THE INTERNET OF THINGS (IOT) IN DISASTER RESPONSE

Disaster management is a complex practice that relies on access to and the usability of critical information to develop strategies for effective decision-making. The emergence of wearable internet of things (IoT) technology has attracted the interests of several major industries, making it one of the fastest-growing technologies to date. This thesis asks, How can disaster management incorporate wearable IoT technology in operations and decision-making practices in disaster response? How IoT is applied in other prominent industries, including construction, manufacturing and distribution, the Department of Defense, and public safety, provides a basis for furthering its application to challenges affecting agency coordination. The critical needs of disaster intelligence in the context of hurricanes, structural collapses, and wildfires are scrutinized to identify gaps that wearable technology could address in terms of information-sharing in multi-agency coordination and the decision-making practices that routinely occur in disaster response. Last, the specifics of wearable technology from the perspective of the private consumer and commercial industry illustrate its potential to improve disaster response but also acknowledge certain limitations including technical capabilities and information privacy and security.Civilian, Virginia Beach Fire Department / FEMA - USAR VATF-2Approved for public release. Distribution is unlimited

Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review

Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but they now work closely with us to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of the global food supply chain, and there is increasing consumer interest in organically fed and humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitat. This review sets the goal to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm and wild animal welfare. We use the notion of \emph{animal welfare} in broad terms, to review the technologies for assessing whether animals are healthy, free of pain and suffering, and also positively stimulated in their environment. Also the notion of \emph{smart computing and sensing} is used in broad terms, to refer to computing and sensing systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange and analysis. We review smart technologies for domestic animals, indoor and outdoor animal farming, as well as animals in the wild and zoos. The findings of this review are expected to motivate future research and contribute to data, information and communication management as well as policy for animal welfare

Personalized rTMS for Depression: A Review

Personalized treatments are gaining momentum across all fields of medicine. Precision medicine can be applied to neuromodulatory techniques, where focused brain stimulation treatments such as repetitive transcranial magnetic stimulation (rTMS) are used to modulate brain circuits and alleviate clinical symptoms. rTMS is well-tolerated and clinically effective for treatment-resistant depression (TRD) and other neuropsychiatric disorders. However, despite its wide stimulation parameter space (location, angle, pattern, frequency, and intensity can be adjusted), rTMS is currently applied in a one-size-fits-all manner, potentially contributing to its suboptimal clinical response (~50%). In this review, we examine components of rTMS that can be optimized to account for inter-individual variability in neural function and anatomy. We discuss current treatment options for TRD, the neural mechanisms thought to underlie treatment, differences in FDA-cleared devices, targeting strategies, stimulation parameter selection, and adaptive closed-loop rTMS to improve treatment outcomes. We suggest that better understanding of the wide and modifiable parameter space of rTMS will greatly improve clinical outcome

Internet Predictions

More than a dozen leading experts give their opinions on where the Internet is headed and where it will be in the next decade in terms of technology, policy, and applications. They cover topics ranging from the Internet of Things to climate change to the digital storage of the future. A summary of the articles is available in the Web extras section

A Review of Transcranial Magnetic Stimulation and Multimodal Neuroimaging to Characterize Post-Stroke Neuroplasticity

Following stroke, the brain undergoes various stages of recovery where the central nervous system can reorganize neural circuitry (neuroplasticity) both spontaneously and with the aid of behavioral rehabilitation and non-invasive brain stimulation. Multiple neuroimaging techniques can characterize common structural and functional stroke-related deficits, and importantly, help predict recovery of function. Diffusion tensor imaging (DTI) typically reveals increased overall diffusivity throughout the brain following stroke, and is capable of indexing the extent of white matter damage. Magnetic resonance spectroscopy (MRS) provides an index of metabolic changes in surviving neural tissue after stroke, serving as a marker of brain function. The neural correlates of altered brain activity after stroke have been demonstrated by abnormal activation of sensorimotor cortices during task performance, and at rest, using functional magnetic resonance imaging (fMRI). Electroencephalography (EEG) has been used to characterize motor dysfunction in terms of increased cortical amplitude in the sensorimotor regions when performing upper limb movement, indicating abnormally increased cognitive effort and planning in individuals with stroke. Transcranial magnetic stimulation (TMS) work reveals changes in ipsilesional and contralesional cortical excitability in the sensorimotor cortices. The severity of motor deficits indexed using TMS has been linked to the magnitude of activity imbalance between the sensorimotor cortices. In this paper, we will provide a narrative review of data from studies utilizing DTI, MRS, fMRI, EEG, and brain stimulation techniques focusing on TMS and its combination with uni- and multimodal neuroimaging methods to assess recovery after stroke. Approaches that delineate the best measures with which to predict or positively alter outcomes will be highlighted

From micro to macro:unravelling the underlying mechanisms of Transcranial Magnetic Stimulation (TMS)

This PhD research investigated the underlying mechanisms of transcranial magnetic stimulation (TMS). TMS is a form of non-invasive brain stimulation, which is used both in research to alter brain activity and in the clinic where it is a treatment option for many neuropsychiatric disorders such as major depression. However, not a lot is known about how TMS actually works. This research took an interdisciplinary approach to better understand the mechanisms of TMS. On the microscopic level, it used human neurons which were grown in the lab and stimulated with TMS to look for changes in plasticity such as neuronal firing, gene expression, and morphology. On the macroscopic level, the researcher stimulated human participants and measured indirect outcomes of plasticity, using multimodal setups such as combined TMS-EEG and TMS-EEG-fMRI. Better understanding the mechanisms of TMS is very important. If we fully understand how TMS works, we can optimize stimulation protocols, promoting increased responsiveness and better treatment outcomes in the clinic

Imaging cortical plasticity in the human motor system

Intermittent theta-burst stimulation (iTBS) is a novel form of repetitive transcranial magnetic stimulation (rTMS) inducing increases in cortical excitability that last beyond stimulation. Compared to conventional rTMS protocols iTBS induces strong and long-lasting aftereffects with shorter stimulation time and less stimulation intensity. However, mechanisms underlying iTBS-induced aftereffects as well as factors contributing to a high inter-individual variability between subjects are still poorly understood. The aim of the present study was to gain some new insights into these mechanisms by combining non-invasive brain stimulation with neuroimaging and connectivity analyses of the human motor system. Previous studies suggested a link between rTMS aftereffects and activity as well as connectivity of the stimulated region. However, the mechanisms underlying iTBS-induced plasticity on the systems level are still incompletely understood. Hence, the aim of the first study of the present thesis was to investigate how neural activity and connectivity of the motor system are related to aftereffects of iTBS. Therefore, 12 healthy, right-handed volunteers underwent functional magnetic resonance imaging (fMRI) during rest (resting-state fMRI, rs-fMRI) and while performing a simple hand motor task. Based on this data, resting-state functional connectivity (rsFC) and task-induced activation as well as task-related effective connectivity were assessed. In separate sessions, aftereffects of iTBS applied over the left, primary motor cortex (M1) and the parieto-occipital vertex (sham) were tested for up to 25 min by measuring motor-evoked potentials (MEPs). High MEP increases post stimulation correlated with low movement-induced blood oxygenation level dependent (BOLD) activity in the stimulated M1. MEP changes also correlated positively with the effective connectivity between M1 and different premotor regions. However, no correlation could be found for rsFC. Therefore, our data suggest that changes in cortical plasticity induced by iTBS not only depend on local properties of the stimulated region, but also on activity-dependent properties of the cortical motor system. Furthermore, different studies recently aimed at enhancing iTBS aftereffects by increasing the dose. However, no additive aftereffects could be observed. This may result from the incomplete understanding of the mechanisms underlying the dose-dependent induction of cortical plasticity in humans. The second study, therefore, aimed at investigating the dose-dependency of iTBS aftereffects by applying multiple stimulation blocks within a short time-interval. Possible mechanisms underlying cortical plasticity should be revealed by combining iTBS with connectivity analyses of the motor system. 16 healthy, right-handed subjects received three serially applied blocks of iTBS with an interstimulus-interval of 15 min. Each subject underwent M1- and sham-iTBS in two separate sessions. Aftereffects were tested on both MEP amplitudes as well as rsFC leading to a total of four sessions: M1-iTBS_MEPs, sham-iTBS_MEPs, M1_rs-fMRI, sham_rs-fMRI. For the first time, a dose-dependent buildup of aftereffects after the third block could be found both on the local level (MEPs) as well as on the systems level (rsFC). These increases in MEP amplitudes and rsFC were not linearly correlated, thus, possibly representing two parallel mechanisms underlying iTBS-induced plasticity. Of note, similar dose-dependent alterations of cortical protein expression of distinct subgroups of GABAergic inhibitory interneurons were observed following multiple iTBS blocks in an animal model. Hence, possibly suggesting a similar mechanism to be involved in iTBS aftereffects in humans. Recently, a considerable number of studies addressing the variability of TBS aftereffects reported strong variations across subjects often resulting in no overall effects on the group level. The reasons for this variability remain poorly understood. Moreover, the question arises whether non-responders to iTBS can be turned into responders by increasing the dose. Therefore, in the third study, the data of the second study were re-analyzed with respect to the individual susceptibility to iTBS. Subjects were grouped into responders (n=7) and non-responders (n=9) according to their increase in MEP amplitudes after one iTBS block. When taking the individual responsiveness to iTBS into account a higher rsFC between M1 and premotor areas before stimulation could be found for non-responders compared to responders. Interestingly, non-responders to iTBS after one block could not be turned into responders by increasing the dose, i.e., applying a second or third block of iTBS. In contrast, responders after one block of iTBS featured a dose-dependent increase in MEP amplitudes as well as rsFC after all three iTBS blocks. Hence, our data suggest that responsiveness to iTBS at the local level (i.e., M1 excitability) is related to the capability of modulating network connectivity of the stimulated region (i.e., motor network). A ceiling effect at the systems level might underlie non-responsiveness to iTBS since higher levels of pre-interventional connectivity precluded a further increase upon iTBS. Taken together, the findings of the present thesis add to the understanding of the mechanisms underlying iTBS aftereffects as well as the factors contributing to the high inter-individual variability. Furthermore, our data might help to improve the usefulness of iTBS in both basic research and as a therapeutic intervention

Effects of Spinal Manipulation on Brain Activation in Individuals with Chronic Low Back Pain

Chronic low back pain (cLBP) continues to be one of the most common health conditions in the United States. Despite an enormous amount of research, there are no treatments for this condition that consistently improve outcomes. For decades health professionals have incorporated spinal manipulative therapy (SMT) into their practice, but the evidence to date has shown that SMT has only small to modest effect sizes when treating cLBP. One way to improve the effectiveness of SMT is by getting a better understanding of its underlying mechanisms so that the intervention be more specifically targeted to the appropriate individual. While biomechanical theories exist to help explain how SMT works, they do not sufficiently explain all the phenomena associated with this treatment. To better understand the mechanisms behind SMT, researchers have begun to study the neurophysiological effects of SMT using functional magnetic resonance imaging (fMRI); however, to date there have been no published studies assessing the effects of SMT on the changes in brain activation during the performance of lumbopelvic motor tasks. Therefore, the overall purpose of this body of work was to describe the differences in brain activity between individuals with and without cLBP when performing lumbopelvic motor tasks, and to assess the effects of SMT on brain activity in these populations. Results from this body of work will help health care professionals implement this technique in a more specific and focused manner. Key findings from this study demonstrated how individuals with cLBP exhibit a broader network of brain activation compared to asymptomatic individuals when performing lumbopelvic motor tasks. Specifically, there appears to be two networks that are active during the performance of lumbopelvic tasks: a “motor network” that consists of the precentral gyrus and the supplemental motor area that is common in both groups, and a “motor-pain network” that is only active in individuals with cLBP consist of the Insula and Middle Cingulate Cortex. These two networks seem to share a common hub, the Putamen, that can assist in translating information between these two networks. It is the Putamen that is impacted the most with spinal manipulation. Both the levels of activation and functional connectivity increases with spinal manipulation in individuals with cLBP, but not asymptomatic individuals. This suggests that spinal manipulation might affect the cortico-basal-ganglia motor loop in individuals with cLBP

From hospital to home. The application of e-health solutions for monitoring and management of people with epilepsy

Background. In the last 10 years, there has been an explosion in the development of mobile and wearable technologies. Recent events such as Covid 19 emergency, showed the world how clinicians need to focus more on the application of these technologies to monitor and manage their patients. Despite this, the use of innovative technologies is not now a common practice in epilepsy. This thesis aims to demonstrate how people with epilepsy (PWE) are ready to use these mobile and wearable technologies and how data collected from these solutions can have a direct impact on PWE’s life. Methods. A systematic literature search was performed to provide an accurate overview of new non- invasive EEGs and their applications in epilepsy health care and an online survey was performed to fill the literature gap on this topic. To accurately study the PWE’s experience using wearable sensors, and the value of physiological and non-physiological data collected from wearable sensors, we used EEG data collected from the hospital (RADAR-CNS), and we collected original data from an at-home study (EEG@HOME). The data can be divided into two main categories: qualitative data (online survey, semi-structured interviews), and quantitative data analysis (questionnaires, EEG, and additional non-invasive physiological variables). Results. The systematic review showed us how non-invasive portable EEGs could provide valuable data for clinical purposes in epilepsy and become useful tools in different settings (i.e., rural areas, Hospitals, and homes). These are well accepted and tolerated by PWE and health care providers, especially for the easy application, cost, and comfort. The information obtained on the acceptability of repeated long-term non-invasive measures at home (EEG@HOME) showed that the use of the portable EEG cap was in general well tolerated over the 6 months but, the use of a smartwatch and the e-seizure diary was usually preferred. The level of compliance was good in most of the individuals and any barriers or issues which affected their experience or quality of the data were highlighted (i.e., life events, issues with equipment, and hairstyle of patients). Semi-structured interviews showed that participants found the combination of the three solutions very well-integrated and easy to use. The support received and the possibility to be trained and monitored remotely were well accepted and no privacy issues were reported by any of the participants. Most of the participants also suggested how they will be happy to have a mobile solution in the future to help to monitor their condition. The graph theory measures extracted from short and/or repeated EEG segments recorded from hospitals (RADAR-CNS) allowed us to explore the temporal evolution of brain activity prior to a seizure. Finally, physiological data and non-physiological data (EEG@HOME) were combined to understand and develop a model for each participant which explained a higher or lower risk of seizure over time. We also evaluated the value of repeated unsupervised resting state EEG recorded at home for seizure detection. Conclusion. The use of new technologies is well accepted by PWE in different settings. This thesis gives a detailed overview of two main points. First: PWE can be monitored in the hospital or at home using new wearable sensors or smartphone apps, and they are ready to use them after a short training and minimal supervision. Second: repeated data collection could provide a new way of a monitor, managing, and diagnosing people with epilepsy. Future studies should focus on balancing the acceptability of the solutions and the quality of the data collected. We also suggest that more studies focusing on seizure forecasting and detection using data collected from long-term monitoring need to be conducted. Digital health is the future of clinical practice and will increase PWE safety, independency, treatment, and monitoring