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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&apos;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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - 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)

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