Blood pressure waveform characteristics exposed by spectral analysis and neural nets

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To Examine the Effects of Exercise & Instructional Based Interventions on Executive Functioning, Motor Learning & Emotional Intelligence Abilities Among Older Adults

Motor skills are a vital part of our life, and there might be situations where we will be required to either learn a new skill or relearn a known one. We examined the effectiveness of two different interventions - eccentric exercise and motivation-based instructions on enhancing the ability of older adults to learn a novel motor skill. Exercise intervention studies have shown that as little as 12 weeks of exercise can lead to improvements in both physical fitness and cognitive function in older adults, particularly executive control. But it is still unclear whether those improvements translate to improvements in other domains that rely on executive control, like motor skill learning and emotional intelligence. Study 1 explored the effect of eccentric exercise on these domains, specifically the ability to handle proactive interference in motor learning. 22 healthy adults (65-85 years of age) were recruited and randomly assigned either to a non-exercise control group, or to an exercise intervention group that performed 12 weeks of low to moderate intensity eccentric leg exercise (Eccentron). Corresponding neurophysiological measures were also recorded using EEG. We found that the control group experienced more proactive interference from baseline learning to post-test compared to the exercise group. The latter also displayed a higher level of emotional processing abilities than controls. They provide preliminary evidence that the cognitive benefits of exercise for older adults can be extended to domains outside of but related to executive control and memory. In study 2, we examined the effectiveness of an intervention based on the OPTIMAL theory of motor learning and performance on skill acquisition in both younger and older adults. We recruited 39 younger adults and 30 older adults and randomly assigned them to either the experimental group or to the control group. The intervention affected the two groups differentially. It was somewhat successful at improving learning in the older adults, but not in the younger adults. In fact, the intervention may have interfered with learning in the latter

Dissecting The Tissue-Specific Contributions To Seizures, Cardiorespiratory Dysfunction, And Sudden Death In The Kv1.1 Mouse Model Of Epilepsy Using Conditional Knockout Approaches

Sudden unexpected death in epilepsy (SUDEP), the primary cause of mortality in epilepsy, remains poorly understood. Studies suggest seizures may trigger dangerous signals affecting the heart and lungs leading to collapse and death. The Kv1.1 deficiency mouse model mirrors clinical SUDEP cases, showing spontaneous seizures, cardiorespiratory issues, and premature death. However, this model lacks regional specificity in Kv1.1 deletion, hindering insights into SUDEP’s mechanisms and anatomical substrates. This dissertation employs three distinct conditional knockout (cKO) techniques to investigate the individual roles for the forebrain, brainstem, and heart in SUDEP related phenotypes. The findings reveal that the forebrain alone can trigger spontaneous seizures and premature death. Additionally, the brainstem may play a significant role in regulating blood oxygen levels and may show gender differences in respiratory measures. Lastly, Kv1.1 in the heart is essential for cardiomyocyte action potential repolarization but does not significantly impact overall cardiac function measured in mice. This research highlights how distinct brain circuits contribute to SUDEP mechanisms, providing insights specifically for researchers using the Kv1.1 deficiency model as to where the crucial anatomical substrates may be found in future studies

Big data analysis of cyclic alternating pattern during sleep using deep learning

Sleep scoring has been of great interest since the invention of the polysomnography method, which enabled the recording of physiological signals overnight. With the surge in wearable devices in recent years, the topic of what is high-quality sleep, how can it be determined and how can it be achieved attracted increasing interest. In the last two decades, cyclic alternating pattern (CAP) was introduced as a scoring alternative to traditional sleep staging. CAP is known as a synonym for sleep microstructure and describes sleep instability. Manual CAP scoring performed by sleep experts is a very exhausting and time-consuming task. Hence, an automatic method would facilitate the processing of sleep data and provide a valuable tool to enhance the understanding of the role of CAP. This thesis aims to expand the knowledge about CAP by developing a high-performance automated CAP scoring system that can reliably detect and classify CAP events in sleep recordings. The automated system is equipped with state-of-the-art signal processing methods and exploits the dynamic, temporal information in brain activity using deep learning. The automated scoring system is validated using large community-based cohort studies and comparing the output to verified values in the literature. Our findings present novel clinical results on the relationship between CAP and age, gender, subjective sleep quality, and sleep disorders demonstrating that automated CAP analysis of large population based studies can lead to new findings on CAP and its subcomponents. Next, we study the relationship between CAP and behavioural, cognitive, and quality-of-life measures and the effect of adenotonsillectomy on CAP in children with obstructive sleep apnoea as the link between CAP and cognitive functioning in children is largely unknown. Finally, we investigate cortical-cardiovascular interactions during CAP to gain novel insights into the causal relationships between cortical and cardiovascular activity that are underpinning the microstructure of sleep. In summary, the research outcomes in this thesis outline the importance of a fully automated end-to-end CAP scoring solution for future studies on sleep microstructure. Furthermore, we present novel critical information for a better understanding of CAP and obtain first evidence on physiological network dynamics between the central nervous system and the cardiovascular system during CAP.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 202

Leveraging Artificial Intelligence to Improve EEG-fNIRS Data Analysis

La spectroscopie proche infrarouge fonctionnelle (fNIRS) est apparue comme une technique de neuroimagerie qui permet une surveillance non invasive et à long terme de l'hémodynamique corticale. Les technologies de neuroimagerie multimodale en milieu clinique permettent d'étudier les maladies neurologiques aiguës et chroniques. Dans ce travail, nous nous concentrons sur l'épilepsie - un trouble chronique du système nerveux central affectant près de 50 millions de personnes dans le monde entier prédisposant les individus affectés à des crises récurrentes. Les crises sont des aberrations transitoires de l'activité électrique du cerveau qui conduisent à des symptômes physiques perturbateurs tels que des changements aigus ou chroniques des compétences cognitives, des hallucinations sensorielles ou des convulsions de tout le corps. Environ un tiers des patients épileptiques sont récalcitrants au traitement pharmacologique et ces crises intraitables présentent un risque grave de blessure et diminuent la qualité de vie globale. Dans ce travail, nous étudions 1. l'utilité des informations hémodynamiques dérivées des signaux fNIRS dans une tâche de détection des crises et les avantages qu'elles procurent dans un environnement multimodal par rapport aux signaux électroencéphalographiques (EEG) seuls, et 2. la capacité des signaux neuronaux, dérivé de l'EEG, pour prédire l'hémodynamique dans le cerveau afin de mieux comprendre le cerveau épileptique. Sur la base de données rétrospectives EEG-fNIRS recueillies auprès de 40 patients épileptiques et utilisant de nouveaux modèles d'apprentissage en profondeur, la première étude de cette thèse suggère que les signaux fNIRS offrent une sensibilité et une spécificité accrues pour la détection des crises par rapport à l'EEG seul. La validation du modèle a été effectuée à l'aide de l'ensemble de données CHBMIT open source documenté et bien référencé avant d'utiliser notre ensemble de données EEG-fNIRS multimodal interne. Les résultats de cette étude ont démontré que fNIRS améliore la détection des crises par rapport à l'EEG seul et ont motivé les expériences ultérieures qui ont déterminé la capacité prédictive d'un modèle d'apprentissage approfondi développé en interne pour décoder les signaux d'état de repos hémodynamique à partir du spectre complet et d'une bande de fréquences neuronale codée spécifique signaux d'état de repos (signaux sans crise). Ces résultats suggèrent qu'un autoencodeur multimodal peut apprendre des relations multimodales pour prédire les signaux d'état de repos. Les résultats suggèrent en outre que des gammes de fréquences EEG plus élevées prédisent l'hémodynamique avec une erreur de reconstruction plus faible par rapport aux gammes de fréquences EEG plus basses. De plus, les connexions fonctionnelles montrent des modèles spatiaux similaires entre l'état de repos expérimental et les prédictions fNIRS du modèle. Cela démontre pour la première fois que l'auto-encodage intermodal à partir de signaux neuronaux peut prédire l'hémodynamique cérébrale dans une certaine mesure. Les résultats de cette thèse avancent le potentiel de l'utilisation d'EEG-fNIRS pour des tâches cliniques pratiques (détection des crises, prédiction hémodynamique) ainsi que l'examen des relations fondamentales présentes dans le cerveau à l'aide de modèles d'apprentissage profond. S'il y a une augmentation du nombre d'ensembles de données disponibles à l'avenir, ces modèles pourraient être en mesure de généraliser les prédictions qui pourraient éventuellement conduire à la technologie EEG-fNIRS à être utilisée régulièrement comme un outil clinique viable dans une grande variété de troubles neuropathologiques.----------ABSTRACT Functional near-infrared spectroscopy (fNIRS) has emerged as a neuroimaging technique that allows for non-invasive and long-term monitoring of cortical hemodynamics. Multimodal neuroimaging technologies in clinical settings allow for the investigation of acute and chronic neurological diseases. In this work, we focus on epilepsy—a chronic disorder of the central nervous system affecting almost 50 million people world-wide predisposing affected individuals to recurrent seizures. Seizures are transient aberrations in the brain's electrical activity that lead to disruptive physical symptoms such as acute or chronic changes in cognitive skills, sensory hallucinations, or whole-body convulsions. Approximately a third of epileptic patients are recalcitrant to pharmacological treatment and these intractable seizures pose a serious risk for injury and decrease overall quality of life. In this work, we study 1) the utility of hemodynamic information derived from fNIRS signals in a seizure detection task and the benefit they provide in a multimodal setting as compared to electroencephalographic (EEG) signals alone, and 2) the ability of neural signals, derived from EEG, to predict hemodynamics in the brain in an effort to better understand the epileptic brain. Based on retrospective EEG-fNIRS data collected from 40 epileptic patients and utilizing novel deep learning models, the first study in this thesis suggests that fNIRS signals offer increased sensitivity and specificity metrics for seizure detection when compared to EEG alone. Model validation was performed using the documented open source and well referenced CHBMIT dataset before using our in-house multimodal EEG-fNIRS dataset. The results from this study demonstrated that fNIRS improves seizure detection as compared to EEG alone and motivated the subsequent experiments which determined the predictive capacity of an in-house developed deep learning model to decode hemodynamic resting state signals from full spectrum and specific frequency band encoded neural resting state signals (seizure free signals). These results suggest that a multimodal autoencoder can learn multimodal relations to predict resting state signals. Findings further suggested that higher EEG frequency ranges predict hemodynamics with lower reconstruction error in comparison to lower EEG frequency ranges. Furthermore, functional connections show similar spatial patterns between experimental resting state and model fNIRS predictions. This demonstrates for the first time that intermodal autoencoding from neural signals can predict cerebral hemodynamics to a certain extent. The results of this thesis advance the potential of using EEG-fNIRS for practical clinical tasks (seizure detection, hemodynamic prediction) as well as examining fundamental relationships present in the brain using deep learning models. If there is an increase in the number of datasets available in the future, these models may be able to generalize predictions which would possibly lead to EEG-fNIRS technology to be routinely used as a viable clinical tool in a wide variety of neuropathological disorders

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 183

This bibliography lists 273 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1978

Design, Evaluation, and Application of Heart Rate Variability Analysis Software (HRVAS)

The analysis of heart rate variability (HRV) has become an increasingly popular and important tool for studying many disease pathologies in the past twenty years. HRV analyses are methods used to non-invasively quantify variability within heart rate. Purposes of this study were to design, evaluate, and apply an easy to use and open-source HRV analysis software package (HRVAS). HRVAS implements four major categories of HRV techniques: statistical and time-domain analysis, frequency-domain analysis, nonlinear analysis, and time-frequency analysis. Software evaluations were accomplished by performing HRV analysis on simulated and public congestive heart failure (CHF) data. Application of HRVAS included studying the effects of hyperaldosteronism on HRV in rats. Simulation and CHF results demonstrated that HRVAS was a dependable HRV analysis tool. Results from the rat hyperaldosteronism model showed that 5 of 26 HRV measures were statistically significant (p\u3c0.05). HRVAS provides a useful tool for HRV analysis to researchers

Measuring blood flow and pro-inflammatory changes in the rabbit aorta

Atherosclerosis is a chronic inflammatory disease that develops as a consequence of progressive entrapment of low density lipoprotein, fibrous proteins and inflammatory cells in the arterial intima. Once triggered, a myriad of inflammatory and atherogenic factors mediate disease progression. However, the role of pro-inflammatory activity in the initiation of atherogenesis and its relation to altered mechanical stresses acting on the arterial wall is unclear. Estimation of wall shear stress (WSS) and the inflammatory mediator NF-κB is consequently useful. In this thesis novel ultrasound tools for accurate measurement of spatiotemporally varying 2D and 3D blood flow, with and without the use of contrast agents, have been developed. This allowed for the first time accurate, broad-view quantification of WSS around branches of the rabbit abdominal aorta. A thorough review of the evidence for a relationship between flow, NF-κB and disease was performed which highlighted discrepancies in the current literature and was used to guide the study design. Subsequently, methods for the measurement and colocalization of the spatial distribution of NF-κB, arterial permeability and nuclear morphology in the aorta of New Zealand White rabbits were developed. It was demonstrated that endothelial pro-inflammatory changes are spatially correlated with patterns of WSS, nuclear morphology and arterial permeability in vivo in the rabbit descending and abdominal aorta. The data are consistent with a causal chain between WSS, macromolecule uptake, inflammation and disease, and with the hypothesis that lipids are deposited first, through flow-mediated naturally occurring transmigration that, in excessive amounts, leads to subsequent inflammation and disease.Open Acces