Danhong Wang

This paper introduces robust lesion network mapping (rLNM) to address statistical validity concerns in lesion network mapping, showing it recovers distinct symptom-specific networks and predicts postoperative language decline.

This study identifies a functionally connected glioma network overlapping with the action-mode network and shows its prognostic significance and association with acetylcholine transporter expression.

The paper characterizes the auditory cortex's function through dynamic coactivation patterns identified in fMRI data, showing consistency between resting-state and task conditions.

DeepPrep accelerates neuroimaging preprocessing by 11-fold using deep learning, outperforming current state-of-the-art methods in scalability and robustness.

The paper maps individual auditory cortex subareas using functional network analysis and fMRI, showing high intraindividual reproducibility and meaningful interindividual variability.

The paper identifies individualized functional connectivity markers in Parkinson's disease patients that predict both motor and mood symptoms using functional MRI data.

This study identifies the somato-cognitive action network (SCAN) as a key pathway in Parkinson's disease, showing its dysfunction and link to neuromodulatory treatments.

The paper examines brain activity patterns in stroke patients, finding that increased time in a frontoparietal brain state correlates with better motor recovery in those with dominant-hand corticospinal tract damage.

This study uses graph matching to analyze functional connectivity reorganization in stroke patients, showing it correlates with motor recovery and structural disconnection.

This study examines individual variability in the functional organization of human and monkey auditory cortex using intrinsic connectivity and task-based fMRI, finding greater variability in non-primary regions, compared to visual areas, and left hemisphere lateralization.