390 research outputs found

    Diffusion imaging and tractography of congenital brain malformations.

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    Diffusion imaging is an MRI modality that measures the microscopic molecular motion of water in order to investigate white matter microstructure. The modality has been used extensively in recent years to investigate the neuroanatomical basis of congenital brain malformations. We review the basic principles of diffusion imaging and of specific techniques, including diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI). We show how DTI and HARDI, and their application to fiber tractography, has elucidated the aberrant connectivity underlying a number of congenital brain malformations. Finally, we discuss potential uses for diffusion imaging of developmental disorders in the clinical and research realms

    Evaluating metabolites in patients with major depressive disorder who received mindfulness-based cognitive therapy and healthy controls using short echo MRSI at 7 Tesla.

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    ObjectivesOur aim was to evaluate differences in metabolite levels between unmedicated patients with major depressive disorder (MDD) and healthy controls, to assess changes in metabolites in patients after they completed an 8-week course of mindfulness-based cognitive therapy (MBCT), and to exam the correlation between metabolites and depression severity.Materials and methodsSixteen patients with MDD and ten age- and gender-matched healthy controls were studied using 3D short echo-time (20 ms) magnetic resonance spectroscopic imaging (MRSI) at 7 Tesla. Relative metabolite ratios were estimated in five regions of interest corresponding to insula, anterior cingulate cortex (ACC), caudate, putamen, and thalamus.ResultsIn all cases, MBCT reduced severity of depression. The ratio of total choline-containing compounds/total creatine (tCr) in the right caudate was significantly increased compared to that in healthy controls, while ratios of N-acetyl aspartate (NAA)/tCr in the left ACC, myo-inositol/tCr in the right insula, and glutathione/tCr in the left putamen were significantly decreased. At baseline, the severity of depression was negatively correlated with my-inositol/tCr in the left insula and putamen. The improvement in depression severity was significantly associated with changes in NAA/tCr in the left ACC.ConclusionsThis study has successfully evaluated regional differences in metabolites for patients with MDD who received MBCT treatment and in controls using 7 Tesla MRSI

    Local Identification of Subsets of Quantum states: A Stronger Quantum Nonlocality

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    Nonolocality makes quantum theory nontrivially sacred and useful in the paradigm of information theoretic tasks. Apart from Bell nonlocality, which deals with measurement outcome statistics of spatially separated agents, there is also another kind of quantum nonlocality, that is associated with perfect distinguishability of quantum states by local operations and classical communication (LOCC). We propose a distributed task: perfect identification of subsets of a known set of multipartite orthogonal states by LOCC, namely, local subset identification. Failure in accomplishing this task guarantees a new notion of quantum nonlocality, viz., local subset unidentifiability. Here, we show that both local distinguishability and local markability of quantum states implies local subset identifiability, but the converse is not necessarily true. This makes local subset unidentifiability a stronger quantum nonlocal phenomenon than its predecessors -- local indistinguishability and local unmarkability. Moreover, we also present an even stronger version of local subset unidentifiablity involving more than two spatially separated parties namely, genuine local subset unidentifiability, where a given subset becomes identifiable if and only if all the parties come together in a common lab.Comment: Initial draft, New results added, Comments are welcom

    System Identification and Control of Front-Steered Ackermann Vehicles through Differentiable Physics

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    In this paper, we address the problem of system identification and control of a front-steered vehicle which abides by the Ackermann geometry constraints. This problem arises naturally for on-road and off-road vehicles that require reliable system identification and basic feedback controllers for various applications such as lane keeping and way-point navigation. Traditional system identification requires expensive equipment and is time consuming. In this work we explore the use of differentiable physics for system identification and controller design and make the following contributions: i)We develop a differentiable physics simulator (DPS) to provide a method for the system identification of front-steered class of vehicles whose system parameters are learned using a gradient-based method; ii) We provide results for our gradient-based method that exhibit better sample efficiency in comparison to other gradient-free methods; iii) We validate the learned system parameters by implementing a feedback controller to demonstrate stable lane keeping performance on a real front-steered vehicle, the F1TENTH; iv) Further, we provide results exhibiting comparable lane keeping behavior for system parameters learned using our gradient-based method with lane keeping behavior of the actual system parameters of the F1TENTH.Comment: Accepted for IROS 202
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