliferating neural progenitor cells. ( D ) Cortical surface area shows genetic correlation with psychiatric and cognitive traits. Error bars indicate SE. IMAGE CREDITS: (A) K. COURTNEY; (C) M. R. GLASS

Deficiencies in Theory of Mind (ToM) are consistently found in individuals with schizophrenia (SZ), major depressive disorder (MDD), and bipolar disorder (BD). However, the character of these deficits and their role in the pathogenesis of mental illness remains poorly understood. This systematic review synthesizes the available secondary literature pertaining to ToM functioning in individuals with MDD, BD, or SZ, and their respective spectrum disorders in order to delineate disorder or symptom specific patterns of ToM impairment. Literature suggests that ToM deficits increase in severity along the affective-psychotic spectrum, with mild deficits in patients with MDD, and severe deficits in patients with mania or psychosis. Furthermore, ToM deficits appear to be part of a broader developmental phenotype associated with SZ and BD, as suggested by findings of attenuated impairments in ToM in remitted patients with SZ or BD, unaffected first-degree relatives of patients, and clinical high-risk groups. Future psychiatric research on ToM should aim to disentangle relationships between ToM deficits and specific symptom dimensions transdiagnostically, and employ standardized, construct-specific ToM tasks.

Background.: Studying offspring of schizophrenia (SZo) and bipolar disorder patients (BDo) provides important information on the putative neurodevelopmental trajectories underlying development toward severe mental illnesses. We compared intracranial volume (ICV), as a marker for neurodevelopment, and global and local brain measures between SZo or BDo and control offspring (Co) in relation to IQ and psychopathology.Methods.: T1-weighted magnetic resonance imaging (MRI) brain scans were obtained from 146 participants (8–19 years; 40 SZo, 66 BDo, 40 Co). Linear mixed models were applied to compare ICV, global, and local brain measures between groups. To investigate the effect of ICV, IQ (four subtests Wechsler Intelligence Scale for Children/Wechsler Adult Intelligence Scale-III) or presence of psychopathology these variables were each added to the model.Results.: SZo and BDo had significantly lower IQ and more often met criteria for a lifetime psychiatric disorder than Co. ICV was significantly smaller in SZo than in BDo (d= −0.56) and Co (d= −0.59), which was largely independent of IQ (respectively,d= −0.54 andd= −0.35). After ICV correction, the cortex was significantly thinner in SZo than in BDo (d= −0.42) and Co (d= −0.75) and lateral ventricles were larger in BDo than in Co (d= 0.55). Correction for IQ or lifetime psychiatric diagnosis did not change these findings.Conclusions.: Despite sharing a lower IQ and a higher prevalence of psychiatric disorders, brain abnormalities in BDo appear less pronounced (but are not absent) than in SZo. Lower ICV in SZo implies that familial risk for schizophrenia has a stronger association with stunted early brain development than familial risk for bipolar disorder.

Background: Lower intelligence quotient (IQ) has frequently been reported in patients with schizophrenia. However, it is unclear whether IQ declines (further) after illness onset and what the familial contribution is to this change. Therefore, we investigate IQ changes during the course of illness in patients with non-affective psychosis, their siblings and controls.Methods: Data are part of the longitudinal Genetic Risk and Outcome of Psychosis (GROUP) study in the Netherlands and Belgium. Participants underwent three measurements, each approximately 3 years apart. A total of 1022 patients with non-affective psychosis [illness duration: 4.34 (s.d.= 4.50) years], 977 of their siblings, and 565 controls had at least one measure of IQ (estimated from four subtests of the WAIS-III).Results: At baseline, IQ was significantly lower in patients (IQ = 97.8) and siblings (IQ = 108.2;p< 0.0001) than in controls (IQ = 113.0;p< 0.0001), and in patients as compared with siblings (p< 0.0001). Over time, IQ increased in all groups. In siblings, improvement in IQ was significantly more pronounced (+0.7 points/year) than in patients (+0.5 points/year;p< 0.0001) and controls (+0.3 points/year;p< 0.0001). IQ increase was not significantly correlated with improvement in (sub)clinical outcome in any of the groups.Conclusions: During the first 10 years of the illness, IQ increases to a similar (and subtle) extent in a relatively high-functioning group of schizophrenia patients and controls, despite the lower IQ in patients at baseline. In addition, the siblings’ IQ was intermediate at baseline, but over time the increase in IQ was more pronounced.

Background: Two established staging models outline the longitudinal progression in bipolar disorder (BD) based on episode recurrence or inter-episodic functioning. However, underlying neurobiological mechanisms and corresponding biomarkers remain unexplored. This study aimed to investigate if global and (sub)cortical brain structures, along with brain-predicted age difference (brain-PAD) reflect illness progression as conceptualized in these staging models, potentially identifying brain-PAD as a biomarker for BD staging.Methods: In total, 199 subjects with bipolar-I-disorder and 226 control subjects from the Dutch Bipolar Cohort with a high-quality T1-weighted magnetic resonance imaging scan were analyzed. Global and (sub)cortical brain measures and brain-PAD (the difference between biological and chronological age) were estimated. Associations between individual brain measures and the stages of both staging models were explored.Results: A higher brain-PAD (higher biological age than chronological age) correlated with an increased likelihood of being in a higher stage of the inter-episodic functioning model, but not in the model based on number of mood episodes. However, after correcting for the confounding factors lithium-use and comorbid anxiety, the association lost significance. Global and (sub)cortical brain measures showed no significant association with the stages.Conclusions: These results suggest that brain-PAD may be associated with illness progression as defined by impaired inter-episodic functioning. Nevertheless, the significance of this association changed after considering lithium-use and comorbid anxiety disorders. Further research is required to disentangle the intricate relationship between brain-PAD, illness stages, and lithium intake or anxiety disorders. This study provides a foundation for potentially using brain-PAD as a biomarker for illness progression.

Over 50% of children with a parent with severe mental illness will develop mental illness by early adulthood. However, intergenerational transmission of risk for mental illness in one’s children is insufficiently considered in clinical practice, nor is it sufficiently utilised into diagnostics and care for children of ill parents. This leads to delays in diagnosing young offspring and missed opportunities for protective actions and resilience strengthening. Prior twin, family, and adoption studies suggest that the aetiology of mental illness is governed by a complex interplay of genetic and environmental factors, potentially mediated by changes in epigenetic programming and brain development. However, how these factors ultimately materialise into mental disorders remains unclear. Here, we present the FAMILY consortium, an interdisciplinary, multimodal (e.g., (epi)genetics, neuroimaging, environment, behaviour), multilevel (e.g., individual-level, family-level), and multisite study funded by a European Union Horizon-Staying-Healthy-2021 grant. FAMILY focuses on understanding and prediction of intergenerational transmission of mental illness, using genetically informed causal inference, multimodal normative prediction, and animal modelling. Moreover, FAMILY applies methods from social sciences to map social and ethical consequences of risk prediction to prepare clinical practice for future implementation. FAMILY aims to deliver: (i) new discoveries clarifying the aetiology of mental illness and the process of resilience, thereby providing new targets for prevention and intervention studies; (ii) a risk prediction model within a normative modelling framework to predict who is at risk for developing mental illness; and (iii) insight into social and ethical issues related to risk prediction to inform clinical guidelines.