Ankit N. Khambhati

This study decodes chronic pain states from intracranial recordings and identifies network-specific neural signatures.

This paper develops a method to detect brain stimulation artifacts in iEEG using synthetic data and a ResNet classifier, achieving high accuracy on real data.

This study decodes naturalistic affective behaviors from multi-day intracranial recordings using classifiers, finding high accuracy in distinguishing positive, negative, and affectless behaviors.

The paper shows that dynamic changes in brain functional connectivity correlate with learning adjustments in uncertain environments, particularly increased integration between fronto-parietal and other systems.

This study investigates how brain activity and connectivity reorganize during learning, finding distinct patterns in early and later stages of value learning.

This study uses direct neural recordings to identify two subnetworks associated with depression, characterized by hypoconnectivity and hyperconnectivity in specific brain regions.

This study predicts ictal and preictal brain network dynamics in epilepsy using anatomical, connectivity, and genetic features, finding white matter plays a greater role in seizure dynamics than geometry or genetics.

The paper identifies cognitive control subgraphs in brain networks using fMRI data and shows their temporal expression relates to cognitive demands and task performance.

This study uses network control theory to predictably reconfigure brain network interactions through targeted neurostimulation in humans.

This paper uses network control theory to model how white matter tracts guide electrical stimulation in the brain, showing that real white matter structures better predict state transitions than artificial models.

This study uses machine learning to uncover dynamic subgraphs in brain networks, revealing how they constrain modular organization and explain inter-individual differences in network reconfiguration.

This paper reviews modeling approaches for dynamic brain networks and discusses statistical testing and interpretation of dynamic graph architecture.

The paper uses machine learning to analyze brain network dynamics in epilepsy patients, finding similar subgraph structures in seizure and baseline states and identifying predictive patterns for seizure onset.

The paper uses virtual cortical resection to identify push-pull control mechanisms that predict seizure evolution in epilepsy.