In nonstationary images, cluster inference depends on the local image smoothness, as clusters tend to be larger in smoother regions by chance alone. In order to correct the inference for such nonstationary, cluster sizes can be adjusted according to a local smoothness estimate. In this study, adjusted cluster sizes are used in a permutation-testing framework for both cluster-based and threshold-free cluster enhancement (TFCE) inference and tested on both simulated and real data. We find that TFCE inference is already fairly robust to nonstationarity in the data, while cluster-based inference requires an adjustment to ensure homogeneity. A group of possible multi-level adjustments are introduced and their results on simulated and real data are assessed using a new performance index. We also find that adjusting for local smoothness via a separate resampling procedure is more effective at removing nonstationarity than an adjustment via a random field theory based smoothness estimator.

Original publication

DOI

10.1016/j.neuroimage.2010.09.088

Type

Journal article

Journal

Neuroimage

Publication Date

01/02/2011

Volume

54

Pages

2006 - 2019

Keywords

Algorithms, Alzheimer Disease, Brain, Brain Mapping, Cluster Analysis, Cognition Disorders, Computer Simulation, Data Interpretation, Statistical, False Positive Reactions, Humans, Image Processing, Computer-Assisted, Linear Models, Magnetic Resonance Imaging, Normal Distribution, ROC Curve, Reproducibility of Results