Hierarchical Bayesian Analyses for Modeling BOLD Time Series Data

Molloy, M. Fiona; Bahg, Giwon; Li, Xiangrui; Steyvers, Mark; Lü, Zhong-Lin; Turner, B. E.

doi:10.1007/s42113-018-0013-5

Cited by 12 publications

(8 citation statements)

References 69 publications

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“…Although it has been used in many different disciplines, such as astronomy ( Thrane & Talbot, 2019 ), ecology ( Reum, Hovel, & Greene, 2015 ; Wikle, 2003 ), genetics ( Storz & Beaumont, 2002 ), machine learning ( Li & Perona, 2005 ), cognitive science ( Ahn, Krawitz, Kim, Busmeyer, & Brown, 2011 ; Lee, 2006 ; Lee & Mumford, 2003 ; Merkle, Smithson, & Verkuilen, 2011 ; Molloy, Bahg, Li, Steyvers, Lu, & Turner, 2018 ; Molloy, Bahg, Lu, & Turner, 2019 ; Rouder & Lu, 2005 ; Rouder et al, 2003 ; Wilson et al, 2020 ) and visual acuity ( Zhao, Lesmes, Dorr, & Lu, 2021 ), HBM has not been applied to analyze the CSF. Here, we develop a three-level HBM to model the entire CSF dataset in a single-factor (luminance), multi-condition (3 luminance conditions), and within-subject experiment design.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Bayesian modeling of contrast sensitivity functions in a within-subject design

et al. 2021

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Section: Introductionmentioning

confidence: 99%

Hierarchical Bayesian modeling of contrast sensitivity functions in a within-subject design

et al. 2021

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“…In addition, larger effect estimate uncertainty, outliers, and skewed distributions may occur due to high collinearity among neighboring trials or head motion; experimental design choices, such as the inter-trial interval, can be made to help reduce these issues. Recent investigations (Molloy et al 2018;Chen et al, 2020;Chen et al, 2021) provide some solutions to handle such complex situations under the conventional and Bayesian frameworks.…”

Section: Trial-level Versus Condition-level Modeling: Accounting For Cross-trial Variabilitymentioning

confidence: 99%

Hyperbolic trade-off: the importance of balancing trial and subject sample sizes in neuroimaging

Chen

Pine

Brotman

et al. 2021

Preprint

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Big data initiatives have gained popularity for leveraging a large sample of subjects to study a wide range of effect magnitudes in the brain. On the other hand, most task-based FMRI designs feature relatively small number of subjects, so that resulting parameter estimates may be associated with compromised precision. Nevertheless, little attention has been given to another important dimension of experimental design, which can equally boost a study's statistical efficiency: the trial sample size. Here, we systematically explore the different factors that impact effect uncertainty, drawing on evidence from hierarchical modeling, simulations and an FMRI dataset of 42 subjects who completed a large number of trials of a commonly used cognitive task. We find that, due to the presence of relatively large cross-trial variability: 1) trial sample size has nearly the same impact as subject sample size on statistical efficiency; 2) increasing both trials and subjects improves statistical efficiency more effectively than focusing on subjects alone; 3) trial sample size can be leveraged with the number of subjects to improve the cost-effectiveness of an experimental design; 4) for small trial sample sizes, rather than the common practice of condition-level modeling through summary statistics, trial-level modeling may be necessary to accurately assess the standard error of an effect estimate. Lastly, we make practical recommendations for improving experimental designs across neuroimaging and behavioral studies.

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“…The 11 participants analyzed in this study were part of a larger study involving multiple cognitive tasks and wellbeing inventories (Gaut et al, 2019;Molloy et al, 2018). These 11 participants, in contrast to the rest of the group from the first scan, were recruited to take part in a second experiment.…”

Section: Participantsmentioning

confidence: 99%

“…Although both models are complex relative to the C-GLM, we previously found that these particular hierarchical levels allow better constraint and generalizability. In Molloy et al (2018), we built five increasingly complex models, all with single-stimulus estimates, of the neural time series of the stop signal task. The simplest model had no hierarchical component.…”

Section: Model Specificationmentioning

confidence: 99%

Individual Differences in the Neural Dynamics of Response Inhibition

Molloy

Bahg

Lü

et al. 2019

Journal of Cognitive Neuroscience

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Response inhibition is a widely studied aspect of cognitive control that is particularly interesting because of its applications to clinical populations. Although individual differences are integral to cognitive control, so too is our ability to aggregate information across a group of individuals, so that we can powerfully generalize and characterize the group's behavior. Hence, an examination of response inhibition would ideally involve an accurate estimation of both group- and individual-level effects. Hierarchical Bayesian analyses account for individual differences by simultaneously estimating group and individual factors and compensate for sparse data by pooling information across participants. Hierarchical Bayesian models are thus an ideal tool for studying response inhibition, especially when analyzing neural data. We construct hierarchical Bayesian models of the fMRI neural time series, models assuming hierarchies across conditions, participants, and ROIs. Here, we demonstrate the advantages of our models over a conventional generalized linear model in accurately separating signal from noise. We then apply our models to go/no-go and stop signal data from 11 participants. We find strong evidence for individual differences in neural responses to going, not going, and stopping and in functional connectivity across the two tasks and demonstrate how hierarchical Bayesian models can effectively compensate for these individual differences while providing group-level summarizations. Finally, we validated the reliability of our findings using a larger go/no-go data set consisting of 179 participants. In conclusion, hierarchical Bayesian models not only account for individual differences but allow us to better understand the cognitive dynamics of response inhibition.

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Hierarchical Bayesian Analyses for Modeling BOLD Time Series Data

Cited by 12 publications

References 69 publications

Hierarchical Bayesian modeling of contrast sensitivity functions in a within-subject design

Hierarchical Bayesian modeling of contrast sensitivity functions in a within-subject design

Hyperbolic trade-off: the importance of balancing trial and subject sample sizes in neuroimaging

Individual Differences in the Neural Dynamics of Response Inhibition

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