Frontiers in 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
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.
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Frontiers in neuroscience · Jan 2015
Transcranial random noise stimulation-induced plasticity is NMDA-receptor independent but sodium-channel blocker and benzodiazepines sensitive.
Application of transcranial random noise stimulation (tRNS) between 0.1 and 640 Hz of the primary motor cortex (M1) for 10 min induces a persistent excitability increase lasting for at least 60 min. However, the mechanism of tRNS-induced cortical excitability alterations is not yet fully understood. ⋯ In contrast to transcranial direct current stimulation (tDCS), aftereffects of tRNS are seem to be not NMDA receptor dependent and can be suppressed by benzodiazepines suggesting that tDCS and tRNS depend upon different mechanisms.
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Frontiers in neuroscience · Jan 2015
ReviewDoes neuroinflammation turn on the flame in Alzheimer's disease? Focus on astrocytes.
Data from animal models and Alzheimer's disease (AD) subjects provide clear evidence for an activation of inflammatory pathways during the pathogenetic course of such illness. Biochemical and neuropathological studies highlighted an important cause/effect relationship between inflammation and AD progression, revealing a wide range of genetic, cellular, and molecular changes associated with the pathology. In this context, glial cells have been proved to exert a crucial role. ⋯ Neuroinflammation is certainly a multi-faceted and complex phenomenon and, especially in the early stages, exerts a reparative intent. However, for reasons not yet all well known, this process goes beyond the physiologic control and contributes to the exacerbation of the damage. Here we scrutinize some evidence supporting the role of astrocytes in the neuroinflammatory process and the possibility that these cells could be considered a promising target for future AD therapies.
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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.