Spatial Regularization of Functional Connectivity Using High-Dimensional Markov Random Fields

Liu, Wei; Zhu, Peihong; Anderson, Jeffrey S.; Yurgelun‐Todd, Deborah; Fletcher, P. Thomas

doi:10.1007/978-3-642-15745-5_45

Cited by 12 publications

(9 citation statements)

References 10 publications

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“…For rs-fMRI processing, MRF were used for signal denoising (Descombes et al, 1998) and segmentation (Lashkari et al, 2010b; Liu et al, 2010, 2012, 2011). Most of the graph-based segmentation schemes proposed so far rely on continuous models, that are optimized by Gibbs sampling (Lashkari et al, 2010b; Liu et al, 2010) or variational methods (Lashkari and Golland, 2009). …”

Section: Methodsmentioning

confidence: 99%

GraSP: Geodesic Graph-based Segmentation with Shape Priors for the functional parcellation of the cortex

et al. 2015

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Resting-state functional MRI is a powerful technique for mapping the functional organization of the human brain. However, for many types of connectivity analysis, high-resolution voxelwise analyses are computationally infeasible and dimensionality reduction is typically used to limit the number of network nodes. Most commonly, network nodes are defined using standard anatomic atlases that do not align well with functional neuroanatomy or regions of interest covering a small portion of the cortex. Data-driven parcellation methods seek to overcome such limitations, but existing approaches are highly dependent on initialization procedures and produce spatially fragmented parcels or overly isotropic parcels that are unlikely to be biologically grounded. In this paper, we propose a novel graph-based parcellation method that relies on a discrete Markov Random Field framework. The spatial connectedness of the parcels is explicitly enforced by shape priors. The shape of the parcels is adapted to underlying data through the use of functional geodesic distances. Our method is initialization-free and rapidly segments the cortex in a single optimization. The performance of the method was assessed using a large developmental cohort of more than 850 subjects. Compared to two prevalent parcellation methods, our approach provides superior reproducibility for a similar data fit. Furthermore, compared to other methods, it avoids incoherent parcels. Finally, the method’s utility is demonstrated through its ability to detect strong brain developmental effects that are only weakly observed using other methods.

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Section: Methodsmentioning

confidence: 99%

GraSP: Geodesic Graph-based Segmentation with Shape Priors for the functional parcellation of the cortex

et al. 2015

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“…We whiten the time‐series assuming an AR(1) model and used the QuIC implementation [Hsieh et al, ] of Graphical Lasso [Friedman et al, ] to produce Markov networks that quantify the relationship between each pair of regions [Smith et al, ] for each subject. Stability selection [Liu et al, ; Meinshausen and Bühlmann, ] was used to determine the optimal network sparsity or number of edges in the network. The primary benefit of stability selection is that it retains only the most stable edges in the network, therefore eliminating the need for “hard” thresholding of the network (i.e., all values less than 0.3 would be arbitrarily discarded).…”

Section: Methodsmentioning

confidence: 99%

Resting state functional MRI reveals abnormal network connectivity in neurofibromatosis 1

Tomson

Schreiner

Narayan

et al. 2015

Human Brain Mapping

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Neurofibromatosis type I (NF1) is a genetic disorder caused by mutations in the neurofibromin 1 gene at locus 17q11.2. Individuals with NF1 have an increased incidence of learning disabilities, attention deficits and autism spectrum disorders. As a single gene disorder, NF1 represents a valuable model for understanding gene-brain-behavior relationships. While mouse models have elucidated molecular and cellular mechanisms underlying learning deficits associated with this mutation, little is known about functional brain architecture in human subjects with NF1. To address this question, we used resting state functional connectivity MRI (rs-fcMRI) to elucidate the intrinsic network structure of 30 NF1 participants compared with 30 healthy demographically matched controls during an eyes-open rs-fcMRI scan. Novel statistical methods were employed to quantify differences in local connectivity (edge strength) and modularity structure, in combination with traditional global graph theory applications. Our findings suggest that individuals with NF1 have reduced anterior-posterior connectivity, weaker bilateral edges, and altered modularity clustering relative to healthy controls. Further, edge strength and modular clustering indices were correlated with IQ and internalizing symptoms. These findings suggest that Ras signaling disruption may lead to abnormal functional brain connectivity; further investigation into the functional consequences of these alterations in both humans and in animal models is warranted.

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“…The first work about fMRI analysis on the GPU is the work by Gembris et al [14] that used the GPU to speedup the calculation of correlations between voxel time series, a technique that commonly is used in resting state fMRI [18] for identifying functional brain networks. Liu et al [19] have also used the GPU to speedup correlation analysis. We recently used the GPU to create an interactive interface, with 3D visualization, for exploratory functional connectivity analysis [6].…”

Section: Introductionmentioning

confidence: 99%

fMRI analysis on the GPU—Possibilities and challenges

Eklund

Andersson

Knutsson

2012

Computer Methods and Programs in Biomedicine

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Functional magnetic resonance imaging (fMRI) makes it possible to non-invasively measure brain activity with high spatial resolution. There are however a number of issues that have to be addressed. One is the large amount of spatio-temporal data that needs to be processed. In addition to the statistical analysis itself, several preprocessing steps, such as slice timing correction and motion compensation, are normally applied. The high computational power of modern graphic cards has already successfully been used for MRI and fMRI. Going beyond the first published demonstration of GPU-based analysis of fMRI data, all the preprocessing steps and two statistical approaches, the general linear model (GLM) and canonical correlation analysis (CCA), have been implemented on a GPU. For an fMRI dataset of typical size (80 volumes with 64 x 64 x 22 voxels), all the preprocessing takes about 0.5 s on the GPU, compared to 5 s with an optimized CPU implementation and 120 s with the commonly used statistical parametric mapping (SPM) software. A random permutation test with 10 000 permutations, with smoothing in each permutation, takes about 50 s if three GPUs are used, compared to 0.5 -2.5 h with an optimized CPU implementation. The presented work will save time for researchers and clinicians in their daily work and enables the use of more advanced analysis, such as non-parametric statistics, both for conventional fMRI and for real-time fMRI.

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Spatial Regularization of Functional Connectivity Using High-Dimensional Markov Random Fields

Cited by 12 publications

References 10 publications

GraSP: Geodesic Graph-based Segmentation with Shape Priors for the functional parcellation of the cortex

GraSP: Geodesic Graph-based Segmentation with Shape Priors for the functional parcellation of the cortex

Resting state functional MRI reveals abnormal network connectivity in neurofibromatosis 1

fMRI analysis on the GPU—Possibilities and challenges

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