Optimizing Data for Modeling Neuronal Responses

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 acquisi...
Ausführliche Beschreibung

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Autor*in:	Peter Zeidman [verfasserIn] Samira M. Kazan [verfasserIn] Nick Todd [verfasserIn] Nikolaus Weiskopf [verfasserIn] Karl J. Friston [verfasserIn] Martina F. Callaghan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	dynamic causal modeling DCM fMRI PEB multiband

Übergeordnetes Werk:	In: Frontiers in Neuroscience - Frontiers Media S.A., 2008, 12(2019)
Übergeordnetes Werk:	volume:12 ; year:2019

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fnins.2018.00986

Katalog-ID:	DOAJ044915136

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spelling	10.3389/fnins.2018.00986 doi (DE-627)DOAJ044915136 (DE-599)DOAJ6a8163f9227446a1b4e54727abdeb2da DE-627 ger DE-627 rakwb eng RC321-571 Peter Zeidman verfasserin aut Optimizing Data for Modeling Neuronal Responses 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. dynamic causal modeling DCM fMRI PEB multiband Neurosciences. Biological psychiatry. Neuropsychiatry Samira M. Kazan verfasserin aut Nick Todd verfasserin aut Nikolaus Weiskopf verfasserin aut Nikolaus Weiskopf verfasserin aut Karl J. Friston verfasserin aut Martina F. Callaghan verfasserin aut In Frontiers in Neuroscience Frontiers Media S.A., 2008 12(2019) (DE-627)55908109X (DE-600)2411902-7 1662453X nnns volume:12 year:2019 https://doi.org/10.3389/fnins.2018.00986 kostenfrei https://doaj.org/article/6a8163f9227446a1b4e54727abdeb2da kostenfrei https://www.frontiersin.org/article/10.3389/fnins.2018.00986/full kostenfrei https://doaj.org/toc/1662-453X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2019
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allfieldsGer	10.3389/fnins.2018.00986 doi (DE-627)DOAJ044915136 (DE-599)DOAJ6a8163f9227446a1b4e54727abdeb2da DE-627 ger DE-627 rakwb eng RC321-571 Peter Zeidman verfasserin aut Optimizing Data for Modeling Neuronal Responses 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. dynamic causal modeling DCM fMRI PEB multiband Neurosciences. Biological psychiatry. Neuropsychiatry Samira M. Kazan verfasserin aut Nick Todd verfasserin aut Nikolaus Weiskopf verfasserin aut Nikolaus Weiskopf verfasserin aut Karl J. Friston verfasserin aut Martina F. Callaghan verfasserin aut In Frontiers in Neuroscience Frontiers Media S.A., 2008 12(2019) (DE-627)55908109X (DE-600)2411902-7 1662453X nnns volume:12 year:2019 https://doi.org/10.3389/fnins.2018.00986 kostenfrei https://doaj.org/article/6a8163f9227446a1b4e54727abdeb2da kostenfrei https://www.frontiersin.org/article/10.3389/fnins.2018.00986/full kostenfrei https://doaj.org/toc/1662-453X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2019
allfieldsSound	10.3389/fnins.2018.00986 doi (DE-627)DOAJ044915136 (DE-599)DOAJ6a8163f9227446a1b4e54727abdeb2da DE-627 ger DE-627 rakwb eng RC321-571 Peter Zeidman verfasserin aut Optimizing Data for Modeling Neuronal Responses 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. dynamic causal modeling DCM fMRI PEB multiband Neurosciences. Biological psychiatry. Neuropsychiatry Samira M. Kazan verfasserin aut Nick Todd verfasserin aut Nikolaus Weiskopf verfasserin aut Nikolaus Weiskopf verfasserin aut Karl J. Friston verfasserin aut Martina F. Callaghan verfasserin aut In Frontiers in Neuroscience Frontiers Media S.A., 2008 12(2019) (DE-627)55908109X (DE-600)2411902-7 1662453X nnns volume:12 year:2019 https://doi.org/10.3389/fnins.2018.00986 kostenfrei https://doaj.org/article/6a8163f9227446a1b4e54727abdeb2da kostenfrei https://www.frontiersin.org/article/10.3389/fnins.2018.00986/full kostenfrei https://doaj.org/toc/1662-453X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2019
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container_title	Frontiers in Neuroscience
authorswithroles_txt_mv	Peter Zeidman @@aut@@ Samira M. Kazan @@aut@@ Nick Todd @@aut@@ Nikolaus Weiskopf @@aut@@ Karl J. Friston @@aut@@ Martina F. Callaghan @@aut@@
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callnumber-first	R - Medicine
author	Peter Zeidman
spellingShingle	Peter Zeidman misc RC321-571 misc dynamic causal modeling misc DCM misc fMRI misc PEB misc multiband misc Neurosciences. Biological psychiatry. Neuropsychiatry Optimizing Data for Modeling Neuronal Responses
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title	Optimizing Data for Modeling Neuronal Responses
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title_sort	optimizing data for modeling neuronal responses
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title_auth	Optimizing Data for Modeling Neuronal Responses
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.
abstractGer	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.
abstract_unstemmed	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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title_short	Optimizing Data for Modeling Neuronal Responses
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