Cross-frequency cortex-muscle interactions are abnormal in young people with dystonia.

Sensory processing and sensorimotor integration are abnormal in dystonia, including impaired modulation of beta-corticomuscular coherence. However, cortex-muscle interactions in either direction are rarely described, with reports limited predominantly to investigation of linear coupling, using corticomuscular coherence or Granger causality. Information-theoretic tools such as transfer entropy detect both linear and non-linear interactions between processes. This observational case-control study applies transfer entropy to determine intra- and cross-frequency cortex-muscle coupling in young people with dystonia/dystonic cerebral palsy. Fifteen children with dystonia/dystonic cerebral palsy and 13 controls, aged 12-18 years, performed a grasp task with their dominant hand. Mechanical perturbations were provided by an electromechanical tapper. Bipolar scalp EEG over contralateral sensorimotor cortex and surface EMG over first dorsal interosseous were recorded. Multi-scale wavelet transfer entropy was applied to decompose signals into functional frequency bands of oscillatory activity and to quantify intra- and cross-frequency coupling between brain and muscle. Statistical significance against the null hypothesis of zero transfer entropy was established, setting individual 95% confidence thresholds. The proportion of individuals in each group showing significant transfer entropy for each frequency combination/direction was compared using Fisher's exact test, correcting for multiple comparisons. Intra-frequency transfer entropy was detected in all participants bidirectionally in the beta (16-32 Hz) range and in most participants from EEG to EMG in the alpha (8-16 Hz) range. Cross-frequency transfer entropy across multiple frequency bands was largely similar between groups, but a specific coupling from low-frequency EMG to beta EEG was significantly reduced in dystonia [P = 0.0061 (corrected)]. The demonstration of bidirectional cortex-muscle communication in dystonia emphasizes the value of transfer entropy for exploring neural communications in neurological disorders. The novel finding of diminished coupling from low-frequency EMG to beta EEG in dystonia suggests impaired cortical feedback of proprioceptive information with a specific frequency signature that could be relevant to the origin of the excessive low-frequency drive to muscle.

Subband Independent Component Analysis for Coherence Enhancement.

OBJECTIVE: Cortico-muscular coherence (CMC) is becoming a common technique for detection and characterization of functional coupling between the motor cortex and muscle activity. It is typically evaluated between surface electromyogram (sEMG) and electroencephalogram (EEG) signals collected synchronously during controlled movement tasks. However, the presence of noise and activities unrelated to observed motor tasks in sEMG and EEG results in low CMC levels, which often makes functional coupling difficult to detect. METHODS: In this paper, we introduce Coherent Subband Independent Component Analysis (CoSICA) to enhance synchronous cortico-muscular components in mixtures captured by sEMG and EEG. The methodology relies on filter bank processing to decompose sEMG and EEG signals into frequency bands. Then, it applies independent component analysis along with a component selection algorithm for re-synthesis of sEMG and EEG designed to maximize CMC levels. RESULTS: We demonstrate the effectiveness of the proposed method in increasing CMC levels across different signal-to-noise ratios first using simulated data. Using neurophysiological data, we then illustrate that CoSICA processing achieves a pronounced enhancement of original CMC. CONCLUSION: Our findings suggest that the proposed technique provides an effective framework for improving coherence detection. SIGNIFICANCE: The proposed methodologies will eventually contribute to understanding of movement control and has high potential for translation into clinical practice.

The neurophysiologic landscape of the sleep onset: a systematic review.

Background: The sleep onset process is an ill-defined complex process of transition from wakefulness to sleep, characterized by progressive modifications at the subjective, behavioural, cognitive, and physiological levels. To this date, there is no international consensus which could aid a principled characterisation of this process for clinical research purposes. The current review aims to systemise the current knowledge about the underlying mechanisms of the natural heterogeneity of this process. Methods: In this systematic review, studies investigating the process of the sleep onset from 1970 to 2022 were identified using electronic database searches of PsychINFO, MEDLINE, and Embase. Results: A total of 139 studies were included; 110 studies in healthy participants and 29 studies in participants with sleep disorders. Overall, there is a limited consensus across a body of research about what distinct biomarkers of the sleep onset constitute. Only sparse data exists on the physiology, neurophysiology and behavioural mechanisms of the sleep onset, with majority of studies concentrating on the non-rapid eye movement stage 2 (NREM 2) as a potentially better defined and a more reliable time point that separates sleep from the wake, on the sleep wake continuum. Conclusions: The neurophysiologic landscape of sleep onset bears a complex pattern associated with a multitude of behavioural and physiological markers and remains poorly understood. The methodological variation and a heterogenous definition of the wake-sleep transition in various studies to date is understandable, given that sleep onset is a process that has fluctuating and ill-defined boundaries. Nonetheless, the principled characterisation of the sleep onset process is needed which will allow for a greater conceptualisation of the mechanisms underlying this process, further influencing the efficacy of current treatments for sleep disorders.

Similarity Maps for Ventricular Arrhythmia Classification.

Ventricular arrhythmias are the primary arrhythmias that cause sudden cardiac death. In current clinical and preclinical research, the discovery of new therapies and their translation is hampered by the lack of consistency in diagnostic criteria for distinguishing between ventricular tachycardia (VT) and ventricular fibrillation (VF). This study develops a new set of features, similarity maps, for discrimination between VT and VF using deep neural network architectures. The similarity maps are designed to capture the similarity and the regularity within an ECG trace. Our experiments show that the similarity maps lead to a substantial improvement in distinguishing VT and VF.

Multiscale Wavelet Transfer Entropy With Application to Corticomuscular Coupling Analysis.

OBJECTIVE: Functional coupling between the motor cortex and muscle activity is commonly detected and quantified by cortico-muscular coherence (CMC) or Granger causality (GC) analysis, which are applicable only to linear couplings and are not sufficiently sensitive: some healthy subjects show no significant CMC and GC, and yet have good motor skills. The objective of this work is to develop measures of functional cortico-muscular coupling that have improved sensitivity and are capable of detecting both linear and non-linear interactions. METHODS: A multiscale wavelet transfer entropy (TE) methodology is proposed. The methodology relies on a dyadic stationary wavelet transform to decompose electroencephalogram (EEG) and electromyogram (EMG) signals into functional bands of neural oscillations. Then, it applies TE analysis based on a range of embedding delay vectors to detect and quantify intra- and cross-frequency band cortico-muscular coupling at different time scales. RESULTS: Our experiments with neurophysiological signals substantiate the potential of the developed methodologies for detecting and quantifying information flow between EEG and EMG signals for subjects with and without significant CMC or GC, including non-linear cross-frequency interactions, and interactions across different temporal scales. The obtained results are in agreement with the underlying sensorimotor neurophysiology. CONCLUSION: These findings suggest that the concept of multiscale wavelet TE provides a comprehensive framework for analyzing cortex-muscle interactions. SIGNIFICANCE: The proposed methodologies will enable developing novel insights into movement control and neurophysiological processes more generally.

Unravelling Causal Relationships Between Cortex and Muscle with Errors-in-variables Models.

Corticomuscular communications are commonly estimated by Granger causality (GC) or directed coherence, with the aim of assessing the linear causal relationship between electroencephalogram (EEG) and electromyogram (EMG) signals. However, conventional GC based on standard linear regression (LR) models may be substantially underestimated in the presence of noise in both EEG and EMG signals: some healthy subjects with good motor skills show no significant GC. In this study, errors-in-variables (EIV) models are investigated for the purpose of estimating underlying linear time-invariant systems in the context of GC. The performance of the proposed method is evaluated using both simulated data and neurophysiological recordings, and compared with conventional GC. It is demonstrated that the inferred EIV-based causality offers an advantage over typical LR-based GC when detecting communication between the cortex and periphery using noisy EMG and EEG signals.

Dictionary Learning Strategies for Cortico-Muscular Coherence Detection and Estimation.

The spectral method of cortico-muscular coherence (CMC) can reveal the communication patterns between the cerebral cortex and muscle periphery, thus providing guidelines for the development of new therapies for movement disorders and insights into fundamental motor neuroscience. The method is applied to electroencephalogram (EEG) and surface electromyogram (sEMG) recorded synchronously during a motor task. However, synchronous EEG and sEMG components are typically too weak compared to additive noise and background activities making significant coherence very difficult to detect. Dictionary learning and sparse representation have been proved effective in enhancing CMC levels. In this paper, we explore the potential of a recently proposed dictionary learning algorithm in combination with an improved component selection algorithm for CMC enhancement. The effectiveness of the method was demonstrated using neurophysiological data where it achieved considerable improvements in CMC levels.

Abnormal patterns of corticomuscular and intermuscular coherence in childhood dystonia.

OBJECTIVE: Sensorimotor processing is abnormal in Idiopathic/Genetic dystonias, but poorly studied in Acquired dystonias. Beta-Corticomuscular coherence (CMC) quantifies coupling between oscillatory electroencephalogram (EEG) and electromyogram (EMG) activity and is modulated by sensory stimuli. We test the hypothesis that sensory modulation of CMC and intermuscular coherence (IMC) is abnormal in Idiopathic/Genetic and Acquired dystonias. METHODS: Participants: 11 children with Acquired dystonia, 5 with Idiopathic/Genetic dystonia, 13 controls (12-18 years). CMC and IMC were recorded during a grasp task, with mechanical perturbations provided by an electromechanical tapper. Coherence patterns pre- and post-stimulus were compared across groups. RESULTS: Beta-CMC increased post-stimulus in Controls and Acquired dystonia (p = 0.001 and p = 0.010, respectively), but not in Idiopathic/Genetic dystonia (p = 0.799). The modulation differed between groups, being larger in both Controls and Acquired dystonia compared with Idiopathic/Genetic dystonia (p = 0.003 and p = 0.022). Beta-IMC increased significantly post-stimulus in Controls (p = 0.004), but not in dystonia. Prominent 4-12 Hz IMC was seen in all dystonia patients and correlated with severity (rho = 0.618). CONCLUSION: Idiopathic/Genetic and Acquired dystonia share an abnormal low-frequency IMC. In contrast, sensory modulation of beta-CMC differed between the two groups. SIGNIFICANCE: The findings suggest that sensorimotor processing is abnormal in Acquired as well as Idiopathic/Genetic dystonia, but that the nature of the abnormality differs.

Methods for Improved Discrimination Between Ventricular Fibrillation and Tachycardia.

Differentiating between ventricular tachycardia and ventricular fibrillation in clinical and preclinical research is based on subjective definitions that have yet to be validated using objective criteria. This is partly due to shortcomings in the discrimination ability of current objective approaches, typified by the algorithms that perform cardiac rhythm classification using low-dimensional feature representations of electrocardiogram (ECG) signals. These identify ventricular tachyarrhythmias, but do not discriminate between ventricular tachycardia and ventricular fibrillation. In order to address this limitation, we have tested the utility of high-dimensional feature vectors, in particular, magnitude spectra and classifier ensembles that take into account local context information from ECG signals. Using these approaches, we categorized rhythms into three classes: ventricular tachycardia, ventricular fibrillation, and any other possible rhythm, defined here as "nonventricular rhythms." The high-dimensional spectral features achieved a substantial improvement in the discrimination between ventricular tachycardia and ventricular fibrillation, but exhibited a decreased sensitivity to nonventricular rhythms. In order to deal with the reduced sensitivity for the detection of nonventricular rhythms, methods were elaborated for combining the strengths of different feature spaces, and this substantially improved the identification sensitivities of all three classes.

Cortico-muscular coherence enhancement via coherent Wavelet enhanced Independent Component Analysis.

Functional coupling between the motor cortex and muscle activity is usually detected and characterized using the spectral method of cortico-muscular coherence (CMC) between surface electromyogram (sEMG) and electroencephalogram (EEG) recorded synchronously under motor control task. However, CMC is often weak and not easily detectable in all individuals. One of the reasons for the low levels of CMC is the presence of noise and components unrelated to the considered tasks in recorded sEMG and EEG signals. In this paper we propose a method for enhancing relative levels of sEMG components coherent with synchronous EEG signals via a variant of Wavelet Independent Component Analysis combined with a novel component selection algorithm. The effectiveness of the proposed algorithm is demonstrated using data collected in neurophysiologcal experiments.

Corticomuscular Coherence With Time Lag With Application to Delay Estimation.

Functional coupling between the motor cortex and muscle activity is usually detected and characterized using the spectral method of corticomuscular coherence (CMC). This functional coupling occurs with a time delay, which, if not properly accounted for, may decrease the coherence and make the synchrony difficult to detect. In this paper, we introduce the concept of CMC with time lag (CMCTL), that is the coherence between segments of motor cortex electroencephalogram (EEG) and electromyography (EMG) signals displaced from a central observation point. This concept is motivated by the need to compensate for the unknown delay between coupled cortex and muscle processes. We demonstrate using simulated data that under certain conditions the time lag between EEG and EMG segments at points of local maxima of CMCTL corresponds to the average delay along the involved corticomuscular conduction pathways. Using neurophysiological data, we then show that CMCTL with appropriate time lag enhances the coherence between cortical and muscle signals, and that time lags which correspond to local maxima of CMCTL provide estimates of delays involved in corticomuscular coupling that are consistent with the underlying physiology.

Structured prediction for differentiating between normal rhythms, ventricular tachycardia, and ventricular fibrillation in the ECG.

Recent studies have been performed on feature selection for diagnostics between non-ventricular rhythms and ventricular arrhythmias, or between non-ventricular fibrillation and ventricular fibrillation. However they did not assess classification directly between non-ventricular rhythms, ventricular tachycardia and ventricular fibrillation, which is important in both a clinical setting and preclinical drug discovery. In this study it is shown that in a direct multiclass setting, the selected features from these studies are not capable at differentiating between ventricular tachycardia and ventricular fibrillation. A high dimensional feature space, Fourier magnitude spectra, is proposed for classification, in combination with the structured prediction method conditional random fields. An improvement in overall accuracy, and sensitivity of every category under investigation is achieved.

Modulation of corticomuscular coherence by peripheral stimuli.

The purpose of this study was to investigate the effects of peripheral afferent stimuli on the synchrony between brain and muscle activity as estimated by corticomuscular coherence (CMC). Electroencephalogram (EEG) from sensorimotor cortex and electromyogram (EMG) from two intrinsic hand muscles were recorded during a key grip motor task, and the modulation of CMC caused by afferent electrical and mechanical stimulation was measured. The particular stimuli used were graded single-pulse electrical stimuli, above threshold for perception and activating cutaneous afferents, applied to the dominant or non-dominant index finger, and a pulsed mechanical displacement of the gripped object causing the subject to feel as if the object may be dropped. Following electrical stimulation of the dominant index finger, the level of ß-range (14-36 Hz) CMC was reduced in a stimulus intensity-dependent fashion for up to 400 ms post-stimulus, then returned with greater magnitude before falling to baseline levels over 2.5 s, outlasting the reflex and evoked changes in EMG and EEG. Subjects showing no baseline ß-range CMC nevertheless showed post-stimulus increases in ß-range CMC with the same time course as those with baseline ß-range CMC. The mechanical stimuli produced similar modulation of ß-range CMC. Electrical stimuli to the non-dominant index finger produced no significant increase in ß-range CMC. The results suggest that both cutaneous and proprioceptive afferents have access to circuits generating CMC, but that only a functionally relevant stimulus produces significant modulation of the background ß-range CMC, providing further evidence that ß-range CMC has an important role in sensorimotor integration.

Voxel-wise quantification of myocardial perfusion by cardiac magnetic resonance. Feasibility and methods comparison.

The purpose of this study is to enable high spatial resolution voxel-wise quantitative analysis of myocardial perfusion in dynamic contrast-enhanced cardiovascular MR, in particular by finding the most favorable quantification algorithm in this context. Four deconvolution algorithms--Fermi function modeling, deconvolution using B-spline basis, deconvolution using exponential basis, and autoregressive moving average modeling--were tested to calculate voxel-wise perfusion estimates. The algorithms were developed on synthetic data and validated against a true gold-standard using a hardware perfusion phantom. The accuracy of each method was assessed for different levels of spatial averaging and perfusion rate. Finally, voxel-wise analysis was used to generate high resolution perfusion maps on real data acquired from five patients with suspected coronary artery disease and two healthy volunteers. On both synthetic and perfusion phantom data, the B-spline method had the highest error in estimation of myocardial blood flow. The autoregressive moving average modeling and exponential methods gave accurate estimates of myocardial blood flow. The Fermi model was the most robust method to noise. Both simulations and maps in the patients and hardware phantom showed that voxel-wise quantification of myocardium perfusion is feasible and can be used to detect abnormal regions.

Rectification of the EMG is an unnecessary and inappropriate step in the calculation of Corticomuscular coherence.

Corticomuscular coherence (CMC) estimation is a frequency domain method used to detect a linear coupling between rhythmic activity recorded from sensorimotor cortex (EEG or MEG) and the electromyogram (EMG) of active muscles. In motor neuroscience, rectification of the surface EMG is a common pre-processing step prior to calculating CMC, intended to maximize information about action potential timing, whilst suppressing information relating to motor unit action potential (MUAP) shape. Rectification is believed to produce a general shift in the EMG spectrum towards lower frequencies, including those around the mean motor unit discharge rate. However, there are no published data to support the claim that EMG rectification enhances the detection of CMC. Furthermore, performing coherence analysis after the non-linear procedure of rectification, which results in a significant distortion of the EMG spectrum, is considered fundamentally flawed in engineering and digital signal processing. We calculated CMC between sensorimotor cortex EEG and EMG of two hand muscles during a key grip task in 14 healthy subjects. CMC calculated using unrectified and rectified EMG was compared. The use of rectified EMG did not enhance the detection of CMC, nor was there any evidence that MUAP shape information had an adverse effect on the CMC estimation. EMG rectification had inconsistent effects on the power and coherence spectra and obscured the detection of CMC in some cases. We also provide a comprehensive theoretical analysis, which, along with our empirical data, demonstrates that rectification is neither necessary nor appropriate in the calculation of CMC.

Locally adaptive wavelet-based image interpolation.

We describe a spatially adaptive algorithm for image interpolation. The algorithm uses a wavelet transform to extract information about sharp variations in the low-resolution image and then implicitly applies interpolation which adapts to the image local smoothness/singularity characteristics. The proposed algorithm yields images that are sharper compared to several other methods that we have considered in this paper. Better performance comes at the expense of higher complexity.

[The content of copper and zinc in human ulcerated carotid plaque]. / Sadrzaj bakra i cinka u humanom ulcerisanom karotidnom plaku.

INTRODUCTION: Copper and zinc have significant antiatherogenic effect influencing activity of antioxidant enzymes (glutathione-peroxidase and superoxide-dismutase), mechanism of apoptosis and other mechanisms. Few studies showed increased copper and zinc concentration in atherosclerotic plaque in comparison to normal vascular tissue. AIM: The aim of the study was to compare copper and zinc concentrations in carotid artery tissue without significant atherosclerotic changes and human ulcered atherosclerotic plaque. MATERIAL AND METHODS: Study was conducted on 66 patients. Carotid endarterectomy due to the significant carotid atherosclerotic changes with cerebrovascular disorders was performed in 54 patients (81.8%). Control group consisted of 12 patients (18.2%) without carotid atherosclerotic changes operated due to the symptomatic kinking and coiling of carotid artery. Operated group consisted of 38 men (62.96%) and 16 women (37.04%). Control group had the same number of patients: six men (50%) and six women (50%). Preoperatively, all patients were examined by vascular surgeon, neurologist and cardiologist. Duplex sonography of carotid and vertebral arteries was performed by Aloca DSD 630 ultrasound with mechanical and linear transducer 7.7 MHz. Indication for surgical treatment was obtained according to non-invasive diagnostic protocol and neurological symptoms. Copper and zinc concentration in human ulcered atherosclerotic plaque and carotid artery segment were estimated by spectrophotometry (Varian AA-5). RESULTS: Average age of our patients was 59.8+/-8.1 years. For males average age was 76.1+/-9.8 years. And for females 42.4+/-5.8 years. In group with carotid endarterectomy female patients were significantly younger than male patients (p<0.01). In group with carotid endarterectomy clinically determined neurological disorders were found in 47 patients (87.03%)--35 male (74.47%) and 12 female patients (25.53%). Regarding risk factors for cardiovascular diseases, no significant difference among groups was found for blood pressure and smoking. However, patients with carotid endarterectomy had significantly more diabetes mellitus (p<0.05), obesity (p<0.01) and hypercholesterolemia (p<0.01). DISCUSSION: Our study showed significantly lower total copper value in the group with human ulcered atherosclerotic plaque in comparison with the control group (p<0.05). We also found significantly lower total zinc value in the group with human ulcered atherosclerotic plaque in comparison with the control group (p<0.05). CONCLUSION: Our study revealed significant difference in copper and zinc content between human ulcered atherosclerotic plaque and normal carotid tissue. Closer correlation of these oligoelements and endothelial dysfunction will be established in future investigations.