Effective Prediction of Neurofeedback based on Functional Connection Characteristics of Brain Network in Insomnia

Kai Li, Zhi Zou, Huan Zhang, Linyuan Wang, Ying Zeng, Fei Qi, Chi Zhang

2022

Abstract

Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-nf) is a new means of emotion regulation in insomnia, however, due to personal physiological and psychological differences, the effect of neurofeedback training on different patients is significantly different. Using brain imaging data to predict the curative effect is of great significance to improve the individual adaptability of clinical application of neurofeedback training, reduce the treatment cost and reduce the burden of patients. In this article, we raise a neurofeedback training effectiveness prediction method based on brain network functional connection. In this method, network connection matrices of the default mode network (DMN), salience network (SAN), executive control network (ECN), basal ganglia (BG), sensorimotor (SM) et.al. in insomnia are used as features to construct the prediction model, so as to predict the training effect of patients’ neurofeedback by using machine learning method. The experimental results through the cross validation of CatBoost model with leave one method show that, the prediction accuracy of whether an insomnia patient can benefit from emotion regulation method produced by rt-fMRI-nf is 75%. This method can initially provide a reference basis for insomnia patients to choose treatment methods.

Paper Citation

in Harvard Style

Li K., Zou Z., Zhang H., Wang L., Zeng Y., Qi F. and Zhang C. (2022). Effective Prediction of Neurofeedback based on Functional Connection Characteristics of Brain Network in Insomnia. In Proceedings of the 1st International Conference on Health Big Data and Intelligent Healthcare - Volume 1: ICHIH, ISBN 978-989-758-596-8, pages 151-156. DOI: 10.5220/0011235400003438

in Bibtex Style

@conference{ichih22,
author={Kai Li and Zhi Zou and Huan Zhang and Linyuan Wang and Ying Zeng and Fei Qi and Chi Zhang},
title={Effective Prediction of Neurofeedback based on Functional Connection Characteristics of Brain Network in Insomnia},
booktitle={Proceedings of the 1st International Conference on Health Big Data and Intelligent Healthcare - Volume 1: ICHIH,},
year={2022},
pages={151-156},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011235400003438},
isbn={978-989-758-596-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Health Big Data and Intelligent Healthcare - Volume 1: ICHIH,
TI - Effective Prediction of Neurofeedback based on Functional Connection Characteristics of Brain Network in Insomnia
SN - 978-989-758-596-8
AU - Li K.
AU - Zou Z.
AU - Zhang H.
AU - Wang L.
AU - Zeng Y.
AU - Qi F.
AU - Zhang C.
PY - 2022
SP - 151
EP - 156
DO - 10.5220/0011235400003438