Migraine Subclassification via a Data‐Driven Automated Approach Using Multimodality Factor Mixture Modeling of Brain Structure Measurements

Schwedt, Todd J.; Si, Bing; Li, Jing; Wu, Teresa; Chong, Catherine D.

doi:10.1111/head.13121

Cited by 22 publications

(18 citation statements)

References 50 publications

(115 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Others have reported an association between allodynia and certain subgroups of migraine. 6,27 Given the association between allodynia and central sensitization, 28 it might be expected that allodynia would attenuate HRs for members of classes with pain comorbidities (i.e., respiratory/pain and pain classes). While allodynia attenuated the HR, to a degree, the magnitude of the effect was relatively uniform across comorbidity classes.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Migraine progression in subgroups of migraine based on comorbidities

Lipton

Fanning²,

Buse

et al. 2019

Neurology

View full text Add to dashboard Cite

ObjectiveTo test the hypothesis that statistically defined subgroups of migraine (based on constellations of comorbidities and concomitant conditions; henceforth comorbidities), previously identified using Chronic Migraine Epidemiology and Outcomes (CaMEO) Study data, differ in prognosis, as measured by rates of progression from episodic migraine (EM) to chronic migraine (CM).MethodsThe onset of CM was assessed up to 4 times over 12 months in individuals with EM and ≥1 comorbidity at baseline, based on constellations of comorbidities (comorbidity classes). The “fewest comorbidities” class served as reference. Individuals completing ≥1 follow-up survey from the web-based CaMEO Study were included. Covariates included sociodemographic variables and headache characteristics. Sex, income, cutaneous allodynia, and medication overuse were modeled as binary variables; age, body mass index, headache-related disability (Migraine Disability Assessment [MIDAS]), and Migraine Symptom Severity Scale as continuous variables. CM onset was assessed using discrete time analysis.ResultsIn the final sociodemographic model, all comorbidity classes had significantly elevated hazard ratios (HRs) for risk of progression to CM from EM, relative to fewest comorbidities. HRs for CM onset ranged from 5.34 (95% confidence interval [CI] 3.89–7.33; p ≤ 0.001) for most comorbidities to 1.53 (95% CI 1.17–2.01; p < 0.05) for the respiratory class. After adjusting for headache covariates independently, each comorbidity class significantly predicted CM onset, although HRs were attenuated.ConclusionsSubgroups of migraine identified by comorbidity classes at cross-section predicted progression from EM (with ≥1 comorbidity at baseline) to CM. The relationship of comorbidity group to CM onset remained after adjusting for indicators of migraine severity, such as MIDAS.Clinicaltrials.gov identifierNCT01648530.

show abstract

Section: Discussionmentioning

confidence: 99%

“…2 Increased headache day frequency, medication overuse, depression, and cutaneous allodynia are associated with progression to CM. 3,4 MRI 5,6 and gene association studies 1 have previously identified subgroups of migraine with features predisposing to progression to CM.…”

mentioning

confidence: 99%

Migraine progression in subgroups of migraine based on comorbidities

Lipton

Fanning²,

Buse

et al. 2019

Neurology

View full text Add to dashboard Cite

show abstract

“…Recent machine learning studies have focused on the diagnosis of migraine. Machine learning algorithms based on brain resting state functional magnetic resonance imaging (MRI), or morphometric MRI data have been used to identify brain signatures that discriminate migraine patients from controls (17)(18)(19). The functional connectivity of brain regions involved with processing the affective components of pain, like the insula, amygdala, temporal, and frontal lobes, discriminated migraine patients from controls with an accuracy rate of 86%.…”

mentioning

confidence: 99%

“…The altered patterns of functional connections that distinguish migraine patients from controls could represent migraine biomarkers that are further reinforced by recurrent pain (17). On the other hand, an unsupervised machine learning approach was not able to clearly separate migraineurs from healthy controls based upon brain morphometric measures (19). An improvement in classification performance in migraine identification can be achieved integrating functional and structural imaging metrics that disclose complementary information regarding the underlying biological processes (20).…”

mentioning

confidence: 99%

“…A data-driven classification study identified two subgroups of migraine patients based upon their brain structures, with one subgroup having longer disease duration, higher migraine-related disability and more severe allodynia symptoms during migraine attacks. Thus, highlighting the role of machine learning models in identifying migraine patients with different disease courses (19). Future machine learning studies combining clinical, structural, and functional imaging measures will be valuable for identifying episodic migraine patients who are at risk of evolving into a chronic form.…”

mentioning

confidence: 99%

See 1 more Smart Citation

What We Gain From Machine Learning Studies in Headache Patients

Messina

Filippi

2020

Front. Neurol.

View full text Add to dashboard Cite

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...

show abstract