Dataset Information

The application of a mathematical model linking structural and functional connectomes in severe brain injury.

ABSTRACT: Following severe injuries that result in disorders of consciousness, recovery can occur over many months or years post-injury. While post-injury synaptogenesis, axonal sprouting and functional reorganization are known to occur, the network-level processes underlying recovery are poorly understood. Here, we test a network-level functional rerouting hypothesis in recovery of patients with disorders of consciousness following severe brain injury. This hypothesis states that the brain recovers from injury by restoring normal functional connections via alternate structural pathways that circumvent impaired white matter connections. The so-called network diffusion model, which relates an individual's structural and functional connectomes by assuming that functional activation diffuses along structural pathways, is used here to capture this functional rerouting. We jointly examined functional and structural connectomes extracted from MRIs of 12 healthy and 16 brain-injured subjects. Connectome properties were quantified via graph theoretic measures and network diffusion model parameters. While a few graph metrics showed groupwise differences, they did not correlate with patients' level of consciousness as measured by the Coma Recovery Scale - Revised. There was, however, a strong and significant partial Pearson's correlation (accounting for age and years post-injury) between level of consciousness and network diffusion model propagation time (r = 0.76, p < 0.05, corrected), i.e. the time functional activation spends traversing the structural network. We concluded that functional rerouting via alternate (and less efficient) pathways leads to increases in network diffusion model propagation time. Simulations of injury and recovery in healthy connectomes confirmed these results. This work establishes the feasibility for using the network diffusion model to capture network-level mechanisms in recovery of consciousness after severe brain injury.

SUBMITTER: Kuceyeski A

PROVIDER: S-EPMC4864323 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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The application of a mathematical model linking structural and functional connectomes in severe brain injury.

Kuceyeski A A Shah S S Dyke J P JP Bickel S S Abdelnour F F Schiff N D ND Voss H U HU Raj A A

NeuroImage. Clinical 20160414

Following severe injuries that result in disorders of consciousness, recovery can occur over many months or years post-injury. While post-injury synaptogenesis, axonal sprouting and functional reorganization are known to occur, the network-level processes underlying recovery are poorly understood. Here, we test a network-level functional rerouting hypothesis in recovery of patients with disorders of consciousness following severe brain injury. This hypothesis states that the brain recovers from ...[more]

PMID: 27200264

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Project description:BACKGROUND:Understanding the interdependencies among inflammatory mediators of tissue damage following traumatic brain injury (TBI) is essential in providing effective, patient-specific care. Activated microglia and elevated concentrations of inflammatory signaling molecules reflect the complex cascades associated with acute neuroinflammation and are predictive of recovery after TBI. However, clinical TBI studies to date have not focused on modeling the dynamic temporal patterns of simultaneously evolving inflammatory mediators, which has potential in guiding the design of future immunomodulation intervention studies. METHODS:We derived a mathematical model consisting of ordinary differential equations (ODE) to represent interactions between pro- and anti-inflammatory cytokines, M1- and M2-like microglia, and central nervous system (CNS) tissue damage. We incorporated variables for several cytokines, interleukin (IL)-1?, IL-4, IL-10, and IL-12, known to have roles in microglial activation and phenotype differentiation. The model was fit to cerebrospinal fluid (CSF) cytokine data, collected during the first 5 days post-injury in n?=?89 adults with severe TBI. Ensembles of model fits were produced for three patient subgroups: (1) a favorable outcome group (GOS?=?4,5) and (2) an unfavorable outcome group (GOS?=?1,2,3) both with lower pro-inflammatory load, and (3) an unfavorable outcome group (GOS?=?1,2,3) with higher pro-inflammatory load. Differences in parameter distributions between subgroups were ranked using Bhattacharyya metrics to identify mechanistic differences underlying the neuroinflammatory patterns of patient groups with different TBI outcomes. RESULTS:Optimal model fits to data showed different microglial and damage responses by patient subgroup. Upon comparison of model parameter distributions, unfavorable outcome groups were characterized by either a prolonged, pathophysiological or a transient, sub-physiological course of neuroinflammation. CONCLUSION:By developing a mathematical characterization of inflammatory processes informed by clinical data, we have created a system for exploring links between acute neuroinflammatory components and patient outcome in severe TBI.

Project description:OBJECTIVES:To employ metabolomics-based pathway and network analyses to evaluate the cerebrospinal fluid metabolome after severe traumatic brain injury in children and the capacity of combination therapy with probenecid and N-acetylcysteine to impact glutathione-related and other pathways and networks, relative to placebo treatment. DESIGN:Analysis of cerebrospinal fluid obtained from children enrolled in an Institutional Review Board-approved, randomized, placebo-controlled trial of a combination of probenecid and N-acetylcysteine after severe traumatic brain injury (Trial Registration NCT01322009). SETTING:Thirty-six-bed PICU in a university-affiliated children's hospital. PATIENTS AND SUBJECTS:Twelve children 2-18 years old after severe traumatic brain injury and five age-matched control subjects. INTERVENTION:Probenecid (25?mg/kg) and N-acetylcysteine (140?mg/kg) or placebo administered via naso/orogastric tube. MEASUREMENTS AND MAIN RESULTS:The cerebrospinal fluid metabolome was analyzed in samples from traumatic brain injury patients 24 hours after the first dose of drugs or placebo and control subjects. Feature detection, retention time, alignment, annotation, and principal component analysis and statistical analysis were conducted using XCMS-online. The software "mummichog" was used for pathway and network analyses. A two-component principal component analysis revealed clustering of each of the groups, with distinct metabolomics signatures. Several novel pathways with plausible mechanistic involvement in traumatic brain injury were identified. A combination of metabolomics and pathway/network analyses showed that seven glutathione-centered pathways and two networks were enriched in the cerebrospinal fluid of traumatic brain injury patients treated with probenecid and N-acetylcysteine versus placebo-treated patients. Several additional pathways/networks consisting of components that are known substrates of probenecid-inhibitable transporters were also identified, providing additional mechanistic validation. CONCLUSIONS:This proof-of-concept neuropharmacometabolomics assessment reveals alterations in known and previously unidentified metabolic pathways and supports therapeutic target engagement of the combination of probenecid and N-acetylcysteine treatment after severe traumatic brain injury in children.

Dataset Information

The application of a mathematical model linking structural and functional connectomes in severe brain injury.

Publications

The application of a mathematical model linking structural and functional connectomes in severe brain injury.

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