MIGRAINE: MRI Graph Reliability Analysis and Inference for Connectomics

Roncal, William Gray; Koterba, Zachary H.; Mhembere, Disa; Kleissas, Dean; Vogelstein, Joshua T.; Burns, Randal; Bowles, Anita R.; Donavos, Dimitrios K.; Ryman, Sephira G.; Jung, Rex E.; Wu, Lei; Calhoun, Vince D.; Vogelstein, R. Jacob

doi:10.1109/globalsip.2013.6736878

Cited by 29 publications

(36 citation statements)

References 18 publications

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“…Previous studies show that intra-hemispheric connections are more likely than interhemispheric connections for healthy individuals (Gray Roncal et al, 2013). This is also evident in our dataset, with subjects having a proportion of intra-hemispheric edges of 0.55 over the total number of possible intra-hemispheric connections, against a proportion of about 0.21 for the inter-hemispheric ones.…”

Section: Application To Human Brain Network and Creativitysupporting

confidence: 67%

“…We consider H = 2 mixture components, with π 0(h) defined as in (2.9). Brain networks are typically characterized by tighter intrahemispheric than inter-hemispheric connections (Gray Roncal et al, 2013). Hence, we consider two node blocks V 1 = {1, .…”

Section: Simulation Settingsmentioning

confidence: 99%

“…As directional diffusion of water within the brain tends to occur along white matter tracts, current connectome pre-processing pipelines (e.g Craddock et al, 2013;Gray Roncal et al, 2013) can produce an adjacency matrix A i for each individual i = 1, . .…”

Section: Low Creativity Subject High Creativity Subjectmentioning

confidence: 99%

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Bayesian Inference and Testing of Group Differences in Brain Networks

Durante¹,

Dunson²

2018

Bayesian Anal.

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Abstract. Network data are increasingly collected along with other variables of interest. Our motivation is drawn from neurophysiology studies measuring brain connectivity networks for a sample of individuals along with their membership to a low or high creative reasoning group. It is of paramount importance to develop statistical methods for testing of global and local changes in the structural interconnections among brain regions across groups. We develop a general Bayesian procedure for inference and testing of group differences in the network structure, which relies on a nonparametric representation for the conditional probability mass function associated with a network-valued random variable. By leveraging a mixture of low-rank factorizations, we allow simple global and local hypothesis testing adjusting for multiplicity. An efficient Gibbs sampler is defined for posterior computation. We provide theoretical results on the flexibility of the model and assess testing performance in simulations. The approach is applied to provide novel insights on the relationships between human brain networks and creativity.

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Section: Application To Human Brain Network and Creativitysupporting

confidence: 67%

Section: Simulation Settingsmentioning

confidence: 99%

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Bayesian Inference and Testing of Group Differences in Brain Networks

Durante¹,

Dunson²

2018

Bayesian Anal.

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“…The Frobenius Norm is an established matrix distance measure 36 , referred to as Frobenius Distance when assessing graph similarity. The result from Roncal et al's study 35 has not proven to be exactly reproducible with the published dataset 25 , as the scan-rescan comparisons do not always produce the lowest values (i.e. greatest similarity).…”

Section: Brain Similarity Detectionmentioning

confidence: 93%

“…The CDI method is applied in this section on large-scale human connectomes generated by Roncal et al 35 from magnetic resonance imaging (MRI) scans carried out by Landman et al 25 . Landman et al used 21 healthy volunteers, where each subject was scanned twice with a short break between scan and rescan (scan 1 and scan 2 respectively) 25 .…”

Section: Brain Similarity Detectionmentioning

confidence: 99%

Network Communities of Dynamical Influence

Clark

Punzo

Macdonald

2019

Sci Rep

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Fuelled by a desire for greater connectivity, networked systems now pervade our society at an unprecedented level that will affect it in ways we do not yet understand. In contrast, nature has already developed efficient networks that can instigate rapid response and consensus when key elements are stimulated. We present a technique for identifying these key elements by investigating the relationships between a system’s most dominant eigenvectors. This approach reveals the most effective vertices for leading a network to rapid consensus when stimulated, as well as the communities that form under their dynamical influence. In applying this technique, the effectiveness of starling flocks was found to be due, in part, to the low outdegree of every bird, where increasing the number of outgoing connections can produce a less responsive flock. A larger outdegree also affects the location of the birds with the most influence, where these influentially connected birds become more centrally located and in a poorer position to observe a predator and, hence, instigate an evasion manoeuvre. Finally, the technique was found to be effective in large voxel-wise brain connectomes where subjects can be identified from their influential communities.

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Spatial modeling of brain connectivity data via latent distance models with nodes clustering

Aliverti

Durante

2019

Statistical Analysis

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Brain network data-measuring structural interconnections among brain regions of interest-are increasingly collected for multiple individuals. Moreover, recent analyses provide additional information on the brain regions under study. These predictors typically include the three-dimensional anatomical coordinates of the regions, and their membership to hemispheres and lobes. Although recent studies have explored the spatial effects underlying brain networks, there is still a lack of statistical analyses on the net connectivity structure which is not explained by the physical proximity of the brain regions. We answer this question via a predictor-dependent latent space model for replicated brain network data which provides a meaningful representation for the net connectivity architecture via a set of latent positions having a mixture of Gaussians prior. This model allows for flexible inference on brain network patterns which are not explained by the anatomical structure, and facilitates clustering among brain regions according to local similarities in the latent space. Our findings offer novel insights on wiring mechanisms among subsets of brain regions which interestingly departs from the anatomical proximity structure. K E Y W O R D Slatent space model, mixture of Gaussians prior, spatial effect, structural brain network

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MIGRAINE: MRI Graph Reliability Analysis and Inference for Connectomics

Cited by 29 publications

References 18 publications

Bayesian Inference and Testing of Group Differences in Brain Networks

Bayesian Inference and Testing of Group Differences in Brain Networks

Network Communities of Dynamical Influence

Spatial modeling of brain connectivity data via latent distance models with nodes clustering

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