Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures

the Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

In this paper, we propose a heat kernel based regional shape descriptor that may be capable of better exploiting volumetric morphological information than other available methods, thereby improving statistical power on brain magnetic resonance imaging (MRI) analysis. The mechanism of our analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral meshes. In order to capture profound brain grey matter shape changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between white-grey matter and CSF-grey matter boundary surfaces by computing the streamlines in a tetrahedral mesh. Secondly, we propose multi-scale grey matter morphology signatures to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the grey matter morphology signatures and generate the internal structure features. With the sparse linear discriminant analysis, we select a concise morphology feature set with improved classification accuracies. In our experiments, the proposed work outperformed the cortical thickness features computed by FreeSurfer software in the classification of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, on publicly available data from the Alzheimer's Disease Neuroimaging Initiative. The multi-scale and physics based volumetric structure feature may bring stronger statistical power than some traditional methods for MRI-based grey matter morphology analysis.

Original languageEnglish (US)
Pages (from-to)360-380
Number of pages21
JournalNeuroImage
Volume147
DOIs
StatePublished - Feb 15 2017

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Hot Temperature
Alzheimer Disease
Magnetic Resonance Imaging
Prodromal Symptoms
Physics
Brain
Discriminant Analysis
Neuroimaging
Gray Matter
Software

Keywords

  • Alzheimer's disease
  • Computer-Aided Diagnosis
  • Heat Kernel
  • Magnetic resonance imaging (MRI)
  • Shape analysis

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

Cite this

Towards a Holistic Cortical Thickness Descriptor : Heat Kernel-Based Grey Matter Morphology Signatures. / the Alzheimer's Disease Neuroimaging Initiative.

In: NeuroImage, Vol. 147, 15.02.2017, p. 360-380.

Research output: Contribution to journalArticle

the Alzheimer's Disease Neuroimaging Initiative. / Towards a Holistic Cortical Thickness Descriptor : Heat Kernel-Based Grey Matter Morphology Signatures. In: NeuroImage. 2017 ; Vol. 147. pp. 360-380.
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