title: MRI pattern recognition in multiple sclerosis normal-appearing brain areas.
abstract: OBJECTIVE
  Here, we use pattern-classification to investigate diagnostic information for multiple sclerosis (MS; relapsing-remitting type) in lesioned areas, areas of normal-appearing grey matter (NAGM), and normal-appearing white matter (NAWM) as measured by standard MR techniques.
  
  
  METHODS
  A lesion mapping was carried out by an experienced neurologist for Turbo Inversion Recovery Magnitude (TIRM) images of individual subjects. Combining this mapping with templates from a neuroanatomic atlas, the TIRM images were segmented into three areas of homogenous tissue types (Lesions, NAGM, and NAWM) after spatial standardization. For each area, a linear Support Vector Machine algorithm was used in multiple local classification analyses to determine the diagnostic accuracy in separating MS patients from healthy controls based on voxel tissue intensity patterns extracted from small spherical subregions of these larger areas. To control for covariates, we also excluded group-specific biases in deformation fields as a potential source of information.
  
  
  RESULTS
  Among regions containing lesions a posterior parietal WM area was maximally informative about the clinical status (96% accuracy, p<10(-13)). Cerebellar regions were maximally informative among NAGM areas (84% accuracy, p<10(-7)). A posterior brain region was maximally informative among NAWM areas (91% accuracy, p<10(-10)).
  
  
  CONCLUSIONS
  We identified regions indicating MS in lesioned, but also NAGM, and NAWM areas. This complements the current perception that standard MR techniques mainly capture macroscopic tissue variations due to focal lesion processes. Compared to current diagnostic guidelines for MS that define areas of diagnostic information with moderate spatial specificity, we identified hotspots of MS associated tissue alterations with high specificity defined on a millimeter scale.
authors.affiliation: Bernstein Center for Computational Neuroscience Berlin, Charité - University Medicine, Berlin, Germany. martin.weygandt@bccn-berlin.de
authors.forename: Martin
authors.initials: M
authors.lastname: Weygandt
authors.forename: Kerstin
authors.initials: K
authors.lastname: Hackmack
authors.forename: Caspar
authors.initials: C
authors.lastname: Pfüller
authors.forename: Judith
authors.initials: J
authors.lastname: Bellmann-Strobl
authors.forename: Friedemann
authors.initials: F
authors.lastname: Paul
authors.forename: Frauke
authors.initials: F
authors.lastname: Zipp
authors.forename: John-Dylan
authors.initials: JD
authors.lastname: Haynes
mesh_terms: D000328:Adult; D001921:Brain / Q000166:cytology* / Q000473:pathology*; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009103:Multiple Sclerosis / Q000175:diagnosis* / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods*