Chandra Sripada

This study examines how task-control network functioning, cognition, and environmental factors co-develop in early adolescence using longitudinal data from the ABCD Study.

The paper identifies a stable, age-invariant neural signature of task-control network functioning using latent variable modeling across developmental stages.

The paper links shorter sleep duration in adolescents to somatomotor network disconnection, socioeconomic factors, screen time, cognition, and psychopathology using network neuroscience methods.

The paper assesses neurocognitive maturation in early adolescence by comparing resting-state functional connectivity to adult and infant networks, finding it predicts cognitive performance and development.

The paper shows that flexible adaptation of task-positive brain networks predicts individual differences in evidence accumulation efficiency, explaining 36%-39% of the variance.

This paper expands a normative model repository to include lifespan trajectories of brain structure and function, showing their advantage over raw data in neuroimaging tasks.

This study finds that socioeconomic resources in childhood are associated with widespread changes in brain connectivity, primarily driven by parental education, which is partially explained by home enrichment and child cognitive factors.

This work develops normative models of brain growth and aging using neuroimaging data to quantify variability across disorders and improve case-control inferences.

This paper introduces a data-driven method to identify dynamic functional connectivity states in resting state fMRI by leveraging activation changes, showing improved accuracy and efficiency over sliding window approaches.

The paper identifies evidence accumulation efficiency (EEA) and its neural correlates as predictors of substance use in emerging adults.

This study shows predictive models applied to resting state fMRI data achieve higher test-retest reliability than individual features across various scanning and processing conditions.

This study uses large neuroimaging datasets to develop a predictive model classifying children who stutter from fluent peers, achieving 72% accuracy.

This study builds predictive neuromarkers of children's neurocognitive abilities from brain connectivity data, showing reliable generalization across sites and explaining over 14% of variance.

This study identifies a connectome-wide neurosignature of the P factor, a general risk factor for psychopathology, and shows its generalizability across adolescent brain imaging data.

The paper uses cognitive modeling to analyze neural activations during a go/no-go task, finding that error-related brain activity correlates with cognitive performance and externalizing psychopathology.

The paper develops a CNN for automated fetal brain masking in rs-fMRI data, achieving good performance across different scanners and populations.

The paper identifies FPN-DMN network separation as a mediator of general intelligence, showing it predicts intelligence scores and reverses in different task conditions.

The paper identifies a low-rank structure in resting state connectomes, showing that a small number of components account for most inter-individual variation and can predict neurocognitive and clinical variables.

This study identifies resting state fMRI connectivity patterns predicting neurocognitive profiles in youth across three domains, showing cross-site generalizability for General Ability.

The paper predicts intelligence from whole-brain task activation patterns, achieving a 0.68 correlation, better than other modalities.