Typicality of Functional Connectivity Robustly Captures Motion Artifacts in rs‐fMRI across Datasets, Atlases, and Preprocessing Pipelines

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00532231" target="_blank" >RIV/67985807:_____/20:00532231 - isvavai.cz</a>

Result on the web

<a href="http://hdl.handle.net/11104/0310801" target="_blank" >http://hdl.handle.net/11104/0310801</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/hbm.25195" target="_blank" >10.1002/hbm.25195</a>

Result language

angličtina

Original language name

Typicality of Functional Connectivity Robustly Captures Motion Artifacts in rs‐fMRI across Datasets, Atlases, and Preprocessing Pipelines

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA17-01251S" target="_blank" >GA17-01251S: Metalearning for Extraction of Rules with Numerical Consequents</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

HUMAN BRAIN MAPPING

ISSN

1097-0193

e-ISSN

—

Volume of the periodical

41

Issue of the periodical within the volume

18

Country of publishing house

US - UNITED STATES

Number of pages

16

Pages from-to

5325-5340

UT code for WoS article

000565321100001

EID of the result in the Scopus database

2-s2.0-85090109459