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Prediction of cognitive performance differences in older age from multimodal neuroimaging data.

Authors
  • Krämer, Camilla1, 2
  • Stumme, Johanna1, 2
  • da Costa Campos, Lucas1, 2
  • Dellani, Paulo1, 2
  • Rubbert, Christian3
  • Caspers, Julian3
  • Caspers, Svenja1, 2
  • Jockwitz, Christiane4, 5
  • 1 Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany. , (Germany)
  • 2 Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany. , (Germany)
  • 3 Department of Diagnostic and Interventional Radiology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany. , (Germany)
  • 4 Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany. [email protected]. , (Germany)
  • 5 Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany. [email protected]. , (Germany)
Type
Published Article
Journal
GeroScience
Publication Date
Feb 01, 2024
Volume
46
Issue
1
Pages
283–308
Identifiers
DOI: 10.1007/s11357-023-00831-4
PMID: 37308769
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Differences in brain structure and functional and structural network architecture have been found to partly explain cognitive performance differences in older ages. Thus, they may serve as potential markers for these differences. Initial unimodal studies, however, have reported mixed prediction results of selective cognitive variables based on these brain features using machine learning (ML). Thus, the aim of the current study was to investigate the general validity of cognitive performance prediction from imaging data in healthy older adults. In particular, the focus was with examining whether (1) multimodal information, i.e., region-wise grey matter volume (GMV), resting-state functional connectivity (RSFC), and structural connectivity (SC) estimates, may improve predictability of cognitive targets, (2) predictability differences arise for global cognition and distinct cognitive profiles, and (3) results generalize across different ML approaches in 594 healthy older adults (age range: 55-85 years) from the 1000BRAINS study. Prediction potential was examined for each modality and all multimodal combinations, with and without confound (i.e., age, education, and sex) regression across different analytic options, i.e., variations in algorithms, feature sets, and multimodal approaches (i.e., concatenation vs. stacking). Results showed that prediction performance differed considerably between deconfounding strategies. In the absence of demographic confounder control, successful prediction of cognitive performance could be observed across analytic choices. Combination of different modalities tended to marginally improve predictability of cognitive performance compared to single modalities. Importantly, all previously described effects vanished in the strict confounder control condition. Despite a small trend for a multimodal benefit, developing a biomarker for cognitive aging remains challenging. © 2023. The Author(s).

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