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) |
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Article number | 119192 |
Journal | NeuroImage |
Volume | 255 |
DOIs | |
State | Published - Jul 15 2022 |
Bibliographical note
Publisher Copyright:© 2022
Keywords
- Cluster inference
- Group-level activation
- Neuroimaging data analysis
- Resampling
- Spatial autocorrelation modelling
- Task-fMRI