2016
DOI: 10.1371/journal.pone.0163875
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Discriminative Analysis of Migraine without Aura: Using Functional and Structural MRI with a Multi-Feature Classification Approach

Abstract: Magnetic resonance imaging (MRI) is by nature a multi-modality technique that provides complementary information about different aspects of diseases. So far no attempts have been reported to assess the potential of multi-modal MRI in discriminating individuals with and without migraine, so in this study, we proposed a classification approach to examine whether or not the integration of multiple MRI features could improve the classification performance between migraine patients without aura (MWoA) and healthy c… Show more

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Cited by 44 publications
(41 citation statements)
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“…In meta-analyses, the prefrontal cortex was identified as being the most crucial brain area associated with structural change in migraine [ 32 , 33 ]. A discriminative analysis of migraine without aura (vs. controls) also identified, using a machine learning classifier, the superior frontal gyrus as one of the most discriminative GM features [ 34 ]. The present finding of a lower prefrontal GM volume in patients with migraine has three clinical implications.…”
Section: Discussionmentioning
confidence: 99%
“…In meta-analyses, the prefrontal cortex was identified as being the most crucial brain area associated with structural change in migraine [ 32 , 33 ]. A discriminative analysis of migraine without aura (vs. controls) also identified, using a machine learning classifier, the superior frontal gyrus as one of the most discriminative GM features [ 34 ]. The present finding of a lower prefrontal GM volume in patients with migraine has three clinical implications.…”
Section: Discussionmentioning
confidence: 99%
“…Upon comparing the results obtained using the two different CNNS, we found that the Inception module-based CNN shows a better performance than the AlexNet-based CNN. Compared with the support vector machine classifier that we previously analyzed (a final classification accuracy of 83.67%) [ 20 ], our approach provides preliminary support for deep learning methods combined with the fMRI features as a method for improving the discriminative power for migraine. In fact, we obtained an accuracy as high as 99.25% when using the RFCS feature in deep learning-based frameworks.…”
Section: Discussionmentioning
confidence: 99%
“…The first model we used was the CNN network based on AlexNet [ 21 ], and the second model was the CNN with Google’s Inception module. As a lot of information can be lost when decomposing 4D rs-fMRI data into 2D data, many fMRI studies use feature mapping instead of raw data as the original input [ 17 , 20 ]. Thus, after preprocessing, we extracted three indices—ALFF, ReHo and RFCS—as inputs to improve the classification results for migraine patients.…”
Section: Introductionmentioning
confidence: 99%
“…Similar changes in the reward circuitry had been observed before in episodic migraine patients, during the course of spontaneous migraine headache attacks ( DaSilva et al, 2014 ). However, in that case, instead of the NAc, the ÎĽ-opioid activation, represented by a decreased availability of ÎĽ-opioid receptors was found in the mPFC ( Figure 3 ), suggesting the possible contribution of this area to the migraine pathophysiology ( Zhang et al, 2016 ). The results of the two aforementioned studies also denote that specific regions within the reward system could be active by different chronic pain syndromes, though many other limbic regions that not the PFC (e.g., insula and amygdala) have been related to migraine ( Stankewitz and May, 2011 ).…”
Section: Plastic Changes In the Reward System Related To Chronic Painmentioning
confidence: 92%