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dc.contributor.author
Haker, Helene
dc.contributor.author
Schneebeli, Maya
dc.contributor.author
Stephan, Klaas
dc.date.accessioned
2019-06-25T15:10:25Z
dc.date.available
2017-06-12T08:07:17Z
dc.date.available
2019-06-25T14:34:54Z
dc.date.available
2019-06-25T14:40:18Z
dc.date.available
2019-06-25T15:10:25Z
dc.date.issued
2016-06-17
dc.identifier.issn
1664-0640
dc.identifier.other
10.3389/fpsyt.2016.00107
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/117744
dc.identifier.doi
10.3929/ethz-b-000117744
dc.description.abstract
Diagnosis and individualized treatment of autism spectrum disorder (ASD) represent major problems for contemporary psychiatry. Tackling these problems requires guidance by a pathophysiological theory. In this paper, we consider recent theories that re-conceptualize ASD from a “Bayesian brain” perspective, which posit that the core abnormality of ASD resides in perceptual aberrations due to a disbalance in the precision of prediction errors (sensory noise) relative to the precision of predictions (prior beliefs). This results in percepts that are dominated by sensory inputs and less guided by top-down regularization and shifts the perceptual focus to detailed aspects of the environment with difficulties in extracting meaning. While these Bayesian theories have inspired ongoing empirical studies, their clinical implications have not yet been carved out. Here, we consider how this Bayesian perspective on disease mechanisms in ASD might contribute to improving clinical care for affected individuals. Specifically, we describe a computational strategy, based on generative (e.g., hierarchical Bayesian) models of behavioral and functional neuroimaging data, for establishing diagnostic tests. These tests could provide estimates of specific cognitive processes underlying ASD and delineate pathophysiological mechanisms with concrete treatment targets. Written with a clinical audience in mind, this article outlines how the development of computational diagnostics applicable to behavioral and functional neuroimaging data in routine clinical practice could not only fundamentally alter our concept of ASD but eventually also transform the clinical management of this disorder.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Frontiers Research Foundation
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Autism spectrum disorder
en_US
dc.subject
Asperger syndrome
en_US
dc.subject
Translational research
en_US
dc.subject
Diagnostic tests
en_US
dc.subject
Generative modeling
en_US
dc.subject
Bayesian inference
en_US
dc.subject
Bayesian models
en_US
dc.subject
Neuroimaging
en_US
dc.title
Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice?
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
Frontiers in Psychiatry
ethz.journal.volume
7
en_US
ethz.pages.start
107
en_US
ethz.size
17 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.publication.place
Lausanne
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02631 - Institut für Biomedizinische Technik / Institute for Biomedical Engineering::03955 - Stephan, Klaas E. / Stephan, Klaas E.
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02631 - Institut für Biomedizinische Technik / Institute for Biomedical Engineering::03955 - Stephan, Klaas E. / Stephan, Klaas E.
ethz.date.deposited
2017-06-12T08:11:52Z
ethz.source
ECIT
ethz.identifier.importid
imp5936547f3f62219771
ethz.ecitpid
pub:179661
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-15T15:17:24Z
ethz.rosetta.lastUpdated
2019-06-25T15:10:45Z
ethz.rosetta.versionExported
true
ethz.COinS
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