Drugs and the autonomic nervous system

The nervous systems can initially be divided up into the central and peripheral nervous systems. The central nervous system is the brain and spinal cord and drugs that modify the central nervous system are considered as a subject in systematic pharmacology (therapeutics) section. Everything neural, other that the central nervous system, can be considered peripheral nervous systems. The peripheral nervous systems can be divided into the autonomic(involuntary) nervous system, which is the system that performs without your conscious help, and the somatic or voluntary nervous system, which you can consciously control(Figure 7.1). In addition the autonomic nervous system is divided into the sympathetic and parasympathetic nervous systems...

Cardiac state diagnosis using higher order spectra of heart rate variability

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart, by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are nonlinear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of nonlinear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and seven classes of arrhythmia. We present some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. We also extracted features from the HOS and performed an analysis of variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test.

Analysis of cardiac and epileptic signals using higher order spectra

The theory of nonlinear dyamic systems provides some new methods to handle complex systems. Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals. In recent years, researchers are applying the concepts from this theory to bio-signal analysis. In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory. In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia. This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. Several features were extracted from the HOS and subjected an Analysis of Variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification. The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions. The classifier achieved a sensitivity of 90% and a specificity of 89%. This system is ready to run on larger data sets. In EEG analysis, the search for hidden information for identification of seizures has a long history. Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier. Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features. About 2 hours of EEG recordings from 10 patients were used in this study. This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals. These plots reveal distinct patterns. The patterns are useful for visual interpretation by those without a deep understanding of spectral analysis such as medical practitioners. It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers.

The low-affinity site of the β1-adrenoceptor and its relevance to cardiovascular pharmacology

β-Adrenoceptor blocking agents (β-blockers) that at low concentrations antagonize cardiostimulant effects of catecholamines, but at high concentrations also cause cardiostimulation, have been appearing since the late 1960s. These cardiostimulant β-blockers, coined non-conventional partial agonists, antagonize the effects of catecholamines through a high-affinity site (β1HAR), but cause cardiostimulation mainly through a low-affinity site (β1LAR) of the myocardial β1-adrenoceptor. The experimental non-conventional partial agonist (−)-CGP12177 increases cardiac L-type Ca2+ current density and Ca2+ transients, shortens action potential duration but augments action potential plateau, increases heart rate and force, as well as causes arrhythmic Ca2+ transients and arrhythmic cardiocyte contractions. Other β-blockers, which do not cause cardiostimulation, consistently have lower affinity for β1LAR than β1HAR. These sites were verified and the cardiac pharmacology of non-conventional partial agonists confirmed on recombinant β1-adrenoceptors and on β1-adrenoceptors overexpressed into the heart. A targeted mutation of Asp138 to Glu138 virtually abolished the pharmacology of β1HAR but left intact the pharmacology of β1LAR. Non-conventional partial agonists may be beneficial for the treatment of peripheral autonomic neuropathy but probably due to their arrhythmic propensities, may be harmful for the treatment of chronic heart failure.

Cardiac health diagnosis using higher order spectra and support vector machine

The Electrocardiogram (ECG) is an important bio-signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. The HRV signal can be used as a base signal to observe the heart's functioning. These signals are non-linear and non-stationary in nature. So, higher order spectral (HOS) analysis, which is more suitable for non-linear systems and is robust to noise, was used. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, we have extracted seven features from the heart rate signals using HOS and fed them to a support vector machine (SVM) for classification. Our performance evaluation protocol uses 330 subjects consisting of five different kinds of cardiac disease conditions. We demonstrate a sensitivity of 90% for the classifier with a specificity of 87.93%. Our system is ready to run on larger data sets.

The relationship between Bayesian motor unit number estimation and histological measurements of motor neurons in wild-type and SOD1 G93A mice

Objective: To assess the relationship between Bayesian MUNE and histological motor neuron counts in wild-type mice and in an animal model of ALS. Methods: We performed Bayesian MUNE paired with histological counts of motor neurons in the lumbar spinal cord of wild-type mice and transgenic SOD1 G93A mice that show progressive weakness over time. We evaluated the number of acetylcholine endplates that were innervated by a presynaptic nerve. Results: In wild-type mice, the motor unit number in the gastrocnemius muscle estimated by Bayesian MUNE was approximately half the number of motor neurons in the region of the spinal cord that contains the cell bodies of the motor neurons supplying the hindlimb crural flexor muscles. In SOD1 G93A mice, motor neuron numbers declined over time. This was associated with motor endplate denervation at the end-stage of disease. Conclusion: The number of motor neurons in the spinal cord of wild-type mice is proportional to the number of motor units estimated by Bayesian MUNE. In SOD1 G93A mice, there is a lower number of estimated motor units compared to the number of spinal cord motor neurons at the end-stage of disease, and this is associated with disruption of the neuromuscular junction. Significance: Our finding that the Bayesian MUNE method gives estimates of motor unit numbers that are proportional to the numbers of motor neurons in the spinal cord supports the clinical use of Bayesian MUNE in monitoring motor unit loss in ALS patients. © 2012 International Federation of Clinical Neurophysiology.

Nutritional status in Parkinson's disease patients undergoing deep-brain stimulation surgery : a pilot study

People with Parkinson’s disease (PD) are at higher risk of malnutrition due to PD symptoms and pharmacotherapy side effects. Poorer outcomes are associated with higher amounts of weight loss (>5%) and lower levels of fat free mass. When pharmacotherapy is no longer effective for symptom control, deep-brain stimulation (DBS) surgery may be considered. People with PD scheduled for DBS surgery were recruited from a Brisbane neurological clinic (n=11 out of 16). The Scale for Outcomes of Parkinson’s disease –Autonomic (SCOPA-AUT), Modified Constipation Assessment Scale (MCAS), and a 3-day food diary were mailed to participants’ homes for completion prior to hospital admission. During admission, the Patient-Generated Subjective Global Assessment (PG-SGA), weight, height and body composition were assessed. Mean(±s.d.) PD duration from diagnosis and time since occurrence of PD symptoms was 9.0(±8.0) and 12(±8.8) years, respectively. Five participants reported unintentional weight loss (average loss of 15.6%). PD duration but not years since symptom onset significantly predicted PG-SGA scores (β=4.2, t(8)=2.7, p<.05). Both were positively correlated with PG-SGA score (r = .667, r=.587). On average, participants classified as well-nourished (SGA-A) (n=4) were younger, had shorter disease durations, lower PG-SGA scores, higher body mass (BMI) and fat free mass (FFMI) indices when compared to malnourished participants (SGA-B) (n=7). They also reported fewer non-motor symptoms on the SCOPA-AUT and MCAS. Three participants had previously received dietetic advice but not in relation to PD. These findings demonstrate that malnutrition remains unrecognised and untreated in this group despite unintentional weight loss and a high prevalence of malnutrition.

Risk factors for malnutrition in community-dwelling people with Parkinson’s disease

In the elderly, the risks for protein-energy malnutrition from older age, dementia, depression and living alone have been well-documented. Other risk factors including anorexia, gastrointestinal dysfunction, loss of olfactory and taste senses and early satiety have also been suggested to contribute to poor nutritional status. In Parkinson’s disease (PD), it has been suggested that the disease symptoms may predispose people with PD to malnutrition. However, the risks for malnutrition in this population are not well-understood. The current study’s aim was to determine malnutrition risk factors in community-dwelling adults with PD. Nutritional status was assessed using the Patient-Generated Subjective Global Assessment (PG-SGA). Data about age, time since diagnosis, medications and living situation were collected. Levodopa equivalent doses (LDED) and LDED per kg body weight (mg/kg) were calculated. Depression and anxiety were measured using the Beck’s Depression Inventory (BDI) and Spielberger Trait Anxiety questionnaire, respectively. Cognitive function was assessed using the Addenbrooke’s Cognitive Examination (ACE-R). Non-motor symptoms were assessed using the Scales for Outcomes in Parkinson's disease-Autonomic (SCOPA-AUT) and Modified Constipation Assessment Scale (MCAS). A total of 125 community-dwelling people with PD were included, average age of 70.2±9.3(35-92) years and average time since diagnosis of 7.3±5.9(0–31) years. Average body mass index (BMI) was 26.0±5.5kg/m2. Of these, 15% (n=19) were malnourished (SGA-B). Multivariate logistic regression analysis revealed that older age (OR=1.16, CI=1.02-1.31), more depressive symptoms (OR=1.26, CI=1.07-1.48), lower levels of anxiety (OR=.90, CI=.82-.99), and higher LDED per kg body weight (OR=1.57, CI=1.14-2.15) significantly increased malnutrition risk. Cognitive function, living situation, number of prescription medications, LDED, years since diagnosis and the severity of non-motor symptoms did not significantly influence malnutrition risk. Malnutrition results in poorer health outcomes. Proactively addressing the risk factors can help prevent declines in nutritional status. In the current study, older people with PD with depression and greater amounts of levodopa per body weight were at increased malnutrition risk.

Markers of disease severity are associated with malnutrition in Parkinson's disease

Objective In Parkinson's disease (PD), commonly reported risk factors for malnutrition in other populations commonly occur. Few studies have explored which of these factors are of particular importance in malnutrition in PD. The aim was to identify the determinants of nutritional status in people with Parkinson's disease (PWP). Methods Community-dwelling PWP (>18 years) were recruited (n = 125; 73M/52F; Mdn 70 years). Self-report assessments included Beck's Depression Inventory (BDI), Spielberger Trait Anxiety Inventory (STAI), Scales for Outcomes in Parkinson's disease – Autonomic (SCOPA-AUT), Modified Constipation Assessment Scale (MCAS) and Freezing of Gait Questionnaire (FOG-Q). Information about age, PD duration, medications, co-morbid conditions and living situation was obtained. Addenbrooke's Cognitive Examination (ACE-R), Unified Parkinson's Disease Rating Scale (UPDRS) II and UPDRS III were performed. Nutritional status was assessed using the Subjective Global Assessment (SGA) as part of the scored Patient-Generated Subjective Global Assessment (PG-SGA). Results Nineteen (15%) were malnourished (SGA-B). Median PG-SGA score was 3. More of the malnourished were elderly (84% vs. 71%) and had more severe disease (H&Y: 21% vs. 5%). UPDRS II and UPDRS III scores and levodopa equivalent daily dose (LEDD)/body weight(mg/kg) were significantly higher in the malnourished (Mdn 18 vs. 15; 20 vs. 15; 10.1 vs. 7.6 respectively). Regression analyses revealed older age at diagnosis, higher LEDD/body weight (mg/kg), greater UPDRS III score, lower STAI score and higher BDI score as significant predictors of malnutrition (SGA-B). Living alone and higher BDI and UPDRS III scores were significant predictors of a higher log-adjusted PG-SGA score. Conclusions In this sample of PWP, the rate of malnutrition was higher than that previously reported in the general community. Nutrition screening should occur regularly in those with more severe disease and depression. Community support should be provided to PWP living alone. Dopaminergic medication should be reviewed with body weight changes.

A population genomics overview of the neuronal nitric oxide synthase (nNOS) gene and its relationship to migraine susceptibility

The ubiquitous chemical messenger molecule nitric oxide (NO) has been implicated in a diverse range of biological activities including neurotransmission, smooth muscle motility and mediation of nociception. Endogenous synthesis of NO by the neuronal isoform of the nitric oxide synthase gene family has an essential role within the central and peripheral nervous systems in addition to the autonomic innervation of cerebral blood vessels. To investigate the potential role of NO and more specifically the neuronal nitric oxide synthase (nNOS) gene in migraine susceptibility, we investigated two microsatellite repeat variants residing within the 5′ and 3′ regions of the nNOS gene. Population genomic evaluation of the two nNOS repeat variants indicated significant linkage disequilibrium between the two loci. Z-DNA conformational sequence structures within the 5′ region of the nNOS gene have the potential to enhance or repress gene promoter activity. We suggest that genetic analysis of this 5′ repeat variant is the more functional variant expressing gene wide information that could affect endogenous NO synthesis and potentially result in diseased states. However, no association with migraine (with or without aura) was seen in our extensive case-control cohort (n = 579 affected with matched controls), when both the 5′ and 3′ genetic variants were investigated.

Cardiac parasympathetic reactivation following exercise : implications for training prescription

The objective of exercise training is to initiate desirable physiological adaptations that ultimately enhance physical work capacity. Optimal training prescription requires an individualized approach, with an appropriate balance of training stimulus and recovery and optimal periodization. Recovery from exercise involves integrated physiological responses. The cardiovascular system plays a fundamental role in facilitating many of these responses, including thermoregulation and delivery/removal of nutrients and waste products. As a marker of cardiovascular recovery, cardiac parasympathetic reactivation following a training session is highly individualized. It appears to parallel the acute/intermediate recovery of the thermoregulatory and vascular systems, as described by the supercompensation theory. The physiological mechanisms underlying cardiac parasympathetic reactivation are not completely understood. However, changes in cardiac autonomic activity may provide a proxy measure of the changes in autonomic input into organs and (by default) the blood flow requirements to restore homeostasis. Metaboreflex stimulation (e.g. muscle and blood acidosis) is likely a key determinant of parasympathetic reactivation in the short term (0–90 min post-exercise), whereas baroreflex stimulation (e.g. exercise-induced changes in plasma volume) probably mediates parasympathetic reactivation in the intermediate term (1–48 h post-exercise). Cardiac parasympathetic reactivation does not appear to coincide with the recovery of all physiological systems (e.g. energy stores or the neuromuscular system). However, this may reflect the limited data currently available on parasympathetic reactivation following strength/resistance-based exercise of variable intensity. In this review, we quantitatively analyse post-exercise cardiac parasympathetic reactivation in athletes and healthy individuals following aerobic exercise, with respect to exercise intensity and duration, and fitness/training status. Our results demonstrate that the time required for complete cardiac autonomic recovery after a single aerobic-based training session is up to 24 h following low-intensity exercise, 24–48 h following threshold-intensity exercise and at least 48 h following high-intensity exercise. Based on limited data, exercise duration is unlikely to be the greatest determinant of cardiac parasympathetic reactivation. Cardiac autonomic recovery occurs more rapidly in individuals with greater aerobic fitness. Our data lend support to the concept that in conjunction with daily training logs, data on cardiac parasympathetic activity are useful for individualizing training programmes. In the final sections of this review, we provide recommendations for structuring training microcycles with reference to cardiac parasympathetic recovery kinetics. Ultimately, coaches should structure training programmes tailored to the unique recovery kinetics of each individual.

Langerhans cells in the cornea of diabetic patients : relevance to diabetic neuropathy

Purpose The presence of a lymphocytic infiltration in autonomic ganglia and an increased prevalence of autoantibodies and iritis in diabetic patients with autonomic neuropathy suggests a role for autoimmune mechanisms in the development of diabetic and perhaps somatic neuropathy. Corneal Langerhans cells are antigenpresenting cells which can be identified in corneal immunologic conditions using in-vivo confocal microscopy. The aim of this study was to assess the presence and density of Langerhans cells (LCs) in Bowman’s layer of the cornea in diabetic patients with varying degrees of neuropathy compared to healthy control subjects. Method 128 diabetic patients aged 58±1 years with differing severity of neuropathy (NDS – 4.7±0.28) and 26 control subjects aged 53±3 years were examined with in-vivo corneal confocal microscopy to quantify the density of “Langerhans cells” (LCs). Results LCs were observed more often in diabetic patients (73.8%) compared to control subjects (46.1%), P = 0.001. The LC density (number/mm2) was also significantly increased in diabetic patients (17.73±1.45) compared to control subjects (6.94±1.58, P = 0.001). There was a significant correlation between the density of LCs with age (r = 0.162, P = 0.047) and severity of neuropathy assessed by NDS (r =−0.202, P = 0.02). Conclusions In vivo corneal confocal microscopy enables quantification of Langerhans cells in Bowman’s layer of the cornea. There is a relationship between density of LCs and the degree of nerve damage. Corneal confocal microscopy could be a valuable tool to establish the role of immune mediated corneal nerve damage and provide insights into the pathogenesis of diabetic neuropathy.

Higher order spectra based support vector machine for arrhythmia classification

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. HRV analysis is an important tool to observe the heart’s ability to respond to normal regulatory impulses that affect its rhythm. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. A computer-based arrhythmia detection system of cardiac states is very useful in diagnostics and disease management. In this work, we studied the identification of the HRV signals using features derived from HOS. These features were fed to the support vector machine (SVM) for classification. Our proposed system can classify the normal and other four classes of arrhythmia with an average accuracy of more than 85%.