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

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 region-based 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?=?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 memories leading to re-experiencing, hyperarousal and flashbacks observed in soldiers with PTSD and mTBI. Hum Brain Mapp 39:264-287, 2018. © 2017 Wiley Periodicals, Inc.

SUBMITTER: Rangaprakash D

PROVIDER: S-EPMC6866312 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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

Rangaprakash D D Dretsch Michael N MN Venkataraman Archana A Katz Jeffrey S JS Denney Thomas S TS Deshpande Gopikrishna G

Human brain mapping 20171023 1

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 region-based 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-var ...[more]

PMID: 29058357

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Project description:Brain functioning relies on various segregated/specialized neural regions functioning as an integrated-interconnected network (i.e., metastability). Various psychiatric and neurologic disorders are associated with aberrant functioning of these brain networks. In this study, we present a novel framework integrating the strength and temporal variability of metastability in brain networks. We demonstrate that this approach provides novel mechanistic insights which enables better imaging-based predictions. Using whole-brain resting-state fMRI and a graph-theoretic framework, we integrated strength and temporal-variability of complex-network properties derived from effective connectivity networks, obtained from 87 U.S. Army soldiers consisting of healthy combat controls (n = 28), posttraumatic stress disorder (PTSD; n = 17), and PTSD with comorbid mild-traumatic brain injury (mTBI; n = 42). We identified prefrontal dysregulation of key subcortical and visual regions in PTSD/mTBI, with all network properties exhibiting lower variability over time, indicative of poorer flexibility. Larger impairment in the prefrontal-subcortical pathway but not prefrontal-visual pathway differentiated comorbid PTSD/mTBI from the PTSD group. Network properties of the prefrontal-subcortical pathway also had significant association (R 2 = 0.56) with symptom severity and neurocognitive performance; and were also found to possess high predictive ability (81.4% accuracy in classifying the disorders, explaining 66-72% variance in symptoms), identified through machine learning. Our framework explained 13% more variance in behaviors compared to the conventional framework. These novel insights and better predictions were made possible by our novel framework using static and time-varying network properties in our three-group scenario, advancing the mechanistic understanding of PTSD and comorbid mTBI. Our contribution has wide-ranging applications for network-level characterization of healthy brains as well as mental disorders.

Project description:IntroductionPost-stroke aphasia (PSA) is a language disorder caused by left hemisphere stroke. Electroacupuncture (EA) is a minimally invasive therapeutic option for PSA treatment. Tongli (HT5) and Xuanzhong (GB39), two important language-associated acupoints, are frequently used in the rehabilitation of patients with PSA. Preliminary evidence indicated functional activation in distributed cortical areas upon HT5 and GB39 stimulation. However, research on the modulation of dynamic and static functional connectivity (FC) in the brain by EA in PSA is lacking.MethodThis study aimed to investigate the PSA-related effects of EA stimulation at HT5 and GB39 on neural processing. Thirty-five participants were recruited, including 19 patients with PSA and 16 healthy controls (HCs). The BOLD signal was analyzed by static independent component analysis, generalized psychophysiological interactions, and dynamic independent component analysis, considering variables such as age, sex, and years of education.ResultsThe results revealed that PSA showed activated clusters in the left putamen, left postcentral gyrus (PostCG), and left angular gyrus in the salience network (SN) compared to the HC group. The interaction effect on temporal properties of networks showed higher variability of SN (F = 2.23, positive false discovery rate [pFDR] = 0.017). The interaction effect on static FC showed increased functional coupling between the right calcarine and right lingual gyrus (F = 3.16, pFDR = 0.043). For the dynamic FC, at the region level, the interaction effect showed lower variability and higher frequencies of circuit 3, with the strongest connections between the supramarginal gyrus and posterior cingulum (F = 5.42, pFDR = 0.03), middle cingulum and PostCG (F = 5.27, pFDR = 0.036), and triangle inferior frontal and lingual gyrus (F = 5.57, pFDR = 0.026). At the network level, the interaction effect showed higher variability in occipital network-language network (LN) and cerebellar network (CN) coupling, with stronger connections between the LN and CN (F = 4.29, pFDR = 0.042). Dynamic FC values between the triangle inferior frontal and lingual gyri were anticorrelated with transcribing, describing, and dictating scores in the Chinese Rehabilitation Research Center for Chinese Standard Aphasia Examination.DiscussionThese findings suggest that EA stimulation may improve language function, as it significantly modulated the nodes of regions/networks involved in the LN, SN, CN, occipital cortex, somatosensory regions, and cerebral limbic system.

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

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

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