Frontiers in neuroscience
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Frontiers in neuroscience · Jan 2015
ReviewDisease-modifying therapeutic directions for Lewy-Body dementias.
Dementia with Lewy bodies (DLB) is the second leading cause of dementia following Alzheimer's disease (AD) and accounts for up to 25% of all dementia. DLB is distinct from AD in that it involves extensive neuropsychiatric symptoms as well as motor symptoms, leads to enormous societal costs in terms of direct medical care and is associated with high financial and caregiver costs. Although, there are no disease-modifying therapies for DLB, we review several new therapeutic directions in treating DLB. We discuss progress in strategies to decrease the level of alpha-synuclein, to prevent the cell to cell transmission of misfolded alpha-synuclein, and the potential of brain stimulation in DLB.
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Frontiers in neuroscience · Jan 2015
ReviewEmotion, rationality, and decision-making: how to link affective and social neuroscience with social theory.
In this paper, we argue for a stronger engagement between concepts in affective and social neuroscience on the one hand, and theories from the fields of anthropology, economics, political science, and sociology on the other. Affective and social neuroscience could provide an additional assessment of social theories. We argue that some of the most influential social theories of the last four decades-rational choice theory, behavioral economics, and post-structuralism-contain assumptions that are inconsistent with key findings in affective and social neuroscience. ⋯ The former can provide more precise formulations of the social phenomena that neuroscientific models have targeted, can help neuroscientists who build these models become more aware of their social and cultural biases, and can even improve the models themselves. To illustrate, we show how plural rationality theory can be used to further specify and test the somatic marker hypothesis. Thus, we aim to accelerate the much-needed merger of social theories with affective and social neuroscience.
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Frontiers in neuroscience · Jan 2015
Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach.
Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as to lessen the time and cost of clinical trials. Magnetic Resonance (MR)-related biomarkers have been recently identified by the use of machine learning methods for the in vivo differential diagnosis of AD. However, the vast majority of neuroimaging papers investigating this topic are focused on the difference between AD and patients with mild cognitive impairment (MCI), not considering the impact of MCI patients who will (MCIc) or not convert (MCInc) to AD. ⋯ CN, 66% MCIc vs. MCInc (nested 20-fold cross validation). Our data encourage the application of computer-based diagnosis in clinical practice of AD opening new prospective in the early management of AD patients.
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Frontiers in neuroscience · Jan 2015
Case ReportsNeuroplastic Effects of Transcranial Direct Current Stimulation on Painful Symptoms Reduction in Chronic Hepatitis C: A Phase II Randomized, Double Blind, Sham Controlled Trial.
Pegylated Interferon Alpha (Peg-IFN) in combination with other drugs is the standard treatment for chronic hepatitis C infection (HCV) and is related to severe painful symptoms. The aim of this study was access the efficacy of transcranial direct current stimulation (tDCS) in controlling the painful symptoms related to Peg-IFN side effects. ⋯ Five sessions of tDCS were effective in reducing the painful symptoms in HCV patients undergoing Peg-IFN treatment. These findings support the efficacy of tDCS as a promising therapeutic tool to improve the tolerance of the side effects related to the use of Peg-IFN. Future larger studies (phase III and IV trials) are needed to confirm the clinical use of the therapeutic effects of tDCS in such condition.
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Frontiers in neuroscience · Jan 2015
Segmentation of brain magnetic resonance images based on multi-atlas likelihood fusion: testing using data with a broad range of anatomical and photometric profiles.
We propose a hierarchical pipeline for skull-stripping and segmentation of anatomical structures of interest from T1-weighted images of the human brain. The pipeline is constructed based on a two-level Bayesian parameter estimation algorithm called multi-atlas likelihood fusion (MALF). In MALF, estimation of the parameter of interest is performed via maximum a posteriori estimation using the expectation-maximization (EM) algorithm. ⋯ As a result, we demonstrate subject-level differences in the performance of the proposed pipeline, which may be accounted for by age, diagnosis, or the imaging parameters (particularly the field strength). For the subcortical and ventricular structures of the two datasets, the hierarchical pipeline is capable of producing automated segmentations with Dice overlaps ranging from 0.8 to 0.964 when compared with the gold standard. Comparisons with other representative segmentation algorithms are presented, relative to which the proposed hierarchical pipeline demonstrates comparative or superior accuracy.