A state-of-the-art review of functional magnetic resonance imaging technique integrated with advanced statistical modeling and machine learning for primary headache diagnosis

Frontiers in Human Neuroscience

Neuroscience

Ming-Lin Li¹  Yi-Fei Wang¹  Han-Yong Luo¹  Zi-Wei Quan¹  Jia-He Wang¹  Fei Zhang¹  Yi-Yang Chen²  Le-Tian Huang³ 

[1] Department of Family Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China;Department of Family Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China;Department of Family Medicine, Liaoning Health Industry Group Fukuang General Hospital, Fushun, Liaoning, China;Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China;

关键词: functional magnetic resonance imaging;    pathophysiology;    machine learning;    statistical modeling;    primary headaches;    diagnosis;   

DOI  :  10.3389/fnhum.2023.1256415

 received in 2023-07-10, accepted in 2023-08-14,  发布年份 2023

来源: Frontiers

【 摘 要 】

Primary headache is a very common and burdensome functional headache worldwide, which can be classified as migraine, tension-type headache (TTH), trigeminal autonomic cephalalgia (TAC), and other primary headaches. Managing and treating these different categories require distinct approaches, and accurate diagnosis is crucial. Functional magnetic resonance imaging (fMRI) has become a research hotspot to explore primary headache. By examining the interrelationships between activated brain regions and improving temporal and spatial resolution, fMRI can distinguish between primary headaches and their subtypes. Currently the most commonly used is the cortical brain mapping technique, which is based on blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI). This review sheds light on the state-of-the-art advancements in data analysis based on fMRI technology for primary headaches along with their subtypes. It encompasses not only the conventional analysis methodologies employed to unravel pathophysiological mechanisms, but also deep-learning approaches that integrate these techniques with advanced statistical modeling and machine learning. The aim is to highlight cutting-edge fMRI technologies and provide new insights into the diagnosis of primary headaches.

【 授权许可】

Unknown   
Copyright © 2023 Li, Zhang, Chen, Luo, Quan, Wang, Huang and Wang.