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Activity Number: 132 - Statistical Advances in Dimension Reduction and Feature Interpretability in Neuroimaging
Type: Topic Contributed
Date/Time: Monday, August 8, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Imaging
Abstract #320841
Title: CLEAN: Leveraging Spatial Autocorrelation in Neuroimaging Data in Clusterwise Inference
Author(s): Jun Young Park* and Mark Fiecas
Companies: University of Toronto and University of Minnesota
Keywords: Clusterwise inference; Neuroimaging; Multivariate; Spatial statistics; Data integration

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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