Dataset Information

Estimating Memory Deterioration Rates Following Neurodegeneration and Traumatic Brain Injuries in a Hopfield Network Model.

ABSTRACT: Neurodegenerative diseases and traumatic brain injuries (TBI) are among the main causes of cognitive dysfunction in humans. At a neuronal network level, they both extensively exhibit focal axonal swellings (FAS), which in turn, compromise the information encoded in spike trains and lead to potentially severe functional deficits. There are currently no satisfactory quantitative predictors of decline in memory-encoding neuronal networks based on the impact and statistics of FAS. Some of the challenges of this translational approach include our inability to access small scale injuries with non-invasive methods, the overall complexity of neuronal pathologies, and our limited knowledge of how networks process biological signals. The purpose of this computational study is three-fold: (i) to extend Hopfield's model for associative memory to account for the effects of FAS, (ii) to calibrate FAS parameters from biophysical observations of their statistical distribution and size, and (iii) to systematically evaluate deterioration rates for different memory-recall tasks as a function of FAS injury. We calculate deterioration rates for a face-recognition task to account for highly correlated memories and also for a discrimination task of random, uncorrelated memories with a size at the capacity limit of the Hopfield network. While it is expected that the performance of any injured network should decrease with injury, our results link, for the first time, the memory recall ability to observed FAS statistics. This allows for plausible estimates of cognitive decline for different stages of brain disorders within neuronal networks, bridging experimental observations following neurodegeneration and TBI with compromised memory recall. The work lends new insights to help close the gap between theory and experiment on how biological signals are processed in damaged, high-dimensional functional networks, and towards positing new diagnostic tools to measure cognitive deficits.

SUBMITTER: Weber M

PROVIDER: S-EPMC5684180 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Estimating Memory Deterioration Rates Following Neurodegeneration and Traumatic Brain Injuries in a Hopfield Network Model.

Weber Melanie M Maia Pedro D PD Kutz J Nathan JN

Frontiers in neuroscience 20171109

Neurodegenerative diseases and traumatic brain injuries (TBI) are among the main causes of cognitive dysfunction in humans. At a neuronal network level, they both extensively exhibit focal axonal swellings (FAS), which in turn, compromise the information encoded in spike trains and lead to potentially severe functional deficits. There are currently no satisfactory quantitative predictors of decline in memory-encoding neuronal networks based on the impact and statistics of FAS. Some of the challe ...[more]

PMID: 29170624

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Project description:BackgroundMillions of Americans experience traumatic orthopaedic injuries (TOIs) annually. Post-injury symptoms of acute stress disorder (ASD), anxiety, depression, pain, and sleep disturbance are common. Symptoms often present in clusters. Symptom cluster profiles phenotypically characterize TOI survivors' experiences with clustered symptoms. Expression of brain-derived neurotrophic factor (BDNF) may contribute to the biological underpinnings of symptom cluster profile membership.MethodsWe recruited hospitalized TOI survivors within 72 hours of injury. We measured symptoms of ASD with the Acute Stress Disorder Scale and symptoms of anxiety, depression, pain, and sleep disturbance with Patient-Reported Outcomes Measurement Information System (PROMIS) short forms. We measured serum BDNF concentrations with enzyme-linked immunosorbent assay (ELISA) and identified rs6265 genotypes with TaqMan real-time PCR. We performed latent profile analysis to identify the symptom cluster profiles. We identified the variables associated with symptom cluster profile membership with unadjusted and adjusted multinomial logistic regression.ResultsWe identified 4 symptom cluster profiles characterized by symptom severity that we labelled Physical Symptoms Only, and Mild, Moderate, and Severe Psychological Distress. Age, self-identified Black race, resilience, and serum BDNF concentrations were associated with lower odds, and female sex with higher odds, of being in the Psychological Distress clusters. Clinical characteristics and rs6265 genotypes were not associated with symptom cluster profile membership.ConclusionTOI survivors experience distinct symptom cluster profiles. Sociodemographic characteristics and serum BDNF concentrations, not clinical characteristics, were associated with symptom cluster profile membership. These findings support comprehensive symptom screening and treatment for all TOI survivors and further evaluating BDNF as a biomarker of post-injury symptom burden.

Project description:ObjectiveTraumatic brain injury (TBI) is a heterogeneous disease with multiple neurological deficits that evolve over time. It is also associated with an increased incidence of neurodegenerative diseases. Accordingly, clinicians need better tools to predict a patient's long-term prognosis.MethodsDiffusion-weighted and anatomical MRI data were collected from 17 adolescents (mean age = 15y8mo) with moderate-to-severe TBI and 19 healthy controls. Using a network diffusion model (NDM), we examined the effect of progressive deafferentation and gray matter thinning in young TBI patients. Moreover, using a novel automated inference method, we identified several injury epicenters in order to determine the neural degenerative patterns in each TBI patient.ResultsWe were able to identify the subject-specific patterns of degeneration in each patient. In particular, the hippocampus, temporal cortices, and striatum were frequently found to be the epicenters of degeneration across the TBI patients. Orthogonal transformation of the predicted degeneration, using principal component analysis, identified distinct spatial components in the temporal-hippocampal network and the cortico-striatal network, confirming the vulnerability of these networks to injury. The NDM model, best predictive of the degeneration, was significantly correlated with time since injury, indicating that NDM can potentially capture the pathological progression in the chronic phase of TBI.InterpretationThese findings suggest that network spread may help explain patterns of distant gray matter thinning, which would be consistent with Wallerian degeneration of the white matter connections (i.e., "diaschisis") from diffuse axonal injuries and multifocal contusive injuries, and the neurodegenerative patterns of abnormal protein aggregation and transmission, which are hallmarks of brain changes in TBI. NDM approaches could provide highly subject-specific biomarkers relevant for disease monitoring and personalized therapies in TBI.

Project description:Following traumatic brain injury (TBI), activation of microglia and peripherally derived inflammatory macrophages occurs in association with tissue damage. This neuroinflammatory response may have beneficial or detrimental effects on neuronal survival, depending on the functional polarization of these cells along a continuum from M1-like to M2-like activation states. The mechanisms that regulate M1-like and M2-like activation after TBI are not well understood, but appear in part to reflect the redox state of the lesion microenvironment. NADPH oxidase (NOX2) is a critical enzyme system that generates reactive oxygen species in microglia/macrophages. After TBI, NOX2 is strongly up-regulated in M1-like, but not in M2-like polarized cells. Therefore, we hypothesized that NOX2 drives M1-like neuroinflammation and contributes to neurodegeneration and loss of neurological function after TBI. In the present studies we inhibited NOX2 activity using NOX2-knockout mice or the selective peptide inhibitor gp91ds-tat. We show that NOX2 is highly up-regulated in infiltrating macrophages after injury, and that NOX2 deficiency reduces markers of M1-like activation, limits tissue loss and neurodegeneration, and improves motor recovery after moderate-level control cortical injury (CCI). NOX2 deficiency also promotes M2-like activation after CCI, through increased IL-4Rα signaling in infiltrating macrophages, suggesting that NOX2 acts as a critical switch between M1- and M2-like activation states after TBI. Administration of gp91ds-tat to wild-type CCI mice starting at 24h post-injury reduces deficits in cognitive function and increased M2-like activation in the hippocampus. Collectively, our data indicate that increased NOX2 activity after TBI drives M1-like activation that contributes to inflammatory-mediated neurodegeneration, and that inhibiting this pathway provides neuroprotection, in part by altering M1-/M2-like balance towards the M2-like neuroinflammatory response.

Dataset Information

Estimating Memory Deterioration Rates Following Neurodegeneration and Traumatic Brain Injuries in a Hopfield Network Model.

Publications

Estimating Memory Deterioration Rates Following Neurodegeneration and Traumatic Brain Injuries in a Hopfield Network Model.

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