The effects of hemodynamic lag on functional connectivity and behavior after stroke

Stroke disrupts the brain's vascular supply, not only within but also outside areas of infarction. We investigated temporal delays (lag) in resting state functional magnetic resonance imaging signals in 130 stroke patients scanned two weeks, three months and 12 months post stroke onset. Thirty controls were scanned twice at an interval of three months. Hemodynamic lag was determined using cross-correlation with the global gray matter signal. Behavioral performance in multiple domains was assessed in all patients. Regional cerebral blood flow and carotid patency were assessed in subsets of the cohort using arterial spin labeling and carotid Doppler ultrasonography. Significant hemodynamic lag was observed in 30% of stroke patients sub-acutely. Approximately 10% of patients showed lag at one-year post-stroke. Hemodynamic lag corresponded to gross aberrancy in functional connectivity measures, performance deficits in multiple domains and local and global perfusion deficits. Correcting for lag partially normalized abnormalities in measured functional connectivity. Yet post-stroke FC-behavior relationships in the motor and attention systems persisted even after hemodynamic delays were corrected. Resting state fMRI can reliably identify areas of hemodynamic delay following stroke. Our data reveal that hemodynamic delay is common sub-acutely, alters functional connectivity, and may be of clinical importance

Clinical Applications of Neuromonitoring Following Acute Brain Injury

Various invasive and non-invasive cranial monitoring techniques can be applied clinically to describe the extent to which cerebral hemodynamics and subsequently, patient outcome, have been impacted following acute brain injury (ABI). This Ph.D. thesis examines both prospective and retrospective patient data in both neurocritical and general intensive care patients. Thirty neurotrauma patients and forty general intensive care patients with neurological complications were prospectively monitored after ABI. Retrospective patient data was harvested from a database of 1,023 traumatic brain injury (TBI) patients with invasive intracranial pressure (ICP), arterial blood pressure (ABP), and transcranial Doppler ultrasonography (TCD) recordings. Data analysis focused on ICP microsensor accuracy, compensatory reserve, the pulsatility of brain signals (ICP and TCD), and cerebral arterial blood volume (CaBV) based on TCD. The main results are summarized below: I. Intracranial hypertension has a profound negative influence on cerebrovascular parameters and patient outcome. II. ICP microsensor accuracy is limited, with an average error of approximately ± 6.0 mm Hg. III. ICP weighted with the compensatory reserve better predicts outcome than mean ICP alone. IV. ICP and TCD pulsatility are functions of mean ICP and cerebral perfusion pressure (CPP). V. Continuous blood flow forward (CFF) and pulsatile blood flow forward (PFF) models can approximate CaBV with derived TCD signals; CFF best models TCD pulsatility. VI. The pressure reactivity index (PRx) and the pulse amplitude index (PAx) can be estimated non-invasively using slow waves of TCD estimated by CaBV with similar outcome-predictive power. VII. Multi-parametric TCD-based monitoring of general intensive care patients is clinically feasible; the joint estimation of autoregulation, dysautonomia, non-invasive ICP, and critical closing pressure is possible. The culmination of these projects should have an impact on current monitoring practices in ABI patients, emphasizing the continued validation and refinement of TCD methodology in clinical neurosciences

Networking Architecture and Key Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive Survey

Digital twin (DT), refers to a promising technique to digitally and accurately represent actual physical entities. One typical advantage of DT is that it can be used to not only virtually replicate a system's detailed operations but also analyze the current condition, predict future behaviour, and refine the control optimization. Although DT has been widely implemented in various fields, such as smart manufacturing and transportation, its conventional paradigm is limited to embody non-living entities, e.g., robots and vehicles. When adopted in human-centric systems, a novel concept, called human digital twin (HDT) has thus been proposed. Particularly, HDT allows in silico representation of individual human body with the ability to dynamically reflect molecular status, physiological status, emotional and psychological status, as well as lifestyle evolutions. These prompt the expected application of HDT in personalized healthcare (PH), which can facilitate remote monitoring, diagnosis, prescription, surgery and rehabilitation. However, despite the large potential, HDT faces substantial research challenges in different aspects, and becomes an increasingly popular topic recently. In this survey, with a specific focus on the networking architecture and key technologies for HDT in PH applications, we first discuss the differences between HDT and conventional DTs, followed by the universal framework and essential functions of HDT. We then analyze its design requirements and challenges in PH applications. After that, we provide an overview of the networking architecture of HDT, including data acquisition layer, data communication layer, computation layer, data management layer and data analysis and decision making layer. Besides reviewing the key technologies for implementing such networking architecture in detail, we conclude this survey by presenting future research directions of HDT

Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation.

After an initial period of recovery, human neurological injury has long been thought to be static. In order to improve quality of life for those suffering from stroke, spinal cord injury, or traumatic brain injury, researchers have been working to restore the nervous system and reduce neurological deficits through a number of mechanisms. For example, neurobiologists have been identifying and manipulating components of the intra- and extracellular milieu to alter the regenerative potential of neurons, neuro-engineers have been producing brain-machine and neural interfaces that circumvent lesions to restore functionality, and neurorehabilitation experts have been developing new ways to revitalize the nervous system even in chronic disease. While each of these areas holds promise, their individual paths to clinical relevance remain difficult. Nonetheless, these methods are now able to synergistically enhance recovery of native motor function to levels which were previously believed to be impossible. Furthermore, such recovery can even persist after training, and for the first time there is evidence of functional axonal regrowth and rewiring in the central nervous system of animal models. To attain this type of regeneration, rehabilitation paradigms that pair cortically-based intent with activation of affected circuits and positive neurofeedback appear to be required-a phenomenon which raises new and far reaching questions about the underlying relationship between conscious action and neural repair. For this reason, we argue that multi-modal therapy will be necessary to facilitate a truly robust recovery, and that the success of investigational microscopic techniques may depend on their integration into macroscopic frameworks that include task-based neurorehabilitation. We further identify critical components of future neural repair strategies and explore the most updated knowledge, progress, and challenges in the fields of cellular neuronal repair, neural interfacing, and neurorehabilitation, all with the goal of better understanding neurological injury and how to improve recovery

Cross-Modal Health State Estimation

Individuals create and consume more diverse data about themselves today than any time in history. Sources of this data include wearable devices, images, social media, geospatial information and more. A tremendous opportunity rests within cross-modal data analysis that leverages existing domain knowledge methods to understand and guide human health. Especially in chronic diseases, current medical practice uses a combination of sparse hospital based biological metrics (blood tests, expensive imaging, etc.) to understand the evolving health status of an individual. Future health systems must integrate data created at the individual level to better understand health status perpetually, especially in a cybernetic framework. In this work we fuse multiple user created and open source data streams along with established biomedical domain knowledge to give two types of quantitative state estimates of cardiovascular health. First, we use wearable devices to calculate cardiorespiratory fitness (CRF), a known quantitative leading predictor of heart disease which is not routinely collected in clinical settings. Second, we estimate inherent genetic traits, living environmental risks, circadian rhythm, and biological metrics from a diverse dataset. Our experimental results on 24 subjects demonstrate how multi-modal data can provide personalized health insight. Understanding the dynamic nature of health status will pave the way for better health based recommendation engines, better clinical decision making and positive lifestyle changes.Comment: Accepted to ACM Multimedia 2018 Conference - Brave New Ideas, Seoul, Korea, ACM ISBN 978-1-4503-5665-7/18/1

Seizure prediction : ready for a new era

Acknowledgements: The authors acknowledge colleagues in the international seizure prediction group for valuable discussions. L.K. acknowledges funding support from the National Health and Medical Research Council (APP1130468) and the James S. McDonnell Foundation (220020419) and acknowledges the contribution of Dean R. Freestone at the University of Melbourne, Australia, to the creation of Fig. 3.Peer reviewedPostprin

The cognitive emotion process

Different theories of emotions have been introduced since the 19th century. Even though a large number of apparent differences between these theories exist, there is a broad consensus today that emotions consist of multiple components such as cognition, physiology, motivation, and subjectively perceived feeling. Appraisal theories of emotions, such as the Component Process Model (CPM) by Klaus Scherer, emphasize that the cognitive evaluation of a stimulus or event is the driving component of the emotion process. It is believed to cause changes in all other components and hence to differentiate emotion states. To test the CPM and gain more insights into the multi-componential emotion process, the present thesis examines two emotion sub-processes – the link between the cognitive and the feeling component (study 1) and the link between the cognitive and the physiological component (study 2) – by using different predictive modeling approaches. In study 1, four theoretically informed models were implemented. The models use a weighted distance metric based on an emotion prototype approach to predict the perceived emotion of participants from self-reported cognitive appraisals. Moreover, they incorporate different weighting functions with weighting parameters that were either derived from theory or estimated from empirical data. The results substantiate the examined link based on the predictive performance of the models. In line with the CPM, the preferred model weighted the appraisal evaluations differently in the distance metric. However, the data-derived weighting parameters of this model deviate from theoretically proposed ones. Study 2 analyzed the link between cognition and physiology by predicting self-reported appraisal dimensions from a large set of physiological features (calculated from different physiological responses to emotional videos) using different linear and non-linear machine learning algorithms. Based on the predictive performance of the models, the study is able to confirm that most cognitive evaluations were interlinked with different physiological responses. The comparison of the different algorithms and the application of methods for interpretable machine learning showed that the relation between these two components is best represented by a non-linear model and that the studied link seems to vary among physiological signals and cognitive dimensions. Both studies substantiate the assumption that the cognitive appraisal process is interlinked with physiology and subjective feelings, accentuating the relevance of cognition in emotion as assumed in appraisal theory. They also demonstrate how computational emotion modeling can be applied in basic research on emotions

A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis

[EN] In the current world we live immersed in online applications, being one of the most present of them Social Network Sites (SNSs), and different issues arise from this interaction. Therefore, there is a need for research that addresses the potential issues born from the increasing user interaction when navigating. For this reason, in this survey we explore works in the line of prevention of risks that can arise from social interaction in online environments, focusing on works using Multi-Agent System (MAS) technologies. For being able to assess what techniques are available for prevention, works in the detection of sentiment polarity and stress levels of users in SNSs will be reviewed. We review with special attention works using MAS technologies for user recommendation and guiding. 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