Mapping the effects of Aβ1-42 levels on the longitudinal changes in healthy aging: Hierarchical modeling based on stationary velocity fields

Marco Lorenzi, Nicholas Ayache, Giovanni B. Frisoni, Xavier Pennec

Research output: Contribution to journalArticlepeer-review

Abstract

Mapping the effects of different clinical conditions on the evolution of the brain structural changes is of central interest in the field of neuroimaging. A reliable description of the cross-sectional longitudinal changes requires the consistent integration of intra and inter-subject variability in order to detect the subtle modifications in populations. In computational anatomy, the changes in the brain are often measured by deformation fields obtained through non rigid registration, and the stationary velocity field (SVF) parametrization provides a computationally efficient registration scheme. The aim of this study is to extend this framework into an efficient and robust multilevel one for accurately modeling the longitudinal changes in populations. This setting is used to investigate the subtle effects of the positivity of the CSF Aβ1-42 levels on brain atrophy in healthy aging. Thanks to the higher sensitivity of our framework, we obtain statistically significant results that highlight the relationship between brain damage and positivity to the marker of Alzheimer's disease and suggest the presence of a presymptomatic pattern of the disease progression.

Original languageEnglish
Pages (from-to)663-670
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6892 LNCS
Issue numberPART 2
DOIs
Publication statusPublished - 2011

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Fingerprint Dive into the research topics of 'Mapping the effects of Aβ<sub>1-42</sub> levels on the longitudinal changes in healthy aging: Hierarchical modeling based on stationary velocity fields'. Together they form a unique fingerprint.

Cite this