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Kopal J, Pidnebesna A, Tomeček D, Tintěra J, Hlinka J. Typicality of functional connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases, and preprocessing pipelines. Hum Brain Mapp 2020; 41:5325-5340. [PMID: 32881215 PMCID: PMC7670643 DOI: 10.1002/hbm.25195] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 08/07/2020] [Accepted: 08/09/2020] [Indexed: 12/25/2022] Open
Abstract
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.
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Affiliation(s)
- Jakub Kopal
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic.,Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, Czech Republic.,Centre de Recherche Cerveau et Cognition, Universite Paul Sabatier, Toulouse, France
| | - Anna Pidnebesna
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic.,National Institute of Mental Health, Klecany, Czech Republic.,Faculty of Electrical Engineering, Czech Technical University, Prague, Czech Republic
| | - David Tomeček
- National Institute of Mental Health, Klecany, Czech Republic.,Faculty of Electrical Engineering, Czech Technical University, Prague, Czech Republic
| | - Jaroslav Tintěra
- National Institute of Mental Health, Klecany, Czech Republic.,Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Jaroslav Hlinka
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic.,National Institute of Mental Health, Klecany, Czech Republic
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