title: Analysis of longitudinal diffusion-weighted images in healthy and pathological aging: An ADNI study.
abstract: The widely used framework of voxel-based morphometry for analyzing neuroimages is extended here to model longitudinal imaging data by exchanging the linear model with a linear mixed-effects model. The new approach is employed for analyzing a large longitudinal sample of 756 diffusion-weighted images acquired in 177 subjects of the Alzheimer's Disease Neuroimaging initiative (ADNI).
  
  
  
  While sample- and group-level results from both approaches are equivalent, the mixed-effect model yields information at the single subject level. Interestingly, the neurobiological relevance of the relevant parameter at the individual level describes specific differences associated with aging. In addition, our approach highlights white matter areas that reliably discriminate between patients with Alzheimer's disease and healthy controls with a predictive power of 0.99 and include the hippocampal alveus, the para-hippocampal white matter, the white matter of the posterior cingulate, and optic tracts. In this context, notably the classifier includes a sub-population of patients with minimal cognitive impairment into the pathological domain.
  
  
  
  Our classifier offers promising features for an accessible biomarker that predicts the risk of conversion to Alzheimer's disease. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how to apply/ADNI Acknowledgement List.pdf. Significance statement This study assesses neuro-degenerative processes in the brain's white matter as revealed by diffusion-weighted imaging, in order to discriminate healthy from pathological aging in a large sample of elderly subjects. The analysis of time-series examinations in a linear mixed effects model allowed the discrimination of population-based aging processes from individual determinants. We demonstrate that a simple classifier based on white matter imaging data is able to predict the conversion to Alzheimer's disease with a high predictive power.
authors.affiliation: Department of Biomedical Engineering, University of California, Irvine, USA. Electronic address: fkruggel@uci.edu.
authors.forename: Frithjof
authors.initials: F
authors.lastname: Kruggel
authors.affiliation: Department of Biomedical Engineering, University of California, Irvine, USA.
authors.forename: Fumitaro
authors.initials: F
authors.lastname: Masaki
authors.affiliation: Department of Anatomy & Neurobiology and Neurology, University of California, Irvine, USA.
authors.forename: Ana
authors.initials: A
authors.lastname: Solodkin
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000473:pathology*; D000544:Alzheimer Disease / Q000145:classification / Q000000981:diagnostic imaging; D001921:Brain / Q000000981:diagnostic imaging*; D038524:Diffusion Magnetic Resonance Imaging; D018450:Disease Progression; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D012306:Risk; D012727:Sex Characteristics; D066127:White Matter / Q000000981:diagnostic imaging
keywords: Aging; Alzheimer’s disease; Biomarker; Linear mixed-effects modeling; Longitudinal imaging; Voxel-based morphometry