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| 001 | 911669 | ||
| 005 | 20250414120453.0 | ||
| 024 | 7 | _ | |a 10.1162/netn_a_00275 |2 doi |
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| 037 | _ | _ | |a FZJ-2022-04924 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Krämer, Camilla |0 P:(DE-Juel1)180200 |b 0 |
| 245 | _ | _ | |a Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach |
| 260 | _ | _ | |a Cambridge, MA |c 2023 |b The MIT Press |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1688023394_23817 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a 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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| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Stumme, Johanna |0 P:(DE-Juel1)171557 |b 1 |u fzj |
| 700 | 1 | _ | |a da Costa Campos, Lucas |0 P:(DE-Juel1)191499 |b 2 |
| 700 | 1 | _ | |a Rubbert, Christian |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Caspers, Julian |0 P:(DE-Juel1)144344 |b 4 |
| 700 | 1 | _ | |a Caspers, Svenja |0 P:(DE-Juel1)131675 |b 5 |u fzj |
| 700 | 1 | _ | |a Jockwitz, Christiane |0 P:(DE-Juel1)145386 |b 6 |e Corresponding author |u fzj |
| 773 | _ | _ | |a 10.1162/netn_a_00275 |g p. 1 - 27 |0 PERI:(DE-600)2900481-0 |n 1 |p 122–147 |t Network neuroscience |v 7 |y 2023 |x 2472-1751 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/911669/files/Invoice_APC600344887.pdf |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/911669/files/K%C3%A4rmer_et%20al_Network%20Neuroscience_2022.pdf |y OpenAccess |
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