Theodore Daniel Satterthwaite

This study evaluates head motion correction methods in diffusion MRI, showing both Eddy and SHORELine perform well but are affected by denoising and sampling schemes.

This study compares machine learning models using functional connectivity and its derivatives to predict schizophrenia, finding that raw connectivity outperforms low-dimensional features.

This study investigates the neural correlates of impulsivity using meta-analytic synthesis and functional connectivity modelling, identifying distinct brain networks associated with decision-making and action control.

XCP-D provides a standardized, reproducible post-processing pipeline for fMRI data, supporting multiple pre-processing pipelines and BIDS best practices.

The paper introduces BABS, a Python package for reproducible and scalable neuroimaging data processing using BIDS Apps and HPC systems.

This study identifies white matter fiber covariance networks in youth, showing their development aligns with executive function and hierarchical cortical maturation patterns.

This paper introduces a distance-based regression method for multimodal data that can handle different data types and dimensionalities, showing its effectiveness through simulations and real-world studies.

This study identifies 928 genes associated with brain weight variation across three datasets and links them to neurodevelopmental processes and brain function.

This study shows that myelination and excitation-inhibition balance influence structure-function coupling in the human cortex, with myelination becoming more important in agranular regions.

This study maps neurophysiological dynamics to cortical micro-architecture, finding spatial correlations between brain activity patterns and microstructural features.

This paper introduces a framework for applying network control theory to the human structural connectome to study neural dynamics and control energy, controllability, and their correlations with behavior and mental health.

The paper investigates how functional connectivity patterns in brain networks predict symptoms of borderline personality disorder in adolescents and young adults using fMRI data.

The paper identifies proteins and molecular processes related to brain connectivity by integrating neuroimaging, proteomic, and gene expression data from human brain regions.

This study uses network control theory to examine how alprazolam affects persistence energy during emotion processing in schizophrenia relatives, revealing compensatory mechanisms and neuromodulatory relationships.

The paper estimates the whole-cortex excitation-inhibition ratio using fMRI and shows it declines during youth, with lower ratios linked to better cognitive performance in children.

This paper integrates multiscale cortical biology by generating spatially dense gene expression maps from postmortem data, revealing transcriptional zones linked to microstructure and neuroimaging features.

The paper identifies two EEG connectivity dimensions linked to ASD symptoms using contrastive learning and sparse canonical correlation analysis in 392 ASD samples.

This paper develops a self-supervised deep learning method to compute personalized brain functional networks from fMRI data, showing it can identify canonical networks and predict individual differences in behavior and schizophrenia status.

This paper identifies and addresses quality-related biases in structural MRI analyses of children by improving automated quality control methods.

This study evaluates six compressed sensing diffusion spectrum imaging (CS-DSI) schemes, finding they provide accurate and reliable white matter structure measures with up to 80% reduced scan time compared to full DSI.

This study tracks cortical activity propagations in youth, finding increased top-down propagations with cognitive control demands and development.

Low phenotypic measurement reliability severely limits out-of-sample brain-behaviour prediction accuracy and prevents models from fully benefiting when neuroimaging sample sizes scale from hundreds to thousands of participants.

The paper benchmarks and optimizes a normative modeling framework for brain morphometry across the lifespan using MFPR, showing excellent accuracy and stability.

This study links individual differences in delay discounting to specific patterns of dorsal prefrontal cortex connectivity in youth using resting-state fMRI.

The paper uses network control theory to show that bottom-up signaling across the sensory-fugal axis of the human connectome requires less energy than top-down signaling.

The paper uses network theory to predict compression levels in neural circuits and finds differences in dimensionality and representational capacity across brain regions and behaviors.

The paper introduces CLEAN-R, a method to test and localize intermodal correspondence in neuroimaging data, demonstrating high statistical power and nominal type I error levels.

The paper defines personalized functional networks in children to predict cognitive differences, finding that fronto-parietal network representation correlates with general cognition.

Neuromaps provides a software package to standardize and compare brain maps across different coordinate systems, enabling comprehensive annotation analysis of the human brain.

CuBIDS streamlines reproducible curation of large BIDS datasets by validating metadata, managing variability, and preparing data for preprocessing pipelines.