Deep learning algorithms have greatly improved our ability to estimate eloquent cortex
regions from resting-state brain scans for patients about to undergo neurosurgery. The use …

Introduction: Accurate diagnosis is crucial for brain tumors, given their low survival rates and
high treatment costs. However, traditional methods relying on manual interpretation of …

Deep learning on resting-state functional MRI (rs-fMRI) has shown great success in
predicting a single cognition or mental disease. Nevertheless, cognitive functions or mental …

Objective: Epileptogenic zone (EZ) localization is a crucial step during diagnostic work up
and therapeutic planning in medication refractory epilepsy. In this paper, we present the first …

Localizing the epileptogenic zone (EZ) is a critical step in the treatment of medically
refractory epilepsy. Resting-state fMRI (rs-fMRI) offers a new window into this task by …

We propose a lesion-aware graph neural network (LEGNet) to predict language ability from
resting-state fMRI (rs-fMRI) connectivity in patients with post-stroke aphasia. Our model …

Over the past decade, there has been a growing emphasis on neuroscience to analyze the
human brain from the perspective of complex networks. Here, connectomics, or the study of …

Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive
neuroimaging modality that quantifies the changes in blood flow and oxygenation in the …