Arjun Ramakrishnan

State-dependent aperiodic EEG dynamics track cortical network reorganization in chronic epilepsy

This study examines how aperiodic EEG dynamics change in chronic epilepsy, showing they reflect both network reorganization and waveform shape effects across different brain states.

Effects of dopamine and serotonin on persistence for delayed rewards in humans

This study investigates how dopamine and serotonin affect persistence for delayed rewards in humans, finding that serotonin increases persistence while dopamine has mixed effects.

Trait anxiety drives premature disengagement despite intact opportunity-cost sensitivity

The paper shows that trait anxiety leads to premature disengagement from tasks despite normal sensitivity to opportunity costs, identified through drift diffusion modeling and controlled for physiological markers.

Attenuation of value-to-evidence translation drives biased decision making in anxiety and depression

The paper shows that anxiety and depression reduce value sensitivity during decision evidence accumulation, leading to biased decision making.

Optimizing electrode placement and information capacity for local field potentials in cortex

This paper introduces tools to optimize electrode placement and maximize information capacity for local field potentials in the cortex using subject-specific modeling.

Anxiety Selectively Impairs Reward Learning Under Uncertainty, While N3 Sleep Recalibrates It

The paper shows high trait anxiety impairs reward learning in noisy environments but not volatile ones, and N3 sleep improves learning and evidence accumulation.

Environmental Conflict Modulates Pavlovian Bias

This paper shows that environmental conflict reduces Pavlovian avoidance and increases instrumental choice, especially in individuals with high trait anxiety.

Decomposing Loss Aversion from Gaze Allocation and Pupil Dilation

This study decomposes loss aversion into valuation and response biases using computational modeling and physiological biomarkers like gaze and pupil dilation.

A machine learning approach to identifying objectivebiomarkers of anxiety and stress

This study uses machine learning to identify objective biomarkers of anxiety and stress using physiological and self-report data.