Neurometrics of intrinsic connectivity networks at rest using fMRI: Retest reliability and cross-validation using a meta-level method

Krista M. Wisner; Gowtham Atluri; Kelvin O. Lim; Angus W. MacDonald

doi:10.1016/j.neuroimage.2013.02.066

Neurometrics of intrinsic connectivity networks at rest using fMRI: Retest reliability and cross-validation using a meta-level method

Krista M. Wisner, Gowtham Atluri, Kelvin O. Lim, Angus W. MacDonald

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52 Scopus citations

Abstract

Functional images of the resting brain can be empirically parsed into intrinsic connectivity networks (ICNs) which closely resemble patterns of evoked task-based brain activity and which have a biological and genetic basis. Recently, ICNs have become popular for investigating brain functioning and brain-behavior relationships. However, the replicability and neurometrics of these networks are only beginning to be reported. Using a meta-level independent component analysis (ICA), we produced ICNs from three data sets collected from two samples of healthy adults. The ICNs from our data sets demonstrated robust and independent replication of 12 intrinsic networks that reflected 17 canonical, task-based, brain networks. We found within-subject reliability of ICNs was modest overall, but ranged from poor to good, and that voxels with the highest measured connectivity rarely had the highest reliability. Networks associated with executive functions, visuospatial reasoning, motor coordination, speech and audition, default mode, vision, and interoception showed moderate to high group-level reproducibility and replicability. However, only the first four of these networks also showed fair or better within-subject reliability over time. Our findings highlight the replicability of ICNs across data sets, the range of within-subject neurometrics across different networks, and the shared characteristics between resting and task-based networks.

Original language	English (US)
Pages (from-to)	236-251
Number of pages	16
Journal	NeuroImage
Volume	76
DOIs	https://doi.org/10.1016/j.neuroimage.2013.02.066
State	Published - Aug 1 2013

Bibliographical note

Funding Information:
We are grateful to Andrew Poppe for optimizing and improving the code implemented during the analyses, and to Chris Bell for his time and efforts involved in the data collection. We are also grateful for the computing resources as well as technical support from the University of Minnesota Supercomputing Institute, and the neuroimaging resources and technical support from the Center for Magnetic Resonance Research at the University of Minnesota. We are further grateful for the analytical insight provided by Angela R. Laird, Christian F. Beckmann, and Stephen M. Smith. Data collection was possible thanks to the financial support of the NIMH grant R01MH060662 and the NIDA grant P20DA024196. A.W.M. was additionally supported by the NIMH grant R01MH084861 and R21MH079262 during the course of this research; K.M.W. was supported by the NIMH Training Grant 5T32MH017069-29 . Parts of this manuscript were previously presented at the 2012 annual meeting for the Cognitive Neuroscience Society and were included in the master's thesis completed by K.M.W in April of 2012.

Keywords

BrainMap
FMRI
ICA
Intrinsic connectivity
Reliability
Reproducibility

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abstract = "Functional images of the resting brain can be empirically parsed into intrinsic connectivity networks (ICNs) which closely resemble patterns of evoked task-based brain activity and which have a biological and genetic basis. Recently, ICNs have become popular for investigating brain functioning and brain-behavior relationships. However, the replicability and neurometrics of these networks are only beginning to be reported. Using a meta-level independent component analysis (ICA), we produced ICNs from three data sets collected from two samples of healthy adults. The ICNs from our data sets demonstrated robust and independent replication of 12 intrinsic networks that reflected 17 canonical, task-based, brain networks. We found within-subject reliability of ICNs was modest overall, but ranged from poor to good, and that voxels with the highest measured connectivity rarely had the highest reliability. Networks associated with executive functions, visuospatial reasoning, motor coordination, speech and audition, default mode, vision, and interoception showed moderate to high group-level reproducibility and replicability. However, only the first four of these networks also showed fair or better within-subject reliability over time. Our findings highlight the replicability of ICNs across data sets, the range of within-subject neurometrics across different networks, and the shared characteristics between resting and task-based networks.",

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author = "Wisner, {Krista M.} and Gowtham Atluri and Lim, {Kelvin O.} and MacDonald, {Angus W.}",

note = "Funding Information: We are grateful to Andrew Poppe for optimizing and improving the code implemented during the analyses, and to Chris Bell for his time and efforts involved in the data collection. We are also grateful for the computing resources as well as technical support from the University of Minnesota Supercomputing Institute, and the neuroimaging resources and technical support from the Center for Magnetic Resonance Research at the University of Minnesota. We are further grateful for the analytical insight provided by Angela R. Laird, Christian F. Beckmann, and Stephen M. Smith. Data collection was possible thanks to the financial support of the NIMH grant R01MH060662 and the NIDA grant P20DA024196. A.W.M. was additionally supported by the NIMH grant R01MH084861 and R21MH079262 during the course of this research; K.M.W. was supported by the NIMH Training Grant 5T32MH017069-29 . Parts of this manuscript were previously presented at the 2012 annual meeting for the Cognitive Neuroscience Society and were included in the master's thesis completed by K.M.W in April of 2012. ",

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AU - Atluri, Gowtham

AU - Lim, Kelvin O.

AU - MacDonald, Angus W.

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KW - Reliability

KW - Reproducibility

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Neurometrics of intrinsic connectivity networks at rest using fMRI: Retest reliability and cross-validation using a meta-level method

Abstract

Bibliographical note

Keywords

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