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While past neuroimaging methods have contributed greatly to our understanding of brain function after traumatic brain injury (TBI), resting state functional MRI (rsfMRI) connectivity methods have more recently provided a far more unbiased approach with which to monitor brain circuitry compared to task-based approaches. However, current knowledge on the physiologic underpinnings of the correlated blood oxygen level dependent signal, and how changes in functional connectivity relate to reorganizational processes that occur following injury is limited. The degree and extent of this relationship remain to be determined in order that rsfMRI methods can be fully adapted for determining the optimal timing and type of rehabilitative interventions that can be used post-TBI to achieve the best outcome. Very few rsfMRI studies exist after experimental TBI and therefore we chose to acquire rsfMRI data before and at 7, 14 and 28 days after experimental TBI using a well-known, clinically-relevant, unilateral controlled cortical impact injury (CCI) adult rat model of TBI. This model was chosen since it has widespread axonal injury, a well-defined time-course of reorganization including spine, dendrite, axonal and cortical map changes, as well as spontaneous recovery of sensorimotor function by 28 d post-injury from which to interpret alterations in functional connectivity. Data were co-registered to a parcellated rat template to generate adjacency matrices for network analysis by graph theory. Making no assumptions about direction of change, we used two-tailed statistical analysis over multiple brain regions in a data-driven approach to access global and regional changes in network topology in order to assess brain connectivity in an unbiased way. Our main hypothesis was that deficits in functional connectivity would become apparent in regions known to be structurally altered or deficient in axonal connectivity in this model. The data show the loss of functional connectivity predicted by the structural deficits, not only within the primary sensorimotor injury site and pericontused regions, but the normally connected homotopic cortex, as well as subcortical regions, all of which persisted chronically. Especially novel in this study is the unanticipated finding of widespread increases in connection strength that dwarf both the degree and extent of the functional disconnections, and which persist chronically in some sensorimotor and subcortically connected regions. Exploratory global network analysis showed changes in network parameters indicative of possible acutely increased random connectivity and temporary reductions in modularity that were matched by local increases in connectedness and increased efficiency among more weakly connected regions. The global network parameters: shortest path-length, clustering coefficient and modularity that were most affected by trauma also scaled with the severity of injury, so that the corresponding regional measures were correlated to the injury severity most notably at 7 and 14 days and especially within, but not limited to, the contralateral cortex. These changes in functional network parameters are discussed in relation to the known time-course of physiologic and anatomic data that underlie structural and functional reorganization in this experiment model of TBI. [ABSTRACT FROM AUTHOR]

Abstract: MRI research examining structural brain atrophy in Alzheimer''s disease (AD) generally focuses on medial temporal and cortical structures, but amyloid and tau deposits also accumulate in the caudate. Here we mapped the 3D profile of caudate atrophy using a surface mapping approach in subjects with AD and mild cognitive impairment (MCI) to identify potential clinical and pathological correlates. 3D surface models of the caudate were automatically extracted from 400 baseline MRI scans (100 AD, 200 MCI, 100 healthy elderly). Compared to controls, caudate volumes were lower in MCI (2.64% left, 4.43% right) and AD (4.74% left, 8.47% right). Caudate atrophy was associated with age, sum-of-boxes and global Clinical Dementia Ratings, Delayed Logical Memory scores, MMSE decline 1 year later, and body mass index. Reduced right (but not left) volume was associated with MCI-to-AD conversion and CSF tau levels. Normal caudate asymmetry (with the right 3.9% larger than left) was lost in AD, suggesting preferential right caudate atrophy. Automated caudate maps may complement other MRI-derived measures of disease burden in AD. [Copyright &y& Elsevier]

Abstract: Objectives: To assess quantitatively the cortical pattern profile of regional FDDNP binding to beta-amyloid and neurofibrillary tangles on MR derived cortical maps, FDDNP PET images were corrected for movement and partial volume (PV), and optimized for kernel size. Methods: FDDNP DVR PET images from 23 subjects (7 with Alzheimer''s disease (AD), 6 with mild cognitive impairment and 10 controls) were obtained from Logan analysis using cerebellum as reference. A hemispheric cortical surface model for each subject was extracted from the MRI. The same transformations were applied to the FDDNP DVR PET images to map them into the same space. The cortical map with PV correction was calculated as the ratio of the DVR cortical surface and that of the simulated map, created from the mask derived from MRI and smoothed to the PET resolution. Discriminant analysis was used to order the FDDNP DVR cortical surfaces based on subjects'' disease state. Linear regression was used to assess the rate of change of DVR vs. MMSE for each hemispheric cortical surface point. Results: The FDDNP DVR cortical surface corrected for movement and PV had less hemispheric asymmetry. Optimal kernel size was determined to be 9 mm. The corrected cortical surface map of FDDNP DVR showed clear spatial pattern that was consistent with the known pathological progression of AD. Conclusion: Correcting for movement, PV as well as optimizing kernel size provide sensitive statistical analysis of FDDNP distribution which confirms in the living brain known pathology patterns earlier observed with cognitive decline with brain specimens. [Copyright &y& Elsevier]

Abstract: To assess the morphological changes of the hippocampus in Lewy body dementia (LBD) patients we used radial atrophy mapping, a mathematical modeling method sensitive to subtle differences in hippocampal shape. T1-weighted high resolution magnetic resonance (MR) scans were acquired from 14 LBD and 28 controls of similar age and gender, and were compared to those of 28 patients with Alzheimer''s disease (AD) described previously. MR images were normalized by linear (12 parameter) transformation to a customized template. The hippocampal formation was isolated by manual tracing. Group differences were assessed with algorithms that average hippocampal shapes across subjects, using three-dimensional parametric surface mesh models. In LBD patients, significant tissue loss amounting to 10–20% was found in the hippocampal subregions corresponding to the anterior portion of the CA1 field on both sides, along the longitudinal midline in the dorsal aspect within the CA2–3 field, and in the subiculum and presubiculum. The direct comparisons between LBD and AD patients showed that this pattern of local atrophy is different from that characteristic of AD. LBD pattern of hippocampal atrophy might be related to the peculiar neuropathology of the disease. [Copyright &y& Elsevier]

Abstract: This paper presents a novel approach to feature-based brain image warping, by using a hybrid implicit/explicit framework, which unifies many prior approaches in a common framework. In the first step, we develop links between image warping and the level-set method, and we formulate the fundamental mathematics required for this hybrid implicit/explicit approach. In the second step, we incorporate the large-deformation models into these formulations, leading to a complete and elegant treatment of anatomical structure matching. In this latest approach, exact matching of anatomy is achieved by comparing the target to the warped source structure under the forward mapping and the source to the warped target structure under the backward mapping. Because anatomy is represented nonparametrically, a path is constructed linking the source to the target structure without prior knowledge of their point correspondence. The final point correspondence is constructed based on the linking path with the minimal energy. Intensity-similarity measures can be naturally incorporated in the same framework as landmark constraints by combining them in the gradient descent body forces. We illustrate the approach with two applications: (1) tensor-based morphometry of the corpus callosum in autistic children; and (2) matching cortical surfaces to measure the profile of cortical anatomic variation. In summary, the new mathematical techniques introduced here contribute fundamentally to the mapping of brain structure and its variation and provide a framework that unites feature and intensity-based image registration techniques. [Copyright &y& Elsevier]