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Review
.2023 Sep 1:17:1256415.
doi: 10.3389/fnhum.2023.1256415. eCollection 2023.

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

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Review

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

Ming-Lin Li et al. Front Hum Neurosci..

Abstract

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.

Keywords: diagnosis; functional magnetic resonance imaging; machine learning; pathophysiology; primary headaches; statistical modeling.

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

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

FIGURE 1
FIGURE 1
Literature search flow chart.
FIGURE 2
FIGURE 2
Trends over time for all articles included in this review.
FIGURE 3
FIGURE 3
The basic process of functional magnetic resonance imaging combined with machine learning for the diagnosis of primary headache.
FIGURE 4
FIGURE 4
Functional magnetic resonance imaging (FMRI) studies reveal recurrent brain areas in migraine patients.
FIGURE 5
FIGURE 5
Changes in functional connectivity in the insular in migraine patients.
FIGURE 6
FIGURE 6
Changes in functional connectivity in the brainstem in migraine patients.
FIGURE 7
FIGURE 7
Changes in functional connectivity in the limbic system in migraine patients.
FIGURE 8
FIGURE 8
Changes in functional connectivity in the hypothalamus in migraine patients.
FIGURE 9
FIGURE 9
Changes in functional connectivity in the thalamus in migraine patients.
FIGURE 10
FIGURE 10
Machine learning and advanced statistical modeling of fMRI in migraine diagnosis. Adapted from(A)Yang et al. (2018),(B)Sun et al. (2023), and(C)Li et al. (2022b), respectively.
FIGURE 11
FIGURE 11
Changes in the functional connectivity of the hypothalamus in patients with cluster headache.
See this image and copyright information in PMC

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The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by National Key Research and Development Program of China (grant numbers 2018YFC2002100 and 2018YFC2002104) and by Key Research and Development Program of Liaoning Province (Grant Number 2019JH8/10300021).

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