Biology subjects

Humaidan, D.

Publications and source records attributed to Humaidan, D..

4 recordsLinked to original sources

bioRxiv2026

Brain-State-Resolved Consistency of Corticospinal Responses with EEG-TMS

BackgroundTranscranial magnetic stimulation (TMS) over the primary motor cortex (M1) elicits motor-evoked potentials (MEPs), a neurophysiological marker of corticospinal excitability. Ongoing brain activity at the time of stimulation, such as the phase and power of the sensorimotor mu rhythm (8-13 Hz), has a significant impact on MEP amplitudes. However, it remains unclear whether these endogenous excitability states also influence the consistency of MEP amplitudes across repeated trials. ObjectivesWe investigated whether instantaneous mu dynamics modulate not only the magnitude but also the consistency of corticospinal responses to TMS. MethodsTwenty-nine healthy participants received 1200 single TMS pulses over the left M1 during simultaneous EEG recording. Trials were stratified based on pre-stimulus mu power, phase, and interhemispheric M1-M1 functional connectivity. Brain-state-resolved MEP variability was quantified using the coefficient of variation (CV) within subsets of trials defined by similar pre-stimulus mu dynamics. ResultsTrial subsets characterized by high mu power or high M1-M1 functional connectivity were associated with reduced MEP variability, indicating more consistent corticospinal output. In contrast, the mu phase did not significantly influence response consistency. Brain-state-resolved MEP variability showed greater stability across sessions compared to MEP variability estimated from random trial subsampling. ConclusionsPre-stimulus mu dynamics shape not only magnitude but also consistency of corticospinal responses to TMS. We show that corticospinal response consistency reflects a structured, brain-state-dependent property of the sensorimotor network. These findings contribute to our mechanistic understanding of brain-state-dependent neuromodulation and may be leveraged to reduce variability and improve efficacy to TMS. HighlightsO_LIOngoing sensorimotor mu dynamics shape both magnitude and consistency of MEPs. C_LIO_LITrial subsets characterized by high mu power were associated with reduced MEP variability. C_LIO_LIMu phase modulated MEP amplitude but did not influence MEP consistency. C_LIO_LIBrain-state-resolved estimates of MEP variability were more reliable across sessions. C_LIO_LIFuture TMS protocols may reduce effect variability by targeting stable excitability states. C_LI

neuroscience↗

bioRxiv2025

Diazepam alters the shape of alpha oscillations recorded from human cortex using EEG

While neural oscillations are conventionally assessed via their frequency, power and phase, developing literature suggests that their shape also provides neurophysiological and functional information. However, the extent to which the shape of oscillations recorded non-invasively in humans index specific brain processes remains unclear. This study implemented a pharmaco-EEG approach to begin addressing this limitation. In 21 healthy adults, resting-state EEG data was collected before and after placebo or diazepam, a positive allosteric modulator of type A {gamma}-aminobutyric acid (GABAA) receptors. The shape of individual cycles in the alpha band was then derived using empirical mode decomposition, followed by extraction of principal components (PCs) describing specific facets of alpha shape. Results of this approach show that all shape features were unchanged following placebo. In contrast, diazepam was associated with complex changes in several shape features, including peak-trough shape and edge speed. While changes in shape were apparent in all cortical lobes, the strongest alterations were specific to sensorimotor and parietal cortices. Taken together, our results support the neurophysiological utility of waveform shape, particularly with respect to non-invasive human recordings. Furthermore, the regional specificity of effects highlights the need for more granular exploration of waveform diversity.

neuroscience↗

bioRxiv2025

Predictive modeling of TMS-evoked responses: Unraveling instantaneous excitability states

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) and electromyography (EMG) provides a unique window into instantaneous cortical and corticospinal excitability states. We investigated 50 healthy participants to determine how fluctuations in pre-stimulus brain activity influence single-trial TMS-evoked potentials (TEPs) and motor-evoked potentials (MEPs). We developed a novel automated source-level TEP extraction method using individualized spatiotemporal priors that is robust against poor single-trial signal-to-noise ratios (SNRs) and ongoing oscillations. TEP and MEP amplitudes were predicted with linear mixed-effects models based on pre-stimulation EEG band-powers (theta to gamma), while accounting for temporal drifts (within-session trends), coil control, and inter-subject differences. We found that higher pre-stimulus sensorimotor alpha, beta, and gamma power were each associated with larger TEPs, indicating a more excitable cortical state. Increases in alpha and gamma power immediately before stimulation specifically predicted larger MEPs, reflecting increased corticospinal excitability. These results reveal relationships between ongoing oscillatory brain states and TMS response amplitudes, identifying EEG biomarkers of high- and low-excitability states. In conclusion, our study demonstrates the feasibility of single-trial source-level TMS-EEG analysis and shows that spontaneous alpha-, beta-, and gamma-band oscillations modulate motor cortical and corticospinal responsiveness. These findings pave the way for EEG-informed, brain-state-dependent TMS protocols to optimize neuromodulatory interventions in clinical and research applications.

neuroscience↗

bioRxiv2024

Decoding Motor Excitability in TMS using EEG-Features:An Exploratory Machine Learning Approach

BackgroundWith the burgeoning interest in personalized treatments for brain network disorders, closed-loop transcranial magnetic stimulation (TMS) represents a promising frontier. Relying on the real-time adjustment of stimulation parameters based on brain signal decoding, the success of this approach depends on the identification of precise biomarkers for timing the stimulation optimally. ObjectiveWe aimed to develop and validate a supervised machine learning framework for the individualized prediction of motor excitability states, leveraging a broad spectrum of sensor and source space EEG features. MethodsOur approach integrates multi-scale EEG feature extraction and selection within a nested cross-validation scheme, tested on a cohort of 20 healthy participants. We assessed the frameworks performance across different classifiers, feature sets, and experimental protocols to ensure robustness and generalizability. ResultsPersonalized classifiers demonstrated a statistically significant mean predictive accuracy of 72 {+/-} 11%. Consistent performance across various testing conditions highlighted the sufficiency of sensor-derived features for accurate excitability state predictions. Subtype analysis revealed distinct clusters linked to specific brain regions and oscillatory features as well as the need for a more extensive feature set for effective biomarker identification than conventionally considered. ConclusionsOur machine learning framework effectively identifies predictive biomarkers for motor excitability, holding potential to enhance the efficacy of personalized closed-loop TMS interventions. While the clinical applicability of our findings remains to be validated, the consistent performance across diverse testing conditions and the efficacy of sensor-only features suggest promising avenues for clinical research and wider applications in brain signal classification.

neuroscience↗