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  Local striatal reward signals can be predicted from corticostriatal connectivity

Smittenaar, P., Kurth-Nelson, Z., Mohammadi, S., Weiskopf, N., & Dolan, R. J. (2017). Local striatal reward signals can be predicted from corticostriatal connectivity. NeuroImage, 159, 9-17. doi:10.1016/j.neuroimage.2017.07.042.

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Smittenaar_Kurth-Nelson_2017.pdf (Publisher version), 2MB
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 Creators:
Smittenaar, Peter1, Author
Kurth-Nelson, Zeb1, 2, Author
Mohammadi, Siawoosh1, 3, Author
Weiskopf, Nikolaus1, 4, Author           
Dolan, Raymond J.1, 2, Author
Affiliations:
1Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom, ou_persistent22              
2Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom, ou_persistent22              
3Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Germany, ou_persistent22              
4Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              

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 Abstract: A defining feature of the basal ganglia is their anatomical organization into multiple cortico-striatal loops. A central tenet of this architecture is the idea that local striatal function is determined by its precise connectivity with cortex, creating a functional topography that is mirrored within cortex and striatum. Here we formally test this idea using both human anatomical and functional imaging, specifically asking whether within striatal subregions one can predict between-voxel differences in functional signals based on between-voxel differences in corticostriatal connectivity. We show that corticostriatal connectivity profiles predict local variation in reward signals in bilateral caudate nucleus and putamen, expected value signals in bilateral caudate nucleus, and response effector activity in bilateral putamen. These data reveal that, even within individual striatal regions, local variability in corticostriatal anatomical connectivity predicts functional differentiation.

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Language(s): eng - English
 Dates: 2017-05-242017-02-122017-07-192017-07-202017-10-01
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2017.07.042
PMID: 28736307
PMC: PMC5678290
Other: Epub 2017
 Degree: -

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Project name : Non-invasive in vivo histology in health and disease using Magnetic Resonance Imaging (MRI) / HMRI
Grant ID : 616905
Funding program : FP7 (ERC-2013-CoG)
Funding organization : European Research Council
Project name : Mesoscopic characterization of human white-matter: A computational in-vivo MRI framework / MWMI
Grant ID : 658589
Funding program : Horizon 2020
Funding organization : European Commission (EC)
Project name : -
Grant ID : 078865/Z/05/Z
Funding program : -
Funding organization : Wellcome Trust
Project name : -
Grant ID : -
Funding program : -
Funding organization : Max Planck Society

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Title: NeuroImage
Source Genre: Journal
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Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 159 Sequence Number: - Start / End Page: 9 - 17 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166