Genetic Heterogeneity Shapes Brain Connectivity in Psychiatry

BackgroundPolygenicity and genetic heterogeneity pose great challenges for studying psychiatric conditions. Genetically informed approaches have been implemented in neuroimaging studies to address this issue. However, the effects on functional connectivity of rare and common genetic risks for psychiatric disorders are largely unknown. Our objectives were to estimate and compare the effect sizes on brain connectivity of psychiatric genomic risk factors with various levels of complexity: oligogenic copy number variants (CNVs), multigenic CNVs, and polygenic risk scores (PRSs) as well as idiopathic psychiatric conditions and traits.MethodsResting-state functional magnetic resonance imaging data were processed using the same pipeline across 9 datasets. Twenty-nine connectome-wide association studies were performed to characterize the effects of 15 CNVs (1003 carriers), 7 PRSs, 4 idiopathic psychiatric conditions (1022 individuals with autism, schizophrenia, bipolar conditions, or attention-deficit/hyperactivity disorder), and 2 traits (31,424 unaffected control subjects).ResultsEffect sizes on connectivity were largest for psychiatric CNVs (estimates: 0.2–0.65 z score), followed by psychiatric conditions (0.15–0.42), neuroticism and fluid intelligence (0.02–0.03), and PRSs (0.01–0.02). Effect sizes of CNVs on connectivity were correlated to their effects on cognition and risk for disease (r = 0.9, p = 5.93 × 10^?6). However, effect sizes of CNVs adjusted for the number of genes significantly decreased from small oligogenic to large multigenic CNVs (r = ?0.88, p = 8.78 × 10^?6). PRSs had disproportionately low effect sizes on connectivity compared with CNVs conferring similar risk for disease.ConclusionsHeterogeneity and polygenicity affect our ability to detect brain connectivity alterations underlying psychiatric manifestations.     

Brain Imaging in Stroke: Insight Beyond Diagnosis

Stroke, whether hemorrhagic or ischemic in nature, has the ability to lead to devastating and debilitating patient outcomes, which not only has direct implications from a healthcare standpoint, but its effects are longstanding and they impact the community as a whole. For decades, the goal of advancement and refinement in imaging modalities has been to develop the most precise, convenient, widely available and reproducible interpretable modality for the detection of stroke, not only in its hyperacute phase, but a method to be able to predict its evolution through the natural course of disease. Diagnosis is one of the most important initial roles, which imaging fulfills after the identification of existent pathology. However, imaging fulfills an even more important goal by using a combination of imaging modalities and their precise interpretation, which lends itself to understanding the mechanisms and pathophysiology of underlying disease, and therefore guides therapeutic decision-making in a patient-tailored fashion. This review explores the most commonly used brain imaging modalities, computer tomography, and magnetic resonance imaging, with an aim to demonstrate their dynamic use in uncovering stroke mechanism, facilitating prognostication, and potentially guiding therapy.