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At Risk for Neuropsychiatric Disorders: An Affective Neuroscience Approach to Understanding the Spectrum

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889450268 Year: Pages: 260 DOI: 10.3389/978-2-88945-026-8 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Neurology
Added to DOAB on : 2018-02-27 16:16:44
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Abstract

Neuropsychiatric disorders such as schizophrenia, bipolar disorder, depression, anxiety disorders, and other mental disorders constitute about 13% of the global burden of disease surpassing both cardiovascular disease and cancer. The total cost worldwide of these diseases is estimated to exceed 100 million disability-adjusted life years. In order to begin to address this important problem, the present Research Topic brings together a group of leading affective neuroscience researchers to present their state-of-the-art findings using an affective neuroscience approach to investigate the spectrum of neuropsychiatric disorders from patients to those at risk. They focus on different aspects of the emotional and social cognitive disturbances which are core features of neuropsychiatric disorders. While progress has been slow over last couple of decades, we are finally beginning to glimpse some of the underlying neural mechanisms of the emotional and social cognitive disturbances in patients and those at risk. With the technological advances in affective neuroscience and neuroimaging presented in this volume, we hope that progress will be much swifter in the coming years such that we can provide better care for patients and those at risk.

Integrating Computational and Neural Findings in Visual Object Perception

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889198733 Year: Pages: 137 DOI: 10.3389/978-2-88919-873-3 Language: English
Publisher: Frontiers Media SA
Subject: Neurology --- Science (General)
Added to DOAB on : 2016-01-19 14:05:46
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The articles in this Research Topic provide a state-of-the-art overview of the current progress in integrating computational and empirical research on visual object recognition. Developments in this exciting multidisciplinary field have recently gained momentum: High performance computing enabled breakthroughs in computer vision and computational neuroscience. In parallel, innovative machine learning applications have recently become available for datamining the large-scale, high resolution brain data acquired with (ultra-high field) fMRI and dense multi-unit recordings. Finally, new techniques to integrate such rich simulated and empirical datasets for direct model testing could aid the development of a comprehensive brain model. We hope that this Research Topic contributes to these encouraging advances and inspires future research avenues in computational and empirical neuroscience.

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