We present a novel shape metric for quantification of shape differences between the spatial components obtained from
independent component analysis (ICA) of group functional magnetic resonance imaging (fMRI) data. This metric is
utilized to measure the difference in shapes of the activation regions obtained from different subjects within a group
(healthy controls or patients). The parameters comprising the metric are computed for each pixel on the outermost
contour (edge) of an activation region for each slice. These parameters are in the form of (r, θ) pairs that may be
interpreted as the length and orientation of a vector originating from the centroid of the activation region to the pixel
belonging to the boundary contour. Using this information we extract three features that quantify the shape difference
between the two shapes under observation. The reference and observation shapes may be selected in two ways: (a)
activation maps from two different subjects or (b) mean activation map compared against subject-wise activations, as
obtained from group ICA. We present different methods to visualize the shape differences, thus providing a tool to
observe the spatial differences within a group or across groups. In addition to the above results, we also address a few
special cases where two or more activation contours are present in a single slice and present potential solutions for
accounting for these regions as special measures. Our results show that this metric has utility in creating a better
understanding of the variability in brain activity among different groups of subjects performing the same task.
KEYWORDS: Smoothing, Functional magnetic resonance imaging, Denoising, Wavelets, Independent component analysis, Interference (communication), Signal to noise ratio, Signal detection, Stationary wavelet transform, Magnetic resonance imaging
Functional MRI (fMRI) data analysis deals with the problem of detecting very weak signals in very noisy data.
Smoothing with a Gaussian kernel is often used to decrease noise at the cost of losing spatial specificity. We present a
novel wavelet-based 3-D technique to remove noise in fMRI data while preserving the spatial features in the component
maps obtained through group independent component analysis (ICA). Each volume is decomposed into eight volumetric
sub-bands using a separable 3-D stationary wavelet transform. Each of the detail sub-bands are then treated through the
main denoising module. This module facilitates computation of shrinkage factors through a hierarchical framework. It
utilizes information iteratively from the sub-band at next higher level to estimate denoised coefficients at the current
level. These de-noised sub-bands are then reconstructed back to the spatial domain using an inverse wavelet transform.
Finally, the denoised group fMRI data is analyzed using ICA where the data is decomposed in to clusters of functionally
correlated voxels (spatial maps) as indicators of task-related neural activity. The proposed method enables the
preservation of shape of the actual activation regions associated with the BOLD activity. In addition it is able to achieve
high specificity as compared to the conventionally used FWHM (full width half maximum) Gaussian kernels for
smoothing fMRI data.
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