Kanhao Zhao

This study benchmarks foundation models for resting-state fMRI time series in cognitive aging, finding mixed results on their utility for cognitive assessment and prediction.

This study uses machine learning and transcriptomic integration to identify brain network and molecular signatures associated with amyloid-beta and tau in Alzheimer's disease.

This study identifies a functional connectivity signature, ARC, that distinguishes superagers from AD patients and predicts cognitive decline and amyloid-beta accumulation.

This study identifies two transdiagnostic subtypes in ADHD and ASD using contrastive graph machine learning on brain connectivity patterns from resting-state fMRI.

The paper identifies dementia subtypes based on neuropsychiatric symptom-linked brain connectivity patterns using neuroimaging data.

The paper identifies two EEG connectivity dimensions linked to ASD symptoms using contrastive learning and sparse canonical correlation analysis in 392 ASD samples.