title: Deep gray matter involvement on brain MRI scans is associated with clinical progression in multiple sclerosis.
abstract: BACKGROUND
  Conventional brain MRI lesion measures have unreliable associations with clinical progression in multiple sclerosis (MS). Gray matter imaging may improve clinical-MRI correlations.
  
  
  METHODS
  We tested if gray matter MRI measures and conventional measures of lesions/atrophy predicted clinical progression in a 4-year longitudinal study of 97 patients with MS. Baseline and follow-up brain MRI were analyzed for basal ganglia and thalamic normalized T2 signal intensity, whole brain T2-hyperintense lesion volume, and whole brain atrophy. Logistic regression tested the ability of baseline or on-study change in MRI to predict disability progression, as reported by area under the receiver operator characteristics curve (AUC).
  
  
  RESULTS
  Lower caudate T2-intensity at baseline (P= .04; AUC = .69) and on-study decreasing T2-intensity in the putamen (P= .03; AUC = .70) and thalamus (P= .01; AUC = .71) were the MRI variables associated with clinical progression when regression modeling was adjusted for length of follow-up interval, baseline EDSS, disease duration, age, and sex.
  
  
  CONCLUSIONS
  Gray matter T2-hypointensity, suggestive of excessive iron deposition is associated with worsening disability in patients with MS. Gray matter MRI assessment may be able to capture neurodegenerative aspects of the disease, with more clinical relevance than derived from conventional MRI measures. J Neuroimaging 2009;19:3-8.
authors.affiliation: Department of Neurology, Brigham and Women's Hospital, Partners MS Center, Harvard Medical School, Boston, Massachusetts 02115, USA.
authors.forename: Mohit
authors.initials: M
authors.lastname: Neema
authors.forename: Ashish
authors.initials: A
authors.lastname: Arora
authors.forename: Brian C
authors.initials: BC
authors.lastname: Healy
authors.forename: Zachary D
authors.initials: ZD
authors.lastname: Guss
authors.forename: Steven D
authors.initials: SD
authors.lastname: Brass
authors.forename: Yang
authors.initials: Y
authors.lastname: Duan
authors.forename: Guy J
authors.initials: GJ
authors.lastname: Buckle
authors.forename: Bonnie I
authors.initials: BI
authors.lastname: Glanz
authors.forename: Lynn
authors.initials: L
authors.lastname: Stazzone
authors.forename: Samia J
authors.initials: SJ
authors.lastname: Khoury
authors.forename: Howard L
authors.initials: HL
authors.lastname: Weiner
authors.forename: Charles R G
authors.initials: CR
authors.lastname: Guttmann
authors.forename: Rohit
authors.initials: R
authors.lastname: Bakshi
mesh_terms: D000328:Adult; D019540:Area Under Curve; D001921:Brain / Q000378:metabolism* / Q000473:pathology; D016009:Chi-Square Distribution; D018450:Disease Progression; D005260:Female; D006801:Humans; D007501:Iron / Q000378:metabolism*; D016015:Logistic Models; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000378:metabolism* / Q000473:pathology; D012189:Retrospective Studies
chemical_list: D007501:Iron