Zheng Wang

The paper uses computational modeling to explore how theta burst stimulation affects brain oscillations and plasticity through calcium-dependent mechanisms, revealing resonance with alpha rhythms and implications for personalized treatment.

This study identifies neurophysiological signatures predicting response to TMS in treatment-resistant depression using combined EEG and computational modeling.

This paper applies a corticothalamic model to analyze sleep EEG data from multiple datasets, revealing consistent physiological patterns and drug-related effects during sleep stages.

This study investigates how functional connectivity in resting-state networks depends on structural connectivity, showing state-dependent interactions between lower and higher order networks.

GREMI improves multi-omics disease prediction by integrating biomolecular interactions and provides interpretable module identification.

This paper investigates how inhibition levels affect fMRI resting-state negative correlations (NCs) using simulations and connectome-based modeling, showing that greater inhibition increases NC magnitude and spatial extent.

The paper uses electrophysiology and modeling to show that high-order brain regions have higher excitability due to recurrent feedback, revealing a gradient linked to cortical hierarchy.

This study investigates LSD's effects on brain connectivity using fMRI and finds increased interregional connectivity and reduced self-inhibition, suggesting a disruption of the Excitation/Inhibition balance.

This paper investigates how TMS-evoked responses are influenced by recurrent large-scale network dynamics and local reverberatory activity, using TMS-EEG and connectome-based modeling.

The paper introduces an automated pipeline for generating connectome-based inputs for TheVirtualBrain using UK Biobank neuroimaging data.

The paper introduces a deep learning approach to estimate parameters in connectome-based neural mass models using fMRI data and demonstrates its effectiveness on synthetic and real resting-state fMRI data.

The paper maps excitatory axonal projections from the macaque ventrolateral prefrontal cortex to the whole brain at submicron resolution, revealing discrepancies with diffusion tractography.

The paper introduces virtual connectomic datasets by simulating missing connectivity features from existing neuroimaging data, enabling machine learning classification comparable to empirical data.

The paper develops a transfer-learning U-Net model for brain extraction in non-human primates using human data, achieving better performance than traditional methods and popular MRI packages.

The paper identifies shared circuit-level abnormalities between MECP2 transgenic monkeys and a subgroup of human autism patients, highlighting the need for a multimodal approach to study genetic effects in a primate model.

The paper measures brain signal complexity during music listening and reward tasks, finding that perceptual tasks show better complexity matching with music complexity than emotional tasks.

A cross-species machine learning approach using transgenic primate data improves diagnostic classification of ASD and OCD with higher accuracy than human-only models.

This paper uses functional brain connectomes to predict optimal deep brain stimulation targets for Parkinson’s disease patients, finding that globus pallidus and subthalamic nucleus are top choices for a significant proportion of patients.