Converging on consistent functional connectomics
Rattachement africain : ca, gb, us, ie, au, fr, es. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Abstract Functional interactions between brain regions can be viewed as a network, empowering neuroscientists to leverage network science to investigate distributed brain function. However, obtaining a brain network from functional neuroimaging data involves multiple steps of data manipulation, which can drastically affect the organisation and validity of the estimated brain network and its properties. Here, we provide a systematic evaluation of 576 unique data-processing pipelines for functional connectomics from resting-state functional MRI, obtained from all possible recombinations of popular choices for brain atlas type and size, connectivity definition and selection, and global signal regression. We use the portrait divergence, an information-theoretic measure of differences in network topology across scales, to quantify the influence of analytic choices on the overall organisation of the derived functional connectome. We evaluate each pipeline across an entire battery of criteria, seeking pipelines that (i) minimise spurious test-retest discrepancies of network topology, while simultaneously (ii) mitigating motion confounds, and being sensitive to both (iii) inter-subject differences and (iv) experimental effects of interest, as demonstrated by propofol-induced general anaesthesia. Our findings reveal vast and systematic variability across pipelines’ suitability for functional connectomics. Choice of the wrong data-processing pipeline can lead to results that are not only misleading, but systematically so, distorting the functional connectome more drastically than the passage of several months. We also found that the majority of pipelines failed to meet at least one of our criteria. However, we identified 8 candidates satisfying all criteria across each of four independent datasets spanning minutes, weeks, and months, ensuring the generalisability of our recommendations. Our results also generalise to alternative acquisition parameters and preprocessing and denoising choices. By providing the community with a full breakdown of each pipeline’s performance across this multi-dataset, multi-criteria, multi-scale and multi-step approach, we establish a comprehensive set of benchmarks to inform future best practices in functional connectomics.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Converging on consistent functional connectomics
- Date Crossref
- 26/06/2023
- Éditeur
- openRxiv
- Type
- posted-content
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Montreal Neurological Institute and Hospital pays non établi dans la noticeÉtablissement de santé
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University of Cambridge Department of Clinical Neurosciences pays non établi dans la noticeUniversité ou école supérieure
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Leverhulme Trust pays non établi dans la noticeOrganisation à but non lucratif
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The Alan Turing Institute pays non établi dans la noticeStructure de recherche
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McGill University Montreal Neurological Institute pays non établi dans la noticeUniversité ou école supérieure
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Western University Department of Psychology and Department of Physiology and Pharmacology pays non établi dans la noticeUniversité ou école supérieure
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Western University of Health Sciences pays non établi dans la noticeUniversité ou école supérieure
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Trinity College Dublin pays non établi dans la noticeUniversité ou école supérieure
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Mental Health Research Institute pays non établi dans la noticeStructure de recherche
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Cognitive Neuroimaging Lab pays non établi dans la noticeStructure de recherche
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Universitat de Barcelona pays non établi dans la noticeUniversité ou école supérieure
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Cardiff University Neuroinformatics Group pays non établi dans la noticeUniversité ou école supérieure
Montreal Neurological Institute and Hospital, Department of Clinical Neurosciences — University of Cambridge et Leverhulme Trust, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.