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We present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of bundle shapes and trajectories over subjects. For each example subject, an atlas is maintained to allow the example data of a subject to be added or deleted flexibly. A voting scheme is proposed to facilitate the multi-atlas exploitation of example data. For conceptual simplicity, we adopt the same metrics in both example data construction and WM tract labeling. Due to the huge number of WM tracts in a subject, it is time-consuming to label each WM tract individually. Thus, the WM tracts are grouped according to their shape similarity, and WM tracts within each group are labeled simultaneously. To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU). Through nested cross-validation we demonstrated that our approach yielded high classification performance. The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively.

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Cognitive reserve is the ability to sustain cognitive function even with a certain amount of brain damages. Here we investigate the neural compensation mechanism of cognitive reserve from the perspective of structural brain connectivity. Our goal was to show that normal people with high education levels (i.e., cognitive reserve) maintain abundant pathways connecting any two brain regions, providing better compensation or resilience after brain damage. Accordingly, patients with high education levels show more deterioration in structural brain connectivity than those with low education levels before symptoms of Alzheimer's disease (AD) become apparent. To test this hypothesis, we use network flow measuring the number of alternative paths between two brain regions in the brain network. The experimental results show that for normal aging, education strengthens network reliability, as measured through flow values, in a subnetwork centered at the supramarginal gyrus. For AD, a subnetwork centered at the left middle frontal gyrus shows a negative correlation between flow and education, which implies more collapse in structural brain connectivity for highly educated patients. We conclude that cognitive reserve may come from the ability of network reorganization to secure the information flow within the brain network, therefore making it more resistant to disease progress.

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We propose a physically-based approach to separate reflection using multiple polarized images with a background scene captured behind glass. The input consists of three polarized images, each captured from the same view point but with a different polarizer angle separated by 45 degrees. The output is the high-quality separation of the reflection and background layers from each of the input images. A main technical challenge for this problem is that the mixing coefficient for the reflection and background layers depends on the angle of incidence and the orientation of the plane of incidence, which are spatially varying over the pixels of an image. Exploiting physical properties of polarization for a double-surfaced glass medium, we propose a multiscale scheme which automatically finds the optimal separation of the reflection and background layers. Through experiments, we demonstrate that our approach can generate superior results to those of previous methods.

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BACKGROUND: Electroconvulsive therapy (ECT) is a widely used, highly effective antidepressant treatment. Except for the most severely ill patients, right unilateral (RUL) electrode placement is the most frequent initial treatment choice. In current practice, RUL ECT is administered at several multiples of seizure threshold (ST) based on reports that lower stimulus intensity results in lower response/remission rates. Many patients, as part of an initial dose titration to determine ST, will receive a single treatment with low-dose RUL ECT and subsequent treatments with a stimulus at a multiple of ST. OBJECTIVE: To assess response to the first ECT. METHODS: A retrospective analysis of charts from clinical practice at Mount Sinai Medical Center was performed. RESULTS: A single treatment with low-dose (presumably near ST) RUL ECT had a significant and immediate antidepressant effect in our sample of patients with major depression. We determined that this response is similar to that of patients receiving a single initial treatment with high-dose RUL ECT (at a multiple of ST). CONCLUSIONS: These data suggest, contrary to commonly held belief, that RUL ECT may be effective at a low stimulus dose. This argues against restimulating at 6 times ST in the initial session, based on the belief that the near-threshold seizure has no antidepressant efficacy. Our findings suggest a need for further investigation of cases in which low-dose RUL ECT may be an effective antidepressant treatment. Further prospective studies, including larger numbers of patients who receive randomized treatment with low- or high-dose RUL with longer follow-up, are indicated.

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