Journal Article

FZJ-2020-04355

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Age differences in predicting working memory performance from network-based functional connectivity

Pläschke, R. N. (Corresponding author)FZJ* ; Patil, K. R.FZJ* ; Cieslik, E. C.FZJ* ; Nostro, A. D. ; Varikuti, D. P. ; Plachti, A.FZJ* ; Lösche, P. ; Hoffstaedter, F.FZJ* ; Kalenscher, T. ; Langner, R.FZJ* ; Eickhoff, S. B. (Corresponding author)FZJ*

2020
Elsevier New York, NY

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Please use a persistent id in citations: http://hdl.handle.net/2128/26131  doi:10.1016/j.cortex.2020.08.012

Abstract: Deterioration in working memory capacity (WMC) has been associated with normal aging, but it remains unknown how age affects the relationship between WMC and connectivity within functional brain networks. We therefore examined the predictability of WMC from fMRI-based resting-state functional connectivity (RSFC) within eight meta-analytically defined functional brain networks and the connectome in young and old adults using relevance vector machine in a robust cross-validation scheme. Particular brain networks have been associated with mental functions linked to WMC to a varying degree and are associated with age-related differences in performance. Comparing prediction performance between the young and old sample revealed age-specific effects: In young adults, we found a general unpredictability of WMC from RSFC in networks subserving WM, cognitive action control, vigilant attention, theory-of-mind cognition, and semantic memory, whereas in older adults each network significantly predicted WMC. Moreover, both WM-related and WM-unrelated networks were differently predictive in older adults with low versus high WMC. These results indicate that the within-network functional coupling during task-free states is specifically related to individual task performance in advanced age, suggesting neural-level reorganization. In particular, our findings support the notion of a decreased segregation of functional brain networks, deterioration of network integrity within different networks and/or compensation by reorganization as factors driving associations between individual WMC and within-network RSFC in older adults. Thus, using multivariate pattern regression provided novel insights into age-related brain reorganization by linking cognitive capacity to brain network integrity.

Note: This study was supported by the Deutsche Forschungsgemeinschaft (DFG), contract grantnumbers: EI 816/4-1, LA 3071/3-1; the National Institute of Mental Health, contract grantnumber: R01-MH074457; the Helmholtz Association Theme “Supercomputing and Modelingfor the Human Brain”; and the European Union’s Horizon 2020 Research and InnovationProgramme, contract grant number: 7202070 (HBP SGA1).

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Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)

Research Program(s):

1. 572 - (Dys-)function and Plasticity (POF3-572) (POF3-572)
2. SMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017) (HGF-SMHB-2013-2017)
3. HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270) (720270)

Appears in the scientific report 2020

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Database coverage:
; ; ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Current Contents - Social and Behavioral Sciences ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Social Sciences Citation Index ; Web of Science Core Collection

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