Analysis of longitudinal diffusion-weighted images in healthy and pathological aging: An ADNI study

Abstract

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.

Hippocampal gene expression patterns linked to late-life physical activity oppose age and AD-related transcriptional decline

“…Further, many of the anti-Aging/AD genes showing increased expression with physical activity target myelin and axon health, suggesting that physical activity may promote white matter integrity and axonal connectivity and function. The strong relationship between physical activity and expression of genes targeting axon functioning are important, given that white matter degeneration and reduced axonal efficacy are notable components of cognitive decline in both normal aging and in AD pathogenesis (Bartzokis et al, 2003;Fletcher et al, 2018;Kruggel et al, 2017).…”

Extensive Evaluation of Morphological Statistical Harmonization for Brain Age Prediction

“…Characterizing both neurodevelopmental and aging brain structural trajectories is important for understanding normal biological processes and atypical patterns that are related to pathological phenomena. As an example, accelerated aging atrophy has been found in neurodegenerative diseases, such as Alzheimer’s Diseases [ 11 , 12 ]; other studies have investigated altered morphological patterns during neurodevelopment in neurological disorders, such as Autism Spectrum Disorder (ASD) [ 13 , 14 ]. Recently, a comprehensive index has been introduced to describe the brain age of a subject [ 15 ].…”

Personalized pathology maps to quantify diffuse and focal brain damage

“…A more traditional approach to compare single-patient data to a cohort of healthy individuals is based on a voxel basis by using voxel-based morphometry (VBM) analysis (Beeson et al, 2011; Colliot et al, 2006; Kruggel et al, 2017; Muhlau et al, 2009; Sajjadi et al, 2013). Voxel-based methods rely on the non-rigid registration of the patient brain volume to a group of healthy control volumes in a common space prior to the voxel-wise comparison.…”