Brain functional connectivity mirrors genetic pleiotropy in psychiatric conditions

Diversity of Intraspecific Patterns of Brain Region Size Covariation in Fish

Traits often do not evolve in isolation or vary independently of other traits. Instead, they can be affected by covariation, both within and across species. However, the importance of within-species trait covariation and, critically, the degree to which it varies between species has yet to be thoroughly studied. Brain morphology is a trait of great ecological and behavioral importance, with regions that are hypothesized to vary in size based on behavioral and cognitive demands. Sizes of brain regions have also been shown to covary with each other across various taxa. Here, we test the degree to which covariation in brain region sizes within species has been conserved across 10 teleost fish species. These 10 species span five orders, allowing us to examine how phylogenetic proximity influences similarities in intraspecific trait covariation. Our results showed a trend that similar patterns of brain region size covariation occur in more closely related species. Interestingly, there were certain brain region pairs that showed similar levels of covariation across all species regardless of phylogenetic distance, such as the telencephalon and optic tectum, while others, such as the olfactory bulb and the hypothalamus, varied more independently. Ultimately, the patterns of brain region covariation shown here suggest that evolutionary mechanisms or constraints can act on specific brain regions independently, and that these constraints can change over evolutionary time.

Integrative genetic analysis: cornerstone of precision psychiatry

The role of genetic testing in the domain of neurodevelopmental and psychiatric disorders (NPDs) is gradually changing from providing etiological explanation for the presence of NPD phenotypes to also identifying young individuals at high risk of developing NPDs before their clinical manifestation. In clinical practice, the latter implies a shift towards the availability of individual genetic information predicting a certain liability to develop an NPD (e.g., autism, intellectual disability, psychosis etc.). The shift from mostly a posteriori explanation to increasingly a priori risk prediction is the by-product of the systematic implementation of whole exome or genome sequencing as part of routine diagnostic work-ups during the neonatal and prenatal periods. This rapid uptake of genetic testing early in development has far-reaching consequences for psychiatry: Whereas until recently individuals would come to medical attention because of signs of abnormal developmental and/or behavioral symptoms, increasingly, individuals are presented based on genetic liability for NPD outcomes before NPD symptoms emerge. This novel clinical scenario, while challenging, also creates opportunities for research on prevention interventions and precision medicine approaches. Here, we review why optimization of individual risk prediction is a key prerequisite for precision medicine in the sphere of NPDs, as well as the technological and statistical methods required to achieve this ambition.

Genetic and phenotypic similarity across major psychiatric disorders: a systematic review and quantitative assessment

There is widespread overlap across major psychiatric disorders, and this is the case at different levels of observations, from genetic variants to brain structures and function and to symptoms. However, it remains unknown to what extent these commonalities at different levels of observation map onto each other. Here, we systematically review and compare the degree of similarity between psychiatric disorders at all available levels of observation. We searched PubMed and EMBASE between January 1, 2009 and September 8, 2022. We included original studies comparing at least four of the following five diagnostic groups: Schizophrenia, Bipolar Disorder, Major Depressive Disorder, Autism Spectrum Disorder, and Attention Deficit Hyperactivity Disorder, with measures of similarities between all disorder pairs. Data extraction and synthesis were performed by two independent researchers, following the PRISMA guidelines. As main outcome measure, we assessed the Pearson correlation measuring the degree of similarity across disorders pairs between studies and biological levels of observation. We identified 2975 studies, of which 28 were eligible for analysis, featuring similarity measures based on single-nucleotide polymorphisms, gene-based analyses, gene expression, structural and functional connectivity neuroimaging measures. The majority of correlations (88.6%) across disorders between studies, within and between levels of observation, were positive. To identify a consensus ranking of similarities between disorders, we performed a principal component analysis. Its first dimension explained 51.4% (95% CI: 43.2, 65.4) of the variance in disorder similarities across studies and levels of observation. Based on levels of genetic correlation, we estimated the probability of another psychiatric diagnosis in first-degree relatives and showed that they were systematically lower than those observed in population studies. Our findings highlight that genetic and brain factors may underlie a large proportion, but not all of the diagnostic overlaps observed in the clinic.

Longitudinal Development of Thalamocortical Functional Connectivity in 22q11.2 Deletion Syndrome

BACKGROUND

The 22q11.2 deletion syndrome (22qDel) is a genetic copy number variant that strongly increases risk for schizophrenia and other neurodevelopmental disorders. Disrupted functional connectivity between the thalamus and the somatomotor/frontoparietal cortex has been implicated in cross-sectional studies of 22qDel, idiopathic schizophrenia, and youths at clinical high risk for psychosis. Here, we used a novel functional atlas approach to investigate longitudinal age-related changes in network-specific thalamocortical functional connectivity (TCC) in participants with 22qDel and typically developing (TD) control participants.

METHODS

TCC was calculated for 9 functional networks derived from resting-state functional magnetic resonance imaging scans collected from 65 participants with 22qDel (63.1% female) and 69 demographically matched TD control participants (49.3% female) ages 6 to 23 years. Analyses included 86 longitudinal follow-up scans. Nonlinear age trajectories were characterized with generalized additive mixed models.

RESULTS

In participants with 22qDel, TCC in the frontoparietal network increased until approximately age 13, while somatomotor TCC and cingulo-opercular TCC decreased from age 6 to 23. In contrast, no significant relationships between TCC and age were found in TD control participants. Somatomotor connectivity was significantly higher in participants with 22qDel than in TD control participants in childhood, but lower in late adolescence. Frontoparietal TCC showed the opposite pattern.

CONCLUSIONS

22qDel is associated with aberrant development of functional network connectivity between the thalamus and cortex. Younger individuals with 22qDel have lower frontoparietal connectivity and higher somatomotor connectivity than control individuals, but this phenotype may normalize or partially reverse by early adulthood. Altered maturation of this circuitry may underlie elevated neuropsychiatric disease risk in this syndrome.

Quantifying brain connectivity signatures by means of polyconnectomic scoring

A broad range of neuropsychiatric disorders are associated with alterations in macroscale brain circuitry and connectivity. Identifying consistent brain patterns underlying these disorders by means of structural and functional MRI has proven challenging, partly due to the vast number of tests required to examine the entire brain, which can lead to an increase in missed findings. In this study, we propose polyconnectomic score (PCS) as a metric designed to quantify the presence of disease-related brain connectivity signatures in connectomes. PCS summarizes evidence of brain patterns related to a phenotype across the entire landscape of brain connectivity into a subject-level score. We evaluated PCS across four brain disorders (autism spectrum disorder, schizophrenia, attention deficit hyperactivity disorder, and Alzheimer's disease) and 14 studies encompassing ~35,000 individuals. Our findings consistently show that patients exhibit significantly higher PCS compared to controls, with effect sizes that go beyond other single MRI metrics ([min, max]: Cohen's d = [0.30, 0.87], AUC = [0.58, 0.73]). We further demonstrate that PCS serves as a valuable tool for stratifying individuals, for example within the psychosis continuum, distinguishing patients with schizophrenia from their first-degree relatives (d = 0.42, p = 4 × 10-3, FDR-corrected), and first-degree relatives from healthy controls (d = 0.34, p = 0.034, FDR-corrected). We also show that PCS is useful to uncover associations between brain connectivity patterns related to neuropsychiatric disorders and mental health, psychosocial factors, and body measurements.