Alik S. Widge

This study shows that transcranial magnetic stimulation (TMS) effects on cognitive control depend on the cognitive state during stimulation, enhancing outcomes when aligned with active cognitive tasks.

This paper develops a continuous rolling arena framework for optimizing deep brain stimulation using a multi-armed bandit algorithm to improve cognitive control in psychiatric conditions.

This study shows unilateral striatal deep brain stimulation improves cognitive control similarly to bilateral stimulation in rodents, matching human findings and highlighting potential clinical benefits.

This study identifies right dorsal prefrontal cortical pathways mediating cognitive control improvements from VCVS DBS.

The paper investigates sex differences in cognitive flexibility using a novel touchscreen Set Shift task in mice, finding females perform better with fewer errors and distinct computational strategies.

This study shows that optogenetic stimulation of mPFC axons, not midSTR neurons, enhances cognitive control in a rodent Set-Shift task, suggesting mPFC axons are key drivers of DBS therapeutic effects.

This study uses Bayesian Optimization to find optimal DBS parameters in rats for improving cognitive control, showing consistent effects across acute and chronic settings.

The paper identifies a neural mechanism for flexible cognitive control involving theta-phase coordination in the right anterior cingulate cortex and shows that internal capsule stimulation can enhance this mechanism.

This study investigates sex differences in avoidance strategies using a modified platform-mediated avoidance task in rats, finding females prioritize safety while males pursue rewards despite risk.

NeuroTest evaluates sensing-capable electrophysiology and neurostimulation systems through a low-cost, open-source benchtop platform to standardize testing and reduce variability across institutions.

The paper examines how a history of depressive symptoms in individuals with epilepsy affects fronto-temporo-limbic connectivity during cognitive conflict encoding using sEEG and DCM.

This paper decodes defensive behaviors from LFP signals in rats using feature selection and machine learning, finding that high-gamma power and inter-regional connectivity are most informative.

The paper develops online methods to track non-stationary neural oscillations and recover phase response curves (PRCs) and amplitude response curves (ARC) despite estimation errors and changing dynamics.

The paper develops an animal model of human brain stimulation therapy, showing it enhances cognitive control by improving the brain's ability to process conflicting evidence.

The paper validates a foraging task as a reliable measure of decision-making with clinical correlates in psychiatry.

This paper introduces a method using accelerometry integrated with electrophysiology headstages to quantify defensive behaviors, showing it correlates with video and bar press metrics but captures distinct aspects of behavior.

The paper develops biophysical models of neural dynamics from EEG data to identify biomarkers for depression subtyping, showing better classification than traditional EEG features.

The paper introduces TORTE, an open-source toolkit for real-time oscillatory tracking and estimation, enabling accurate phase and amplitude extraction for closed-loop experiments.

The paper examines how stimulation location relative to grey and white matter affects single pulse electrical stimulation responses in the human brain, finding location and parameter-specific effects.

This paper introduces a state space phase estimator for real-time neural phase tracking, showing improved performance over existing methods in simulations and in vivo data.

This study failed to replicate compulsive behavior in rats following optogenetic stimulation of the OFC-VMS circuit, unlike previous findings in mice.

This study uses state-space modeling and intracranial stimulation to modulate hidden emotional states during conflict resolution, showing bidirectional effects on behavior.

This paper introduces a statistical framework to assess cross-frequency coupling while accounting for confounding effects, showing improved performance in simulations and applications to in vivo data.