Bayesian Connective Field Modeling: a Markov Chain Monte Carlo approach

Invernizzi, Azzurra; Haak, Koen V.; Carvalho, Joana; Rj, Renken; Fw, Cornelissen

doi:10.1101/2020.09.03.281162

Cited by 2 publications

(18 citation statements)

References 38 publications

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“…As in the standard CF modeling, the eccentricity and polar angle values associated with the CF centers are inferred from a pRF mapping. For sake of completeness, the complete fitting procedure of Bayesian CF model (option B) is described in the supplementary material (Invernizzi et al, 2020).…”

Section: Bayesian Connective Field Mappingmentioning

confidence: 99%

“…Based on a quantile analysis of the posterior distribution (Invernizzi et al, 2020), we computed a voxelwise uncertainty measure for each CF parameter by subtracting the upper (…”

Section: Bayesian Analysismentioning

confidence: 99%

“…To evaluate the goodness of the corrected beta-thresholding method in the voxel selection on RS data, we compared the model VE, each CF parameter and the uncertainty associated, respectively (Figure 4, CF size and Figure 2S, beta parameter).Both thresholds: VE is higher than 15% and the FWE corrected effect size ( >95% Figure 4, Panel A; for more details, see Invernizzi et al, 2020) are indicated. Based on a direct comparison of FWE beta-corrected threshold (CI) to the standard VE of the model (Figure 4, Panel B and C), the 95% FWE CI-based threshold proved to be more conservative.…”

Section: Bayesian Cf Thresholding Applicationmentioning

confidence: 99%

“…Moreover, in order to assess the suitability of 3T data in unique patient cases, we will look beyond the classical variance explained as an indicator of modeling performance and we will directly assess the uncertainty of model parameter estimates using a Bayesian approach. These parameters are available to us by applying our recently developed Bayesian framework for the CF model (Bayesian CF, Invernizzi et al , 2020). In particular, this approach allows to estimate the variability for each CF parameter estimate such as CF size and beta.…”

Section: Introductionmentioning

confidence: 99%

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Assessing uncertainty in connective field estimations from resting state fMRI activity

Invernizzi

Gravel

Haak³

et al. 2020

Preprint

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Connective Field (CF) modeling estimates the local spatial integration between signals in distinct cortical visual field areas. As we have shown previously using 7T data, CF can reveal the visuotopic organization of visual cortical areas even when applied to BOLD activity recorded in the absence of external stimulation. This indicates that CF modeling can be used to evaluate cortical processing in participants in which the visual input may be compromised. Furthermore, by using Bayesian CF modelling it is possible to estimate the co-variability of the parameter estimates and therefore, apply CF modeling to single cases. However, no previous studies evaluated the (Bayesian) CF model using 3T resting-state fMRI data, although this is important since 3T scanners are much more abundant and more often used in clinical research than 7T ones. In this study, we investigate whether it is possible to obtain meaningful CF estimates from 3T resting state (RS) fMRI data. To do so, we applied the standard and Bayesian CF modeling approaches on two RS scans interleaved by the acquisition of visual stimulation in 12 healthy participants. Our results show that both approaches reveal good agreement between RS- and visual field (VF)- based maps. Moreover, the 3T observations were similar to those previously reported at 7T. In addition, to quantify the uncertainty associated with each estimate in both RS and VF data, we applied our Bayesian CF framework to provide the underlying marginal distribution of the CF parameters. Finally, we show how an additional CF parameter, beta, can be used as a data-driven threshold on the RS data to further improve CF estimates. We conclude that Bayesian CF modeling can characterize local functional connectivity between visual cortical areas from RS data at 3T. In particular, we expect the ability to assess parameter uncertainty in individual participants will be important for future clinical studies.

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Section: Bayesian Connective Field Mappingmentioning

confidence: 99%

“…Based on a quantile analysis of the posterior distribution (Invernizzi et al, 2020), we computed a voxelwise uncertainty measure for each CF parameter by subtracting the upper (…”

Section: Bayesian Analysismentioning

confidence: 99%

Section: Bayesian Cf Thresholding Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assessing uncertainty in connective field estimations from resting state fMRI activity

Invernizzi

Gravel

Haak³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…To do so, we will assess the uncertainty of model parameter estimates using a Bayesian approach. These parameters are available to us by applying our recently developed Bayesian framework for the CF model (Bayesian CF, Invernizzi et al, 2020 ). In particular, this approach allows to estimate the variability for each CF parameter estimate such as CF size and beta.…”

Section: Introductionmentioning

confidence: 99%

Assessing Uncertainty and Reliability of Connective Field Estimations From Resting State fMRI Activity at 3T

et al. 2021

Self Cite

View full text Add to dashboard Cite

Connective Field (CF) modeling estimates the local spatial integration between signals in distinct cortical visual field areas. As we have shown previously using 7T data, CF can reveal the visuotopic organization of visual cortical areas even when applied to BOLD activity recorded in the absence of external stimulation. This indicates that CF modeling can be used to evaluate cortical processing in participants in which the visual input may be compromised. Furthermore, by using Bayesian CF modeling it is possible to estimate the co-variability of the parameter estimates and therefore, apply CF modeling to single cases. However, no previous studies evaluated the (Bayesian) CF model using 3T resting-state fMRI data. This is important since 3T scanners are much more abundant and more often used in clinical research compared to 7T scanners. Therefore in this study, we investigate whether it is possible to obtain meaningful CF estimates from 3T resting state (RS) fMRI data. To do so, we applied the standard and Bayesian CF modeling approaches on two RS scans, which were separated by the acquisition of visual field mapping data in 12 healthy participants. Our results show good agreement between RS- and visual field (VF)- based maps using either the standard or Bayesian CF approach. In addition to quantify the uncertainty associated with each estimate in both RS and VF data, we applied our Bayesian CF framework to provide the underlying marginal distribution of the CF parameters. Finally, we show how an additional CF parameter, beta, can be used as a data-driven threshold on the RS data to further improve CF estimates. We conclude that Bayesian CF modeling can characterize local functional connectivity between visual cortical areas from RS data at 3T. Moreover, observations obtained using 3T scanners were qualitatively similar to those reported for 7T. In particular, we expect the ability to assess parameter uncertainty in individual participants will be important for future clinical studies.

show abstract

Bayesian Connective Field Modeling: a Markov Chain Monte Carlo approach

Cited by 2 publications

References 38 publications

Assessing uncertainty in connective field estimations from resting state fMRI activity

Assessing uncertainty in connective field estimations from resting state fMRI activity

Assessing Uncertainty and Reliability of Connective Field Estimations From Resting State fMRI Activity at 3T

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