Keith Wakefield Jamison

The paper develops a pipeline to estimate structural and functional connectomes from lesion masks to predict post-stroke recovery outcomes, showing better performance than traditional methods.

This study uses longitudinal multimodal neuroimaging to analyze changes in brain injury biomarkers over time, revealing dynamic relationships between structural, functional, and PET-derived measures.

The paper analyzes sex differences in brain networks across the lifespan using a new AI-based method, finding minimal differences in early childhood, pronounced in adulthood, and diverging in later life.

The paper uses TSPO-PET to show higher inflammation in white matter disrupted by paramagnetic rim lesions in MS patients, linked to disability.

This study finds that cocaine use disorder is linked to reduced brain state transition energy in specific networks, particularly those involved in excitatory and higher-order functions.

This study uses Network Control Theory to analyze differences in brain energy landscapes between MCI patients and healthy controls, finding lower transition energy in the limbic network and reduced global entropy in MCI.

This study uses estimated structural and functional connectivity networks from MRI and AI to predict cognition in multiple sclerosis patients, finding strong predictive accuracy for follow-up scores.

This study examines sex-specific brain activity dynamics in youth with and without a family history of substance use disorder, finding differences in transition energy across brain networks.

The paper introduces Krakencoder, a tool that translates and fuses structural and functional brain connectomes across atlases and processing strategies, achieving higher identifiability than existing models.

This study examines brain dynamics and structural connectivity in individuals with heavy alcohol use using multimodal imaging and finds reduced brain complexity and increased control energy.

This study compares data-driven and theory-based biomarkers for predicting chronic motor outcomes after stroke, finding data-driven models perform better, especially when using structural disconnection measures.

The paper identifies early structural disconnection biomarkers in multiple sclerosis linked to disability and cognitive impairment progression.

This study examines how brain lesion volume in multiple sclerosis patients correlates with changes in brain activity dynamics and entropy.

This study introduces a graph-matching based metric to quantify functional connectome differences between individuals and finds it correlates with age, cognitive performance, and familial relationships.

The paper examines brain activity patterns in stroke patients, finding that increased time in a frontoparietal brain state correlates with better motor recovery in those with dominant-hand corticospinal tract damage.

The paper investigates non-neural factors affecting BOLD response magnitudes in human participants and proposes a correction method to improve fMRI data interpretation.

This paper introduces BrainSurfCNN, a surface-based CNN that predicts task-based contrast maps from resting-state fMRI, achieving state-of-the-art accuracy and generalization.

The paper examines sex-independent and sex-specific relationships between functional connectivity and cognitive abilities in healthy adults using the Human Connectome Project data.

This study uses graph matching to analyze functional connectivity reorganization in stroke patients, showing it correlates with motor recovery and structural disconnection.

The paper quantifies regional structural-functional connectome coupling in adults and finds it varies with age, sex, cognitive scores, and is highly heritable.

This study shows deep neural networks can predict cortical responses to naturalistic stimuli across large brain areas beyond visual and auditory regions.

The paper integrates functional and structural brain connectivity to predict individual cognitive abilities, finding that multimodal integration outperforms single-modal approaches.

This paper compares models using observed and estimated connectivity networks to classify multiple sclerosis patients into disability groups, finding estimated networks perform better.

This paper introduces TDM, a method to remove venous effects from task-based fMRI by decomposing responses into microvascular and macrovascular components, improving spatial accuracy.

This study identifies sex differences in brain regions using temporal dependence and volume measures from resting-state fMRI data, achieving high classification accuracy.

The paper examines how changes in structural and functional connectomes relate to cognitive recovery after mild TBI, finding that increased network diffusion model propagation time, structural segregation, and functional integration correlate with better recovery.

This study evaluates ultra-high-resolution fMRI's ability to capture fine-scale BOLD activity, identifies venous effects as a confounding factor, and provides practical recommendations for data interpretation.

The paper describes a large-scale retinotopy dataset from the HCP and presents population receptive field analysis results from both individual subjects and group averages.