NeuroImage
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There have been several methods proposed so far using diffusion tensor imaging (DTI) for the assessment of normal-appearing brain tissue (NABT) injury in multiple sclerosis (MS). However, for these methods, the analyses of the NABT injury at the cellular level, wherein histological examinations can be used, still present challenging problems. We developed a method of segregating NABT into the following anatomical structures using lambda chart analysis associated with a two-dimensional Gaussian deconvolution of diffusion characteristic functions: 1) structures primarily composed of small neurons and glia; 2) structures primarily composed of large neurons; 3) structures primarily composed of short axons; and 4) structures primarily composed of long axons. ⋯ Furthermore, the volume fractions of the structures primarily composed of short axons markedly decreased, whereas those of the structures primarily composed of small neurons and glia markedly increased. These results suggest that axonal loss and glial proliferation predominantly occurred in the subcortical white matter and adjacent deep cortical layer, namely, the juxtacortical region. This cell-oriented analysis of NABT injury using DTI confirmed in vivo the histological observation that the juxtacortical region is the most vulnerable site in MS.
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A post-processing method for group discriminant analysis of fMRI is proposed. It assumes that the fMRI data have been pre-processed and analyzed so that each voxel is given a statistic specifying task-related activation(s), and that individually specific regions of interest (ROIs) have been drawn for each subject. The method then utilizes Local Linear Discriminant Analysis (LLDA) to jointly optimize the individually-specific and group linear combinations of ROIs that maximally discriminates between groups (or between tasks, if using the same subjects). ⋯ We applied the method to data recorded from 10 normal subjects during a motor task expected to activate both cortical and subcortical structures. The proposed method detected activation in multiple cortical and subcortical structures that were not present when the data were analyzed by warping the data to a common space. We suggest that the method be applied to group fMRI data when warping to a common space may be ill-advised, such as examining activation in small subcortical structures susceptible to mis-registration, or examining older or neurological patient populations.
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Preliminary data suggest an association of posterior cortical gray matter reduction with poor outcome in schizophrenia. We made a systematic MRI assessment of regional gray and white matter volumes, parcellated into 40 Brodmann's areas, in 104 patients with schizophrenia (51 with good outcomes, 53 with poor outcomes) and 41 normal comparison subjects, and investigated correlations of regional morphometry with outcome and severity of the illness. Schizophrenia patients displayed differential reductions in frontal and to a lesser degree temporal gray matter volumes in both hemispheres, most pronounced in the frontal pole and lateral temporal cortex. ⋯ While gray matter deficits in the granular cortex were observed in all schizophrenia patients, agranular cortical deficits in the left hemisphere were peculiar to patients with poor outcomes. These results provide support for frontotemporal gray matter reduction and frontoparietal white matter expansion in schizophrenia. Poor outcome is associated with more posterior distribution (posteriorization) of both gray and white matter changes, and with preferential impairment in the unimodal visual and paralimbic cortical regions.
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A component based method (CompCor) for the reduction of noise in both blood oxygenation level-dependent (BOLD) and perfusion-based functional magnetic resonance imaging (fMRI) data is presented. In the proposed method, significant principal components are derived from noise regions-of-interest (ROI) in which the time series data are unlikely to be modulated by neural activity. These components are then included as nuisance parameters within general linear models for BOLD and perfusion-based fMRI time series data. ⋯ For both functional perfusion and BOLD data, the application of CompCor significantly increased the number of activated voxels as compared to no correction. In addition, for functional BOLD data, there were significantly more activated voxels detected with CompCor as compared to RETROICOR. In comparison to RETROICOR, CompCor has the advantage of not requiring external monitoring of physiological fluctuations.