Journal of visualized experiments : JoVE · 2026
A Protocol For Uncovering Neural Mechanisms Of Neurotherapeutic Effects On Electroencephalography Using The Human Neocortical Neurosolver.
СтатьяVideo-Audio MediaПоддержка NIH: внешняя программаНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Tolley Nicholas, Zhou David W, Soplata Austin E, Daniels Dylan S, Duecker Katharina, Pujol Carolina Fernandez, Gao Joyce, Jones Stephanie R
Аннотация
Electroencephalography (EEG) and electrophysiological methods provide millisecond-resolution biomarkers for central nervous system disorders and are widely used to assess treatment-related effects. However, limited understanding of the neural mechanisms generating these biomarkers impedes the development of diagnostics and therapeutics based on these signals. The Human Neocortical Neurosolver (HNN) is an open-source biophysical modeling software that links localized EEG biomarkers to their multiscale neural generators. This protocol demonstrates a hypothesis-driven workflow using HNN to test neural mechanisms of neurotherapeutic-induced EEG biomarkers by optimizing model parameters to achieve a fit between simulated and empirical current source waveforms. Corresponding multiscale cell- and circuit-level activity can then be visualized and quantified, providing validation targets for model predictions in follow-up empirical studies. An example is provided demonstrating how to examine the neural mechanisms underlying early event-related potential components of an auditory evoked response (P1, N1, and P2), and to assess changes following neurotherapeutic-induced modifications in neural circuit activity. This protocol enables the design of simulation experiments to generate testable predictions linking EEG biomarkers to underlying neural circuit mechanisms. A similar workflow can be applied to study disease mechanisms or other therapeutic interventions.