Linked dimensions of psychopathology and connectivity in functional brain networks

Abstract

Neurobiological abnormalities associated with psychiatric disorders do not map well to existing diagnostic categories. High co-morbidity suggests dimensional circuit-level abnormalities that cross diagnoses. Here we seek to identify brain-based dimensions of psychopathology using sparse canonical correlation analysis in a sample of 663 youths. This analysis reveals correlated patterns of functional connectivity and psychiatric symptoms. We find that four dimensions of psychopathology – mood, psychosis, fear, and externalizing behavior – are associated (r = 0.68–0.71) with distinct patterns of connectivity. Loss of network segregation between the default mode network and executive networks emerges as a common feature across all dimensions. Connectivity linked to mood and psychosis becomes more prominent with development, and sex differences are present for connectivity related to mood and fear. Critically, findings largely replicate in an independent dataset (n = 336). These results delineate connectivity-guided dimensions of psychopathology that cross clinical diagnostic categories, which could serve as a foundation for developing network-based biomarkers in psychiatry.

Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology

“…One example is the general psychopathology (or p) factor (10)(11)(12)(13)(14)(15), which is thought to reflect individuals' susceptibility to develop "any and all forms of common psychopathologies" (16). The p factor has also been extended to other clinical dimensions, e.g., internalizing/ externalizing symptoms (10,11,17). Importantly, these factors were extracted from the general population, supporting the idea that psychopathology lies on a spectrum spanning healthy and disease states.…”

“…Indeed, studies have found significant overlap in neural circuits altered in different disorders, suggesting common neurobiological mechanisms (20)(21)(22)(23). RSFC alterations in higher-order (e.g., default mode and executive) networks are also associated with the p factor (14,17) or clinical symptoms (24). However, many psychiatric imaging studies have focused on higher-order association networks (19,25), neglecting the potential influence of huge swaths of cortex.…”

“…In this study, we utilized data from the UCLA Consortium for Neuropsychiatric Phenomics (29), a unique dataset in which the imaging and behavioral assessment were focused on working memory and inhibitory control, two domains that are exceedingly relevant in multiple psychiatric disorders. This allowed us to examine behavioral phenotypes beyond the clinical symptoms typically examined in many transdiagnostic studies (10,11,14,15,17). Using a multivariate data-driven approach, we extracted latent components that simultaneously link a large set of behavioral measures spanning clinical, cognitive and personality domains with whole-brain RSFC patterns across healthy individuals (HC) and individuals with schizophrenia (SZ), schizoaffective disorder (SZAD), bipolar I disorder (BD) or attention deficit/hyperactivity disorder (ADHD).…”

Neurocognitive patterns dissociating semantic processing from executive control are linked to more detailed off-task mental time travel

“…The implementation of the confound removal method was retrieved from Python library Nilearn (http://nilearn.github.io/, version 0.3.1). As features that do not vary across subjects cannot be predictive of individual differences, we limited our analysis of connectivity data to the top 5% most variable connections, as measured by median absolute deviation, which is more robust against outliers than standard deviation 54 . Thus the input data to the subsequent analysis consist of 247 ROI-ROI connectivity and 13 cognitive task measures.…”

Multi-Scale Network Regression for Brain-Phenotype Associations

“…A community refers to a collection of nodes that are highly connected to each other and have little connection to nodes outside of the community. Prior work has demonstrated that brain network architecture present on these different scales is associated with development and aging (Power et al 2010;Gu et al 2015;Betzel et al 2014), cognition (Crossley et al 2013;Park and Friston 2013;Bressler and Menon 2010), and neuropsychiatric diseases (Yu et al 2019;Braun et al 2016;Fornito, Zalesky, and Breakspear 2015;Grillon et al 2013;Bassett, Xia, and Satterthwaite 2018;Xia et al 2018;Kernbach et al 2018).…”