We apply the initial momentum shape representation of diffeomorphic metric mapping from a template region of interest
(ROI) to a given ROI as a morphometic marker in Parkinson's disease. We used a three-step segmentation-registrationmomentum
process to derive feature vectors from ROIs in a group of 42 subjects consisting of 19 Parkinson's Disease
(PD) subjects and 23 normal control (NC) subjects. Significant group differences between PD and NC subjects were
detected in four basal ganglia structures including the caudate, putamen, thalamus and globus pallidus. The magnitude of
regionally significant between-group differences detected ranged between 34-75%. Visualization of the different
structural deformation pattern between-groups revealed that some parts of basal ganglia structure actually hypertrophy,
presumably as a compensatory response to more widespread atrophy. Our results of both hypertrophy and atrophy in the
same structures further demonstrate the importance of morphological measures as opposed to overall volume in the
assessment of neurodegenerative disease.
We apply a recently developed automated brain segmentation method, FS+LDDMM, to brain MRI scans from
Parkinson's Disease (PD) subjects, and normal age-matched controls and compare the results to manual segmentation
done by trained neuroscientists. The data set consisted of 14 PD subjects and 12 age-matched control
subjects without neurologic disease and comparison was done on six subcortical brain structures (left and right
caudate, putamen and thalamus). Comparison between automatic and manual segmentation was based on Dice
Similarity Coefficient (Overlap Percentage), L1 Error, Symmetrized Hausdorff Distance and Symmetrized Mean
Surface Distance. Results suggest that FS+LDDMM is well-suited for subcortical structure segmentation and
further shape analysis in Parkinson's Disease. The asymmetry of the Dice Similarity Coefficient over shape
change is also discussed based on the observation and measurement of FS+LDDMM segmentation results.
Conventional voxel-based group analysis of functional magnetic resonance imaging (fMRI) data typically requires
warping each subject's brain images onto a common template to create an assumed voxel correspondence. The implicit
assumption is that aligning the anatomical structures would correspondingly align the functional regions of the subjects.
However, due to anatomical and functional inter-subject variability, mis-registration often occurs. Moreover, wholebrain
warping is likely to distort the spatial patterns of activation, which have been shown to be important markers of
task-related activation. To reduce the amount of mis-registration and distortions, warping at the brain region level has
recently been proposed. In this paper, we investigate the effects of both whole-brain and region-level warping on the
spatial patterns of activation statistics within certain regions of interests (ROIs). We have chosen to examine the bilateral
thalami and cerebellar hemispheres during a bulb-squeezing experiment, as these regions are expected to incur taskrelated
activation changes. Furthermore, the appreciable size difference between the thalamus and cerebellum allows for
exploring the effects of warping on various ROI sizes. By applying our recently proposed 3D moment-based invariant
spatial features to characterize the spatial pattern of fMRI activation statistics, we demonstrate that whole-brain warping
generally reduced discriminability of task-related activation differences. Applying the same spatial analysis to ROIs
warped at the region level showed some improvements over whole-brain warping, but warp-free analysis resulted in the
best performance. We hence suggest that spatial analysis of fMRI data that includes spatial warping to a common space
must be interpreted with caution.
Independent Component Analysis (ICA) has proved a powerful exploratory analysis method for fMRI. In the ICA model, the fMRI data at a given time point are modeled as the linear superposition of spatially independent (and spatially stationary) component maps. The ICA model has been recently applied to positron emission tomography (PET) data with some success (Human Brain Mapping 18:284-295(2003), IEEE Trans. BME, Naganawa et al, in press). However, in PET imaging each frame is, in fact, activity integrated over a relatively long period of time, making the assumption that the underlying component maps are spatially stationary (and hence no head movement has taken place during the frame collection) very tenuous. Here we extend the application of the ICA model to <sup>11</sup>C-methylphenidate PET data by assuming that each frame is actually composed of the superposition of rigidly transformed underlying spatial components. We first determine the “noisy” initial spatially independent components of a data set under the erroneous assumption of no intra or inter-frame motion. Aspects of the initial components that reliably track spatial perturbations of the data are then determined to produce the motion-compensated components. Initial components included ring-like spatial distributions, indicating that movement corrupts the statistical properties of the data. The final intra-frame motion-compensated components included more plausible symmetric and robust activity in the striatum as would be expected compared to the raw data and the initial components. We conclude that 1) intra-frame motion is a serious confound in PET imaging which affects the statistical properties of the data and 2) our proposed procedure ameliorates such motion effects.