A hierarchical model for simultaneous detection and estimation in multi-subject fMRI studies

Degras, David; Lindquist, Martin A.

doi:10.1016/j.neuroimage.2014.04.052

Cited by 25 publications

(26 citation statements)

References 39 publications

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“…Some solutions have been proposed for this joint comparison, yet none is widely accepted yet [51,55]. Recent work combining estimation of the HRF shape with detection of activation in the same optimization seems a step in the right direction [56,57], though these approaches currently assume that HRF shape is similar across subjects in a given brain region, which may not be a valid assumption when investigating individual differences.…”

Section: Validity: Are Individual Differences Attributable To Brain Fmentioning

confidence: 99%

Building a Science of Individual Differences from fMRI

Dubois

Adolphs

2016

Trends in Cognitive Sciences

630

482

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To date, fMRI research has been concerned primarily with evincing generic principles of brain function through averaging data from multiple subjects. Given rapid developments in both hardware and analysis tools, the field is now poised to study fMRI-derived measures in individual subjects, and to relate these to psychological traits or genetic variations. We discuss issues of validity and reliability that arise when the focus shifts to individual subjects and that are widely applicable across imaging modalities. We also emphasize that individual assessment of neural function with fMRI presents specific challenges and necessitates careful consideration of anatomical and vascular between-subject variability, sources of within-subject variability, and statistical power.

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Section: Validity: Are Individual Differences Attributable To Brain Fmentioning

confidence: 99%

Building a Science of Individual Differences from fMRI

Dubois

Adolphs

2016

Trends in Cognitive Sciences

630

482

View full text Add to dashboard Cite

show abstract

“…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). Methods for brain connectivity are developed in Friston et al (2003); Harrison et al (2003); Bowman et al (2008); Cribben et al (2012); Kang et al (2012); Gorrostieta et al (2013); Luo (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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“…Group analyses involve combining individual SPMs across subjects. A multisubject approach analyzes all subjects at the same time to estimate a group level activation and the shape of the HRF simultaneously (Degras & Lindquist, 2014;Makni, Ciuciu, Idier, & Poline, 2005;Makni et al, 2008;Vincent, Risser, & Ciuciu, 2010). In this work, we focus on the subject-wise analysis assuming that individual maps are provided after statistical modeling of voxel time series.…”

Section: Introductionmentioning

confidence: 99%

Regularized aggregation of statistical parametric maps

Wang

Chung

Park

et al. 2018

Human Brain Mapping

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Combining statistical parametric maps (SPM) from individual subjects is the goal in some types of group-level analyses of functional magnetic resonance imaging data. Brain maps are usually combined using a simple average across subjects, making them susceptible to subjects with outlying values. Furthermore, t tests are prone to false positives and false negatives when outlying values are observed. We propose a regularized unsupervised aggregation method for SPMs to find an optimal weight for aggregation, which aids in detecting and mitigating the effect of outlying subjects. We also present a bootstrap-based weighted t test using the optimal weights to construct an activation map robust to outlying subjects. We validate the performance of the proposed aggregation method and test using simulated and real data examples. Results show that the regularized aggregation approach can effectively detect outlying subjects, lower their weights, and produce robust SPMs.

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A hierarchical model for simultaneous detection and estimation in multi-subject fMRI studies

Cited by 25 publications

References 39 publications

Building a Science of Individual Differences from fMRI

Building a Science of Individual Differences from fMRI

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

Regularized aggregation of statistical parametric maps

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