Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2012 Mar;60(1):400-8.
doi: 10.1016/j.neuroimage.2011.12.070. Epub 2012 Jan 5.

Multivariate Pattern Classification of Gray Matter Pathology in Multiple Sclerosis


Multivariate Pattern Classification of Gray Matter Pathology in Multiple Sclerosis

Kerstin Bendfeldt et al. Neuroimage. .


Univariate analyses have identified gray matter (GM) alterations in different groups of MS patients. While these methods detect differences on the basis of the single voxel or cluster, multivariate methods like support vector machines (SVM) identify the complex neuroanatomical patterns of GM differences. Using multivariate linear SVM analysis and leave-one-out cross-validation, we aimed at identifying neuroanatomical GM patterns relevant for individual classification of MS patients. We used SVM to separate GM segmentations of T1-weighted three-dimensional magnetic resonance (MR) imaging scans within different age- and sex-matched groups of MS patients with either early (n=17) or late MS (n=17) (contrast I), low (n=20) or high (n=20) white matter lesion load (contrast II), and benign MS (BMS, n=13) or non-benign MS (NBMS, n=13) (contrast III) scanned on a single 1.5 T MR scanner. GM patterns most relevant for individual separation of MS patients comprised cortical areas of all the cerebral lobes as well as deep GM structures, including the thalamus and caudate. The patterns detected were sufficiently informative to separate individuals of the respective groups with high sensitivity and specificity in 85% (contrast I), 83% (contrast II) and 77% (contrast III) of cases. The study demonstrates that neuroanatomical spatial patterns of GM segmentations contain information sufficient for correct classification of MS patients at the single case level, thus making multivariate SVM analysis a promising clinical application.

Similar articles

See all similar articles

Cited by 19 articles

See all "Cited by" articles

LinkOut - more resources