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Typicality of Functional Connectivity Robustly Captures Motion Artifacts in rs‐fMRI across Datasets, Atlases, and Preprocessing Pipelines
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SYSNO ASEP 0532231 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Typicality of Functional Connectivity Robustly Captures Motion Artifacts in rs‐fMRI across Datasets, Atlases, and Preprocessing Pipelines Author(s) Kopal, Jakub (UIVT-O) RID, ORCID, SAI
Pidnebesna, Anna (UIVT-O) SAI, ORCID, RID
Tomeček, D. (CZ)
Tintěra, J. (CZ)
Hlinka, Jaroslav (UIVT-O) RID, SAI, ORCIDNumber of authors 5 Source Title Human Brain Mapping. - : Wiley - ISSN 1065-9471
Roč. 41, č. 18 (2020), s. 5325-5340Number of pages 16 s. Publication form Online - E Language eng - English Country US - United States Keywords atlas ; functional connectivity ; motion ; quality ; rs‐fMRI Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects GA17-01251S GA ČR - Czech Science Foundation (CSF) Method of publishing Open access Institutional support UIVT-O - RVO:67985807 UT WOS 000565321100001 EID SCOPUS 85090109459 DOI 10.1002/hbm.25195 Annotation Functional connectivity analysis of resting‐state fMRI data has recently become one of the most common approaches to characterizing individual brain function. It has been widely suggested that the functional connectivity matrix is a useful approximate representation of the brain's connectivity, potentially providing behaviorally or clinically relevant markers. However, functional connectivity estimates are known to be detrimentally affected by various artifacts, including those due to in‐scanner head motion. Moreover, as individual functional connections generally covary only very weakly with head motion estimates, motion influence is difficult to quantify robustly, and prone to be neglected in practice. Although the use of individual estimates of head motion, or group‐level correlation of motion and functional connectivity has been suggested, a sufficiently sensitive measure of individual functional connectivity quality has not yet been established. We propose a new intuitive summary index, Typicality of Functional Connectivity, to capture deviations from standard brain functional connectivity patterns. In a resting‐state fMRI dataset of 245 healthy subjects, this measure was significantly correlated with individual head motion metrics. The results were further robustly reproduced across atlas granularity, preprocessing options, and other datasets, including 1,081 subjects from the Human Connectome Project. In principle, Typicality of Functional Connectivity should be sensitive also to other types of artifacts, processing errors, and possibly also brain pathology, allowing extensive use in data quality screening and quantification in functional connectivity studies as well as methodological investigations. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2021 Electronic address http://hdl.handle.net/11104/0310801
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