Bayesian models for functional magnetic resonance imaging data analysis

Zhang, Linlin; Guindani, Michele; Vannucci, Marina

doi:10.1002/wics.1339

Cited by 62 publications

(55 citation statements)

References 138 publications

(216 reference statements)

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“…Lindquist (2008) and Zhang et al (2015) provide detailed reviews on existing methods. Methods for brain activation are proposed in Friston et al (1994); Worsley and Friston (1995); Smith and Fahrmeir (2007); David et al (2008); Guo and Pagnoni (2008); Xu et al (2009); Degras and Lindquist (2014).…”

Section: Introductionmentioning

confidence: 99%

Understanding the Impact of Stroke on Brain Motor Function: A Hierarchical Bayesian Approach

Yu¹,

Prado²,

Quinlan³

et al. 2016

Journal of the American Statistical Association

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Stroke is a disturbance in blood supply to the brain resulting in the loss of brain functions, particularly motor function. A study was conducted by the UCI Neurorehabilitation Lab to investigate the impact of stroke on motor-related brain regions. Functional MRI (fMRI) data were collected from stroke patients and healthy controls while the subjects performed a simple motor task. In addition to affecting local neuronal activation strength, stroke might also alter communications (i.e., connectivity) between brain regions. We develop a hierarchical Bayesian modeling approach for the analysis of multi-subject fMRI data that allows us to explore brain changes due to stroke. Our approach simultaneously estimates activation and condition-specific connectivity at the group level, and provides estimates for region/subject-specific hemodynamic response functions. Moreover, our model uses spike and slab priors to allow for direct posterior inference on the connectivity network. Our results indicate that motor-control regions show greater activation in the unaffected hemisphere and the midline surface in stroke patients than those same regions in healthy controls during the simple motor task. We also note increased connectivity within secondary motor regions in stroke subjects. These findings provide insight into altered neural correlates of movement in subjects who suffered a stroke.

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Section: Introductionmentioning

confidence: 99%

Understanding the Impact of Stroke on Brain Motor Function: A Hierarchical Bayesian Approach

Yu¹,

Prado²,

Quinlan³

et al. 2016

Journal of the American Statistical Association

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“…We further assume that the HRF, denoted

h false(t false)

, is known and is common across all voxels. Voxel‐ and ROI‐specific HRFs have been developed (see, e.g., Degras and Lindquist ()) by assuming that the HRF is the difference between two gamma functions or has a Poisson distribution (Zhang et al., ), and allowing spatially varying parameters. We use the canonical HRF from the Statistical Parametric Mapping software to produce a BOLD response associated with each of the two stimuli.…”

Section: The Statistical Model For Fmri Datamentioning

confidence: 99%

“…The two‐stage approach was the first rigorous framework to acknowledge spatial correlation and has given rise to a large body of literature on Bayesian models for fMRI data (see Zhang et al. () for a comprehensive review). Although this framework has been demonstrated to be flexible and to produce useful information for practitioners (e.g., posterior probability maps for activation), two main factors still present limitations to the development of spatio‐temporal models for fMRI data.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al. () claim that “the large dimensionality of the data makes it impossible to model the entire 3D maps of the data at once.” Here, we demonstrate that it is possible to provide activation maps for the entire brain with nontrivial spatio‐temporal models, provided that appropriate computational power is available and a suitable inferential scheme that fully exploits distributed computing is implemented.…”

Section: Introductionmentioning

confidence: 99%

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A Scalable Multi-Resolution Spatio-Temporal Model for Brain Activation and Connectivity in Fmri Data

2018

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Functional Magnetic Resonance Imaging (fMRI) is a primary modality for studying brain activity. Modeling spatial dependence of imaging data at different scales is one of the main challenges of contemporary neuroimaging, and it could allow for accurate testing for significance in neural activity. The high dimensionality of this type of data (on the order of hundreds of thousands of voxels) poses serious modeling challenges and considerable computational constraints. For the sake of feasibility, standard models typically reduce dimensionality by modeling covariance among regions of interest (ROIs) -coarser or larger spatial units -rather than among voxels. However, ignoring spatial dependence at different scales could drastically reduce our ability to detect activation patterns in the brain and hence produce misleading results. To overcome these problems, we introduce a multi-resolution spatio-temporal model and a computationally efficient methodology to estimate cognitive control related activation and whole-brain connectivity. The proposed model allows for testing voxel-specific activation while accounting for non-stationary local spatial dependence within anatomically defined ROIs, as well as regional dependence (between-ROIs). Furthermore, the model allows for detection of interpretable connectivity patterns among ROIs using the graphical Least Absolute Shrinkage Selection Operator (LASSO). The model is used in a motor-task fMRI study to investigate brain activation and connectivity patterns aimed at identifying associations between these patterns and regaining motor functionality following a stroke.

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“…al, 2008;Derado et. al, 2010;Kang et al, 2012;Zhang et al, 2015). Nevertheless, a multiplicity issue exists because there are as many models specified as the number of the spatial units, leading to potential overfitting and model inefficiency.…”

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confidence: 99%

Fighting or Embracing Multiplicity in Neuroimaging? Neighborhood Leverage versus Global Calibration

Chen¹,

Taylor²,

Cox³

et al. 2019

Preprint

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Neuroimaging faces the daunting challenge of multiple testing -an instance of multiplicity -that is associated with two other issues to some extent: low inference efficiency and poor reproducibility. Typically, the same statistical model is applied to each spatial unit independently in the approach of massively univariate modeling. In dealing with multiplicity, the general strategy employed in the field is the same regardless of the specifics: trust the local "unbiased" effect estimates while adjusting the extent of statistical evidence at the global level. However, in this approach, modeling efficiency is compromised because each spatial unit (e.g., voxel, region, matrix element) is treated as an isolated and independent entity during massively univariate modeling. In addition, the required step of multiple testing "correction" by taking into consideration spatial relatedness, or neighborhood leverage, Global calibration is accomplished via a Gaussian distribution for the cross-region effects whose properties are not a priori specified, but a posteriori determined by the data at hand through the BML model. Our framework therefore incorporates multiplicity as integral to the modeling structure, not a separate correction step. By turning multiplicity into a strength, we aim to achieve five goals: 1) improve model efficiency with higher predictive accuracy, 2) control the error of incorrect magnitude and incorrect sign, 3) validate each model relative to competing candidates, 4) reduce the reliance and sensitivity on the choice of data space, and 5) encourage full results reporting. Our modeling proposal reverberates with recent proposals to eliminate the dichotomization of statistical evidence ("significant" vs. "non-significant"), to improve the interpretability of study findings, as well as to promote reporting the full gamut of results (not only "significant" ones), thereby enhancing research transparency and reproducibility. * Corresponding author. E-mail address: gangchen@mail.nih.gov 1 The two steps discussed here should not be confused with the "two steps" of first estimating regression coefficients at the individual-participant level and then employing those at a second step of statistical testing across participants.to rethink her approach of controlling for the overall false positive rate (FPR) under the null hypothesis significance testing (NHST) framework. One is not simply worried about errors at a single location but at all locations of the dataset at the same time -hence the need for a second step. This multiple testing problem has led to beautiful and creative solutions by the neuroimaging community in the past quarter of century, including random field theory (Worsley et al., 1992), Monte Carlo simulations (Forman et al., 1995), and permutation testing (Nichols and Holmes, 2001;Smith and Nichols, 2009).However, we believe that the current approaches have an important shortcoming: excessive penalties from correction for multiplicity due to modeling inefficiency.Consider the two-step procedure. In t...

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Bayesian models for functional magnetic resonance imaging data analysis

Cited by 62 publications

References 138 publications

Understanding the Impact of Stroke on Brain Motor Function: A Hierarchical Bayesian Approach

Understanding the Impact of Stroke on Brain Motor Function: A Hierarchical Bayesian Approach

A Scalable Multi-Resolution Spatio-Temporal Model for Brain Activation and Connectivity in Fmri Data

Fighting or Embracing Multiplicity in Neuroimaging? Neighborhood Leverage versus Global Calibration

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