Jingnan Du

The paper investigates network competition in the brain using electrophysiology and neuroimaging, finding temporally layered antagonism between default and task-related networks, with distinct patterns in the hippocampus.

The paper identifies two nested language networks in the human brain using functional mapping across multiple cohorts, showing distinct roles in phonology and syntax.

This paper introduces mRBM-HBP, a Bayesian framework that integrates resting-state and task fMRI to improve cortical parcellation accuracy and individual specificity.

The paper uses fMRI data to estimate hierarchical networks in the human cerebral cortex, revealing a global organization with distinct functional properties across different network orders.

The paper introduces NSI, an objective measure to assess data quality for precision functional MRI, aligning with expert evaluations and enabling principled decisions on data sufficiency.

This study shows that aperiodic exponents from intracranial EEG can differentiate depression symptom severity and correlate with specific brain regions and networks.

This paper shows that brain networks can be precisely mapped within individuals using only task data, producing results similar to resting-state data.

The paper identifies cortical regions linked to the ventral striatum (VS) using functional connectivity MRI, showing its preferential correlation with the Salience network including dorsolateral prefrontal cortex.

The paper maps the human cerebellum using functional MRI to show functionally specialized regions within a megacluster, dissociating cognitive control from domain-specific processing.

The paper identifies distinct functional zones within the caudate nucleus that correspond to different cerebral association networks, revealing specialized circuits within the basal ganglia.

The paper introduces a toolbox to generate functional and genetic annotations for neuroimaging data, demonstrating its utility in interpreting brain regions and psychiatric disorders.