Modeling Brain–Heart Crosstalk Information in Patients with Traumatic Brain Injury

Publisher Copyright: © 2021, The Author(s).Background: Traumatic brain injury (TBI) is an extremely heterogeneous and complex pathology that requires the integration of different physiological measurements for the optimal understanding and clinical management of patients. Information derived from intracranial pressure (ICP) monitoring can be coupled with information obtained from heart rate (HR) monitoring to assess the interplay between brain and heart. The goal of our study is to investigate events of simultaneous increases in HR and ICP and their relationship with patient mortality. Methods: In our previous work, we introduced a novel measure of brain–heart interaction termed brain–heart crosstalks (ctnp), as well as two additional brain–heart crosstalks indicators [mutual information (mict) and average edge overlap (ωct)] obtained through a complex network modeling of the brain–heart system. These measures are based on identification of simultaneous increase of HR and ICP. In this article, we investigated the relationship of these novel indicators with respect to mortality in a multicenter TBI cohort, as part of the Collaborative European Neurotrauma Effectiveness Research in TBI high-resolution work package. Results: A total of 226 patients with TBI were included in this cohort. The data set included monitored parameters (ICP and HR), as well as laboratory, demographics, and clinical information. The number of detected brain–heart crosstalks varied (mean 58, standard deviation 57). The Kruskal–Wallis test comparing brain–heart crosstalks measures of survivors and nonsurvivors showed statistically significant differences between the two distributions (p values: 0.02 for mict, 0.005 for ctnp and 0.006 for ωct). An inverse correlation was found, computed using the point biserial correlation technique, between the three new measures and mortality: − 0.13 for ctnp (p value 0.04), − 0.19 for ωct (p value 0.002969) and − 0.09 for mict (p value 0.1396). The measures were then introduced into the logistic regression framework, along with a set of input predictors made of clinical, demographic, computed tomography (CT), and lab variables. The prediction models were obtained by dividing the original cohort into four age groups (16–29, 30–49, 50–65, and 65–85 years of age) to properly treat with the age confounding factor. The best performing models were for age groups 16–29, 50–65, and 65–85, with the deviance of ratio explaining more than 80% in all the three cases. The presence of an inverse relationship between brain–heart crosstalks and mortality was also confirmed. Conclusions: The presence of a negative relationship between mortality and brain–heart crosstalks indicators suggests that a healthy brain–cardiovascular interaction plays a role in TBI.Peer reviewe

Multilayer network methodologies for brain data analysis and modelling

The term neuroscience includes in itself a plethora of research areas devoted to undercover the most fascinating complex organ of our body: the brain. A common denominator of neuroscience areas, is the need for the application of methodologies to integrate different features. In this thesis, we focused on the analysis of two types of brain data: brain data coming from Traumatic Brain Injury (TBI) patients and data collected for the study of neurocognitive healthy ageing. In both cases there was the need of applying computational techniques able to integrate different features. To do so we used multilayer networks. For two groups of TBI patients (adults and paediatrics), time series data were collected from the observations of IntraCranial Pressure (ICP) and Heart Rate (HR). We first detected events of simultaneous increase of HR and ICP, which we called brain-heart crosstalks. Subsequently time series were translated into graphs, and network measures, during brain-heart crosstalks, were obtained. These were then included as predictors in a mortality outcome model, with crosstalks. Causality measures were also investigated, using a Granger causality approach, to understand the dynamics of signals during these events. We further applied multilayer networks to study neurocognitive ageing. To do so, we implemented a pipeline for community detection, which we called NetRank, applying it to the Cam-CAN, a large cross-sectional cohort for the study of healthy neurocognitive ageing. Using multilayer networks modelling, we identified subgroups of individuals, with similar lifestyles, and we related them to structural and functional brain features. We believe that multilayer networks and their extensions represent a powerful tool to be used in integrative and cross modal neuroscience datasets. New insights on cognitive neuroscience and time series analysis, can in fact be gained trough multilayer network, possibly improving patients managements and allowing to develop new predictive tools.EPSR

Visibility graph analysis of intraspinal pressure signal predicts functional outcome in spinal cord injured patients.

To guide management of patients with acute spinal cord injuries, we developed intraspinal pressure monitoring from the injury site. Here, we examine the complex fluctuations in the intraspinal pressure signal using network theory. We analyzed 7,097 hours of intraspinal pressure data from 58 patients with severe cord injuries. Intraspinal pressure signals were split into hourly windows. Each window was mapped into a visibility graph as follows: Vertical bars were drawn at 0.1 Hz representing signal amplitudes. Each bar produced a node, thus totalling 360 nodes per graph. Two nodes were linked with an edge if the straight line through the nodes did not intersect a bar. We computed several topological metrics for each graph including diameter, modularity, eccentricity and small-worldness. Patients were followed up for 20 months on average. Our data show that the topological structure of intraspinal pressure visibility graphs is highly sensitive to pathological events at the injury site including cord compression (high intraspinal pressure), ischemia (low spinal cord perfusion pressure) and deranged autoregulation (high spinal pressure reactivity index). These pathological changes correlate with long graph diameter, high modularity, high eccentricity and reduced small-worldness. In a multivariate logistic regression model, age, neurological status on admission and average node eccentricity were independent predictors of neurological improvement. We conclude that analysis of intraspinal pressure fluctuations after spinal cord injury as graphs, rather than time series, captures clinically important information. Our novel technique may be applied to other signals recorded from injured CNS e.g intracranial pressure, tissue metabolite and oxygen levels

Advances in neuroproteomics for neurotrauma: unraveling insights for personalized medicine and future prospects

Neuroproteomics, an emerging field at the intersection of neuroscience and proteomics, has garnered significant attention in the context of neurotrauma research. Neuroproteomics involves the quantitative and qualitative analysis of nervous system components, essential for understanding the dynamic events involved in the vast areas of neuroscience, including, but not limited to, neuropsychiatric disorders, neurodegenerative disorders, mental illness, traumatic brain injury, chronic traumatic encephalopathy, and other neurodegenerative diseases. With advancements in mass spectrometry coupled with bioinformatics and systems biology, neuroproteomics has led to the development of innovative techniques such as microproteomics, single-cell proteomics, and imaging mass spectrometry, which have significantly impacted neuronal biomarker research. By analyzing the complex protein interactions and alterations that occur in the injured brain, neuroproteomics provides valuable insights into the pathophysiological mechanisms underlying neurotrauma. This review explores how such insights can be harnessed to advance personalized medicine (PM) approaches, tailoring treatments based on individual patient profiles. Additionally, we highlight the potential future prospects of neuroproteomics, such as identifying novel biomarkers and developing targeted therapies by employing artificial intelligence (AI) and machine learning (ML). By shedding light on neurotrauma’s current state and future directions, this review aims to stimulate further research and collaboration in this promising and transformative field

Brain Injury

The present two volume book "Brain Injury" is distinctive in its presentation and includes a wealth of updated information on many aspects in the field of brain injury. The Book is devoted to the pathogenesis of brain injury, concepts in cerebral blood flow and metabolism, investigative approaches and monitoring of brain injured, different protective mechanisms and recovery and management approach to these individuals, functional and endocrine aspects of brain injuries, approaches to rehabilitation of brain injured and preventive aspects of traumatic brain injuries. The collective contribution from experts in brain injury research area would be successfully conveyed to the readers and readers will find this book to be a valuable guide to further develop their understanding about brain injury

Monitoring the Neuroinflammatory Response Following Acute Brain injury

Traumatic brain injury (TBI) and subarachnoid hemorrhage (SAH) are major contributors to morbidity and mortality. Following the initial insult, patients may deteriorate due to secondary brain damage. The underlying molecular and cellular cascades incorporate components of the innate immune system. There are different approaches to assess and monitor cerebral inflammation in the neuro intensive care unit. The aim of this narrative review is to describe techniques to monitor inflammatory activity in patients with TBI and SAH in the acute setting. The analysis of pro- and anti-inflammatory cytokines in compartments of the central nervous system (CNS), including the cerebrospinal fluid and the extracellular fluid, represent the most common approaches to monitor surrogate markers of cerebral inflammatory activity. Each of these compartments has a distinct biology that reflects local processes and the cross-talk between systemic and CNS inflammation. Cytokines have been correlated to outcomes as well as ongoing, secondary injury progression. Alongside the dynamic, focal assay of humoral mediators, imaging, through positron emission tomography, can provide a global in vivo measurement of inflammatory cell activity, which reveals long-lasting processes following the initial injury. Compared to the innate immune system activated acutely after brain injury, the adaptive immune system is likely to play a greater role in the chronic phase as evidenced by T-cell-mediated autoreactivity toward brain-specific proteins. The most difficult aspect of assessing neuroinflammation is to determine whether the processes monitored are harmful or beneficial to the brain as accumulating data indicate a dual role for these inflammatory cascades following injury. In summary, the inflammatory component of the complex injury cascade following brain injury may be monitored using different modalities. Using a multimodal monitoring approach can potentially aid in the development of therapeutics targeting different aspects of the inflammatory cascade and improve the outcome following TBI and SAH

UWOMJ Volume 64, Number 1, Winter 1994

Schulich School of Medicine & Dentistryhttps://ir.lib.uwo.ca/uwomj/1241/thumbnail.jp

Peripheral Biomarkers of Inflammation Following Blast Exposure in a Clinical Population

Concussions resulting from blast exposures represent a significant source of injury among military service members and the civilian population. Overall, traumatic brain injuries (TBIs) are a significant cause of hospitalization, disability, long-term care, and mortality across all age groups in the United States. Blast induced traumatic brain injury (biTBI) is an increasingly recognized subtype of brain injury, especially among military personnel. Blast exposure may influence a number of neurological processes, such as the inflammatory response, representing a unique biological profile. Outcomes from a TBI vary, even in similar injuries, and biomarkers including proteins and gene expression are increasingly studied to determine potential underlying mechanisms of injury and recovery processes. Biomarkers may yield insight into differential biological pathways in the various severities and subtypes of brain injury. This novel study proposes the examination of clinical and demographic characteristics and the identification of possible biological mechanisms through gene expression and protein analysis following brain injury. This study will be the first to examine gene expression related to inflammatory activation using sequencing and other unique methods to gain insight into immune pathways following blast exposure in clinical populations during the acute and subacute stages of injury. A deeper understanding of the role of inflammatory activation profiles will help direct future research in blast exposure and improve outcomes for individuals affected by this injury

The effects of dexmedetomidine on cerebral glucose metabolism, systemic cytokine release and cerebral autoregulation: Studies on healthy volunteers and aneurysmal subarachnoid haemorrhage patients

Dexmedetomidine is a very selective α2-agonist that has become a popular sedative in the intensive care unit. It has characteristics that make it appealing especially for neurologically compromised patients. Aneurysmal subarachnoid haemorrhage (aSAH) is a complicated disease where cerebral physiology and the regulation of cerebral blood flow, i.e., autoregulation, are often disturbed. It is important that the used anaesthetic and sedative drugs do not cause further damage. The aim of this study was to explore how dexmedetomidine affects cerebral glucose metabolism, systemic cytokine response, and cerebral autoregulation. The first two studies included healthy male volunteers. The first study investigated the effects of dexmedetomidine on cerebral glucose metabolism along with three other anaesthetic drugs (propofol, sevoflurane, S-ketamine) and a placebo group. The second study investigated the effects of dexmedetomidine and propofol on the release of cytokines, chemokines, and growth factors. The third study included 10 aSAH patients. We examined the effects of dexmedetomidine on cerebral autoregulation with three increasing doses after the baseline sedation with propofol and/or midazolam was suspended. In the volunteer studies, we found that the cerebral glucose metabolism was lowest with dexmedetomidine. In addition, we found that dexmedetomidine induced an anti-inflammatory cytokine response, whereas propofol induced a partly pro-inflammatory and slightly anti-inflammatory cytokine response. In aSAH patients, dexmedetomidine did not alter the static cerebral autoregulation compared to baseline. However, after the dose of 1.0 µg/kg/h, we observed a minor but statistically significant decrease in dynamic cerebral autoregulation which may suggest that in aSAH patients sedated with dexmedetomidine, sudden decreases in mean arterial pressure should be avoided