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Salum, G. A.

Publications and source records attributed to Salum, G. A..

5 recordsLinked to original sources

Generalizable sex differences in functional brain organization follow the cortical hierarchy in youth

It remains unknown if sex differences in human functional connectivity follow a generalizable overarching principle. Here, we tested whether sex differences in functional connectivity during childhood and adolescence are organized along the sensorimotor-association (S-A) axis, a cortical hierarchy spanning unimodal sensorimotor to transmodal association cortex. We analyzed four large-scale, independent datasets of youth ages 5-23 years (total N = 3,355). Across datasets, males showed greater connectivity between functional networks, whereas females showed greater connectivity within networks, most prominently within the default mode network. More broadly, the sensorimotor and association ends of the S-A axis were more segregated in females and more integrated in males. This difference extended to macroscale cortical organization: the cortical hierarchy was expanded in females relative to males along the principal gradient of functional connectivity. Together, these findings identify the S-A axis as a generalizable organizing principle of sex differences in functional brain organization during youth.

neuroscience↗

Disentangling Brain-Psychopathology Associations: A Systematic Evaluation of Transdiagnostic Bifactor Models

Identifying robust brain-psychopathology associations with neuroimaging remains difficult, in part due to substantial heterogeneity within and comorbidity between diagnostic categories. Transdiagnostic latent factor models aim to address this structure by separating shared and unique symptom variance. However, it remains unclear whether latent factor modeling translates into stronger and more interpretable brain-psychopathology associations within contemporary whole-brain prediction frameworks. Using two large developmental cohorts, we systematically compared transdiagnostic bifactor models, correlated factor models, and typical summary scores derived from the Child Behaviour Checklist (CBCL) in their reliability and multivariate associations with whole-brain structure (MRI) and function (resting-state fMRI). General psychopathology, internalising, externalising, and attention dimensions could be significantly predicted from resting-state connectivity but not cortical thickness. We found no consistent evidence that latent factors (bifactor or correlated factor models) strengthened reliability or brain-psychopathology associations, relative to summary scores. Neural signatures were also highly consistent across all scoring methods, with triple network (DMN, FPN, CO) involvement in general, internalizing, and externalising psychopathology. Bifactor scores did, however, display more distinct neural signatures between general, internalising, and externalising dimensions than did summary or correlated factor scores. Collectively, results suggest that additional phenotypic modelling of psychopathology alone does not systematically strengthen the predictive utility of neuroimaging, possibly reflecting fundamental limits on the amount of explainable symptom variance by brain features. While latent factor models may aid in distinguishing neural correlates between constructs, improving phenotypic assessment may be necessary for improvements to brain-psychopathology association strength.

neuroscience↗

Reproducible Brain Charts: An open data resource for mapping brain development and its associations with mental health

Major mental disorders are increasingly understood as disorders of brain development. Large and heterogeneous samples are required to define generalizable links between brain development and psychopathology. To this end, we introduce the Reproducible Brain Charts (RBC), an open data resource that integrates data from 5 large studies of brain development in youth from three continents (N=6,346; 45% Female). Confirmatory bifactor models were used to create harmonized psychiatric phenotypes that capture major dimensions of psychopathology. Following rigorous quality assurance, neuroimaging data were carefully curated and processed using consistent pipelines in a reproducible manner with DataLad, the Configurable Pipeline for the Analysis of Connectomes (C-PAC), and FreeSurfer. Initial analyses of RBC data emphasize the benefit of careful quality assurance and data harmonization in delineating developmental effects and associations with psychopathology. Critically, all RBC data - including harmonized psychiatric phenotypes, unprocessed images, and fully processed imaging derivatives - are openly shared without a data use agreement via the International Neuroimaging Data-sharing Initiative. Together, RBC facilitates large-scale, reproducible, and generalizable research in developmental and psychiatric neuroscience.

neuroscience↗

Functional Connectivity Development along the Sensorimotor-Association Axis Enhances the Cortical Hierarchy

Human cortical maturation has been posited to be organized along the sensorimotor-association (S-A) axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the S-A axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3,355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1,207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1,126). In each dataset, the development of functional connectivity systematically varied along the S-A axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These robust and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.

neuroscience↗

Cross-sectional and longitudinal associations of threat and deprivation on cognition, emotional processing and psychopathology in children and adolescents

BackgroundExposure to childhood adversity has been consistently associated with poor developmental outcomes, but it is unclear whether these associations vary across different forms of adversity. We examined cross-sectional and longitudinal associations between two types of adversity--threat and deprivation--with cognition, emotional processing, and psychopathology in a middle-income country. MethodsThe sample consisted of 2,511 children and adolescents (6-17 years old) from the Brazilian High-Risk Cohort for Psychiatric Disorders. Parent reports on childhood adversity were used to construct threat and deprivation latent constructs. Psychopathology was measured by the CBCL which generated a measure of general psychopathology (the "p" factor). Executive function (EF) and attention orienting toward angry faces were assessed using cognitive tasks. All measures were acquired at two time-points 3-years apart. Cross-lagged panel models were estimated to evaluate longitudinal associations. ResultsPsychopathology was associated with threat and deprivation cross-sectionally, and higher levels of threat and deprivation predicted increases in general psychopathology at follow-up. For EF, worse performance was more strongly associated with deprivation than threat at baseline, and only with deprivation at follow-up. Deprivation was associated with attention orienting away from angry faces cross-sectionally, but neither form of adversity was associated with changes over time in attention bias. ConclusionBoth types of adversity are associated with current and future psychopathology and with current, but not future, EF. Threat was more strongly linked to higher psychopathology, whereas deprivation was more strongly linked to lower EF. Lack of longitudinal associations between deprivation and EF mean reverse causality cannot be excluded.

neuroscience↗