EEG cortical activity and connectivity correlates of early sympathetic response during cold pressor test

Previous studies have identified several brain regions involved in the sympathetic response and its integration with pain, cognition, emotions and memory processes. However, little is known about how such regions dynamically interact during a sympathetic activation task. In this study, we analyzed EEG activity and effective connectivity during a cold pressor test (CPT). A source localization analysis identified a network of common active sources including the right precuneus (r-PCu), right and left precentral gyri (r-PCG, l-PCG), left premotor cortex (l-PMC) and left anterior cingulate cortex (l-ACC). We comprehensively analyzed the network dynamics by estimating power variation and causal interactions among the network regions through the direct directed transfer function (dDTF). A connectivity pattern dominated by interactions in α (8–12) Hz band was observed in the resting state, with r-PCu acting as the main hub of information flow. After the CPT onset, we observed an abrupt suppression of such α -band interactions, followed by a partial recovery towards the end of the task. On the other hand, an increase of δ -band (1–4) Hz interactions characterized the first part of CPT task. These results provide novel information on the brain dynamics induced by sympathetic stimuli. Our findings suggest that the observed suppression of α and rise of δ dynamical interactions could reflect non-pain-specific arousal and attention-related response linked to stimulus’ salience

The effect of spontaneous versus paced breathing on EEG, HRV, skin conductance and skin temperature

A dissertation submitted in fulfilment of the requirements for the degree Master of Science in Engineering, in the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg. January 2017 JohannesburgIt is well known that emotional stress has a negative impact on people’s health and physical, emotional and mental performance. Previous research has investigated the effects of stress on various aspects of physiology such as respiration, heart rate, heart rate variability (HRV), skin conductance, skin temperature and electrical activity in the brain. Essentially, HRV, Electroencephalography (EEG), skin conductance and skin temperature appear to reflect a stress response or state of arousal. Whilst the relationship between respiration rate, respiration rhythm and HRV is well documented, less is known about the relationship between respiration rate, EEG, skin conductance and skin temperature, whilst HRV is maximum (when there is resonance between HRV and respiration i.e. in phase with one another). This research project aims to investigate the impact that one session of slow paced breathing has on EEG, heart rate variability (HRV), skin conductance and skin temperature. Twenty male participants were randomly assigned to either a control or intervention group. Physiological data were recorded for the intervention and control group during one breathing session, over a short initial baseline (B1), a main session of 12 minutes, and a final baseline (B2). The only difference between the control and intervention groups was that during the main session, the intervention group practiced slow paced breathing (at 6 breaths per minute), while the control group breathed spontaneously. Wavelet transformation was used to analyse EEG data while Fourier transformation was used to analyse HRV. The study shows that slow-paced breathing significantly increases the low frequency and total power of the HRV but does not change the high frequency power of HRV. Furthermore, skin temperature significantly increased for the control group from B1 to Main, and was significantly higher for the control group when compared to the intervention group during the main session. There were no significant skin temperature changes between sessions for the intervention group. Skin conductance increased significantly from Main to B2 for the control group. No significant changes were found between sessions for the intervention group and between groups. EEG theta power at Cz decreased significantly from Main to B2 for the control group only, while theta power decreased at F4 from Main to B2 for both groups. Lastly, beta power at Cz decreased from B1 to B2 for the control group only. This significant effect that slow-paced breathing has on HRV suggests the hypothesis that with frequent practice, basal HRV would increase, and with it, potential benefits such as a reduction in anxiety and improved performance in specific tasks. Slow-paced breathing biofeedback thus shows promise as a simple, cheap, measurable and effective method to reduce the impact of stress on some physiological signals, suggesting a direction for future research.MT201

Microstates of the cortical brain-heart axis

Electroencephalographic (EEG) microstates are brain states with quasi-stable scalp topography. Whether such states extend to the body level, that is, the peripheral autonomic nerves, remains unknown. We hypothesized that microstates extend at the brain-heart axis level as a functional state of the central autonomic network. Thus, we combined the EEG and heartbeat dynamics series to estimate the directional information transfer originating in the cortex targeting the sympathovagal and parasympathetic activity oscillations and vice versa for the afferent functional direction. Data were from two groups of participants: 36 healthy volunteers who were subjected to cognitive workload induced by mental arithmetic, and 26 participants who underwent physical stress induced by a cold pressure test. All participants were healthy at the time of the study. Based on statistical testing and goodness-of-fit evaluations, we demonstrated the existence of microstates of the functional brain-heart axis, with emphasis on the cerebral cortex, since the microstates are derived from EEG. Such nervous-system microstates are spatio-temporal quasi-stable states that exclusively refer to the efferent brain-to-heart direction. We demonstrated brain-heart microstates that could be associated with specific experimental conditions as well as brain-heart microstates that are non-specific to tasks

Assessing Variability of EEG and ECG/HRV Time Series Signals Using a Variety of Non-Linear Methods

Time series signals, such as Electroencephalogram (EEG) and Electrocardiogram (ECG) represent the complex dynamic behaviours of biological systems. The analysis of these signals using variety of nonlinear methods is essential for understanding variability within EEG and ECG, which potentially could help unveiling hidden patterns related to underlying physiological mechanisms. EEG is a time varying signal, and electrodes for recording EEG at different positions on the scalp give different time varying signals. There might be correlation between these signals. It is important to know the correlation between EEG signals because it might tell whether or not brain activities from different areas are related. EEG and ECG might be related to each other because both of them are generated from one co-ordinately working body. Investigating this relationship is of interest because it may reveal information about the correlation between EEG and ECG signals. This thesis is about assessing variability of time series data, EEG and ECG, using variety of nonlinear measures. Although other research has looked into the correlation between EEGs using a limited number of electrodes and a limited number of combinations of electrode pairs, no research has investigated the correlation between EEG signals and distance between electrodes. Furthermore, no one has compared the correlation performance for participants with and without medical conditions. In my research, I have filled up these gaps by using a full range of electrodes and all possible combinations of electrode pairs analysed in Time Domain (TD). Cross-Correlation method is calculated on the processed EEG signals for different number unique electrode pairs from each datasets. In order to obtain the distance in centimetres (cm) between electrodes, a measuring tape was used. For most of our participants the head circumference range was 54-58cm, for which a medium-sized I have discovered that the correlation between EEG signals measured through electrodes is linearly dependent on the physical distance (straight-line) distance between them for datasets without medical condition, but not for datasets with medical conditions. Some research has investigated correlation between EEG and Heart Rate Variability (HRV) within limited brain areas and demonstrated the existence of correlation between EEG and HRV. But no research has indicated whether or not the correlation changes with brain area. Although Wavelet Transformations (WT) have been performed on time series data including EEG and HRV signals to extract certain features respectively by other research, so far correlation between WT signals of EEG and HRV has not been analysed. My research covers these gaps by conducting a thorough investigation of all electrodes on the human scalp in Frequency Domain (FD) as well as TD. For the reason of different sample rates of EEG and HRV, two different approaches (named as Method 1 and Method 2) are utilised to segment EEG signals and to calculate Pearson’s Correlation Coefficient for each of the EEG frequencies with each of the HRV frequencies in FD. I have demonstrated that EEG at the front area of the brain has a stronger correlation with HRV than that at the other area in a frequency domain. These findings are independent of both participants and brain hemispheres. Sample Entropy (SE) is used to predict complexity of time series data. Recent research has proposed new calculation methods for SE, aiming to improve the accuracy. To my knowledge, no one has attempted to reduce the computational time of SE calculation. I have developed a new calculation method for time series complexity which could improve computational time significantly in the context of calculating a correlation between EEG and HRV. The results have a parsimonious outcome of SE calculation by exploiting a new method of SE implementation. In addition, it is found that the electrical activity in the frontal lobe of the brain appears to be correlated with the HRV in a time domain. Time series analysis method has been utilised to study complex systems that appear ubiquitous in nature, but limited to certain dynamic systems (e.g. analysing variables affecting stock values). In this thesis, I have also investigated the nature of the dynamic system of HRV. I have disclosed that Embedding Dimension could unveil two variables that determined HRV

Effect of Diabetes Mellitus on Human Brain Function

The following thesis contains four clinical studies. Study I, II and IV were based on a cross sectional investigation on subjects with type 1 diabetes (T1DM) studying the effect of the disease on CNS function through electrophysiological parameters coupled with neuropsychological tests. Study III was an interventional study investigating the effect of strict glycaemic control on subjects with type 2 diabetes (T2DM). Several new techniques were applied to the study of EEG in both studies giving a deeper understanding of the effect of diabetes on the brain. Paper I, II & IV: A cross-sectional study was performed in adult patients (N=150) with T1DM. Factors that are important for cognitive impairment in T1DM were identified. Furthermore, the effects of T1DM on auditory event-related potentials (ERP), spectral properties of resting EEG, connectivity between cortical regions and flow of information across the scalp of resting EEG were studied on a subgroup of 119 patients and compared to healthy controls (N=61). The strongest predictor of cognitive decline was found to be long diabetes duration and young age of diabetes onset, however, body mass index (BMI), height, age and compound muscle action potential (CMAP) were also found to predict cognitive decline. Moreover, patients had a significant decrease in auditory N100 amplitude, which correlated with a decrease in psychomotor speed. Furthermore, connectivity and information flow were reduced for patients as was EEG power. There were no significant correlations between the spectral, connectivity and information flow parameters and cognition. The influence of diabetes duration, BMI, height, age and CMAP may suggest that loss of the neuroprotective effects of insulin or insulin-like growth factors plays a role in the decline of cognitive function. Furthermore, the decline in ERP, connectivity and information flow may suggest conduction defects in the white matter and in the cortex. As the above mentioned parameters only had a partial relationship with each other we conclude that the tests measure different functions and are complementary to the cognitive tests and that several tests need to be performed to monitor the effect of T1DM on brain function. Paper III: The mild cognitive decline associated with T2DM has been suggested to be reversible with improved glycaemic control. In order to characterise this cognitive decline and study the effects of improved glycaemic control patients with T2DM (N=28) and healthy control subjects (N=21) were studied. One group of patients with diabetes (N=15) were given a 2-month treatment of intensified glycaemic control, whereas the other group (N=13) maintained their regular treatment. Cognitive function and electrophysiological variables were studied in the two groups of patients and in healthy control subjects before and after the 2-month trial period. There were significant differences at baseline and the change between 1st and 2nd investigation was significantly different in the three groups where patients receiving intensified treatment had an improvement of HbA1c and cerebral function. In conclusion, T2DM had a similar type of effect on brain function as T1DM and intensified therapy improved the function, suggesting that the negative effect of T2DM on the brain is partly reversible

Multidimensional CNN and LSTM for Predicting Epilepsy Seizure Activities

Epilepsy is a chronic neurological disease caused by sudden abnormal brain discharges, leading to temporary brain dysfunction. It can manifest in various ways, including paroxysmal movement, sensory, autonomic nerve, awareness, and mental abnormalities. It is now the second largest neurological disorder worldwide, affecting around 70 million people and increasing by approximately 2 million new cases each year. While about 70% of epilepsy patients can control their seizures with regular antiepileptic drugs, surgery, or nerve stimulation treatments, the remaining 30% suffer from intractable epilepsy without effective treatment, causing significant burden and potential danger to their lives. Early prediction and treatment are crucial to prevent harm to patients. Electroencephalogram (EEG) is a valuable tool for diagnosing epilepsy as it records the brain's electrical activity. EEG can be divided into scalp and intracranial types, and doctors typically analyze EEG signals of epileptic patients into four periods

Investigation of brain networks for personalized rTMS in healthy subjects and patients with major depressive disorder: A translational study

Depression is a complex psychiatric disorder with emotional dysregulation at its core. The first line of treatment includes cognitive behaviour therapy and pharmacological antidepressants. However, up to one third of patients with depression fail to respond to these treatment interventions. The past decades have seen an increasing use of repetitive Transcranial Magnetic Stimulation (rTMS) in clinical studies, as an alternative treatment for depression. Several large-scale, multicentre randomized controlled trials have led the Food and Drugs Administration (FDA), USA to approve two rTMS protocols for clinical application in the treatment of depression - 10 Hz rTMS and intermittent Theta Burst Stimulation (iTBS). However, only 30-50% of patients receiving rTMS respond to the treatment. The large variability in response to rTMS likely stems from multiple reasons, one being the targeting method currently employed for delivering rTMS at the left dorsolateral prefrontal cortex (DLPFC). Previous functional connectivity studies have shown that stimulation at left DLPFC targets with larger negative correlation to the subgenual anterior cingulate cortex (sgACC) may result in greater therapeutic response than those with lower negative correlation. However, current use of rTMS ignores functional connectivity in choosing the left DLPFC target, thus largely discarding functional architectural differences of the brain across subjects. Furthermore, despite widespread clinical use of rTMS, the basic network mechanisms behind these rTMS protocols remain elusive. This work presents a novel personalization method of left DLPFC target selection based on their negative functional connectivity to the sgACC. The default mode network (DMN) is a large-scale brain network commonly involved in self-referential thought processing and plays an essential role in the pathophysiology of depression. I use the novel personalization method and identical study designs to delineate DMN mechanisms from a single session of 10 Hz rTMS and iTBS in healthy subjects. Arguably, an understanding of basic mechanisms of clinically relevant rTMS protocols in healthy subjects will help improve the current therapeutic effect of rTMS, and possibly expand the therapeutic role of rTMS. My work shows, for the first time, strong but different modulations of DMN connectivity by single personalized sessions of 10 Hz rTMS and iTBS. Such modulations can be predicted using the personality trait harm avoidance (HA). Given that initial results show that the method is robust and reproducible, its adaptation to patient cohorts is likely to result in improved therapeutic benefits. Therefore, the novel method of personalization is translated to clinical setting by using accelerated iTBS (aiTBS) in patients with depression. Additionally, a comparison is made between effects resulting from personalized and nonpersonalized (10-20 EEG system F3 position) aiTBS in patients with depression. By evaluating the DMN, and heart rate variability, I show precise modulatory effects of personalized aiTBS, which is not seen in the standard aiTBS group. The work presented here introduces an important method to reduce variability and increase precision in rTMS modulation by personalizing the left DLPFC target selection. Even though DMN and cardiac effects already point towards the advantage of personalization, the still preliminary analysis fails to show significant differences in treatment response. Lack of greater therapeutic benefits viii from personalized aiTBS in this ongoing study probably stems from a still limited sample size. In case personalization proves clinically advantageous to standard iTBS by the final sample size, this work can sediment the first step towards systems medicine in the field of psychiatry.2022-02-0