Accurate Classification of Chronic Migraine via Brain Magnetic Resonance Imaging

被引:102
|
作者
Schwedt, Todd J. [1 ]
Chong, Catherine D. [1 ]
Wu, Teresa [2 ]
Gaw, Nathan [2 ]
Fu, Yinlin [2 ]
Li, Jing [2 ]
机构
[1] Mayo Clin, Dept Neurol, Phoenix, AZ 85054 USA
[2] Arizona State Univ, Sch Comp, Informat, Decis Syst Engn, Phoenix, AZ USA
来源
HEADACHE | 2015年 / 55卷 / 06期
基金
美国国家科学基金会;
关键词
migraine; cortical thickness; cortical surface area; diagnostic classifier; magnetic resonance imaging; STATE FUNCTIONAL CONNECTIVITY; RESTING-STATE; NETWORK CONNECTIVITY; PAIN; ABNORMALITIES; SEGMENTATION; RESPONSES; ATTACKS; MODELS; CORTEX;
D O I
10.1111/head.12584
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Background.-The International Classification of Headache Disorders provides criteria for the diagnosis and subclassification of migraine. Since there is no objective gold standard by which to test these diagnostic criteria, the criteria are based on the consensus opinion of content experts. Accurate migraine classifiers consisting of brain structural measures could serve as an objective gold standard by which to test and revise diagnostic criteria. The objectives of this study were to utilize magnetic resonance imaging measures of brain structure for constructing classifiers: (1) that accurately identify individuals as having chronic vs episodic migraine vs being a healthy control; and (2) that test the currently used threshold of 15 headache days/month for differentiating chronic migraine from episodic migraine. Methods.-Study participants underwent magnetic resonance imaging for determination of regional cortical thickness, cortical surface area, and volume. Principal components analysis combined structural measurements into principal components accounting for 85% of variability in brain structure. Models consisting of these principal components were developed to achieve the classification objectives. Tenfold cross validation assessed classification accuracy within each of the 10 runs, with data from 90% of participants randomly selected for classifier development and data from the remaining 10% of participants used to test classification performance. Headache frequency thresholds ranging from 5-15 headache days/month were evaluated to determine the threshold allowing for the most accurate subclassification of individuals into lower and higher frequency subgroups. Results.-Participants were 66 migraineurs and 54 healthy controls, 75.8% female, with an average age of 36 +/- 11 years. Average classifier accuracies were: (1) 68% for migraine (episodic + chronic) vs healthy controls; (2) 67.2% for episodic migraine vs healthy controls; (3) 86.3% for chronic migraine vs healthy controls; and (4) 84.2% for chronic migraine vs episodic migraine. The classifiers contained principal components consisting of several structural measures, commonly including the temporal pole, anterior cingulate cortex, superior temporal lobe, entorhinal cortex, medial orbital frontal gyrus, and pars triangularis. A threshold of 15 headache days/month allowed for the most accurate subclassification of migraineurs into lower frequency and higher frequency subgroups. Conclusions.-Classifiers consisting of cortical surface area, cortical thickness, and regional volumes were highly accurate for determining if individuals have chronic migraine. Furthermore, results provide objective support for the current use of 15 headache days/month as a threshold for dividing migraineurs into lower frequency (ie, episodic migraine) and higher frequency (ie, chronic migraine) subgroups.
引用
收藏
页码:762 / 777
页数:16
相关论文
共 50 条
  • [21] Efficient brain tumor detection and classification using magnetic resonance imaging
    Sundarasekar, Revathi
    Appathurai, Ahilan
    BIOMEDICAL PHYSICS & ENGINEERING EXPRESS, 2021, 7 (05):
  • [22] A volumetric magnetic resonance imaging study in migraine
    Laila Elmously Naguib
    Ghada Saed Abdel Azim
    Mohammed Abdelrazek Abdellatif
    The Egyptian Journal of Neurology, Psychiatry and Neurosurgery, 57
  • [23] 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.
    HEADACHE, 2017, 57 (07): : 1051 - 1064
  • [24] Relation between migraine pattern and white matter hyperintensities in brain magnetic resonance imaging
    Negm M.
    Housseini A.M.
    Abdelfatah M.
    Asran A.
    The Egyptian Journal of Neurology, Psychiatry and Neurosurgery, 54 (1)
  • [25] Value of Patient-Directed Brain Magnetic Resonance Imaging Scan with a Diagnosis of Migraine
    Mullally, William J.
    Hall, Kathryn E.
    AMERICAN JOURNAL OF MEDICINE, 2018, 131 (04) : 438 - 441
  • [26] Magnetic resonance imaging of brain function and neurochemistry
    Ugurbil, K
    Kim, DS
    Duong, T
    Hu, XP
    Ogawa, S
    Gruetter, R
    Chen, W
    Kim, SG
    Zhu, XH
    Yacoub, E
    Van de Moortele, PF
    Shmuel, A
    Pfeuffer, J
    Merkle, H
    Andersen, P
    Adriany, G
    PROCEEDINGS OF THE IEEE, 2001, 89 (07) : 1093 - 1106
  • [27] Response prediction for chronic migraine preventive treatment by gray matter morphometry in magnetic resonance imaging: a pilot study
    Planchuelo-Gomez, Alvaro
    Garcia-Azorin, David
    Guerrero-Peral, Angel L.
    Aja-Fernandez, Santiago
    Anton-Juarros, Saray
    de Luis-Garcia, Rodrigo
    REVISTA DE NEUROLOGIA, 2020, 71 (11) : 399 - 406
  • [28] Investigations of functional and structural changes in migraine with aura by magnetic resonance imaging
    Hougaard, Anders
    DANISH MEDICAL JOURNAL, 2015, 62 (08):
  • [29] MAGNETIC-RESONANCE-IMAGING IN MIGRAINE AND TENSION-TYPE HEADACHE
    DEBENEDITTIS, G
    LORENZETTI, A
    SINA, C
    BERNASCONI, V
    HEADACHE, 1995, 35 (05): : 264 - 268
  • [30] MICROINFARCTION IN CLASSIC MIGRAINE - A STUDY WITH MAGNETIC-RESONANCE-IMAGING FINDINGS
    FERBERT, A
    BUSSE, D
    THRON, A
    STROKE, 1991, 22 (08) : 1010 - 1014