ArticleHuman brain mapping2020
Neuroimaging features of whole-brain functional connectivity predict attack frequency of migraine.
Article in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 27 citations in OpenAlex.
- Abnormal Regional Brain Functional Activity and Brain Network Connectivity in Primary Trigeminal Neuralgia Patients: An Activation Likelihood Estimation Meta-Analysis Based on Resting-State fMRI.Journal of pain research · 2026Review
- Combining functional, structural, and morphological networks for multimodal classification of developing autistic brains.Brain imaging and behavior · 2025Article
- Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members' vision - part 1.The journal of headache and pain · 2024Review
- Migraine aura discrimination using machine learning: an fMRI study during ictal and interictal periods.Medical & biological engineering & computing · 2024Article
- Article
- Brain connectome-based imaging markers for identifiable signature of migraine with and without aura.Quantitative imaging in medicine and surgery · 2024Article
- Healthy core: Harmonizing brain MRI for supporting multicenter migraine classification studies.PloS one · 2024Article
- Evaluation of a Clustering Approach to Define Distinct Subgroups of Patients With Migraine to Select Electroacupuncture Treatments.Neurology · 2023Article
- Artificial intelligence-directed acupuncture: a review.Chinese medicine · 2022Review
- Circulating exosomal microRNA profiles in migraine patients receiving acupuncture treatment: A placebo-controlled clinical trial.Frontiers in molecular neuroscience · 2022Article
- Disrupted Functional Connectivity of the Amygdala Predicts the Efficacy of Non-steroidal Anti-inflammatory Drugs in Migraineurs Without Aura.Frontiers in molecular neuroscience · 2022Article
- Functional Connectivity Features of Resting-State Functional Magnetic Resonance Imaging May Distinguish Migraine From Tension-Type Headache.Frontiers in neuroscience · 2022Article
- Identifying Alcohol Use Disorder With Resting State Functional Magnetic Resonance Imaging Data: A Comparison Among Machine Learning Classifiers.Frontiers in psychology · 2022Article
- Predicting response to tVNS in patients with migraine using functional MRI: A voxels-based machine learning analysis.Frontiers in neuroscience · 2022Article
- Altered static functional network connectivity predicts the efficacy of non-steroidal anti-inflammatory drugs in migraineurs without aura.Frontiers in molecular neuroscience · 2022Article
- Prediction and associated factors of non-steroidal anti-inflammatory drugs efficacy in migraine treatment.Frontiers in pharmacology · 2022Article
- Interindividual Signatures of fMRI Temporal Fluctuations.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- Neuroimaging features of whole-brain functional connectivity predict attack frequency of migraine.Human brain mapping · 2020Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Migraine is a chronic neurological disorder characterized by attacks of moderate or severe headache accompanying functionally and structurally maladaptive changes in brain. As the headache days/month is often measured by patient self-report and tends to be overestimated than actually experienced, the possibility of using neuroimaging data to predict migraine attack frequency is of great interest. To identify neuroimaging features that could objectively evaluate patients' headache days, a total of 179 migraineurs were recruited from two data center with one dataset used as the training/test cohort and the other used as the validating cohort. The guidelines for controlled trials of prophylactic treatment of chronic migraine in adults were used to identify the frequency of attacks and migraineurs were divided into low (MOl) and high (MOh) subgroups. Whole-brain functional connectivity was used to build multivariate logistic regression models with model iteration optimization to identify MOl and MOh. The best model accurately discriminated MOh from MOl with AUC of 0.91 (95%CI [0.86, 0.95]) in the training/test cohort and 0.79 in the validating cohort. The discriminative features were mainly located within the limbic lobe, frontal lobe, and temporal lobe. Permutation tests analysis demonstrated that the classification performance of these features was significantly better than chance. Furthermore, the indicator of functional connectivity had a higher odds ratio than behavioral variables with implementing a holistic regression analysis. The current findings suggested that the migraine attack frequency could be distinguished by using machine-learning algorithms, and highlighted the role of brain functional connectivity in revealing underlying migraine-related neurobiology.
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