PortalPublikationenAssessing reliability in neuroimaging research through intra...

Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED)

Standard

Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED). / Brandmaier, Andreas M; Wenger, Elisabeth; Bodammer, Nils C; Kühn, Simone; Raz, Naftali; Lindenberger, Ulman.

in: ELIFE, Jahrgang 7, 02.07.2018, S. e35718.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Brandmaier, AM, Wenger, E, Bodammer, NC, Kühn, S, Raz, N & Lindenberger, U 2018, 'Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED)', ELIFE, Jg. 7, S. e35718. https://doi.org/10.7554/eLife.35718

APA

Brandmaier, A. M., Wenger, E., Bodammer, N. C., Kühn, S., Raz, N., & Lindenberger, U. (2018). Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED). ELIFE, 7, e35718. https://doi.org/10.7554/eLife.35718

Vancouver

Brandmaier AM, Wenger E, Bodammer NC, Kühn S, Raz N, Lindenberger U. Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED). ELIFE. 2018 Jul 2;7:e35718. https://doi.org/10.7554/eLife.35718

Bibtex

@article{5f76ba8384a64fbb8b3292b9390e604d,
title = "Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED)",
abstract = "Magnetic resonance imaging has become an indispensable tool for studying associations of structural and functional properties of the brain with behavior in humans. However, generally recognized standards for assessing and reporting the reliability of these techniques are still lacking. Here, we introduce a new approach for assessing and reporting reliability, termed intra-class effect decomposition (ICED). ICED uses structural equation modeling of data from a repeated-measures design to decompose reliability into orthogonal sources of measurement error that are associated with different characteristics of the measurements, for example, session, day, or scanning site. This allows researchers to describe the magnitude of different error components, make inferences about error sources, and inform them in planning future studies. We apply ICED to published measurements of myelin content and resting state functional connectivity. These examples illustrate how longitudinal data can be leveraged separately or conjointly with cross-sectional data to obtain more precise estimates of reliability.",
keywords = "Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural",
author = "Brandmaier, {Andreas M} and Elisabeth Wenger and Bodammer, {Nils C} and Simone K{\"u}hn and Naftali Raz and Ulman Lindenberger",
note = "{\textcopyright} 2018, Brandmaier et al.",
year = "2018",
month = jul,
day = "2",
doi = "10.7554/eLife.35718",
language = "English",
volume = "7",
pages = "e35718",
journal = "ELIFE",
issn = "2050-084X",
publisher = "eLife Sciences Publications",

}

RIS

TY - JOUR

T1 - Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED)

AU - Brandmaier, Andreas M

AU - Wenger, Elisabeth

AU - Bodammer, Nils C

AU - Kühn, Simone

AU - Raz, Naftali

AU - Lindenberger, Ulman

N1 - © 2018, Brandmaier et al.

PY - 2018/7/2

Y1 - 2018/7/2

N2 - Magnetic resonance imaging has become an indispensable tool for studying associations of structural and functional properties of the brain with behavior in humans. However, generally recognized standards for assessing and reporting the reliability of these techniques are still lacking. Here, we introduce a new approach for assessing and reporting reliability, termed intra-class effect decomposition (ICED). ICED uses structural equation modeling of data from a repeated-measures design to decompose reliability into orthogonal sources of measurement error that are associated with different characteristics of the measurements, for example, session, day, or scanning site. This allows researchers to describe the magnitude of different error components, make inferences about error sources, and inform them in planning future studies. We apply ICED to published measurements of myelin content and resting state functional connectivity. These examples illustrate how longitudinal data can be leveraged separately or conjointly with cross-sectional data to obtain more precise estimates of reliability.

AB - Magnetic resonance imaging has become an indispensable tool for studying associations of structural and functional properties of the brain with behavior in humans. However, generally recognized standards for assessing and reporting the reliability of these techniques are still lacking. Here, we introduce a new approach for assessing and reporting reliability, termed intra-class effect decomposition (ICED). ICED uses structural equation modeling of data from a repeated-measures design to decompose reliability into orthogonal sources of measurement error that are associated with different characteristics of the measurements, for example, session, day, or scanning site. This allows researchers to describe the magnitude of different error components, make inferences about error sources, and inform them in planning future studies. We apply ICED to published measurements of myelin content and resting state functional connectivity. These examples illustrate how longitudinal data can be leveraged separately or conjointly with cross-sectional data to obtain more precise estimates of reliability.

KW - Journal Article

KW - Research Support, Non-U.S. Gov't

KW - Research Support, N.I.H., Extramural

U2 - 10.7554/eLife.35718

DO - 10.7554/eLife.35718

M3 - SCORING: Journal article

C2 - 29963984

VL - 7

SP - e35718

JO - ELIFE

JF - ELIFE

SN - 2050-084X

ER -