What We Gain From Machine Learning Studies in Headache Patients

Abstract

Primary headache disorders, such as migraine and cluster headache, are among the most prevalent and debilitating neurological diseases worldwide (1). An increasing recognition of the importance of these diseases has led to a growing interest in understanding their pathophysiology and developing new treatments. From the once popular Vascular theory that described primary headaches as vascular disorders, the field has now moved to the Neuronal theories involving either the peripheral or central nervous system, or both (2). It is now recognized that primary headaches are not simply a disease of recurrent pain attacks but a complex and multifaceted brain disorder. There is evidence that in predisposed headache patients various cortical, subcortical, and brainstem regions are activated, and key neuropeptides are released during the headache attack (3). Neuroimaging techniques have made a tremendous contribution to our understanding of headache pathophysiology, providing insights into human brain networks that might account for the pain and the broad symptomatology characterizing the headache attacks. The brainstem, including the trigeminovascular pathway, thalamus and hypothalamus seem to have a pivotal role in triggering the migraine and cluster headache attacks. Widespread structural and functional alterations in multisensory processing brain areas have also been shown in both conditions during the interictal and ictal phase (4).A better understanding of the mechanisms responsible for the generation of the headache attacks allows the identification of novel therapeutic targets. In conjunction with progress in theories of the pathophysiology of primary headaches, the understanding of the mechanisms of action of acute and preventive treatments for migraine and cluster headache has evolved. A few neuroimaging studies have explored the therapeutic effects of pharmacological and non-pharmacological therapeutic approaches commonly used against migraines and cluster headaches, suggesting a potential central mechanism of action of these therapies (5-7).Although much progress has been made in the understanding of migraine and cluster headache, there are still many unsolved questions to address. Many studies suggested that brain alterations in headache patients might change dynamically over time, since they differ according to the headache phase, frequency of attacks, and disease duration (8,9). However, some brain alterations are not influenced by the disease activity, suggesting that they might represent brain biomarkers that predispose to the disease (10, 11). Further unanswered questions are whether it is possible to identify a specific neuroimaging pattern for each different headache phenotype and if alterations in the function and structure of nociceptive brain areas are headache-specific or common to other chronic pain disorders. Moreover, imaging biomarkers that could predict treatment response of headache patients are scarce.A valuable strategy to reduce the unmet needs in the understanding of primary headaches is to...

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The Spontaneous Activity Pattern of the Middle Occipital Gyrus Predicts the Clinical Efficacy of Acupuncture Treatment for Migraine Without Aura

“…Based on the machine learning technologies and neuroimaging markers, a large number of studies have been conducted on efficacy prediction in recent years ( 12 – 15 ). These studies illustrated that the baseline functional magnetic resonance imaging (fMRI) and the structural MRI properties contributed significant information for the prediction of post-intervention symptom relief.…”

Artificial Intelligence-Aided Headache Classification Based on a Set of Questionnaires: A Short Review

“…Identification of focal cortical dysplasia, the evolvement of neuroimaging biomarkers in Alzheimer’s and prognosticate clinical consequences in depression therapeutics undoubtedly prove AI’s success and expanding boundaries [ 7 ]. With regards to migraine and cluster headaches, there can be a functional variation in terms of activation of separate structures in the brain, namely the trigeminovascular system, brainstem, hypothalamus and cortical areas.…”

“…There could be an adjoining structural alteration of cortical thickness and its surface area. Perhaps, these changes could be related to ictal or interictal phases, and may be dynamic in nature [ 7 ]. The use of ML algorithms in functional and morphometric MRI analysis helps to distinguish these headache features [ 7 ].…”

“…Perhaps, these changes could be related to ictal or interictal phases, and may be dynamic in nature [ 7 ]. The use of ML algorithms in functional and morphometric MRI analysis helps to distinguish these headache features [ 7 ]. It is appropriate to mention here that AI has invested a decade’s drudgery in recognizing just one headache disorder: migraine [ 4 ].…”