Display Settings:

Format

Send to:

Choose Destination
See comment in PubMed Commons below
Neurology. 2012 Jul 3;79(1):80-4. doi: 10.1212/WNL.0b013e31825dce28. Epub 2012 Jun 20.

Early-onset Alzheimer disease clinical variants: multivariate analyses of cortical thickness.

Author information

  • 1Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK. gerard.ridgway@ucl.ac.uk

Abstract

OBJECTIVE:

To assess patterns of reduced cortical thickness in different clinically defined variants of early-onset Alzheimer disease (AD) and to explore the hypothesis that these variants span a phenotypic continuum rather than represent distinct subtypes.

METHODS:

The case-control study included 25 patients with posterior cortical atrophy (PCA), 15 patients with logopenic progressive aphasia (LPA), and 14 patients with early-onset typical amnestic AD (tAD), as well as 30 healthy control subjects. Cortical thickness was measured using FreeSurfer, and differences and commonalities in patterns of reduced cortical thickness were assessed between patient groups and controls. Given the difficulty of using mass-univariate statistics to test ideas of continuous variation, we use multivariate machine learning algorithms to visualize the spectrum of subjects and to assess separation of patient groups from control subjects and from each other.

RESULTS:

Although each patient group showed disease-specific reductions in cortical thickness compared with control subjects, common areas of cortical thinning were identified, mainly involving temporoparietal regions. Multivariate analyses permitted clear separation between control subjects and patients and moderate separation between patients with PCA and LPA, while patients with tAD were distributed along a continuum between these extremes. Significant classification performance could nevertheless be obtained when every pair of patient groups was compared directly.

CONCLUSIONS:

Analyses of cortical thickness patterns support the hypothesis that different clinical presentations of AD represent points in a phenotypic spectrum of neuroanatomical variation. Machine learning shows promise for syndrome separation and for identifying common anatomic patterns across syndromes that may signify a common pathology, both aspects of interest for treatment trials.

PMID:
22722624
[PubMed - indexed for MEDLINE]
PMCID:
PMC3385494
Free PMC Article

Images from this publication.See all images (3)Free text

Figure 1
Figure 2
Figure 3
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Icon for HighWire Icon for PubMed Central
    Loading ...
    Write to the Help Desk