Brian Litt

This study develops an on-demand seizure induction method in chronically epileptic mice to improve therapeutic screening for epilepsy.

This study validates phase synchronization as a biomarker for antiseizure medication load using intracranial EEG in epileptic patients.

This study shows white matter signals during seizures reflect communication between brain regions and differentiate epilepsy pathophysiologies, challenging past assumptions about their limited value.

This paper uses HAVOK analysis to model nonlinear seizure dynamics and improves multi-day seizure risk forecasting by identifying short-period forcings.

This study identifies clinical features preceding genetic epilepsy diagnosis in EMR data from 32,000 individuals, showing early signs can inform earlier treatment.

The paper develops deep learning algorithms to quantify seizure spread beyond onset zones and finds associations with structural connectivity and surgical outcomes.

The paper shows that external inputs affect BOLD signal non-stationarity and presents a method to estimate these inputs using in silico models and fMRI data.

The paper proposes a framework for selecting brain atlases to maximize their descriptive, explanatory, and predictive validity in studying neurological function and epilepsy.

This study links cognitive map formation during sequence learning to neural activity in the temporal lobe using behavioral modeling and iEEG recordings.

This paper introduces transparent graphene microelectrodes to simultaneously record microelectrocorticography and calcium fluorescence in vivo, revealing spatiotemporal seizure dynamics through non-negative matrix factorization.

This paper introduces a dynamical systems framework to estimate both intrinsic brain activity and external stimuli, showing task-related changes and non-stationarities in cortical dynamics.

The paper uses a pairwise maximum entropy model to analyze intracranial EEG data and shows it captures neural dynamics and identifies white matter connections.

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.

This study quantifies structure-function coupling between white matter connections and iEEG in epilepsy patients, finding increased coupling during seizures and its dependence on short-range structural connections.

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.

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.