Variational Bayesian parameter estimation techniques for the general linear model

Starke, Ludger; Ostwald, Dirk

doi:10.31219/osf.io/b8nsd

Cited by 2 publications

(2 citation statements)

References 70 publications

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“…The free energy provides a lower bound on the model evidence ena- (Friston et al, 2002;Penny et al, 2007;Starke & Ostwald, 2017 In summary, as the TR decreased, a greater number of model components was required to accurately prewhiten the time series.…”

Section: Bayesian Model Comparisonmentioning

confidence: 99%

Accurate modeling of temporal correlations in rapidly sampled fMRI time series

Corbin

Todd

Friston

et al. 2018

Human Brain Mapping

100

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Rapid imaging techniques are increasingly used in functional MRI studies because they allow a greater number of samples to be acquired per unit time, thereby increasing statistical power. However, temporal correlations limit the increase in functional sensitivity and must be accurately accounted for to control the false‐positive rate. A common approach to accounting for temporal correlations is to whiten the data prior to estimating fMRI model parameters. Models of white noise plus a first‐order autoregressive process have proven sufficient for conventional imaging studies, but more elaborate models are required for rapidly sampled data. Here we show that when the “FAST” model implemented in SPM is used with a well‐controlled number of parameters, it can successfully prewhiten 80% of grey matter voxels even with volume repetition times as short as 0.35 s. We further show that the temporal signal‐to‐noise ratio (tSNR), which has conventionally been used to assess the relative functional sensitivity of competing imaging approaches, can be augmented to account for the temporal correlations in the time series. This amounts to computing the t‐score testing for the mean signal. We show in a visual perception task that unlike the tSNR weighted by the number of samples, the t‐score measure is directly related to the t‐score testing for activation when the temporal correlations are correctly modeled. This score affords a more accurate means of evaluating the functional sensitivity of different data acquisition options.

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Section: Bayesian Model Comparisonmentioning

confidence: 99%

Accurate modeling of temporal correlations in rapidly sampled fMRI time series

Corbin

Todd

Friston

et al. 2018

Human Brain Mapping

100

View full text Add to dashboard Cite

show abstract

“…Temporal serial correlations in fMRI data were removed using the residuals covariance matrix estimated by the restricted maximum likelihood routine in SPM12 to satisfy the sphericity assumption needed for doing inference (Starke & Ostwald, 2017). Subject-level effects were fitted individually to their design matrices, and the resulting regression coefficients were taken to a random effects group-level analysis, where the final coefficients values and statistics were calculated using the summary statistics trick (Holmes & Friston, 1998).…”

Section: Fmri Statisticsmentioning

confidence: 99%

Action in auctions: neural and computational mechanisms of bidding behaviour

Martinez-Saito

Konovalov

Piradov

et al. 2019

Eur J of Neuroscience

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Competition for resources is a fundamental characteristic of evolution. Auctions have been widely used to model competition of individuals for resources, and bidding behaviour plays a major role in social competition. Yet, how humans learn to bid efficiently remains an open question. We used model‐based neuroimaging to investigate the neural mechanisms of bidding behaviour under different types of competition. Twenty‐seven subjects (nine male) played a prototypical bidding game: a double action, with three “market” types, which differed in the number of competitors. We compared different computational learning models of bidding: directional learning models (DL), where the model bid is “nudged” depending on whether it was accepted or rejected, along with standard reinforcement learning models (RL). We found that DL fit the behaviour best and resulted in higher payoffs. We found the binary learning signal associated with DL to be represented by neural activity in the striatum distinctly posterior to a weaker reward prediction error signal. We posited that DL is an efficient heuristic for valuation when the action (bid) space is continuous. Indeed, we found that the posterior parietal cortex represents the continuous action space of the task, and the frontopolar prefrontal cortex distinguishes among conditions of social competition. Based on our findings, we proposed a conceptual model that accounts for a sequence of processes that are required to perform successful and flexible bidding under different types of competition.

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Variational Bayesian parameter estimation techniques for the general linear model

Cited by 2 publications

References 70 publications

Accurate modeling of temporal correlations in rapidly sampled fMRI time series

Accurate modeling of temporal correlations in rapidly sampled fMRI time series

Action in auctions: neural and computational mechanisms of bidding behaviour

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