Poster (After Call)

FZJ-2026-03523

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Individualized parcellation enhances relationships between brain network and dual-task performance

Jung, K. (Corresponding author)FZJ* ; Paas Oliveros, L. K.FZJ* ; Hu, D. ; Eickhoff, S.FZJ* ; Liu, H. ; Langner, R.FZJ*

2026

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Please use a persistent id in citations: doi:10.34734/FZJ-2026-03523

Abstract: The regional division of the human brain based on cytoarchitectural, functional, or structural signatures, also known as brain atlas or cortical parcellation, is a widely used approach in current neuroimaging research. The numerous existing parcellation schemes1 usually reflect group-level averages to provide robust and generalizable definitions of brain regions. However, such a population atlas can lead to a mitigation of inter-individual variability in a newly measured data as shown in the previous study2. Therefore, data-driven parcellation reflecting individual empirical data can be effective for further analysis in relationship between brain and behavior across individuals. Consequently, we tested whether such an individualized parcellation, as compared to a standard (predefined) parcellation, can more effectively capture individual characteristics of neuroimaging data, leading to enhanced brain-behavior relationships. In particular, this study investigated the impact of individualizing a cortical population atlas on brain network and their relationship with behavioral measures in different age groups. To this end, we employed MRI as well as dual-task performance data collected from young and old participants. Age-related deterioration in dual-tasking is a well-known phenomenon, and also the neural correlates of dual-task performance have been investigated in the context of aging3. Still, it is an open question about the extent to which individual patterns of inter-regional connectivity are linked to individual dual-task performance. Here, we focus on functional connectivity-driven individualization, in which subject-specific resting-state functional MRI data are used to refine a population cortical atlas, yielding individualized functional parcels that serve as network nodes. Thus, we investigated alterations of brain network architecture induced by the individualization and examined the relationship between network properties and task performance.

Note: This work was supported by the Deutsche Forschungsgemeinschaft (DFG, LA 3071/3-1), Portfolio Theme Supercomputing and Modeling for the Human Brain by the Helmholtz association, the German Academic ExchangeService (DAAD; grant no. 57556280), the Human Brain Project and the European Union’s Horizon 2020 Research and Innovation Programme under the Grant Agreements 785907 (HBP SGA2), 945539 (HBP SGA3) and 826421(VirtualBrainCloud)

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Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)

Research Program(s):

1. 5254 - Neuroscientific Data Analytics and AI (POF4-525) (POF4-525)
2. 5252 - Brain Dysfunction and Plasticity (POF4-525) (POF4-525)
3. HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) (945539)

Appears in the scientific report 2026

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