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  Optimizing data for modeling neuronal responses

Zeidman, P., Kazan, S. M., Todd, N., Weiskopf, N., Friston, K. J., & Callaghan, M. F. (2019). Optimizing data for modeling neuronal responses. Frontiers in Neuroscience, 12: 986. doi:10.3389/fnins.2018.00986.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-C0A1-E Version Permalink: http://hdl.handle.net/21.11116/0000-0003-8925-9
Genre: Journal Article

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 Creators:
Zeidman, Peter1, Author
Kazan, Samira M1, Author
Todd, Nick2, Author
Weiskopf, Nikolaus1, 3, Author              
Friston, Karl J1, Author
Callaghan, Martina F1, Author
Affiliations:
1Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom, ou_persistent22              
2Department of Radiology, Harvard Medical School, Boston, MA, USA, ou_persistent22              
3Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              

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Free keywords: dynamic causal modeling; DCM; fMRI; PEB; multiband
 Abstract: In this technical note, we address an unresolved challenge in neuroimaging statistics: how to determine which of several datasets is the best for inferring neuronal responses. Comparisons of this kind are important for experimenters when choosing an imaging protocol—and for developers of new acquisition methods. However, the hypothesis that one dataset is better than another cannot be tested using conventional statistics (based on likelihood ratios), as these require the data to be the same under each hypothesis. Here we present Bayesian data comparison (BDC), a principled framework for evaluating the quality of functional imaging data, in terms of the precision with which neuronal connectivity parameters can be estimated and competing models can be disambiguated. For each of several candidate datasets, neuronal responses are modeled using Bayesian (probabilistic) forward models, such as General Linear Models (GLMs) or Dynamic Casual Models (DCMs). Next, the parameters from subject-specific models are summarized at the group level using a Bayesian GLM. A series of measures, which we introduce here, are then used to evaluate each dataset in terms of the precision of (group-level) parameter estimates and the ability of the data to distinguish similar models. To exemplify the approach, we compared four datasets that were acquired in a study evaluating multiband fMRI acquisition schemes, and we used simulations to establish the face validity of the comparison measures. To enable people to reproduce these analyses using their own data and experimental paradigms, we provide general-purpose Matlab code via the SPM software.

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Language(s): eng - English
 Dates: 2018-06-292018-12-102019-01-10
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.3389/fnins.2018.00986
PMC: PMC6335328
PMID: 30686967
Other: eCollection 2018
 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 : Funding Programme 7 (FP7)
Funding organization : European Commission (EC)
Project name : -
Grant ID : 203147/Z/16/Z
Funding program : -
Funding organization : Wellcome Trust

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Title: Frontiers in Neuroscience
  Other : Front Neurosci
Source Genre: Journal
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Pages: - Volume / Issue: 12 Sequence Number: 986 Start / End Page: - Identifier: ISSN: 1662-4548
ISSN: 1662-453X
CoNE: https://pure.mpg.de/cone/journals/resource/1662-4548