Striatal dysfunction during reversal learning in unmedicated schizophrenia patients
Florian Schlagenhauf,Quentin J. M. Huys,Lorenz Deserno,Michael A. Rapp,Anne Beck,H. J. Heinze,Raymond J. Dolan,Andreas Heinz +7 more
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TLDR
This study shows that VS dysfunction in schizophrenia patients during reward-related reversal learning remains a core deficit even when controlling for task solving strategies, and highlights VS dysfunction is tightly linked to a reward- related reversal learning deficit in early, unmedicated schizophrenia patients.About:
This article is published in NeuroImage.The article was published on 2014-04-01 and is currently open access. It has received 222 citations till now. The article focuses on the topics: First episode & Schizophrenia.read more
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Computational psychiatry as a bridge from neuroscience to clinical applications
TL;DR: This work reviews recent advances in data driven and theory driven Computational psychiatry, with an emphasis on clinical applications, and highlights the utility of combining them.
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A computational psychiatry approach identifies how alpha-2A noradrenergic agonist Guanfacine affects feature-based reinforcement learning in the macaque.
S.A. Hassani,Mariann Oemisch,Matthew Balcarras,Stephanie Westendorff,Salva Ardid,M.A. van der Meer,Paul H. E. Tiesinga,Thilo Womelsdorf +7 more
TL;DR: Parameter estimation suggested that improved learning is not accounted for by varying a single reinforcement learning mechanism, but by changing the set of parameter values to higher learning rates and stronger suppression of non-chosen over chosen feature information.
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Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.
TL;DR: The proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns.
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Ventral striatal activation during reward processing in psychosis: A neurofunctional meta-analysis
Joaquim Radua,André Schmidt,André Schmidt,Stefan Borgwardt,Stefan Borgwardt,Andreas Heinz,Andreas Heinz,Florian Schlagenhauf,Florian Schlagenhauf,Philip McGuire,Paolo Fusar-Poli +10 more
TL;DR: This meta-analysis provides evidence that patients with psychosis demonstrate VS hypoactivation during reward anticipation and the assessment of VS prediction errors seems to be promising, but more studies are needed to draw valid conclusions.
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Allostatic Self-Efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression
Klaas E. Stephan,Zina M. Manjaly,Christoph Mathys,Lilian A.E. Weber,Saee Paliwal,Tim Gard,Marc Tittgemeyer,Stephen M. Fleming,Helene Haker,Anil K. Seth,Frederike H. Petzschner +10 more
TL;DR: A hierarchical Bayesian framework for interoception, homeostatic/allostatic control, and meta-cognition is outlined that connects fatigue and depression to the experience of chronic dyshomeostasis and proposes that the performance of interoceptive- allostatic circuitry is monitored by a metacognitive layer that updates beliefs about the brain's capacity to successfully regulate bodily states.
References
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