From Connectivity Models to Region Labels: Identifying Foci of a Neurological Disorder

Venkataraman, Archana; Kubicki, Marek; Golland, Polina

doi:10.1109/tmi.2013.2272976

Cited by 16 publications

(22 citation statements)

References 43 publications

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“…Unlike traditional connectomics, which compares either pairwise correlation coefficients or average node-based measures between groups [19], our framework explicitly models the altered network topology while simultaneously adapting to both noise and subject variability [20] [21]. Within a Bayesian setting, we estimate a latent or hidden graph that characterizes the spread of altered functional connectivity from the region foci.…”

Section: Methodsmentioning

confidence: 99%

Pivotal response treatment prompts a functional rewiring of the brain among individuals with autism spectrum disorder

et al. 2016

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Behavioral interventions for autism have gained prominence in recent years; however, the neural-systems-level targets of these interventions remain poorly understood. We use a novel Bayesian framework to extract network-based differences before and after a 16-week Pivotal Response Treatment (PRT) regimen. Our results suggest that functional changes induced by PRT localize to the posterior cingulate and are marked by a shift in connectivity from the orbitofrontal cortex to the occipital temporal cortex. Our results illuminate a potential PRT-induced learning mechanism, whereby the neural circuits involved during social perception shift from sensory and attentional systems to higher-level object and face processing areas.

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Section: Methodsmentioning

confidence: 99%

Pivotal response treatment prompts a functional rewiring of the brain among individuals with autism spectrum disorder

et al. 2016

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“…Our prior work [32], [33] describes a straightforward approach to incorporating anatomical data into the Bayesian model. In the future, we might constrain the abnormal functional edges between community members according to the underlying white matter connections.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the distinction between hyper- and hypo-synchronous connections, the overall distribution cannot be represented compactly, as in our previous works [33], [42]. In practice, we expect the latent noise to be small ( ε < 0.5) and the edge density to be moderate (0.4 < η < 0.6).…”

Section: Generative Model Of Abnormal Communitiesmentioning

confidence: 93%

“…Table I summarizes the random variables and nonrandom parameters in our generative model. Although the variable definitions are similar to [33], our current framework specifies a multi-cluster community organization. Below we formalize the mathematical representations.…”

Section: Generative Model Of Abnormal Communitiesmentioning

confidence: 99%

“…From a clinical perspective, we can use the region assignments to perform follow-up analyses (see Neurosynth below); but the connectivity pattern is much harder to interpret. Finally, our previous work [33], [44] suggests that connection-wise differences may not be as robust as region measures.…”

Section: Model Evaluationmentioning

confidence: 99%

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Bayesian Community Detection in the Space of Group-Level Functional Differences

Venkataraman

Yang

Pelphrey

et al. 2016

IEEE Trans. Med. Imaging

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We propose a unified Bayesian framework to detect both hyper-and hypo-active communities within whole-brain fMRI data. Specifically, our model identifies dense subgraphs that exhibit population-level differences in functional synchrony between a control and clinical group. We derive a variational EM algorithm to solve for the latent posterior distributions and parameter estimates, which subsequently inform us about the afflicted network topology. We demonstrate that our method provides valuable insights into the neural mechanisms underlying social dysfunction in autism, as verified by the Neurosynth meta-analytic database. In contrast, both univariate testing and community detection via recursive edge elimination fail to identify stable functional communities associated with the disorder.

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Identifying disease foci from static and dynamic effective connectivity networks: Illustration in soldiers with trauma

Rangaprakash

Dretsch

Venkataraman

et al. 2017

Human Brain Mapping

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7Alabama Advanced Imaging Consortium, USA r r Abstract: Brain connectivity studies report group differences in pairwise connection strengths. While informative, such results are difficult to interpret since our understanding of the brain relies on regionbased properties, rather than on connection information. Given that large disruptions in the brain are often caused by a few pivotal sources, we propose a novel framework to identify the sources of functional disruption from effective connectivity networks. Our approach integrates static and time-varying effective connectivity modeling in a probabilistic framework, to identify aberrant foci and the corresponding aberrant connectomics network. Using resting-state fMRI, we illustrate the utility of this novel approach in U.S. Army soldiers (N 5 87) with posttraumatic stress disorder (PTSD), mild traumatic brain injury (mTBI) and combat controls. Additionally, we employed machine-learning classification to identify those significant connectivity features that possessed high predictive ability. We identified three disrupted foci (middle frontal gyrus [MFG], insula, hippocampus), and an aberrant prefrontal-subcortical-parietal network of information flow. We found the MFG to be the pivotal focus of network disruption, with aberrant strength and temporal-variability of effective connectivity to the insula, amygdala and hippocampus. These connectivities also possessed high predictive ability (giving a classification accuracy of 81%); and they exhibited significant associations with symptom severity and neurocognitive functioning. In summary, dysregulation originating in the MFG caused elevated and temporally less-variable connectivity in subcortical regions, followed by a similar effect on parietal memory-related regions. This mechanism likely contributes to the reduced control over traumatic

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From Connectivity Models to Region Labels: Identifying Foci of a Neurological Disorder

Cited by 16 publications

References 43 publications

Pivotal response treatment prompts a functional rewiring of the brain among individuals with autism spectrum disorder

Pivotal response treatment prompts a functional rewiring of the brain among individuals with autism spectrum disorder

Bayesian Community Detection in the Space of Group-Level Functional Differences

Identifying disease foci from static and dynamic effective connectivity networks: Illustration in soldiers with trauma

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