Migraine classification using magnetic resonance imaging resting-state functional connectivity data.
Background This study used machine-learning techniques to develop discriminative brain-connectivity biomarkers from resting-state functional magnetic resonance neuroimaging ( rs-fMRI) data that distinguish between individual migraine patients and healthy controls. Methods This study included 58 migr...
| Publicado en: | Cephalalgia Vol. 37; no. 9; pp. 828 - 845 |
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| Autores principales: | , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Aug2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=124747905&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124747905 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03331024 F05 jtl: Cephalalgia issn: 03331024 maglogo: Y pubinfo: dt: Aug2017 vid: 37 iid: 9 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 124747905 124747905 NLM27306407 124747905 10.1177/0333102416652091 NLM27306407 124747905 ppf: 828 ppct: 17 formats: tig: atl: Migraine classification using magnetic resonance imaging resting-state functional connectivity data. aug: au: Chong, Catherine D. Gaw, Nathan Yinlin Fu Jing Li Wu, Teresa Schwedt, Todd J. Fu, Yinlin Li, Jing affil: Mayo Clinic Arizona, Department of Neurology, Phoenix, AZ, USA sug: subj: Brain Mapping Methods Migraine Algorithms Magnetic Resonance Imaging Methods Female Migraine Classification Neural Pathways Male Migraine Physiopathology Neural Pathways Physiopathology Adult Human Adult: 19-44 years Female Male ab: Background This study used machine-learning techniques to develop discriminative brain-connectivity biomarkers from resting-state functional magnetic resonance neuroimaging ( rs-fMRI) data that distinguish between individual migraine patients and healthy controls. Methods This study included 58 migraine patients (mean age = 36.3 years; SD = 11.5) and 50 healthy controls (mean age = 35.9 years; SD = 11.0). The functional connections of 33 seeded pain-related regions were used as input for a brain classification algorithm that tested the accuracy of determining whether an individual brain MRI belongs to someone with migraine or to a healthy control. Results The best classification accuracy using a 10-fold cross-validation method was 86.1%. Resting functional connectivity of the right middle temporal, posterior insula, middle cingulate, left ventromedial prefrontal and bilateral amygdala regions best discriminated the migraine brain from that of a healthy control. Migraineurs with longer disease durations were classified more accurately (>14 years; 96.7% accuracy) compared to migraineurs with shorter disease durations (≤14 years; 82.1% accuracy). Conclusions Classification of migraine using rs-fMRI provides insights into pain circuits that are altered in migraine and could potentially contribute to the development of a new, noninvasive migraine biomarker. Migraineurs with longer disease burden were classified more accurately than migraineurs with shorter disease burden, potentially indicating that disease duration leads to reorganization of brain circuitry. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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