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10.1111/head.13121 http://scihub22266oqcxt.onion/10.1111/head.13121 C5507708!5507708!28627714     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28627714.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Headache 2017 ; 57 (7): 1051-64 Nephropedia Template TP {{tp|p=28627714|t=2017. Migraine sub-classification via a data-driven automated approach using multimodality factor mixture modeling of brain structure measurements |pdf=|usr=}}{{28627714}} gab.com Text Migraine sub-classification via a data-driven automated approach using multimodality factor mixture modeling of brain structure measurements JO: Headache http://www.kidney.de/pget.php?&tw=1&pmid=28627714 #FOAvip Background: The current sub-classification of migraine is according to headache frequency and aura status. The variability in migraine symptoms, disease course, and response to treatment suggest the presence of additional heterogeneity or subclasses within migraine. Objective: The study objective was to sub-classify migraine via a data-driven approach, identifying latent factors by jointly exploiting multiple sets of brain structural features obtained via magnetic resonance imaging (MRI). Methods: Migraineurs (n=66) and healthy controls (n=54) had brain MRI measurements of cortical thickness, cortical surface area, and volumes for 68 regions. A multi-modality factor mixture model was used to sub-classify MRIs and to determine the brain structural factors that most contributed to the sub-classification. Clinical characteristics of subjects in each sub-group were compared. Results: Automated MRI classification divided the subjects into two sub-groups. Migraineurs in sub-group #1 had more severe allodynia symptoms during migraines (6.1 +/? 5.3 vs. 3.6 +/?3.2, p = .03), more years with migraine (19.2 +/? 11.3 years vs. 13 +/? 8.3 years, p = .01), and higher Migraine Disability Assessment (MIDAS) scores (25 +/? 22.9 vs. 15.7 +/? 12.2, p = .04). There were not differences in headache frequency or migraine aura status between the two sub-groups. Conclusions: Data-driven sub-classification of brain MRIs based upon structural measurements identified two sub-groups. Amongst migraineurs, the sub-groups differed in allodynia symptom severity, years with migraine, and migraine-related disability. Since allodynia is associated with this imaging-based sub-classification of migraine and prior publications suggest that allodynia impacts migraine treatment response and disease prognosis, future migraine diagnostic criteria could consider allodynia when defining migraine sub-groups. Twit Text FOAVip Migraine sub-classification via a data-driven automated approach using multimodality factor mixture modeling of brain structure measurements ????Headache http://www.kidney.de/pget.php?&tw=1&pmid=28627714 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28627714 #FOAvip English Wikipedia <ref>{{Cite pmid|28627714}}</ref> Migraine sub-classification via a data-driven automated approach using multimodality factor mixture modeling of brain structure measurements #MMPMID28627714 Schwedt TJ; Si B; Li J; Wu T; Chong CD Headache 2017[Jul]; 57 (7): 1051-64 PMID28627714show ga Background: The current sub-classification of migraine is according to headache frequency and aura status. The variability in migraine symptoms, disease course, and response to treatment suggest the presence of additional heterogeneity or subclasses within migraine. Objective: The study objective was to sub-classify migraine via a data-driven approach, identifying latent factors by jointly exploiting multiple sets of brain structural features obtained via magnetic resonance imaging (MRI). Methods: Migraineurs (n=66) and healthy controls (n=54) had brain MRI measurements of cortical thickness, cortical surface area, and volumes for 68 regions. A multi-modality factor mixture model was used to sub-classify MRIs and to determine the brain structural factors that most contributed to the sub-classification. Clinical characteristics of subjects in each sub-group were compared. Results: Automated MRI classification divided the subjects into two sub-groups. Migraineurs in sub-group #1 had more severe allodynia symptoms during migraines (6.1 +/? 5.3 vs. 3.6 +/?3.2, p = .03), more years with migraine (19.2 +/? 11.3 years vs. 13 +/? 8.3 years, p = .01), and higher Migraine Disability Assessment (MIDAS) scores (25 +/? 22.9 vs. 15.7 +/? 12.2, p = .04). There were not differences in headache frequency or migraine aura status between the two sub-groups. Conclusions: Data-driven sub-classification of brain MRIs based upon structural measurements identified two sub-groups. Amongst migraineurs, the sub-groups differed in allodynia symptom severity, years with migraine, and migraine-related disability. Since allodynia is associated with this imaging-based sub-classification of migraine and prior publications suggest that allodynia impacts migraine treatment response and disease prognosis, future migraine diagnostic criteria could consider allodynia when defining migraine sub-groups. äMigraine sub-classification via a data-driven automated approach using(epmc).pdf DeepDyve Pubget Overpricing