Todd B. Parrish

This study examines the effects of therapeutic acute intermittent hypoxia on cerebrovascular function in healthy individuals and finds no significant changes in whole-brain blood flow or reactivity.

The paper identifies speech and language critical cortex using fMRI network features, finding critical sites have distinct connectivity patterns compared to non-critical sites.

This paper introduces an fMRI-based method to map spinal cord vascular reactivity, showing distinct gray matter regions associated with arterial supply territories.

This study assesses cerebral blood flow responses to hypoxia and hypercapnia using MRI, finding poor reliability in hypoxic CVR and paradoxical responses not explained by CO2 changes.

The paper introduces EPISeg, a deep learning model for automated spinal cord segmentation on gradient-echo EPI data, showing improved performance over existing methods.

This study shows body size parameters significantly correlate with brain and spinal cord MRI metrics and should be included in neuroimaging studies.

This study investigates spinal cord fMRI activation during hand-grasping tasks, finding lateralized activity in C5-C8 with differences based on analysis methods and experimental parameters.

This study examines how variability in manually contoured spinal cord masks affects fMRI co-registration and activation mapping, finding that rater and dataset differences significantly impact spatial normalization and signal contrast.

This paper uses geometric deep learning to predict sex and age from brain shape data, achieving high accuracy and correlation.

This paper introduces geometric deep learning frameworks for analyzing brain morphology in Alzheimer's disease, showing improved classification and morphological insights compared to prior methods.

This study uses geometric deep learning to predict fluid intelligence from brain morphology across two cohorts, finding that amygdala, hippocampus, and specific cortical regions are key predictors.

This study shows that theta-burst TMS during fMRI enhances hippocampal memory encoding immediately, improving recollection of visual scenes without affecting a non-memory task.