There is robust evidence for sex differences in domain-specific cognitive performance in the general population, where females typically show an advantage for verbal memory (VM), while males tend to perform better on tasks of spatial memory (SM). Sex differences in brain structure and connectivity are also well-documented and may provide insight into sex differences in cognition. In this study, we examined sex differences in cognition and morphometric brain connectivity of a large healthy sample (N = 31,180) from the UK Biobank dataset. Using T1-weighted magnetic resonance imaging (MRI) scans and regional cortical thickness values, we applied jackknife bias estimation and graph theory to obtain subject-specific measures of morphometric brain connectivity, hypothesizing that sex-related differences in brain network global efficiency, or overall connectivity, would underlie observed cognitive differences. As predicted, females demonstrated better VM performance and males showed an advantage in SM. Females also demonstrated faster processing speed, with no observed sex difference in executive functioning. Males tended to have higher global efficiency, as well as higher regional connectivity (nodal strengths) in both the left and right hemispheres relative to females. Furthermore, higher global efficiency in males was found to mediate observed sex differences in cognition, predicting poorer verbal memory performance, better spatial memory, and slower processing speed in males. These findings contribute to an improved understanding of the way biological sex and differences in cognitive performance are related to morphometric brain connectivity as derived from graph-theoretic methods.
Psychosis represents a heterogeneous collection of biological and behavioural alterations that evolve over time. We propose a multiscale disease progression model of psychosis, in which hippocampal-cortical dysconnectivity precedes impaired episodic memory and social cognition, worsening negative symptoms and lowering functional outcome. In two cross-sectional datasets of first- and multi-episode psychosis (163 patients; 117 controls), we applied a recently developed machine-learning algorithm, SuStaIn, which uniquely integrates clustering and disease progression modeling. SuStaIn identified three patient subtypes, with Subtype 0 showing normal-range performance on all variables. In comparison, Subtype 1 showed lower episodic memory, social cognition, functional outcome, and higher negative symptoms, while Subtype 2 showed lower hippocampal-cortical connectivity. Subtype 1 deteriorated from (social) cognition to symptoms, functioning and hippocampal-cortical dysconnectivity, while Subtype 2 deteriorated from hippocampal-cortical dysconnectivity to (social) cognition, functioning and symptoms. This first application of SuStaIn in a multiscale model of psychiatry provides distinguishable disease trajectories of hippocampal-cortical connectivity, which might drive heterogeneous behavioural alterations in psychosis.
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