Resting state brain activity and association with transfer of cognitive training gains

Faraza, Sofia; Mierau, Andreas; Riedel, David; Binder, Harald; Kollmann, Bianca; Wolf, Dominik; Fellgiebel, Andreas; Knaepen, Kristel; Teipel, Stefan; Tüscher, Oliver; Fischer, Florian U.; Dyrba, Martin

doi:10.1016/j.ynirp.2026.100389

Journal Article

DZNE-2026-00863

Resting state brain activity and association with transfer of cognitive training gains

Faraza, S. ; Dyrba, M.DZNE* ; Wolf, D. ; Fischer, F. U. ; Knaepen, K. ; Riedel, D. ; Kollmann, B. ; Binder, H. ; Mierau, A. ; Fellgiebel, A. ; Tüscher, O. ; Teipel, S. (Last author)DZNE*

2026
Elsevier ScienceDirect [Amsterdam]

Neuroimage: reports 6(3), 100389 (2026) [10.1016/j.ynirp.2026.100389]

This record in other databases:

Please use a persistent id in citations: doi:10.1016/j.ynirp.2026.100389

Abstract: Background: Normal aging is accompanied by cognitive decline, structural and functional brain changes. Cognitive training is a potentially effective intervention for cognitive improvement. Transfer of training gains to untrained tasks is the ultimate goal of cognitive training. However, the neural mechanisms underlying successful transfer remain underinvestigated. Objective: To examine the predictive role of resting-state functional connectivity in the transfer of training gains. Methods: We analyzed resting-state fMRI and cognitive data of 181 healthy older adults (mean age: 68 years) who underwent a 4-week cognitive training at three study sites. The control group consisted of 54 older adults. Participants underwent neuropsychological assessments before and directly after the training, as well as 12 weeks after. We used aggregate scores representing working memory, memory and executive functions to assess transfer effects. Baseline resting-state fMRI was used to investigate functional connectivity. We used a seed-based and an independent component analysis approach to examine brain network activity. Results; The majority of our participants transferred cognitive training gains successfully over a three-month period. Baseline resting-state functional connectivity within the default mode network and the central executive network did not predict transfer of training gains. Conclusions: Baseline resting-state functional connectivity of large-scale networks does not appear to predict who will benefit from cognitive training in healthy older adults. These findings contribute to a better understanding of the functional brain mechanisms underlying transfer of training gains and highlight the need for larger, multi-modal neuroimaging studies to identify reliable neural predictors of cognitive training outcomes.

Classification: