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Evaluation of non-negative matrix factorization of grey matter in age prediction.

Authors
  • Varikuti, Deepthi P1
  • Genon, Sarah2
  • Sotiras, Aristeidis3
  • Schwender, Holger4
  • Hoffstaedter, Felix2
  • Patil, Kaustubh R5
  • Jockwitz, Christiane6
  • Caspers, Svenja6
  • Moebus, Susanne7
  • Amunts, Katrin8
  • Davatzikos, Christos3
  • Eickhoff, Simon B9
  • 1 Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany. Electronic address: [email protected] , (Germany)
  • 2 Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany. , (Germany)
  • 3 Section for Biomedical Image Analysis, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA.
  • 4 Mathematical Institute, Heinrich Heine University Düsseldorf, Düsseldorf, Germany. , (Germany)
  • 5 Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany. , (Germany)
  • 6 Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; C. & O. Vogt Institute for Brain Research, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany; JARA-BRAIN, Juelich-Aachen Research Alliance, Juelich, Germany. , (Germany)
  • 7 Institute of Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Essen, Germany. , (Germany)
  • 8 Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; C. & O. Vogt Institute for Brain Research, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany. , (Germany)
  • 9 Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany. Electronic address: [email protected] , (Germany)
Type
Published Article
Journal
NeuroImage
Publisher
Elsevier
Publication Date
Jun 01, 2018
Volume
173
Pages
394–410
Identifiers
DOI: 10.1016/j.neuroimage.2018.03.007
PMID: 29518572
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

The relationship between grey matter volume (GMV) patterns and age can be captured by multivariate pattern analysis, allowing prediction of individuals' age based on structural imaging. Raw data, voxel-wise GMV and non-sparse factorization (with Principal Component Analysis, PCA) show good performance but do not promote relatively localized brain components for post-hoc examinations. Here we evaluated a non-negative matrix factorization (NNMF) approach to provide a reduced, but also interpretable representation of GMV data in age prediction frameworks in healthy and clinical populations. This examination was performed using three datasets: a multi-site cohort of life-span healthy adults, a single site cohort of older adults and clinical samples from the ADNI dataset with healthy subjects, participants with Mild Cognitive Impairment and patients with Alzheimer's disease (AD) subsamples. T1-weighted images were preprocessed with VBM8 standard settings to compute GMV values after normalization, segmentation and modulation for non-linear transformations only. Non-negative matrix factorization was computed on the GM voxel-wise values for a range of granularities (50-690 components) and LASSO (Least Absolute Shrinkage and Selection Operator) regression were used for age prediction. First, we compared the performance of our data compression procedure (i.e., NNMF) to various other approaches (i.e., uncompressed VBM data, PCA-based factorization and parcellation-based compression). We then investigated the impact of the granularity on the accuracy of age prediction, as well as the transferability of the factorization and model generalization across datasets. We finally validated our framework by examining age prediction in ADNI samples. Our results showed that our framework favorably compares with other approaches. They also demonstrated that the NNMF based factorization derived from one dataset could be efficiently applied to compress VBM data of another dataset and that granularities between 300 and 500 components give an optimal representation for age prediction. In addition to the good performance in healthy subjects our framework provided relatively localized brain regions as the features contributing to the prediction, thereby offering further insights into structural changes due to brain aging. Finally, our validation in clinical populations showed that our framework is sensitive to deviance from normal structural variations in pathological aging. Copyright © 2018 Elsevier Inc. All rights reserved.

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