Christopher C. Cline

SafeLens uses nonlinear machine learning with statistical control to detect subtle, trial-varying neural dynamics in event-locked activity, outperforming traditional methods in sensitivity and specificity.

The paper identifies and mitigates a step-drift artifact in TMS-EEG recordings through a processing pipeline involving polynomial detrending and modified filtering.

This paper uses TMS-EEG to show that prefrontal cortex excitability changes with cognitive load during a multi-source interference task, linking it to theta power and showing region-specific effects.

This study shows that TMS to the dlPFC modulates single-neuron activity in humans, with facilitation of inhibitory firing and suppression of excitatory drive in specific brain networks.

The paper identifies distinct neural mechanisms for encoding rapid facial expressions and sustained mood states using multimodal data from epilepsy patients.

This paper characterizes human prefrontal cortex excitability using noninvasive TMS and EEG, finding sigmoidal input-output curves in 57% of participants and strong correlations between TMS-evoked potentials and motor-evoked potentials.

NaviNIBS provides open-source software for neuronavigated noninvasive brain stimulation, offering tools for imaging data import, target planning, and integration with robotic systems.

This study shows that sensory entrained iTBS (se-iTBS) significantly enhances motor-evoked potentials compared to standard iTBS, with effects lasting at least 30 minutes.

This study introduces seTMS, a method that enhances motor cortex excitability by synchronizing TMS pulses with sensorimotor alpha rhythms using musical rhythms, resulting in larger motor-evoked potentials.

The paper shows scalp EEG can predict intracranial brain activity in humans, with accuracy varying by region, depth, and frequency.

The paper maps stimulus-response patterns in the human cortex using TMS-EEG and relates them to whole-brain neuroimaging measures from a single densely-sampled individual.

This paper develops a real-time closed-loop optimization method to enhance TMS-EEG measurements of prefrontal cortex excitability by reducing artifacts and increasing signal magnitude.

This study evaluates the reliability of TMS-evoked potentials in the dorsolateral prefrontal cortex across different anatomical targets and analytical parameters.

This study investigates how TMS trains affect neural activity in the human prefrontal cortex using noninvasive measurements and finds no evidence of amplitude changes but detects non-local and phase-based neural effects.

The paper investigates how TMS location and angle affect early TEPs in the dlPFC and identifies optimal targets for enhancing EL-TEP responses.

This study shows that mindfulness-based stress reduction improves BCI performance by enhancing control over alpha band neural activity during intentional rest.