Robert Todd Constable

The paper develops a data-driven method to identify individualized disordered connectivity patterns from fMRI data, revealing diverse neurobiological mechanisms underlying transdiagnostic psychiatric symptoms.

The paper examines how environmental exposures affect whole-brain functional connectivity and cognition in youth using machine learning and the ABCD Study data.

This paper introduces a feedback approach to connectome-based predictive modeling to identify which brain systems standardized tests and clinical symptoms reflect, enabling better-targeted test development.

This study shows that flexible brain state engagement predicts cognitive control across multiple psychiatric disorders using machine learning and three transdiagnostic datasets.

This paper improves brain connectome analysis by incorporating global and local features, enhancing individual identification and phenotype prediction.

The paper uses connectome-based predictive modeling to show that circuits related to clinical symptoms and cognitive deficits are localized in specific brain networks.

The paper examines functional connectivity in the social perception pathway in neonates and links it to social attention at 4 months, finding stronger connectivity in the right pathway associated with better social functioning.

The paper uses simultaneous calcium imaging and fMRI in mice to identify cell-type specific signals in the brain with high spatiotemporal resolution.

This paper develops a geometrically constrained framework to map brain regions across species and shows geometry influences regional organization through patterning molecules.

The paper uses whole-brain functional connectivity to predict regional tau PET in preclinical Alzheimer's disease, finding it outperforms amyloid PET models and generalizes to a clinical dataset in tau-elevated individuals.

This study uses dynamic functional connectivity to identify brain states related to behavior and traits, finding that aggregated data across scans performs better than isolated time points for trait prediction.

The paper uses Ca2+ imaging and fMRI-BOLD in mice to compare functional network organization, finding overlapping but distinct patterns between modalities.

The paper shows spatial and temporal autocorrelation explain network topology measures in rs-fMRI data and how they change with aging and drug treatment.

The paper uses connectome-based predictive modeling to show sex differences in brain network predictors of memory performance, with women relying more on the default mode network and men on visual networks.

This study shows PTEN mutations cause ventriculomegaly and cortical network dysfunction through neural precursor and interneuron dysregulation, with mTORC1 inhibition offering therapeutic potential.

This study identifies neural mechanisms of human conscious visual perception using report-independent methods and shows involvement of large-scale subcortical and cortical networks.

The paper examines sex differences in default mode network connectivity across aging adults, finding age-related changes in women that resemble preclinical Alzheimer's disease patterns.

The paper introduces a pipeline for analyzing widefield calcium imaging data and shows how functional network properties differ with wakefulness and across cell types.

This study tracks the development of three brain networks from late gestation to the newborn period, finding slower maturation in the default mode network and environmental influences on the salience network.

This study investigates how arousal levels affect brain network integration using resting state fMRI and pupillometry, showing decreased integration and variability at low arousal.

This paper introduces a connectome-to-connectome state transformation model to quantify brain reorganization across cognitive states using fMRI data.

The paper develops a universal attention measure from fMRI data that predicts both task-general and task-specific attention performance and their neural mechanisms across multiple tasks.

The paper examines functional connectivity between the anterior insula and amygdala in neonates with familial autism history, finding hypoconnectivity linked to autism risk scores.

The paper shows that within-node connectivity changes systematically vary across tasks and subjects, enabling task and subject classification without relying on specific atlases.

This paper uses bootstrap aggregating (bagging) to improve the generalizability of predictive models of fluid intelligence from functional connectivity data across different datasets.

The paper identifies a reproducible structural brain pattern linked to age at first full drink before 21, using JIVE analysis on neuroimaging data from two datasets.

The paper develops predictive models of substance use vulnerability in children using behavioral and brain data from the ABCD study, identifying risk-seeking and familial factors as key predictors.

This study develops a whole-brain functional connectivity model using fMRI data to predict individual differences in long-term memory, showing it is distinct from working memory networks.

The paper describes a pre- and in-scan system to reduce head motion in pediatric fMRI scans, showing improved motion control with formal mock scans.

This study shows that a validated functional connectivity model predicts attention changes across time and under different sedation states.