Diffusion tensor imaging and tract-based spatial statistics in Alzheimer's disease and mild cognitive impairment.
We aimed to explore the changes in fractional anisotropy (FA) in subjects with mild cognitive impairment (MCI) and Alzheimer's disease (AD) by analyzing diffusion tensor imaging (DTI) data using the Tract-Based Spatial Statistics (TBSS). DTI data were collected from 17 AD patients, 27 MCI subjects and 19 healthy controls. Voxel-based analysis with TBSS was used to compare FA among the three groups. Additionally, guided by TBSS findings, a region of interest (ROI)-based analysis along the TBSS skeleton was performed on group-level and the accuracy of the method was assessed by the back-projection of ROIs to the native space FA. Neurofiber tracts with decreased FA included: the parahippocampal white matter, cingulum, uncinate fasciculus, inferior and superior longitudinal fasciculus, corpus callosum, fornix, tracts in brain stem, and cerebellar tracts. Quantitative ROI-analysis further demonstrated the significant decrease on FA values in AD patients relative to controls whereas FA values of MCI patients were found in between the controls and AD patients. We conclude that TBSS is a promising method in examining the degeneration of neurofiber tracts in MCI and AD patients.
Liu
Yawu
Y
Department of Clinical Radiology, Kuopio University Hospital, P.O. Box 1777, FI-70211 Kuopio, Finland.
Spulber
Gabriela
G
Lehtimäki
Kimmo K
KK
Könönen
Mervi
M
Hallikainen
Ilona
I
Gröhn
Heidi
H
Kivipelto
Miia
M
Hallikainen
Merja
M
Vanninen
Ritva
R
Soininen
Hilkka
H
D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis*; D001931:Brain Mapping / Q000379:methods / Q000706:statistics & numerical data; D060825:Cognitive Dysfunction / Q000175:diagnosis*; D056324:Diffusion Tensor Imaging / Q000379:methods* / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods / Q000706:statistics & numerical data; D008297:Male; D012720:Severity of Illness Index