title: White matter integrity in mild cognitive impairment: a tract-based spatial statistics study.
abstract: Mild cognitive impairment (MCI) as a clinical diagnosis has limited specificity, and identifying imaging biomarkers may improve its predictive validity as a pre-dementia syndrome. This study used diffusion tensor imaging (DTI) to detect white matter (WM) structural alterations in MCI and its subtypes, and aimed to examine if DTI can serve as a potential imaging marker of MCI. We studied 96 amnestic MCI (aMCI), 69 non-amnestic MCI (naMCI), and 252 cognitively normal (CN) controls. DTI was performed to measure fractional anisotropy (FA), and tract-based spatial statistics (TBSS) were applied to investigate the characteristics of WM changes in aMCI and naMCI. The diagnostic utility of DTI in distinguishing MCI from CN was further evaluated by using a binary logistic regression model. We found that FA was significantly reduced in aMCI and naMCI when compared with CN. For aMCI subjects, decreased FA was seen in the frontal, temporal, parietal, and occipital WM, together with several commissural, association, and projection fibres. The best discrimination between aMCI and controls was achieved by combining FA measures of the splenium of corpus callosum and crus of fornix, with accuracy of 74.8% (sensitivity 71.0%, specificity 76.2%). For naMCI subjects, WM abnormality was more anatomically widespread, but the temporal lobe WM was relatively spared. These results suggest that aMCI is best characterized by pathology consistent with early Alzheimer's disease, whereas underlying pathology in naMCI is more heterogeneous, and DTI analysis of white matter structural integrity can serve as a potential biomarker of MCI and its subtypes.
authors.affiliation: Brain & Ageing Research Program, School of Psychiatry, University of New South Wales, Sydney, Australia.
authors.forename: Lin
authors.initials: L
authors.lastname: Zhuang
authors.forename: Wei
authors.initials: W
authors.lastname: Wen
authors.forename: Wanlin
authors.initials: W
authors.lastname: Zhu
authors.forename: Julian
authors.initials: J
authors.lastname: Trollor
authors.forename: Nicole
authors.initials: N
authors.lastname: Kochan
authors.forename: John
authors.initials: J
authors.lastname: Crawford
authors.forename: Simone
authors.initials: S
authors.lastname: Reppermund
authors.forename: Henry
authors.initials: H
authors.lastname: Brodaty
authors.forename: Perminder
authors.initials: P
authors.lastname: Sachdev
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D003072:Cognition Disorders / Q000473:pathology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008953:Models, Anatomic; D015233:Models, Statistical; D009413:Nerve Fibers, Myelinated / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity