Generalized tonic-clonic seizures are accompanied by changes of interrelations within the autonomic nervous system.

PURPOSE: A seizure is a strong central stimulus that affects multiple subsystems of the autonomic nervous system (ANS), and results in different interactions across ANS modalities. Here, we aimed to evaluate whether multimodal peripheral ANS measures demonstrate interactions before and after seizures as compared to controls to provide the basis for seizure detection and forecasting based on peripheral ANS signals. METHODS: Continuous electrodermal activity (EDA), heart rate (HR), peripheral body temperature (TEMP), and respiratory rate (RR) calculated based on blood volume pulse were acquired by a wireless multi-sensor device. We selected 45 min of preictal and 60 min of postictal data and time-matched segments for controls. Data were analyzed over 15-min windows. For unimodal analysis, mean values over each time window were calculated for all modalities and analyzed by Friedman's two-way analysis of variance. RESULTS: Twenty-one children with recorded generalized tonic-clonic seizures (GTCS), and 21 age- and gender-matched controls were included. Unimodal results revealed no significant effect for RR and TEMP, but EDA (p = 0.002) and HR (p < 0.001) were elevated 0-15 min after seizures. The averaged bimodal correlation across all pairs of modalities changed for 15-min windows in patients with seizures. The highest correlations were observed immediately before (0.85) and the lowest correlation immediately after seizures. Overall, average correlations for controls were higher. SIGNIFICANCE: Multimodal ANS changes related to GTCS occur within and across autonomic nervous system modalities. While unimodal changes were most prominent during postictal segments, bimodal correlations increased before seizures and decreased postictally. This offers a promising avenue for further research on seizure detection, and potentially risk assessment for seizure recurrence and sudden unexplained death in epilepsy.

Multimodal approach towards understanding the changes in the autonomic nervous system induced by an ultramarathon.

Purpose: Running an ultramarathon can be considered as a multifaceted, intense stressor inducing changes within the autonomic nervous system (ANS). The aim of this study was to examine changes within and across ANS modalities in response to an ultramarathon.Methods: Thirteen runners (44.3 ± 5.9 years) completed a 65 km run. Electrodermal activity (EDA), heart rate (HR), and skin temperature measured at wrist (Temp), were recorded before and after running. Three-minute intervals were analysed. Mean values were compared by t-tests for dependent samples. Joint principal component analysis-canonical correlation analysis (PCA-CCA) and multiset CCA techniques were employed to measure the interactions between either any two or among all modalities.Results: HR (p < 0.01) and EDA (p < 0.01) increased, while Temp decreased (p < 0.01). PCA-CCA revealed one significant component (p < 0.05) for each modality pair in pre and post measures. Component strength increased from pre (mean = 0.73) to post (mean = 0.92) test. Multiset CCA supported the assumption of increasing strength of correlations across modalities.Conclusion: Ultramarathon, an intense physical stressor, increases correlations across modalities pointing towards a reorganization of central ANS control to restore dynamic balance after physical load. This characterization of ANS-states might offer new avenues for training control.

Information-Theoretic Analysis of a Family of Improper Discrete Constellations.

Non-circular or improper Gaussian signaling has proven beneficial in several interference-limited wireless networks. However, all implementable coding schemes are based on finite discrete constellations rather than Gaussian signals. In this paper, we propose a new family of improper constellations generated by widely linear processing of a square M-QAM (quadrature amplitude modulation) signal. This family of discrete constellations is parameterized by κ , the circularity coefficient and a phase ϕ . For uncoded communication systems, this phase should be optimized as ϕ * ( κ ) to maximize the minimum Euclidean distance between points of the improper constellation, therefore minimizing the bit error rate (BER). For the more relevant case of coded communications, where the coded symbols are constrained to be in this family of improper constellations using ϕ * ( κ ) , it is shown theoretically and further corroborated by simulations that, except for a shaping loss of 1.53 dB encountered at a high signal-to-noise ratio (snr), there is no rate loss with respect to the improper Gaussian capacity. In this sense, the proposed family of constellations can be viewed as the improper counterpart of the standard proper M-QAM constellations widely used in coded communication systems.

Sample-poor estimation of order and common signal subspace with application to fusion of medical imaging data.

Due to their data-driven nature, multivariate methods such as canonical correlation analysis (CCA) have proven very useful for fusion of multimodal neurological data. However, being able to determine the degree of similarity between datasets and appropriate order selection are crucial to the success of such techniques. The standard methods for calculating the order of multimodal data focus only on sources with the greatest individual energy and ignore relations across datasets. Additionally, these techniques as well as the most widely-used methods for determining the degree of similarity between datasets assume sufficient sample support and are not effective in the sample-poor regime. In this paper, we propose to jointly estimate the degree of similarity between datasets and their order when few samples are present using principal component analysis and canonical correlation analysis (PCA-CCA). By considering these two problems simultaneously, we are able to minimize the assumptions placed on the data and achieve superior performance in the sample-poor regime compared to traditional techniques. We apply PCA-CCA to the pairwise combinations of functional magnetic resonance imaging (fMRI), structural magnetic resonance imaging (sMRI), and electroencephalogram (EEG) data drawn from patients with schizophrenia and healthy controls while performing an auditory oddball task. The PCA-CCA results indicate that the fMRI and sMRI datasets are the most similar, whereas the sMRI and EEG datasets share the least similarity. We also demonstrate that the degree of similarity obtained by PCA-CCA is highly predictive of the degree of significance found for components generated using CCA.

Finding brain oscillations with power dependencies in neuroimaging data.

Phase synchronization among neuronal oscillations within the same frequency band has been hypothesized to be a major mechanism for communication between different brain areas. On the other hand, cross-frequency communications are more flexible allowing interactions between oscillations with different frequencies. Among such cross-frequency interactions amplitude-to-amplitude interactions are of a special interest as they show how the strength of spatial synchronization in different neuronal populations relates to each other during a given task. While, previously, amplitude-to-amplitude correlations were studied primarily on the sensor level, we present a source separation approach using spatial filters which maximize the correlation between the envelopes of brain oscillations recorded with electro-/magnetoencephalography (EEG/MEG) or intracranial multichannel recordings. Our approach, which is called canonical source power correlation analysis (cSPoC), is thereby capable of extracting genuine brain oscillations solely based on their assumed coupling behavior even when the signal-to-noise ratio of the signals is low. In addition to using cSPoC for the analysis of cross-frequency interactions in the same subject, we show that it can also be utilized for studying amplitude dynamics of neuronal oscillations across subjects. We assess the performance of cSPoC in simulations as well as in three distinctively different analysis scenarios of real EEG data, each involving several subjects. In the simulations, cSPoC outperforms unsupervised state-of-the-art approaches. In the analysis of real EEG recordings, we demonstrate excellent unsupervised discovery of meaningful power-to-power couplings, within as well as across subjects and frequency bands.

Exercise-Induced Changes of Multimodal Interactions Within the Autonomic Nervous Network.

Physical exercise has been shown to modulate activity within the autonomic nervous system (ANS). Considering physical exercise as a holistic stimulus on the nervous system and specifically the ANS, uni- and multimodal analysis tools were applied to characterize centrally driven interactions and control of ANS functions. Nineteen young and physically active participants performed treadmill tests at individually determined moderate and high intensities. Continuous electrodermal activity (EDA), heart rate (HR), and skin temperature at wrist (Temp) were recorded by wireless multisensor devices (Empatica® E4, Milan, Italy) before and 30 min after exercise. Artifact-free continuous 3 min intervals were analyzed. For unimodal analysis, mean values were calculated, for bimodal and multimodal analysis canonical correlation analysis (CCA) was performed. Unimodal results indicate that physical exercise affects ANS activity. More specifically, Temp increased due to physical activity (moderate intensity: from 34.15°C to 35.34°C and high intensity: from 34.11°C to 35.09°C). HR increased more for the high (from 60.76 bpm to 79.89 bpm) than for the moderate (from 64.81 bpm to 70.83 bpm) intensity. EDA was higher for the high (pre: 8.06 µS and post: 9.37 µS) than for the moderate (pre: 4.31 µS and post: 3.91 µS) intensity. Bimodal analyses revealed high variations in correlations before exercise. The overall correlation coefficient showed varying correlations in pretest measures for all modality pairs (EDA-HR, HR-Temp, Temp-EDA at moderate: 0.831, 0.998, 0.921 and high: 0.706, 0, 0.578). After exercising at moderate intensity coefficients changed little (0.828, 0.744, 0.994), but increased substantially for all modality pairs after exercising at high intensity (0.976, 0.898, 0.926). Multimodal analysis confirmed bimodal results. Exercise-induced changes in ANS activity can be found in multiple ANS modalities as well as in their interactions. Those changes are intensity-specific: with higher intensity the interactions increase. Canonical correlations between different ANS modalities may therefore offer a feasible approach to determine exercise induced modulations of ANS activity.