{"id":24186,"date":"2020-02-10T21:27:02","date_gmt":"2020-02-10T21:27:02","guid":{"rendered":"https:\/\/wames.org.uk\/cms-english\/?p=24186"},"modified":"2020-02-10T21:27:48","modified_gmt":"2020-02-10T21:27:48","slug":"a-machine-learning-approach-to-the-differentiation-of-fmri-data-of-cfs-from-a-sedentary-control","status":"publish","type":"post","link":"https:\/\/wames.org.uk\/cms-english\/a-machine-learning-approach-to-the-differentiation-of-fmri-data-of-cfs-from-a-sedentary-control\/","title":{"rendered":"A machine learning approach to the differentiation of fMRI data of CFS from a sedentary control"},"content":{"rendered":"

A machine learning approach to the differentiation of Functional Magnetic Resonance Imaging data of Chronic Fatigue Syndrome (CFS) from a sedentary control,<\/a> by Destie Provenzano,\u00a0 Stuart D Washington and\u00a0 James N Baraniuk in<\/span> Front. Comput. Neurosci.,<\/em> 29 January 2020 [https:\/\/doi.org\/10.3389\/fncom.2020.00002]<\/h3>\n

 <\/p>\n

Chronic Fatigue Syndrome (CFS) is a debilitating condition estimated to impact at least 1 million individuals in the United States, however there persists controversy about its existence.<\/p>\n

Machine learning algorithms have become a powerful methodology for evaluating multi-regional areas of fMRI<\/a> activation that can classify disease phenotype from sedentary control. Uncovering objective biomarkers such as an fMRI pattern is important for lending credibility to diagnosis of CFS.<\/p>\n

fMRI scans were evaluated for 69 patients (38 CFS and 31 Control) taken before (Day 1) and after (Day 2) a submaximal exercise test while undergoing the n-back memory paradigm. A predictive model<\/a> was created by grouping fMRI voxels into the Automated Anatomical Labeling (AAL) atlas, splitting the data into a training and testing dataset, and feeding these inputs into a logistic regression<\/a> to evaluate differences between CFS and control.<\/p>\n

Model results were cross-validated 10 times to ensure accuracy. Model results were able to differentiate CFS from sedentary controls at a 80% accuracy on Day 1 and 76% accuracy on Day 2 (Table 3). Recursive features selection identified 29 ROI’s that significantly distinguished CFS from control on Day 1 and 28 ROI’s on Day 2 with 10 regions of overlap shared with Day 1 (Figure 3). These 10 shared regions included the putamen, inferior frontal gyrus, orbital (F3O), supramarginal gyrus (SMG), temporal pole; superior temporal gyrus (T1P) and caudate ROIs.<\/p>\n

\"Figure<\/a>

Figure 3. Significantly elevated BOLD activity during the 2 > 0 back condition in CFS and control groups before and after exercise<\/p><\/div>\n

This study was able to uncover a pattern of activated neurological regions that differentiated CFS from Control. This pattern provides a first step toward developing fMRI as a diagnostic biomarker and suggests this methodology could be emulated for other disorders. We concluded that a logistic regression model performed on fMRI data significantly differentiated CFS from Control.<\/p>\n","protected":false},"excerpt":{"rendered":"

A machine learning approach to the differentiation of Functional Magnetic Resonance Imaging data of Chronic Fatigue Syndrome (CFS) from a sedentary control, by Destie Provenzano,\u00a0 Stuart D Washington and\u00a0 James N Baraniuk in Front. Comput. Neurosci., 29 January 2020 [https:\/\/doi.org\/10.3389\/fncom.2020.00002] … Continue reading →<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[1],"tags":[5605,614,3894,1026,2037,5606,3381,5604],"class_list":["post-24186","post","type-post","status-publish","format-standard","hentry","category-news","tag-destie-provenzano","tag-diagnostic-marker","tag-dr-james-n-baraniuk","tag-fmri","tag-functional-magnetic-resonance-imaging","tag-logistic-regression-model","tag-machine-learning","tag-stuart-d-washington"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p5qkYK-6i6","_links":{"self":[{"href":"https:\/\/wames.org.uk\/cms-english\/wp-json\/wp\/v2\/posts\/24186","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wames.org.uk\/cms-english\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wames.org.uk\/cms-english\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wames.org.uk\/cms-english\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/wames.org.uk\/cms-english\/wp-json\/wp\/v2\/comments?post=24186"}],"version-history":[{"count":5,"href":"https:\/\/wames.org.uk\/cms-english\/wp-json\/wp\/v2\/posts\/24186\/revisions"}],"predecessor-version":[{"id":24360,"href":"https:\/\/wames.org.uk\/cms-english\/wp-json\/wp\/v2\/posts\/24186\/revisions\/24360"}],"wp:attachment":[{"href":"https:\/\/wames.org.uk\/cms-english\/wp-json\/wp\/v2\/media?parent=24186"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wames.org.uk\/cms-english\/wp-json\/wp\/v2\/categories?post=24186"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wames.org.uk\/cms-english\/wp-json\/wp\/v2\/tags?post=24186"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}