Predictive structural dynamic network analysis

Causal Network Inference for Neural Ensemble Activity

Interactions among cellular components forming a mesoscopic scale brain network (microcircuit) display characteristic neural dynamics. Analysis of microcircuits provides a system-level understanding of the neurobiology of health and disease. Causal discovery aims to detect causal relationships among variables based on observational data. A key barrier in causal discovery is the high dimensionality of the variable space. A method called Causal Inference for Microcircuits (CAIM) is proposed to reconstruct causal networks from calcium imaging or electrophysiology time series. CAIM combines neural recording, Bayesian network modeling, and neuron clustering. Validation experiments based on simulated data and a real-world reaching task dataset demonstrated that CAIM accurately revealed causal relationships among neural clusters.

Insights into the association between coagulopathy and inflammation: abnormal clot mechanics are a warning of immunologic dysregulation following major injury

Background

Severe injury initiates a complex physiologic response encompassing multiple systems and varies phenotypically between patients. Trauma-induced coagulopathy may be an early warning of a poorly coordinated response at the molecular level, including a deleterious immunologic response and worsening of shock states. The onset of trauma-induced coagulopathy (TIC) may be subtle however. In previous work, we identified an early warning sign of coagulopathy from the admission thromboelastogram, called the MAR ratio. We hypothesized that a low MAR ratio would be associated with specific derangements in the inflammatory response.

Methods

In this prospective, observational study, 88 blunt trauma patients admitted to the intensive care unit (ICU) were identified. Concentrations of inflammatory mediators were recorded serially over the course of a week and the MAR ratio was calculated from the admission thromboelastogram. Correlation analysis was used to assess the relationship between MAR and inflammatory mediators. Dynamic network analysis was used to assess coordination of immunologic response.

Results

Seventy-nine percent of patients were male and mean age was 37 years (SD 12). The mean ISS was 30.2 (SD 12) and mortality was 7.2%. CRITICAL patients (MAR ratio ≤14.2) had statistically higher shock volumes at three time points in the first day compared to NORMAL patients (MAR ratio >14.2). CRITICAL patients had significant differences in IL-6 (P=0.0065), IL-8 (P=0.0115), IL-10 (P=0.0316) and MCP-1 (P=0.0039) concentrations compared to NORMAL. Differences in degree of expression and discoordination of immune response continued in CRITICAL patients throughout the first day.

Conclusions

The admission MAR ratio may be the earliest warning signal of a pathologic inflammatory response associated with hypoperfusion and TIC. A low MAR ratio is an early indication of complicated dysfunction of multiple molecular processes following trauma.

Aberrant Correlation Between the Default Mode and Salience Networks in Mild Traumatic Brain Injury

Objectives: The specific intrinsic network coupling abnormalities in mild traumatic brain injury (mTBI) patients are poorly understood. Our objective is to compare the correlations among the default mode, salience, and central executive networks in patients with mTBI and healthy controls. Methods: This 2-year prospective study included 32 acute mTBI patients and 37 healthy comparisons. We calculated the functional connectivity scores among the default mode, salience, and central executive networks. Then we conducted multilevel correlation analysis to investigate component correlations, global graph, and local functional connectivity changes. Results: Patients with mTBI showed significant increased functional connectivity between the anterior part of the default mode network and the salience network compared with controls (p = 0.013, false discovery rate correction). Hyper-connectivity between the default mode and salience network was significantly positively correlated with the dimensional change card sort score in patients with mTBI (r = 0.40, p = 0.037). The average path length of mTBI patients was significantly higher than that of controls (p = 0.028). Conclusions: Aberrant functional coupling between the default mode and salience networks were identified in acute mTBI patients. Our finding has great potential to improve our understanding of the network architecture of mTBI.

Clinical Concepts Emerging from fMRI Functional Connectomics

Recent advances in connectomics have led to a synthesis of perspectives regarding the brain's functional organization that reconciles classical concepts of localized specialization with an appreciation for properties that emerge from interactions across distributed functional networks. This provides a more comprehensive framework for understanding neural mechanisms of normal cognition and disease. Although fMRI has not become a routine clinical tool, research has already had important influences on clinical concepts guiding diagnosis and patient management. Here we review illustrative examples. Studies demonstrating the network plasticity possible in adults and the global consequences of even focal brain injuries or disease both have had substantial impact on modern concepts of disease evolution and expression. Applications of functional connectomics in studies of clinical populations are challenging traditional disease classifications and helping to clarify biological relationships between clinical syndromes (and thus also ways of extending indications for, or "re-purposing," current treatments). Large datasets from prospective, longitudinal studies promise to enable the discovery and validation of functional connectomic biomarkers with the potential to identify people at high risk of disease before clinical onset, at a time when treatments may be most effective. Studies of pain and consciousness have catalyzed reconsiderations of approaches to clinical management, but also have stimulated debate about the clinical meaningfulness of differences in internal perceptual or cognitive states inferred from functional connectomics or other physiological correlates. By way of a closing summary, we offer a personal view of immediate challenges and potential opportunities for clinically relevant applications of fMRI-based functional connectomics.

Dynamic network model with continuous valued nodes for longitudinal brain morphometry

Longitudinal brain morphometry probes time-related brain morphometric patterns. We propose a method called dynamic network modeling with continuous valued nodes to generate a dynamic brain network from continuous valued longitudinal morphometric data. The mathematical framework of this method is based on state-space modeling. We use a bootstrap-enhanced least absolute shrinkage operator to solve the network-structure generation problem. In contrast to discrete dynamic Bayesian network modeling, the proposed method enables network generation directly from continuous valued high-dimensional short sequence data, being free from any discretization process. We applied the proposed method to a study of normal brain development.

Ten problems and solutions when predicting individual outcome from lesion site after stroke

In this paper, we consider solutions to ten of the challenges faced when trying to predict an individual's functional outcome after stroke on the basis of lesion site. A primary goal is to find lesion-outcome associations that are consistently observed in large populations of stroke patients because consistent associations maximise confidence in future individualised predictions. To understand and control multiple sources of inter-patient variability, we need to systematically investigate each contributing factor and how each factor depends on other factors. This requires very large cohorts of patients, who differ from one another in typical and measurable ways, including lesion site, lesion size, functional outcome and time post stroke (weeks to decades). These multivariate investigations are complex, particularly when the contributions of different variables interact with one another. Machine learning algorithms can help to identify the most influential variables and indicate dependencies between different factors. Multivariate lesion analyses are needed to understand how the effect of damage to one brain region depends on damage or preservation in other brain regions. Such data-led investigations can reveal predictive relationships between lesion site and outcome. However, to understand and improve the predictions we need explanatory models of the neural networks and degenerate pathways that support functions of interest. This will entail integrating the results of lesion analyses with those from functional imaging (fMRI, MEG), transcranial magnetic stimulation (TMS) and diffusor tensor imaging (DTI) studies of healthy participants and patients.

Using Multi-objective Optimization to Identify Dynamical Network Biomarkers as Early-warning Signals of Complex Diseases

Biomarkers have gained immense scientific interest and clinical value in the practice of medicine. With unprecedented advances in high-throughput technologies, research interest in identifying novel and customized disease biomarkers for early detection, diagnosis, or drug responses is rapidly growing. Biomarkers can be identified in different levels of molecular biomarkers, networks biomarkers and dynamical network biomarkers (DNBs). The latter is a recently developed concept which relies on the idea that a cell is a complex system whose behavior is emerged from interplay of various molecules, and this network of molecules dynamically changes over time. A DNB can serve as an early-warning signal of disease progression, or as a leading network that drives the system into the disease state, and thus unravels mechanisms of disease initiation and progression. It is therefore of great importance to identify DNBs efficiently and reliably. In this work, the problem of DNB identification is defined as a multi-objective optimization problem, and a framework to identify DNBs out of time-course high-throughput data is proposed. Temporal gene expression data of a lung injury with carbonyl chloride inhalation exposure has been used as a case study, and the functional role of the discovered biomarker in the pathogenesis of lung injury has been thoroughly analyzed.