John Dominic Medaglia

Brief Trains of Closed-loop TMS Induce Controllable Power in FM-theta Oscillations

This study shows that brief TMS trains triggered at specific FM-theta phases can directionally modulate post-train FM-theta power, suggesting a causal role of oscillations in cognition.

Predicting Post-Stroke Aphasia Speech Performance from Multimodal Data with Explainable Machine Learning

This paper develops a machine learning model to predict post-stroke aphasia speech performance using multimodal data, achieving high accuracy with explainable features.

Individual-level Functional ConnectivityPredicts Cognitive Control Efficiency

The paper uses support vector regression to predict individual cognitive control behaviors from functional connectivity between brain networks during tasks and rest.

Simulated attack reveals how lesions affect network properties in post-stroke aphasia

This study uses simulated attacks on brain networks to assess how stroke lesions affect network properties and aphasia severity, finding that global network measures explain some variance in aphasia symptoms beyond lesion volume.

Glutamate-Weighted Magnetic Resonance Imaging (GluCEST) Detects Effects of Transcranial Magnetic Stimulation to the Motor Cortex

The paper uses GluCEST MRI to detect glutamate concentration changes in the motor cortex after cTBS, finding decreased ipsilateral signal but no consistent contralateral or sham effects.

Structural Disconnection of the Posterior Medial Frontal Cortex Reduces Speech Error Monitoring

The paper investigates how structural disconnections in the posterior medial frontal cortex affect speech error monitoring in post-stroke aphasia patients.

Glass Half Full: Preserved Anatomical Bypasses Predict Variance in Language Functions After Stroke

This study examines how post-stroke bypasses in brain networks predict variance in language functions, finding that bypass length and overlap predict lexical production and auditory comprehension.

Subgraphs of functional brain networks identify dynamical constraints of cognitive control

The paper identifies cognitive control subgraphs in brain networks using fMRI data and shows their temporal expression relates to cognitive demands and task performance.

Improved accuracy of lesion to symptom mapping with multivariate sparse canonical correlations

This study compares multivariate sparse canonical correlation analysis (SCCAN) with mass-univariate voxel-wise lesion-symptom mapping (VLSM) and finds SCCAN provides more accurate results across various scenarios.