title: Automatic detection of preclinical neurodegeneration: presymptomatic Huntington disease.
abstract: BACKGROUND
  Treatment of neurodegenerative diseases is likely to be most beneficial in the very early, possibly preclinical stages of degeneration. We explored the usefulness of fully automatic structural MRI classification methods for detecting subtle degenerative change. The availability of a definitive genetic test for Huntington disease (HD) provides an excellent metric for judging the performance of such methods in gene mutation carriers who are free of symptoms.
  
  
  METHODS
  Using the gray matter segment of MRI scans, this study explored the usefulness of a multivariate support vector machine to automatically identify presymptomatic HD gene mutation carriers (PSCs) in the absence of any a priori information. A multicenter data set of 96 PSCs and 95 age- and sex-matched controls was studied. The PSC group was subclassified into three groups based on time from predicted clinical onset, an estimate that is a function of DNA mutation size and age.
  
  
  RESULTS
  Subjects with at least a 33% chance of developing unequivocal signs of HD in 5 years were correctly assigned to the PSC group 69% of the time. Accuracy improved to 83% when regions affected by the disease were selected a priori for analysis. Performance was at chance when the probability of developing symptoms in 5 years was less than 10%.
  
  
  CONCLUSIONS
  Presymptomatic Huntington disease gene mutation carriers close to estimated diagnostic onset were successfully separated from controls on the basis of single anatomic scans, without additional a priori information. Prior information is required to allow separation when degenerative changes are either subtle or variable.
authors.affiliation: Department of Psychiatry and Psychotherapy, Freiburg Brain Imaging, University Clinic Freiburg, Germany. stefan.kloeppel@uniklinik-freiburg.de
authors.forename: S
authors.initials: S
authors.lastname: Klöppel
authors.forename: C
authors.initials: C
authors.lastname: Chu
authors.forename: G C
authors.initials: GC
authors.lastname: Tan
authors.forename: B
authors.initials: B
authors.lastname: Draganski
authors.forename: H
authors.initials: H
authors.lastname: Johnson
authors.forename: J S
authors.initials: JS
authors.lastname: Paulsen
authors.forename: W
authors.initials: W
authors.lastname: Kienzle
authors.forename: S J
authors.initials: SJ
authors.lastname: Tabrizi
authors.forename: J
authors.initials: J
authors.lastname: Ashburner
authors.forename: R S J
authors.initials: RS
authors.lastname: Frackowiak
mesh_terms: D000328:Adult; D017677:Age Distribution; D017668:Age of Onset; D000368:Aged; D001921:Brain / Q000473:pathology* / Q000503:physiopathology; D018450:Disease Progression; D042241:Early Diagnosis; D001330:Electronic Data Processing / Q000379:methods; D005260:Female; D005820:Genetic Testing; D006579:Heterozygote; D006801:Humans; D006816:Huntington Disease / Q000175:diagnosis* / Q000503:physiopathology; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009410:Nerve Degeneration / Q000175:diagnosis* / Q000503:physiopathology; D011237:Predictive Value of Tests; D055815:Young Adult