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