Brain Sciences (Nov 2021)

Anatomically Standardized Detection of MRI Atrophy Patterns in Early-Stage Alzheimer’s Disease

  • Lukas Lenhart,
  • Stephan Seiler,
  • Lukas Pirpamer,
  • Georg Goebel,
  • Thomas Potrusil,
  • Michaela Wagner,
  • Peter Dal Bianco,
  • Gerhard Ransmayr,
  • Reinhold Schmidt,
  • Thomas Benke,
  • Christoph Scherfler

DOI
https://doi.org/10.3390/brainsci11111491
Journal volume & issue
Vol. 11, no. 11
p. 1491

Abstract

Read online

MRI studies have consistently identified atrophy patterns in Alzheimer’s disease (AD) through a whole-brain voxel-based analysis, but efforts to investigate morphometric profiles using anatomically standardized and automated whole-brain ROI analyses, performed at the individual subject space, are still lacking. In this study we aimed (i) to utilize atlas-derived measurements of cortical thickness and subcortical volumes, including of the hippocampal subfields, to identify atrophy patterns in early-stage AD, and (ii) to compare cognitive profiles at baseline and during a one-year follow-up of those previously identified morphometric AD subtypes to predict disease progression. Through a prospectively recruited multi-center study, conducted at four Austrian sites, 120 patients were included with probable AD, a disease onset beyond 60 years and a clinical dementia rating of ≤1. Morphometric measures of T1-weighted images were obtained using FreeSurfer. A principal component and subsequent cluster analysis identified four morphometric subtypes, including (i) hippocampal predominant (30.8%), (ii) hippocampal-temporo-parietal (29.2%), (iii) parieto-temporal (hippocampal sparing, 20.8%) and (iv) hippocampal-temporal (19.2%) atrophy patterns that were associated with phenotypes differing predominately in the presentation and progression of verbal memory and visuospatial impairments. These morphologically distinct subtypes are based on standardized brain regions, which are anatomically defined and freely accessible so as to validate its diagnostic accuracy and enhance the prediction of disease progression.

Keywords