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bioRxiv · 10.1101/2022.03.02.482664

CLEAN: Leveraging spatial autocorrelation in neuroimaging data in clusterwise inference

Park, J. Y.·Fiecas, M.
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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.

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Park, J. Y., Fiecas, M.. 2022-03-02. CLEAN: Leveraging spatial autocorrelation in neuroimaging data in clusterwise inference. https://doi.org/10.1101/2022.03.02.482664

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