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Typicality of Functional Connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases and preprocessing pipelines

View ORCID ProfileJakub Kopal, View ORCID ProfileAnna Pidnebesna, View ORCID ProfileDavid Tomeček, Jaroslav Tintěra, View ORCID ProfileJaroslav Hlinka
doi: https://doi.org/10.1101/2020.03.06.980193
Jakub Kopal
aInstitute of Computer Science, The Czech Academy of Sciences, Prague, Czech Republic
bDepartment of Computing and Control Engineering, University of Chemistry and Technology, Prague, Czech Republic
dCentre de Recherche Cerveau et Cognition, Universit Paul Sabatier, Toulouse, France
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Anna Pidnebesna
aInstitute of Computer Science, The Czech Academy of Sciences, Prague, Czech Republic
cNational Institute of Mental Health, Klecany, Czech Republic
eFaculty of Electrical Engineering, Czech Technical University, Prague, Czech Republic
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David Tomeček
cNational Institute of Mental Health, Klecany, Czech Republic
eFaculty of Electrical Engineering, Czech Technical University, Prague, Czech Republic
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Jaroslav Tintěra
cNational Institute of Mental Health, Klecany, Czech Republic
fInstitute for Clinical and Experimental Medicine, Prague, Czech Republic
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Jaroslav Hlinka
aInstitute of Computer Science, The Czech Academy of Sciences, Prague, Czech Republic
cNational Institute of Mental Health, Klecany, Czech Republic
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  • For correspondence: hlinka@cs.cas.cz
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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, calculated by correlating signals from regions of interest, 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. Treatment of such artifacts poses a standing challenge because of their high variability. 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, the Typicality of Functional Connectivity, to capture deviations from normal brain functional connectivity pattern. Based on results of resting state fMRI for 245 healthy subjects we show that this measure is significantly correlated with individual head motion metrics. The results were further robustly reproduced across atlas granularity and preprocessing options, as well as other datasets including 1081 subjects from the Human Connectome Project. The Typicality of Functional Connectivity provides individual proxy measure of motion effect on functional connectivity and is more sensitive to inter-individual variation of motion than individual functional connections. In principle it should be sensitive also to other types of artifacts, processing errors and possibly also brain pathology, allowing wide use in data quality screening and quantification in functional connectivity studies as well as methodological investigations.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 06, 2020.
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Typicality of Functional Connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases and preprocessing pipelines
Jakub Kopal, Anna Pidnebesna, David Tomeček, Jaroslav Tintěra, Jaroslav Hlinka
bioRxiv 2020.03.06.980193; doi: https://doi.org/10.1101/2020.03.06.980193
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Typicality of Functional Connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases and preprocessing pipelines
Jakub Kopal, Anna Pidnebesna, David Tomeček, Jaroslav Tintěra, Jaroslav Hlinka
bioRxiv 2020.03.06.980193; doi: https://doi.org/10.1101/2020.03.06.980193

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