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Brain Structure and Function

Erschienen in:

22.06.2023 | Original Article

Using modular connectome-based predictive modeling to reveal brain-behavior relationships of individual differences in working memory

verfasst von: Huayi Yang, Junjun Zhang, Zhenlan Jin, Pouya Bashivan, Ling Li

Erschienen in: Brain Structure and Function | Ausgabe 6/2023

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Abstract

Working memory plays a crucial role in our daily lives, and brain imaging has been used to predict working memory performance. Here, we present an improved connectome-based predictive modeling approach for building a predictive model of individual working memory performance from whole-brain functional connectivity. The model was built using n-back task-based fMRI and resting-state fMRI data from the Human Connectome Project. Compared to prior models, our model was more interpretable, demonstrated a closer connection to the known anatomical and functional network. The model also demonstrates strong generalization on nine other cognitive behaviors from the HCP database and can well predict the working memory performance of healthy individuals in external datasets. By comparing the differences in prediction effects of different brain networks and anatomical feature analysis on n-back tasks, we found the essential role of some networks in differentiating between high and low working memory loads conditions.

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Titel: Using modular connectome-based predictive modeling to reveal brain-behavior relationships of individual differences in working memory
verfasst von: Huayi Yang
Junjun Zhang
Zhenlan Jin
Pouya Bashivan
Ling Li
Publikationsdatum: 22.06.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: Brain Structure and Function / Ausgabe 6/2023
Print ISSN: 1863-2653
Elektronische ISSN: 1863-2661
DOI: https://doi.org/10.1007/s00429-023-02666-3

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