Journal Article

FZJ-2023-01585

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Comparison Between Gradients and Parcellations for Functional Connectivity Prediction of Behavior

Kong, R. ; Tan, Y. R. ; Wulan, N. ; Ooi, L. Q. R. ; Farahibozorg, S.-R. ; Harrison, S. ; Bijsterbosch, J. D. ; Bernhardt, B. C. ; Eickhoff, S.FZJ* ; Yeo, B. T. T. (Corresponding author)

2023
Academic Press Orlando, Fla.

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Please use a persistent id in citations: http://hdl.handle.net/2128/34427  doi:10.1016/j.neuroimage.2023.120044

Abstract: Resting-state functional connectivity (RSFC) is widely used to predict behavioral measures. To predict behavioral measures, representing RSFC with parcellations and gradients are the two most popular approaches. Here, we compare parcellation and gradient approaches for RSFC-based prediction of a broad range of behavioral measures in the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. Among the parcellation approaches, we consider group-average “hard” parcellations (Schaefer et al., 2018), individual-specific “hard” parcellations (Kong et al., 2021a), and an individual-specific “soft” parcellation (spatial independent component analysis with dual regression; Beckmann et al., 2009). For gradient approaches, we consider the well-known principal gradients (Margulies et al., 2016) and the local gradient approach that detects local RSFC changes (Laumann et al., 2015). Across two regression algorithms, individual-specific hard-parcellation performs the best in the HCP dataset, while the principal gradients, spatial independent component analysis and group-average “hard” parcellations exhibit similar performance. On the other hand, principal gradients and all parcellation approaches perform similarly in the ABCD dataset. Across both datasets, local gradients perform the worst. Finally, we find that the principal gradient approach requires at least 40 to 60 gradients to perform as well as parcellation approaches. While most principal gradient studies utilize a single gradient, our results suggest that incorporating higher order gradients can provide significant behaviorally relevant information. Future work will consider the inclusion of additional parcellation and gradient approaches for comparison.

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Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)

Research Program(s):

1. 5252 - Brain Dysfunction and Plasticity (POF4-525) (POF4-525)

Appears in the scientific report 2023

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Database coverage:
; ; ; ; BIOSIS Previews ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; DOAJ Seal ; Ebsco Academic Search ; IF >= 5 ; JCR ; Nationallizenz ; SCOPUS ; Web of Science Core Collection

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