Alterations in Network Connectivity after Traumatic Brain Injury in Mice

Meningher, Inbar; Bernstein‐Eliav, Michal; Rubovitch, Vardit; Pick, Chaim G.; Tavor, Ido

doi:10.1089/neu.2020.7063

Cited by 13 publications

(13 citation statements)

References 56 publications

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“…In terms of topological changes over time, they report decreases in CPL, increased efficiency, consistent with initial work in TBI patients by Nakamura and colleagues (57). Their data also showed increased global network clustering and transiently reduced modularity index (on day 7 compared to pre-injury), which is consistent with diffusion MRI based tractography studies in the closed head weight drop model in mice (64). We did not observe differences in weighted clustering coefficient between the groups (in our between-subjects design), but we did observe a similar but non-significant trend towards increased global transitivity and binarized clustering coefficient in CCI day 2 rats compared to controls (data not shown).…”

Section: Discussionsupporting

confidence: 84%

Compensatory functional connectome changes in a rat model of traumatic brain injury

Yang

Zhu

Pompilus

et al. 2021

Preprint

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Penetrating cortical impact injuries alter neuronal communication beyond the injury epicenter, across regions involved in affective, sensorimotor, and cognitive processing. Understanding how traumatic brain injury (TBI) reorganizes local and brain wide nodal functional interactions may provide valuable quantitative parameters for monitoring pathological progression and functional recovery. To this end, we investigated spontaneous fluctuations in the functional magnetic resonance imaging (fMRI) signal obtained at 11.1 Tesla in rats sustaining controlled cortical impact (CCI) and imaged at 2- and 30-days post-injury. Graph theory-based calculations were applied to weighted undirected matrices constructed from 12,879 pairwise correlations between fMRI signals from 162 regions. Our data indicate that on days 2 and 30 post-CCI there is a significant increase in connectivity strength in nodes located in contralesional cortical, thalamic, and basal forebrain areas. Rats imaged on day 2 post-injury had significantly greater network modularity than controls, with influential nodes (with high eigenvector centrality) contained within the contralesional module and participating less in cross-modular interactions. By day 30, modularity and cross-modular interactions recover, although a cluster of nodes with low strength and low eigenvector centrality remain in the ipsilateral cortex. Our results suggest that changes in node strength, modularity, eigenvector centrality, and participation coefficient track early and late TBI effects on brain functional connectivity. We propose that the observed compensatory functional connectivity reorganization in response to CCI may be unfavorable to brain wide communication in the early post-injury period.

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Section: Discussionsupporting

confidence: 84%

Compensatory functional connectome changes in a rat model of traumatic brain injury

Yang

Zhu

Pompilus

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…These results are in line with a previous study of the white matter integrity of an overlapping patient sample (Yuh et al, 2014). In addition, previous studies of humans (Imms et al, 2019;Kuceyeski, Jamison, Owen, Raj, & Mukherjee, 2019) and animals (Meningher, Bernstein-Eliav, Rubovitch, Pick, & Tavor, 2020) have found the SC of TBI patients are more highly segregated than controls. Modeling studies suggest structural disconnection in TBI patients produces reduced metastability, linked to excitation-inhibition imbalance (Hellyer, Scott, Shanahan, Sharp, & Leech, 2015).…”

supporting

confidence: 92%

Personalized Connectome-Based Modeling in Patients with Semi-Acute Phase TBI: Relationship to Acute Neuroimaging and 6 Month Follow-Up

Good

Schirner

Shen

et al. 2022

eNeuro

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Following traumatic brain injury (TBI), cognitive impairments manifest through interactions between microscopic and macroscopic changes. On the micro-scale a neurometabolic cascade alters neurotransmission, while on the macro-scale diffuse axonal injury impacts the integrity of long-range connections. Large-scale brain network modeling allows us to make predictions across these spatial scales by integrating neuroimaging data with biophysically based models to investigate how microscale changes invisible to conventional neuroimaging influence large-scale brain dynamics. To this end, we analyzed structural and functional neuroimaging data from a well characterized sample of forty-four adult TBI patients recruited from a regional trauma center, scanned at 1-2 weeks post-injury, and with follow-up behavioral outcome assessed six months later. Thirty-six age-matched healthy adults served as comparison participants. Using The Virtual Brain we fit simulations of whole-brain resting-state functional MRI to the empirical static and dynamic functional connectivity of each participant. Multivariate partial least squares (PLS) analysis showed that patients with acute traumatic intracranial lesions had lower cortical regional inhibitory connection strengths than comparison participants, while patients without acute lesions did not differ from the comparison group. Further multivariate PLS analyses found correlations between lower semi-acute regional inhibitory connection strengths and more symptoms and lower cognitive performance at a 6-month follow-up. Critically, patients without acute lesions drove this relationship, suggesting clinical relevance of regional inhibitory 4 connection strengths even when traumatic intracranial lesions were not present. Our results suggest large-scale connectome-based models may be sensitive to pathophysiological changes in semi-acute phase TBI patients and predictive of their chronic outcomes. Significance StatementThe variability of clinical outcomes following mild to moderate traumatic brain injury (TBI) is underscored by complex pathophysiological mechanisms that take effect across spatial scales. We used the neuroinformatics platform, The Virtual Brain, to model individualized brain activity and make inferences across these spatial scales. Specifically, this approach allowed us to link macroscopic brain dynamics with mesoscopic biophysical parameters, distinguishing semi-acute mild to moderate TBI patients from comparison participants and predicting the long-term recovery of these patients. Our results demonstrate the sensitivity of our large-scale brain model to pathophysiological changes following TBI and illustrates how computational modeling may be used to advance understanding of chronic TBI outcome.

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“…In terms of topological changes over time, they report decreases in CPL, increased efficiency, consistent with initial work in TBI patients by Nakamura et al 57 Their data also showed increased global network clustering and transiently reduced modularity index (on Day 7 compared to pre-injury), which is consistent with diffusion MRI based tractography studies in the closed head weight drop model in mice. 64 We did not observe differences in weighted CC between the groups (in our between-subjects design), but we did observe a similar but non-significant trend towards increased global transitivity and binarized CC in CCI Day 2 rats compared to controls (data not shown). A limitation regarding comparisons between rodent TBI studies and human TBI should be noted.…”

Section: Discussionmentioning

confidence: 45%

Compensatory functional connectome changes in a rat model of traumatic brain injury

Yang

Zhu

Pompilus

et al. 2021

Brain Communications

View full text Add to dashboard Cite

Penetrating cortical impact injuries alter neuronal communication beyond the injury epicenter, across regions involved in affective, sensorimotor, and cognitive processing. Understanding how traumatic brain injury reorganizes local and brain wide nodal interactions may provide valuable quantitative parameters for monitoring pathological progression and recovery. To this end, we investigated spontaneous fluctuations in the functional MRI signal obtained at 11.1 Tesla in rats sustaining controlled cortical impact and imaged at 2- and 30-days post-injury. Graph theory-based calculations were applied to weighted undirected matrices constructed from 12,879 pairwise correlations between functional MRI signals from 162 regions. Our data indicate that on days 2 and 30 post-controlled cortical impact there is a significant increase in connectivity strength in nodes located in contralesional cortical, thalamic, and basal forebrain areas. Rats imaged on day 2 post-injury had significantly greater network modularity than controls, with influential nodes (with high eigenvector centrality) contained within the contralesional module and participating less in cross-modular interactions. By day 30, modularity and cross-modular interactions recover, although a cluster of nodes with low strength and low eigenvector centrality remain in the ipsilateral cortex. Our results suggest that changes in node strength, modularity, eigenvector centrality, and participation coefficient track early and late traumatic brain injury effects on brain functional connectivity. We propose that the observed compensatory functional connectivity reorganization in response to controlled cortical impact may be unfavorable to brain wide communication in the early post-injury period.

show abstract

Alterations in Network Connectivity after Traumatic Brain Injury in Mice

Cited by 13 publications

References 56 publications

Compensatory functional connectome changes in a rat model of traumatic brain injury

Compensatory functional connectome changes in a rat model of traumatic brain injury

Personalized Connectome-Based Modeling in Patients with Semi-Acute Phase TBI: Relationship to Acute Neuroimaging and 6 Month Follow-Up

Compensatory functional connectome changes in a rat model of traumatic brain injury

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