Dustin Scheinost

Multimodal Connectome Markers of Disruptive Behavior Problems in Youth: Distinct Patterns of Concurrent Severity and Prospective Symptom Change

The paper uses multimodal connectome data to identify patterns of functional and structural connectivity associated with disruptive behavior severity and change in youth.

Brain age prediction from structural and functional connectivity across the lifespan using connectome-based predictive modeling

This study uses connectome-based predictive modeling to predict chronological age from structural and functional brain connectivity across different lifespan stages and finds that both modalities contribute complementary age-related information.

Mapping the genetic architecture of human cortical expansion and its links to neuropsychiatric disorders

The paper models the genetic architecture of cortical expansion using GWAS data and links it to neuropsychiatric disorders.

Development of the cognitive energy landscape from infancy to adolescence

This study uses network control theory and developmental MRI to show how brain structural maturation supports cognitive development, finding decreasing control energy for most cognitive tasks and varying optimal efficiency ages across cognitive functions.

Testing the Tests: Using Connectome-Based Predictive Models to Reveal the Systems Standardized Tests and Clinical Symptoms Are Reflecting

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.

The Hidden Landscape of Missed Effects in Human Functional Neuroimaging

The paper identifies inflated effect sizes in functional neuroimaging studies and proposes a correction method to improve study planning and detection of brain effects.

Flexible brain state engagement predicts cognitive control transdiagnostically

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

PRISME: A MATLAB Toolbox For Large Data-Driven Multimodal Power Benchmarking

PRISME is a MATLAB toolbox for neuroimaging power benchmarking that enables large-scale empirical power analysis across diverse data types and test types.

Transdiagnostic connectome-based predictive modeling reveals where circuits related to self-reported clinical symptoms impinge upon brain networks supporting cognition

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

Fetal network controllability co-develops with synaptic development and synchronizes with maternal network controllability during pregnancy

The paper uses network control theory to show fetal brain controllability follows a U-shaped trajectory during pregnancy, co-developing with synaptic functions and synchronizing with maternal controllability changes.

Functional connectivity in the social perception pathway at birth is linked with attention to faces at 4 months

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.

Neural Mechanisms of Feedback Processing and Regulation Recalibration during Neurofeedback Training

This paper analyzes fMRI neurofeedback data to identify brain regions and networks involved in feedback processing and regulation recalibration during training.

Trends in Self-citation Rates in High-impact Neurology, Neuroscience, and Psychiatry Journals

This study analyzes self-citation rates across 100,347 articles from 63 neuroscience journals between 2000-2020, revealing trends in self-citation by author position, country, gender, and field.

Growth charts of infant visual neurodevelopment generalize across global contexts

The paper creates longitudinal normative growth curves of visual cortex function using VEP data from infants in three countries, showing generalizability across contexts and associations with early environmental factors.

Brain-handedness associations depend on how and when handedness is measured

The paper shows brain-handedness associations vary based on how and when handedness is measured, using two large datasets.

Overlooked features lead to divergent neurobiological interpretations of brain-based machine learning biomarkers

The paper shows that overlooked brain connectivity features can yield different neurobiological interpretations with similar prediction accuracy in neuroimaging studies.

White-matter controllability at birth predicts social engagement and language outcomes in toddlerhood

The paper uses white-matter controllability at birth to predict social engagement and language outcomes in toddlers.

Brain age prediction and deviations from normative trajectories in the neonatal connectome

This study predicts brain age and deviations from normative trajectories in neonatal connectomes using structural and functional data from infants.

Disruption of the Social Visual Pathway in Autism Spectrum Disorder

This study examines resting-state functional connectivity in the social visual pathway in children with autism spectrum disorder (ASD) and typically developing (TD) children, finding underconnectivity in the right hemisphere STS and its correlation with social impairment.

Variation in moment-to-moment brain state engagement changes across development and contributes to individual differences in executive function

This study examines how variability in brain state engagement changes with age and its relationship to executive function in youth.

Power and reproducibility in the external validation of brain-phenotype predictions

The paper evaluates the statistical power of external validation in brain-phenotype predictions and finds that sample size impacts power and effect size inflation.

Connectome caricatures: removing large-amplitude co-activation patterns in resting-state fMRI emphasizes individual differences

This paper introduces a method to remove task-like co-activation patterns from resting-state fMRI data, revealing individual differences and improving predictive modeling of brain-phenotype associations.

The effects of data leakage on connectome-based machine learning models

This study evaluates how data leakage affects machine learning models using neuroimaging data, finding that some leakage types significantly inflate performance while others reduce it or affect model interpretations.

Brain-phenotype predictions can survive across diverse real-world data

The paper tests predictive models' ability to generalize across diverse, unharmonized neuroimaging datasets and finds that cross-dataset training improves prediction robustness.

Rescuing missing data in connectome-based predictive modeling

This paper improves brain-behavior prediction by imputing missing data in connectome-based models, increasing sample size and explained variance.

Normative Birth Weight Variation is Associated with White Matter Connectivity in Full-Term Neonates

Spatial and temporal autocorrelation weave complexity in brain networks

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

Stacking multiple optimal transport policies to map functional connectomes

This paper extends the CAROT method to stack multiple optimal transport mappings between atlases, improving efficiency while maintaining performance in connectome reconstruction.

Sex differences in default mode network connectivity in healthy aging adults

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.

Developmental trajectories of the default mode, executive control, and salience networks from the third trimester through the newborn period

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.