CLEAN: Leveraging spatial autocorrelation in neuroimaging data in clusterwise inference

Jun Young Park; Mark Fiecas

doi:10.1016/j.neuroimage.2022.119192

CLEAN: Leveraging spatial autocorrelation in neuroimaging data in clusterwise inference

Jun Young Park, Mark Fiecas

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

While clusterwise inference is a popular approach in neuroimaging that improves sensitivity, current methods do not account for explicit spatial autocorrelations because most use univariate test statistics to construct cluster-extent statistics. Failure to account for such dependencies could result in decreased reproducibility. To address methodological and computational challenges, we propose a new powerful and fast statistical method called CLEAN (Clusterwise inference Leveraging spatial Autocorrelations in Neuroimaging). CLEAN computes multivariate test statistics by modelling brain-wise spatial autocorrelations, constructs cluster-extent test statistics, and applies a refitting-free resampling approach to control false positives. We validate CLEAN using simulations and applications to the Human Connectome Project. This novel method provides a new direction in neuroimaging that paces with advances in high-resolution MRI data which contains a substantial amount of spatial autocorrelation.

Original language	English (US)
Article number	119192
Journal	NeuroImage
Volume	255
DOIs	https://doi.org/10.1016/j.neuroimage.2022.119192
State	Published - Jul 15 2022
Externally published	Yes

Bibliographical note

Funding Information:
We would like to thank reviewers for their helpful comments. Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research ; and by the McDonnell Center for Systems Neuroscience at Washington University. We acknowledge the support of the Data Sciences Institute at the University of Toronto, the Masonic Institute for the Developing Brain at the University of Minnesota, and the Natural Sciences and Engineering Research Council of Canada (NSERC) [No: RGPIN-2022-04831].

Publisher Copyright:
© 2022

Keywords

Cluster inference
Group-level activation
Neuroimaging data analysis
Resampling
Spatial autocorrelation modelling
Task-fMRI

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10.1016/j.neuroimage.2022.119192

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abstract = "While clusterwise inference is a popular approach in neuroimaging that improves sensitivity, current methods do not account for explicit spatial autocorrelations because most use univariate test statistics to construct cluster-extent statistics. Failure to account for such dependencies could result in decreased reproducibility. To address methodological and computational challenges, we propose a new powerful and fast statistical method called CLEAN (Clusterwise inference Leveraging spatial Autocorrelations in Neuroimaging). CLEAN computes multivariate test statistics by modelling brain-wise spatial autocorrelations, constructs cluster-extent test statistics, and applies a refitting-free resampling approach to control false positives. We validate CLEAN using simulations and applications to the Human Connectome Project. This novel method provides a new direction in neuroimaging that paces with advances in high-resolution MRI data which contains a substantial amount of spatial autocorrelation.",

keywords = "Cluster inference, Group-level activation, Neuroimaging data analysis, Resampling, Spatial autocorrelation modelling, Task-fMRI",

author = "Park, {Jun Young} and Mark Fiecas",

note = "Funding Information: We would like to thank reviewers for their helpful comments. Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research ; and by the McDonnell Center for Systems Neuroscience at Washington University. We acknowledge the support of the Data Sciences Institute at the University of Toronto, the Masonic Institute for the Developing Brain at the University of Minnesota, and the Natural Sciences and Engineering Research Council of Canada (NSERC) [No: RGPIN-2022-04831]. Publisher Copyright: {\textcopyright} 2022",

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doi = "10.1016/j.neuroimage.2022.119192",

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N2 - While clusterwise inference is a popular approach in neuroimaging that improves sensitivity, current methods do not account for explicit spatial autocorrelations because most use univariate test statistics to construct cluster-extent statistics. Failure to account for such dependencies could result in decreased reproducibility. To address methodological and computational challenges, we propose a new powerful and fast statistical method called CLEAN (Clusterwise inference Leveraging spatial Autocorrelations in Neuroimaging). CLEAN computes multivariate test statistics by modelling brain-wise spatial autocorrelations, constructs cluster-extent test statistics, and applies a refitting-free resampling approach to control false positives. We validate CLEAN using simulations and applications to the Human Connectome Project. This novel method provides a new direction in neuroimaging that paces with advances in high-resolution MRI data which contains a substantial amount of spatial autocorrelation.

AB - While clusterwise inference is a popular approach in neuroimaging that improves sensitivity, current methods do not account for explicit spatial autocorrelations because most use univariate test statistics to construct cluster-extent statistics. Failure to account for such dependencies could result in decreased reproducibility. To address methodological and computational challenges, we propose a new powerful and fast statistical method called CLEAN (Clusterwise inference Leveraging spatial Autocorrelations in Neuroimaging). CLEAN computes multivariate test statistics by modelling brain-wise spatial autocorrelations, constructs cluster-extent test statistics, and applies a refitting-free resampling approach to control false positives. We validate CLEAN using simulations and applications to the Human Connectome Project. This novel method provides a new direction in neuroimaging that paces with advances in high-resolution MRI data which contains a substantial amount of spatial autocorrelation.

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KW - Task-fMRI

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CLEAN: Leveraging spatial autocorrelation in neuroimaging data in clusterwise inference

Abstract

Bibliographical note

Keywords

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