Victoria L. Morgan

The paper generates lifespan normative charts for cervical spinal cord morphometry using MRI data, revealing non-linear changes and sex differences across ages 0-100.

This study characterizes white matter asymmetry across the lifespan using a large dataset, revealing dynamic changes in asymmetry patterns with age.

The paper develops normative reference charts for white matter microstructure and macrostructure across the lifespan using diffusion MRI data from 50 studies.

The paper evaluates the variability of GAMLSS-based brain charts using analytic and empirical methods, finding low coefficient of variation across most white matter features.

This study examines how arousal states affect brain network switching rates and their relationship with cognitive task performance using fMRI, EEG, and eye tracking.

This paper introduces a framework for longitudinal quantitative streamline tractography that reduces spurious changes in white matter connectivity estimates by fixing trajectories and varying density weights between sessions.

This paper analyzes peri-ictal network dynamics in drug-resistant epilepsy patients to show that NIZs increase inhibitory signaling to SOZs during seizures, but this suppression is insufficient to prevent seizure onset.

This study examines how connectivity patterns of arousal regulation centers change with vigilance states using EEG and fMRI, showing distinct network correlations in low and high alertness.

This paper introduces BrainState, a self-supervised method to model complex brain states from EEG data, applied to epilepsy patients to capture pre-seizure states and neuromodulation effects.

This paper develops a deep learning model to accurately segment the nucleus basalis of Meynert on 3T MRI, showing improved performance over standard atlases in both healthy subjects and TLE patients.

The paper tests the interictal suppression hypothesis in focal epilepsy using electrographic and structural data to identify seizure onset zones and develop a classification model with high accuracy.

This paper develops a deep learning model to classify seizure onset zones using cortico-cortical evoked potentials from intracranial neurostimulation recordings.