PDF(Pubmed)
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.
摘要：
原发性头痛是全球范围内非常常见且繁重的功能性头痛，可以归类为偏头痛，紧张型头痛（TTH），三叉神经自主性头痛（TAC），和其他原发性头痛。管理和处理这些不同的类别需要不同的方法，准确的诊断至关重要。功能磁共振成像(fMRI)已成为探讨原发性头痛的研究热点。通过检查激活的大脑区域之间的相互关系并提高时间和空间分辨率，fMRI可以区分原发性头痛及其亚型。目前最常用的是大脑皮层映射技术,这是基于血氧水平依赖性功能磁共振成像（BOLD-fMRI）。这篇综述揭示了基于fMRI技术的原发性头痛及其亚型的数据分析的最新进展。它不仅包括用于揭示病理生理机制的常规分析方法，还有将这些技术与高级统计建模和机器学习相结合的深度学习方法。目的是突出尖端的fMRI技术，并为原发性头痛的诊断提供新的见解。
