Typicality of functional connectivity robustly captures motion artifacts in rs‐fMRI across datasets, atlases, and preprocessing pipelines
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F20%3A43920297" target="_blank" >RIV/00023752:_____/20:43920297 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/20:00345914 RIV/00023001:_____/20:00080324 RIV/60461373:22340/20:43926020
Result on the web
<a href="https://onlinelibrary.wiley.com/doi/10.1002/hbm.25195" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/hbm.25195</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/hbm.25195" target="_blank" >10.1002/hbm.25195</a>
Alternative languages
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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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LO1611" target="_blank" >LO1611: Sustainability for The National Institute of Mental Health</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Human Brain Mapping
ISSN
1065-9471
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