Luppi, Andrea I., Gellersen, Helena M., Liu, Zhen-Qi, Peattie, Alexander R. D., Manktelow, Anne E., Adapa, Ram, Owen, Adrian M., Naci, Lorina, Menon, David K., Dimitriadis, Stavros I. ![]() ![]() |
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Abstract
Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines’ suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline’s performance across criteria and datasets, to inform future best practices in functional connectomics.
Item Type: | Article |
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Date Type: | Publication |
Status: | Published |
Schools: | Psychology Cardiff University Brain Research Imaging Centre (CUBRIC) MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG) |
Additional Information: | License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Type: open-access |
Publisher: | Nature Research |
ISSN: | 2041-1723 |
Date of First Compliant Deposit: | 5 June 2024 |
Date of Acceptance: | 10 May 2024 |
Last Modified: | 05 Jun 2024 09:15 |
URI: | https://orca.cardiff.ac.uk/id/eprint/169484 |
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