Journal Article

FZJ-2022-04924

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach

Krämer, C.FZJ* ; Stumme, J.FZJ* ; da Costa Campos, L. ; Rubbert, C. ; Caspers, J. ; Caspers, S.FZJ* ; Jockwitz, C. (Corresponding author)FZJ*

2023
The MIT Press Cambridge, MA

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Please use a persistent id in citations: http://hdl.handle.net/2128/33218  doi:10.1162/netn_a_00275

Abstract: Age-related cognitive decline varies greatly in healthy older adults, which may partly be explained by differences in the functional architecture of brain networks. Resting-state functional connectivity (RSFC) derived network parameters as widely used markers describing this architecture have even been successfully used to support diagnosis of neurodegenerative diseases. The current study aimed at examining whether these parameters may also be useful in classifying and predicting cognitive performance differences in the normally aging brain by using machine learning (ML). Classifiability and predictability of global and domain-specific cognitive performance differences from nodal and network-level RSFC strength measures were examined in healthy older adults from the 1000BRAINS study (age range: 55–85 years). ML performance was systematically evaluated across different analytic choices in a robust cross-validation scheme. Across these analyses, classification performance did not exceed 60% accuracy for global and domain-specific cognition. Prediction performance was equally low with high mean absolute errors (MAEs ≥ 0.75) and low to none explained variance (R2 ≤ 0.07) for different cognitive targets, feature sets, and pipeline configurations. Current results highlight limited potential of functional network parameters to serve as sole biomarker for cognitive aging and emphasize that predicting cognition from functional network patterns may be challenging.

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Contributing Institute(s):

1. Strukturelle und funktionelle Organisation des Gehirns (INM-1)

Research Program(s):

1. 5251 - Multilevel Brain Organization and Variability (POF4-525) (POF4-525)
2. HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) (945539)
3. JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027) (JL SMHB-2021-2027)

Appears in the scientific report 2022

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