Group MRF for fMRI activation detection

Ng, Bernard; Abugharbieh, Rafeef; Hamarneh, Ghassan

doi:10.1109/cvpr.2010.5540026

Cited by 7 publications

(6 citation statements)

References 16 publications

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“…Thus, the development and validation of robust, effective and accurate approaches for fMRI activation detection have been investigated for decades (e.g., Friston et al, 1994; Heeger and Ress, 2002; Beckmann et al, 2003; Woolrich et al, 2004b; Matthews and Jezzard, 2004; Thirion et al, 2007; Logothetis et al, 2008; Ng et al, 2010). However, due to the challenges of considerable variability in brain structure and function across individuals and the lack of quantitative representation of common brain architectures, novel fMRI activation detection methods have been still in active research in the neuroimaging community.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, we applied a multi-subjects analysis to explore the group-wise activation significance of each landmark among a group of subjects. Our rationale here is the same as those in other group-wise fMRI activation detection methods (Beckmann et al, 2003; Woolrich et al, 2004b; Thirion et al, 2007; Ng et al, 2010), that is, group-wise approach improves the statistical power and reliability of individual fMRI studies. Specifically, for each landmark, we first set the estimated effect sizes from a group of subjects, which were generated from the application of GLM in individual brains, as the input of the mixed-effects model (Everitt and Bullmore, 1999) via the tool of FSL FLAME (Smith et al, 2004).…”

Section: Methodsmentioning

confidence: 99%

“…However, despite the dominant successes of analyzing individual brain’s activation patterns (e.g., Worsley and Friston, 1997; Bullmore et al, 1996; Woolrich et al, 2001a), it has been challenging to derive consistent fMRI activation patterns across different brains and populations, due to the individual variability and different sources of noises (e.g., Thirion et al, 2007; Derrfuss and Mar, 2009; Laird et al, 2009; Hamilton, 2009; Costafreda, 2009; Tahmasebi, 2010). To address this challenge, researchers in the neuroimaging field have developed group-wise activation detection methods, such as the two-level group-wise GLM method (Beckmann et al, 2003), Bayesian inference (Woolrich et al, 2004b), multi-level analysis (Thirion et al, 2007), group ICA analysis (Calhoun et al, 2009), and group Markov Random Field (MRF) methods (Ng et al, 2010), among others. The rationale behind these group-wise fMRI activation detection methods is to leverage the statistical power from multiple brains in order to gain the robustness to noises and the less sensitivity to individual variability.…”

Section: Introductionmentioning

confidence: 99%

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Group-Wise FMRI Activation Detection on DICCCOL Landmarks

Zhu

Zhang

et al. 2014

Neuroinform

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Group-wise activation detection in task-based fMRI has been widely used because of its robustness to noises and its capacity to deal with variability of individual brains. However, current group-wise fMRI activation detection methods typically rely on the co-registration of individual brains’ fMRI images, which has difficulty in dealing with the remarkable anatomic variation of different brains. As a consequence, the resulted misalignments could significantly degrade the required inter-subject correspondences, thus substantially reducing the sensitivity and specificity of group-wise fMRI activation detection. To deal with these challenges, this paper presents a novel approach to detecting group-wise fMRI activation on our recently developed and validated Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL). The basic idea here is that the first-level general linear model (GLM) analysis is first performed on the fMRI signal of each corresponding DICCCOL landmark in individual brain’s own space, and then the estimated effect sizes of the same landmark from a group of subjects are statistically assessed with the mixed-effect model at the group level. Finally, the consistently activated DICCCOL landmarks are determined and declared in a group-wise fashion in response to external block-based stimuli. Our experimental results have demonstrated that the proposed approach can detect meaningful activations.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Group-Wise FMRI Activation Detection on DICCCOL Landmarks

Zhu

Zhang

et al. 2014

Neuroinform

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“…In this paper, we present a new group-wise approach, group MRF (GMRF) [39], for detecting intra-subject activation. 1 .…”

Section: Group-wise Activation Detectionmentioning

confidence: 99%

Modeling Brain Activation in fMRI Using Group MRF

Hamarneh

Abugharbieh

2012

IEEE Trans. Med. Imaging

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Abstract-Noise confounds present serious complications to functional magnetic resonance imaging (fMRI) analysis. The amount of discernible signals within a single dataset of a subject is often inadequate to obtain satisfactory intra-subject activation detection. To remedy this limitation, we propose a novel group Markov random field (GMRF) that extends each subject's neighborhood system to other subjects to enable information coalescing. A distinct advantage of GMRF over standard fMRI group analysis is that no stringent one-to-one voxel correspondence is required. Instead, intra-and inter-subject neighboring voxels are jointly regularized to encourage spatially proximal voxels to be assigned similar labels across subjects. Our proposed group-extended graph structure thus provides an effective means for handling inter-subject variability. Also, adopting a group-wise approach by integrating group information into intra-subject activation, as opposed to estimating a single average group map, permits inter-subject differences to be characterized and studied. GMRF can be elegantly implemented as a single MRF, thus enabling all subjects' activation maps to be simultaneously and collaboratively segmented with global optimality guaranteed in the case of binary labeling. We validate our technique on synthetic and real fMRI data and demonstrate GMRF's superior performance over standard fMRI analysis.Index Terms-Functional magnetic resonance imaging (fMRI), functional neuroimaging, group regularization, inter-subject variability, Markov random field.

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“…While classical analyzes ignore the structure underlying these random effects, there is a growing evidence that some of it can be characterized or even predicted by several factors, such as i) the presence of an average effect in the population (high variability coincides spatially with larger mean effect) [3] ii) anatomical differences, such as the cortical ends of anatomical connections, which has been illustrated by a prediction of faces versus scenes contrast in the fusiform gyrus [4] and iii) individual functional connectivity between brain regions, which has been shown to provide a relevant prior for group analyzes in [5]. Modeling such random effects is important in order to facilitate future correlative studies that compare brain features to genetics and behavior.…”

Section: Introductionmentioning

confidence: 99%

Principal Component Regression Predicts Functional Responses across Individuals

Thirion

Varoquaux

Grisel

et al. 2014

Lecture Notes in Computer Science

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Abstract. Inter-subject variability is a major hurdle for neuroimaging group-level inference, as it creates complex image patterns that are not captured by standard analysis models and jeopardizes the sensitivity of statistical procedures. A solution to this problem is to model random subjects effects by using the redundant information conveyed by multiple imaging contrasts. In this paper, we introduce a novel analysis framework, where we estimate the amount of variance that is fit by a random effects subspace learned on other images; we show that a principal component regression estimator outperforms other regression models and that it fits a significant proportion (10% to 25%) of the betweensubject variability. This proves for the first time that the accumulation of contrasts in each individual can provide the basis for more sensitive neuroimaging group analyzes.

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Group MRF for fMRI activation detection

Abstract: Abstract

Cited by 7 publications

References 16 publications

Group-Wise FMRI Activation Detection on DICCCOL Landmarks

Group-Wise FMRI Activation Detection on DICCCOL Landmarks

Modeling Brain Activation in fMRI Using Group MRF

Principal Component Regression Predicts Functional Responses across Individuals

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