Determining significant connectivity by 4D spatiotemporal wavelet packet resampling of functional neuroimaging data

Abstract

An active area of neuroimaging research involves examining functional relationships between spatially remote brain regions. When determining whether two brain regions exhibit significant correlation due to true functional connectivity, one must account for the background spatial correlation inherent in neuroimaging data. We define background correlation as spatiotemporal correlation in the data caused by factors other than neurophysiologically based functional associations such as scanner induced correlations and image preprocessing. We develop a 4D spatiotemporal wavelet packet resampling method which generates surrogate data that preserves only the average background spatial correlation within an axial slice, across axial slices, and through each voxel time series, while excluding the specific correlations due to true functional relationships. We also extend an amplitude adjustment algorithm which adjusts our surrogate data to closely match the amplitude distribution of the original data. Our method improves upon existing wavelet-based methods and extends them to 4D. We apply our resampling technique to determine significant functional connectivity from resting state and motor task fMRI datasets. D

Linking Brain Connectivity Across Different Time Scales with Electroencephalogram, Functional Magnetic Resonance Imaging, and Diffusion Tensor Imaging

“…Statistical significance of the similarity was tested by randomization statistics following the approach by Patel and associates (2006) and is briefly reviewed here. The t-IAF-BOLD correlation map was used to generate 399 surrogate maps with the same significance values and spatial frequency of their distribution (Britz et al, 2010;Patel et al, 2006). For each of these surrogate maps and FCN combinations, the SS was computed.…”

Surfing the brain

“…Another important question is how to relax the shift-invariance of the transform (e.g., by deploying the redundant transform) without jeopardizing the statistical significance [75]. Finally, an interesting future research topic is functional connectivity, where wavelets could be deployed jointly in the spatial and temporal domain; e.g., for nonparametric tests using bootstrapping in the wavelet domain [76]. Wavelets were primarily applied in medical imaging for denoising in the context of MRI.…”

Statistical Approaches to Functional Neuroimaging Data

“…This was achieved through spatio-temporal resampling of the data in the wavelet domain. Patel and colleagues [65] extended this work to a 4-D wavelet-based nonparametric approach for determining whether the functional connectivity observed in an experiment is significantly greater than the background correlation. Specifically, they presented a spatio-temporal wavelet packet resampling method that generates surrogate data that preserves only the average background correlation within an axial slice, across axial slices, and through each voxel time series, while excluding the specific correlations due to true functional relationships.…”