Aerospace medicine and biology: A continuing bibliography with indexes

This bibliography lists 180 reports, articles and other documents introduced into the NASA scientific and technical information system in February 1985

The role of oscillation population activity in cortico-basal ganglia circuits.

The basal ganglia (BG) are a group of subcortical brain nuclei that are anatomically situated between the cortex and thalamus. Hitherto, models of basal ganglia function have been based solely on the anatomical connectivity and changes in the rate of neurons mediated by inhibitory and excitatory neurotransmitter interactions and modulated by dopamine. Depletion of striatal dopamine as occurs in Parkinson's Disease (PD) however, leads primarily to changes in the rhythmicity of basal ganglia neurons. The general aim of this thesis is to use frontal electrocorticogram (ECoG) and basal ganglia local field potential (LFP) recordings in the rat to further investigate the putative role for oscillations and synchronisation in these structures in the healthy and dopamine depleted brain. In the awake animal, lesion of the SNc lead to a dramatic increase in the power and synchronisation of P-frequency band oscillations in the cortex and subthalamic nucleus (STN) compared to the sham lesioned animal. These results are highly similar to those in human patients and provide further evidence for a direct pathophysological role for p-frequency band oscillations in PD. In the healthy, anaesthetised animal, LFPs recorded in the STN, globus pallidus (GP) and substantia nigra pars reticulata (SNr) were all found to be coherent with the ECoG. A detailed analysis of the interdependence and direction of these activities during two different brain states, prominent slow wave activity (SWA) and global activation, lead to the hypothesis that there were state dependant changes in the dominance of the cortico-subthalamic and cortico-striatal pathways. Multiple LFP recordings in the striatum and GP provided further evidence for this hypothesis, as coherence between the ECoG and GP was found to be dependent on the striatum. Together these results suggest that oscillations and synchronisation may mediate information flow in cortico-basal ganglia networks in both health and disease

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

Genetic Investigations of Sporadic Inclusion Body Myositis and Myopathies with Structural Abnormalities and Protein Aggregates in Muscle

The application of whole-exome sequencing (WES) has not only dramatically accelerated the discovery of pathogenic genes of Mendelian diseases, but has also shown promising findings in complex diseases. This thesis focuses on exploring genetic risk factors for a large series of sporadic inclusion body myositis (sIBM) cases, and identifying disease-causing genes for several groups of patients with abnormal structure and/or protein aggregates in muscle. Both conventional and advanced techniques were applied. Based on the International IBM Genetics Consortium (IIBMGC), the largest sIBM cohort of blood and muscle tissue for DNA analysis was collected as the initial part of this thesis. Candidate gene studies were carried out and revealed a disease modifying effect of an intronic polymorphism in TOMM40, enhanced by the APOE ε3/ε3 genotype. Rare variants in SQSTM1 and VCP genes were identified in seven of 181 patients, indicating a mutational overlap with neurodegenerative diseases. Subsequently, a first whole-exome association study was performed on 181 sIBM patients and 510 controls. This reported statistical significance of several common variants located on chromosome 6p21, a region encompassing genes related to inflammation/infection. WES was performed on a group of 35 cases with tubular aggregates/cylindrical spirals, and detected rare variants in known/candidate genes. Disease-causing genes were identified in four families with protein aggregates in muscle also by WES. In one family identified with a novel homozygous deletion in SBF1 with a rare autosomal-recessive neuromuscular condition, functional analysis was carried out indicating a loss-of-function mechanism underlying the pathogenesis of the disease. The collection of a large series of sIBM patients through the IIBMGC has been shown here to reveal important genetic findings and will be a valuable resource for the future. WES proved to be important in sIBM and also to be an efficient method to investigate the genetics basis of rare complex muscle disorders

Oscillatory activity in the basal ganglia - is it relevant to movement disorders therapy?

Chronic high frequency stimulation of the basal ganglia can be a highly effective intervention for movement disorders in patients. In the past decade, therapeutic benefits have been seen with stimulation of the subthalamic nucleus and globus pallidus interna for Parkinson's disease (PD) and dystonia, respectively. These procedures have allowed direct recording of basal ganglia activity and have suggested that abnormal synchronisation of neurons in these nuclei may contribute to motor impairment. This thesis explores the possible correlation between synchronised activity in the basal ganglia, as evidenced by oscillations in local field potentials, and movement disorders. In Chapter 3, we demonstrate the correlation between synchronization at frequencies under 10 Hz in the globus pallidus interna and dystonic EMG. This low frequency activity is shown to be locked to neuronal activity within GPi in patients with dystonia (Chapter 4). Deep brain stimulation is thought to suppress spontaneous pathological activity in the basal ganglia. Equally, however, it must also suppress any residual physiological activity in these nuclei. In Chapter 5, we demonstrate that the basal ganglia are involved in the processing of simple limb movements in the human, by separating the effects of deep brain stimulation on pathological and physiological activities based on baseline task performance. An impairment of motor performance was seen during high frequency stimulation in those patients with the best task performance at baseline. This deleterious effect, however, should be distinguished from the effect of direct stimulation at 20 Hz in Parkinson's disease. Oscillatory activity at around 20 Hz is thought to be a core feature in Parkinson's disease. In Chapter 6, we demonstrate that the excessive synchronization imposed by stimulation of the subthalamic nucleus at 20 Hz slows movement, in those patients with the best task performance at baseline. This supports the notion that synchronization around 20 Hz may be causally linked to bradykinesia. Last, the therapeutic effectiveness of DBS therapy for patients with PD partially relies on the accurate localisation of the motor region of the subthalamic nucleus. In Chapter 7, we propose an alternative method for the localization of this region using the spontaneous pathological 20 Hz activity to be found in this nucleus. The findings of these studies provide evidence that basal ganglia oscillatory activities of differing frequencies contribute to movement disorders

Non-invasive methods to investigate brain function in health and disease

Ph. D.Non-invasive methods to investigate brain function have been used in research laboratories for many decades, however their popularity has increased in recent years given the ease of use and broad application. Such methods have proved valuable in improving our knowledge about numerous areas of basic brain function. Many non-invasive techniques have also been applied to patient groups to allow further identification of pathological mechanisms, but critically a new role has been found for some as biomarkers of disease. Neurodegenerative disease is fast becoming one of the biggest medical problems in the first world. An aging population has caused the relative incidence of many conditions to rise dramatically and studies suggest that this trend will continue. Although our knowledge surrounding these conditions has improved significantly, most remain notoriously difficult to diagnose and to treat. The recent introduction of neuroprotective drugs offers the potential to slow the progression of some diseases. However, to take full advantage of these disease-modifying treatments, administration must occur early in the disease course which fuels the demand for selective and specific diagnostic tests. There is currently a great need to enhance the clinical diagnostic repertoire with reliable, robust and specific biomarkers of neurodegenerative disease. However, careful, rigorous studies are required to validate the use of non-invasive techniques in this role. The same level of care should also be applied to techniques used in basic research; without a fundamental understanding of the mechanisms underpinning these techniques, their utility in the investigation of specific processes or pathways is questionable. This thesis aims to address specific cases to evaluate existing techniques and to screen potential new disease biomarkers

Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference

Electromyographic patterns of hand muscles during rhythmic finger movements and handwriting.

Available from British Library Document Supply Centre-DSC:DXN012508 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Temporal Associations between Regional Sleep Spindles and Slow Oscillations during NREM Sleep in Older Adults with and without Mild Cognitive Impairment

Sleep is acknowledged as an emerging modifiable risk factor for dementia. Sleep spindles and slow oscillations are distinct electroencephalographic (EEG) features of non-rapid eye movement (NREM) sleep. Deficiencies in these neuro-oscillations, as well as their temporal coordination have been linked to impairments in sleep-dependent memory consolidation. Mild cognitive impairment (MCI), considered the prodromal stage of dementia, may present a window of opportunity for targeted intervention. Sleep structure abnormalities have been reported in MCI, including some evidence to suggest deficits in sleep spindle and slow wave activities. Preliminary evidence additionally demonstrates how the co-occurrence or coupling of slow oscillations and sleep spindles may also contribute to sleep-dependent memory consolidation. However, it is currently unclear whether changes in the coupling between slow oscillations and sleep spindles are present in MCI. The present study investigates the regional differences in slow oscillation-sleep spindle (SO-SP) coupling between older adults with MCI and cognitively-intact controls, and the associations between coupling strength and overnight memory consolidation. This thesis is the first to compare SO-SP coupling, as well as explore the relationship between coupling and sleep-dependent memory consolidation in older adults with and without MCI. Findings suggest that regional deficits in spindle activity are present in older adults at risk of dementia development, and may thus provide potential utility as a sleep biomarker of impaired overnight memory processing. High-density EEG provides high spatial sensitivity to investigate localised changes in NREM sleep neuro-oscillatory activity. Future multi-modal brain imaging studies may elucidate underlying regional brain pathology linked to altered sleep neurophysiology, SO-SP coupling dynamics and sleep-dependent memory consolidation processes in this prodromal group