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Updated: Aug 27, 2025

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

Multimodal resting-state connectivity predicts affective neurofeedback performance.

Lucas R Trambaiolli¹, Raymundo Cassani², Claudinei E Biazoli^3,4

¹Basic Neuroscience Division, McLean Hospital-Harvard Medical School, Belmont, MA, United States.

Frontiers in Human Neuroscience

|September 26, 2022

Summary

This summary is machine-generated.

Resting-state functional connectivity measured with EEG and fNIRS can predict neurofeedback performance. These brain activity patterns are coupled, suggesting combined use for outcome prediction.

Keywords:

brain connectivity electroencephalography functional near-infrared spectroscopy neurofeedback resting-state

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Published on: June 20, 2012

Area of Science:

Neuroscience
Biomedical Engineering
Psychiatry

Background:

Neurofeedback shows promise as a complementary therapy for psychiatric disorders.
Predicting individual performance and outcomes in neurofeedback is crucial for treatment efficacy.

Purpose of the Study:

To investigate if resting-state functional connectivity predicts performance in an affective neurofeedback task.
To evaluate the correlation of predictive connectivity profiles between EEG and fNIRS.

Main Methods:

Functional connectivity modeling using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS).
Resting-state data (fNIRS oxyhemoglobin/deoxyhemoglobin, EEG beta-m-alpha/gamma-m-alpha) used to estimate connectivity.
Support Vector Regressor (SVR) models trained to predict neurofeedback performance based on connectivity summary scores (CSS).

Main Results:

The predictive model achieved a mean absolute error below 20%.
fNIRS oxyhemoglobin CSS significantly correlated with EEG gamma-m-alpha CSS (r = -0.456, p = 0.030).

Conclusions:

Pre-task resting-state electrophysiological and hemodynamic connectivity can predict neurofeedback performance.
Joint EEG-fNIRS connectivity serves as a potential outcome predictor and a tool for investigating functional connectivity coupling.