Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach

• Published in: Network neuroscience (Cambridge, Mass.) Vol. 7; no. 1; pp. 122 - 147
• Main Authors: Krämer, Camilla, Stumme, Johanna, da Costa Campos, Lucas, Rubbert, Christian, Caspers, Julian, Caspers, Svenja, Jockwitz, Christiane
• Format: Journal Article
• Language: English
• Published: One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA MIT Press 01.01.2023; MIT Press Journals, The; The MIT Press
• Subjects: Adults; Age; Aging; Biomarkers; Brain; Brain architecture; Classification; Cognition; Cognition & reasoning; Cognitive ability; Cognitive tasks; Graph-theoretical analyses; Learning algorithms; Machine learning; Neural networks; Neurodegenerative diseases; Older people; Parameters; Performance evaluation; Performance prediction; Predictions; Quality of life; Resting-state functional connectivity; Aging; Resting-state functional connectivity; Cognition; Graph-theoretical analyses; Machine learning
• ISSN: 2472-1751; 2472-1751
• DOI: 10.1162/netn_a_00275

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 ( s ≥ 0.75) and low to none explained variance ( ≤ 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. In recent years, new insights into brain network communication related to cognitive performance differences in older age have been gained. Simultaneously, an increasing number of studies has turned to machine learning (ML) approaches for the development of biomarkers in health and disease. Given the increasing aging population and the impact cognition has on the quality of life of older adults, automated markers for cognitive aging gain importance. This study addressed the classification and prediction power of resting-state functional connectivity (RSFC) strength measures for cognitive performance in healthy older adults using a battery of standard ML approaches. Classifiability and predictability of cognitive abilities was found to be low across analytic choices. Results emphasize limited potential of these metrics as sole biomarker for cognitive aging.