The Cardiac Caval Index: Improving Noninvasive Assessment of Cardiac Preload.

OBJECTIVES: Inferior vena cava (IVC) pulsatility quantified by the Caval Index (CI) is characterized by poor reliability, also due to the irregular magnitude of spontaneous respiratory activity generating the major pulsatile component. The aim of this study was to test whether the IVC cardiac oscillatory component could provide a more stable index (Cardiac CI-CCI) compared to CI or respiratory CI (RCI). METHODS: Nine healthy volunteers underwent long-term monitoring in supine position of IVC, followed by 3 minutes passive leg raising (PLR). CI, RCI, and CCI were extracted from video recordings by automated edge-tracking and CCI was averaged over each respiratory cycle (aCCI). Cardiac output (CO), mean arterial pressure (MAP) and heart rate (HR) were also recorded during baseline (1 minutes prior to PLR) and PLR (first minute). RESULTS: In response to PLR, all IVC indices decreased (P < .01), CO increased by 4 ± 4% (P = .055) while HR and MAP did not vary. The Coefficient of Variation (CoV) of aCCI (13 ± 5%) was lower than that of CI (17 ± 5%, P < .01), RCI (26 ± 7%, P < .001) and CCI (25 ± 7%, P < .001). The mutual correlations in time of the indices were 0.81 (CI-RCI), 0.49 (CI-aCCI) and 0.2 (RCI-aCCI). CONCLUSIONS: Long-term IVC monitoring by automated edge-tracking allowed us to evidence that 1) respiratory and averaged cardiac pulsatility components are uncorrelated and thus carry different information and 2) the new index aCCI, exhibiting the lowest CoV while maintaining good sensitivity to blood volume changes, may overcome the poor reliability of CI and RCI.

Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors.

Surface electromyography (sEMG) has been the subject of thousands of scientific articles, but many barriers limit its clinical applications. Previous work has indicated that the lack of time, competence, training, and teaching is the main barrier to the clinical application of sEMG. This work follows up and presents a number of analogies, metaphors, and simulations using physical and mathematical models that provide tools for teaching sEMG detection by means of electrode pairs (1D signals) and electrode grids (2D and 3D signals). The basic mechanisms of sEMG generation are summarized and the features of the sensing system (electrode location, size, interelectrode distance, crosstalk, etc.) are illustrated (mostly by animations) with examples that teachers can use. The most common, as well as some potential, applications are illustrated in the areas of signal presentation, gait analysis, the optimal injection of botulinum toxin, neurorehabilitation, ergonomics, obstetrics, occupational medicine, and sport sciences. The work is primarily focused on correct sEMG detection and on crosstalk. Issues related to the clinical transfer of innovations are also discussed, as well as the need for training new clinical and/or technical operators in the field of sEMG.

A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson's Disease.

BACKGROUND: Freezing of Gait (FOG) is one of the most disabling motor complications of Parkinson's disease, and consists of an episodic inability to move forward, despite the intention to walk. FOG increases the risk of falls and reduces the quality of life of patients and their caregivers. The phenomenon is difficult to appreciate during outpatients visits; hence, its automatic recognition is of great clinical importance. Many types of sensors and different locations on the body have been proposed. However, the advantages of a multi-sensor configuration with respect to a single-sensor one are not clear, whereas this latter would be advisable for use in a non-supervised environment. METHODS: In this study, we used a multi-modal dataset and machine learning algorithms to perform different classifications between FOG and non-FOG periods. Moreover, we explored the relevance of features in the time and frequency domains extracted from inertial sensors, electroencephalogram and skin conductance. We developed both a subject-independent and a subject-dependent algorithm, considering different sensor subsets. RESULTS: The subject-independent and subject-dependent algorithms yielded accuracies of 85% and 88% in the leave-one-subject-out and leave-one-task-out test, respectively. Results suggest that the inertial sensors positioned on the lower limb are generally the most significant in recognizing FOG. Moreover, the performance impairment experienced when using a single tibial accelerometer instead of the optimal multi-modal configuration is limited to 2-3%. CONCLUSIONS: The achieved results disclose the possibility of getting a good FOG recognition using a minimally invasive set-up made of a single inertial sensor. This is very significant in the perspective of implementing a long-term monitoring of patients in their homes, during activities of daily living.

Vena Cava Responsiveness to Controlled Isovolumetric Respiratory Efforts.

OBJECTIVES: Respirophasic variation of inferior vena cava (IVC) size is affected by large variability with spontaneous breathing. This study aims at characterizing the dependence of IVC size on controlled changes in intrathoracic pressure. METHODS: Ten healthy subjects, in supine position, performed controlled isovolumetric respiratory efforts at functional residual capacity, attaining positive (5, 10, and 15 mmHg) and negative (-5, -10, and -15 mmHg) alveolar pressure levels. The isovolumetric constraint implies that equivalent changes are exhibited by alveolar and intrathoracic pressures during respiratory tasks. RESULTS: The IVC cross-sectional area equal to 2.88 ± 0.43 cm2 at baseline (alveolar pressure = 0 mmHg) was progressively decreased by both expiratory and inspiratory efforts of increasing strength, with diaphragmatic efforts producing larger effects than thoracic ones: -55 ± 15% decrease, at +15 mmHg of alveolar pressure (P < .01), -80 ± 33 ± 12% at -15 mmHg diaphragmatic (P < .01), -33 ± 12% at -15 mmHg thoracic. Significant IVC changes in size (P < .01) and pulsatility (P < .05), along with non significant reduction in the response to respiratory efforts, were also observed during the first 30 minutes of supine rest, detecting an increase in vascular filling, and taking place after switching from the standing to the supine position. CONCLUSIONS: This study quantified the dependence of the IVC cross-sectional area on controlled intrathoracic pressure changes and evidenced the stronger influence of diaphragmatic over thoracic activity. Individual variability in thoracic/diaphragmatic respiratory pattern should be considered in the interpretation of the respirophasic modulations of IVC size.

Prognostic value of EEG indexes for the Glasgow outcome scale of comatose patients in the acute phase.

The purpose of this work is the estimation of the Glasgow outcome scale (GOS) from a single continuous electroencephalogram (c-EEG) routinely recorded to monitor comatose patients in the neurosurgical intensive care unit. c-EEG was recorded from 13 patients in the acute phase: five with GOS = 5, four with GOS = 3 and four with GOS = 1. Different indexes were extracted from epochs of c-EEG (classical: amplitude and spectral estimators; non classical: from recurrence quantification analysis-RQA-and approximate entropy). Descriptors of different indexes (temporal variation and mean, standard deviation, skewness of the distribution across epochs) were used to train support vector machines to identify the correct GOS. We found classifiers allowing correct classification of the patients. Spectral indexes allowed to get optimal performances in classifying GOS 1 and 3. Nonlinear indexes (especially determinism from RQA) were optimal for identifying GOS = 5. Thus, the integration of information from classical/linear and nonlinear c-EEG descriptors in a multi-index classifier is important for GOS estimation.

Evaluation of autonomic nervous system in sleep apnea patients using pupillometry under occlusal stress: a pilot study.

AIMS: Recently, it has been proposed that obstructive sleep apnea syndrome (OSAS) is characterized by an imbalance in autonomic nervous tone. Pupil size has been considered a valid test for studying the autonomic nervous system (ANS). Pupillometry is a simple and non-invasive tool to assess the size and dynamics of the pupil. The purpose of this study was to evaluate, by pupillometry, the hypothesis that subjects with OSAS present ANS dysregulation. METHODS: The study group included 10 males aged between 40 and 50 years with polysomnographic diagnoses of mild OSAS. The control group included 10 males with similar ages with an apnea-hypopnea index (AHI) of less than 5, after polysomnography. Pupillometry was performed by digital infrared pupillometer (25 frame/s). Recordings were processed to measure the area of the pupil frame by frame. The subjects underwent four subsequent recordings: infrared light at rest mandible position (RP); infrared light at forced habitual occlusion (FHO); yellow-green light at RP; and yellow-green light at FHO. According to literature, linear and non-linear information was extracted from the recordings. RESULTS: As expected, the two groups did not differ statistically in age and body mass index (BMI), while there was a significant difference in the AHI. In the within-group comparison of pupil size, there were significant differences between RP and FHO under infrared conditions in the control group. There was a significant difference in the determinism percentage (Det%) in the RP infrared condition between the control and OSAS groups. CONCLUSIONS: The results of the current study confirm ANS dysregulation in OSAS patients and provide a new possible strategy for studying this pathology by using pupillometry through linear and non-linear mathematical models.

Nonlinear spatio-temporal filter to reduce crosstalk in bipolar electromyogram.

Objective.The wide detection volume of surface electromyogram (EMG) makes it prone to crosstalk, i.e. the signal from other muscles than the target one. Removing this perturbation from bipolar recordings is an important open problem for many applications.Approach.An innovative nonlinear spatio-temporal filter is developed to estimate the EMG generated by the target muscle by processing noisy signals from two bipolar channels, placed over the target and the crosstalk muscle, respectively. The filter is trained on some calibration data and then can be applied on new signals. Tests are provided in simulations (considering different thicknesses of the subcutaneous tissue, inter-electrode distances, locations of the EMG channels, force levels) and experiments (from pronator teres and flexor carpi radialis of 8 healthy subjects).Main results.The proposed filter allows to reduce the effect of crosstalk in all investigated conditions, with a statistically significant reduction of its root mean squared of about 20%, both in simulated and experimental data. Its performances are also superior to those of a blind source separation method applied to the same data.Significance.The proposed filter is simple to be applied and feasible in applications in which single bipolar channels are placed over the muscles of interest. It can be useful in many fields, such as in gait analysis, tests of myoelectric fatigue, rehabilitation with EMG biofeedback, clinical studies, prosthesis control.

Closed-Loop Transcranial Electrical Neurostimulation for Sustained Attention Enhancement: A Pilot Study towards Personalized Intervention Strategies.

Sustained attention is pivotal for tasks like studying and working for which focus and low distractions are necessary for peak productivity. This study explores the effectiveness of adaptive transcranial direct current stimulation (tDCS) in either the frontal or parietal region to enhance sustained attention. The research involved ten healthy university students performing the Continuous Performance Task-AX (AX-CPT) while receiving either frontal or parietal tDCS. The study comprised three phases. First, we acquired the electroencephalography (EEG) signal to identify the most suitable metrics related to attention states. Among different spectral and complexity metrics computed on 3 s epochs of EEG, the Fuzzy Entropy and Multiscale Sample Entropy Index of frontal channels were selected. Secondly, we assessed how tDCS at a fixed 1.0 mA current affects attentional performance. Finally, a real-time experiment involving continuous metric monitoring allowed personalized dynamic optimization of the current amplitude and stimulation site (frontal or parietal). The findings reveal statistically significant improvements in mean accuracy (94.04 vs. 90.82%) and reaction times (262.93 vs. 302.03 ms) with the adaptive tDCS compared to a non-stimulation condition. Average reaction times were statistically shorter during adaptive stimulation compared to a fixed current amplitude condition (262.93 vs. 283.56 ms), while mean accuracy stayed similar (94.04 vs. 93.36%, improvement not statistically significant). Despite the limited number of subjects, this work points out the promising potential of adaptive tDCS as a tailored treatment for enhancing sustained attention.

Processing Ultrasound Scans of the Inferior Vena Cava: Techniques and Applications.

The inferior vena cava (IVC) is the largest vein in the body. It returns deoxygenated blood to the heart from the tissues placed under the diaphragm. The size and dynamics of the IVC depend on the blood volume and right atrial pressure, which are important indicators of a patient's hydration and reflect possible pathological conditions. Ultrasound (US) assessment of the IVC is a promising technique for evaluating these conditions, because it is fast, non-invasive, inexpensive, and without side effects. However, the standard M-mode approach for measuring IVC diameter is prone to errors due to the vein movements during respiration. B-mode US produces two-dimensional images that better capture the IVC shape and size. In this review, we discuss the pros and cons of current IVC segmentation techniques for B-mode longitudinal and transverse views. We also explored several scenarios where automated IVC segmentation could improve medical diagnosis and prognosis.

Electroencephalography-Based Brain-Machine Interfaces in Older Adults: A Literature Review.

The aging process is a multifaceted phenomenon that affects cognitive-affective and physical functioning as well as interactions with the environment. Although subjective cognitive decline may be part of normal aging, negative changes objectified as cognitive impairment are present in neurocognitive disorders and functional abilities are most impaired in patients with dementia. Electroencephalography-based brain-machine interfaces (BMI) are being used to assist older people in their daily activities and to improve their quality of life with neuro-rehabilitative applications. This paper provides an overview of BMI used to assist older adults. Both technical issues (detection of signals, extraction of features, classification) and application-related aspects with respect to the users' needs are considered.

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends.

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.

Alpha-synuclein oligomers alter the spontaneous firing discharge of cultured midbrain neurons.

The aim of this work was to monitor the effects of extracellular α-synuclein on the firing activity of midbrain neurons dissociated from substantia nigra TH-GFP mice embryos and cultured on microelectrode arrays (MEA). We monitored the spontaneous firing discharge of the network for 21 days after plating and the role of glutamatergic and GABAergic inputs in regulating burst generation and network synchronism. Addition of GABA A , AMPA and NMDA antagonists did not suppress the spontaneous activity but allowed to identify three types of neurons that exhibited different modalities of firing and response to applied L-DOPA: high-rate (HR) neurons, low-rate pacemaking (LR-p), and low-rate non-pacemaking (LR-np) neurons. Most HR neurons were insensitive to L-DOPA, while the majority of LR-p neurons responded with a decrease of the firing discharge; less defined was the response of LR-np neurons. The effect of exogenous α-synuclein (α-syn) on the firing discharge of midbrain neurons was then studied by varying the exposure time (0-48 h) and the α-syn concentration (0.3-70 µM), while the formation of α-syn oligomers was monitored by means of AFM. Independently of the applied concentration, acute exposure to α-syn monomers did not exert any effect on the spontaneous firing rate of HR, LR-p, and LR-np neurons. On the contrary, after 48 h exposure, the firing activity was drastically altered at late developmental stages (14 days in vitro, DIV, neurons): α-syn oligomers progressively reduced the spontaneous firing discharge (IC50 = 1.03 µM), impaired burst generation and network synchronism, proportionally to the increased oligomer/monomer ratio. Different effects were found on early-stage developed neurons (9 DIV), whose firing discharge remained unaltered, regardless of the applied α-syn concentration and the exposure time. Our findings unravel, for the first time, the variable effects of exogenous α-syn at different stages of midbrain network development and provide new evidence for the early detection of neuronal function impairment associated to aggregated forms of α-syn.

Corrigendum: Alpha-synuclein oligomers alter the spontaneous firing discharge of cultured midbrain neurons.

[This corrects the article DOI: 10.3389/fncel.2023.1078550.].

Motor Nerve Conduction Block Estimation in Demyelinating Neuropathies by Deconvolution.

A deconvolution method is proposed for conduction block (CB) estimation based on two compound muscle action potentials (CMAPs) elicited by stimulating a nerve proximal and distal to the region in which the block is suspected. It estimates the time delay distributions by CMAPs deconvolution, from which CB is computed. The slow afterwave (SAW) is included to describe the motor unit potential, as it gives an important contribution in case of the large temporal dispersion (TD) often found in patients. The method is tested on experimental signals obtained from both healthy subjects and pathological patients, with either Chronic Inflammatory Demyelinating Polyneuropathy (CIDP) or Multifocal Motor Neuropathy (MMN). The new technique outperforms the clinical methods (based on amplitude and area of CMAPs) and a previous state-of-the-art deconvolution approach. It compensates phase cancellations, allowing to discriminate among CB and TD: estimated by the methods of amplitude, area and deconvolution, CB showed a correlation with TD equal to 39.3%, 29.5% and 8.2%, respectively. Moreover, a significant decrease of percentage reconstruction errors of the CMAPs with respect to the previous deconvolution approach is obtained (from a mean/median of 19.1%/16.7% to 11.7%/11.2%). Therefore, the new method is able to discriminate between CB and TD (overcoming the important limitation of clinical approaches) and can approximate patients' CMAPs better than the previous deconvolution algorithm. Then, it appears to be promising for the diagnosis of demyelinating polyneuropathies, to be further tested in the future in a prospective clinical trial.

Single Channel Surface Electromyogram Deconvolution is a Useful Pre-Processing for Myoelectric Control.

OBJECTIVE: Myoelectric control requires fast and stable identification of a movement from data recorded from a comfortable and straightforward system. METHODS: We consider a new real-time pre-processing method applied to a single differential surface electromyogram (EMG): deconvolution, providing an estimation of the cumulative firings of motor units. A 2 channel-10 class finger movement problem has been investigated on 10 healthy subjects. We have compared raw EMG and deconvolution signals, as sources of information for two specific classifiers (based on either Support Vector Machines or k-Nearest Neighbours), with classical time-domain input features selected using Mutual Component Analysis. RESULTS: Using the proposed pre-processing technique, classification performances statistically improve. For example, the true positive rates of the best-tested configurations were 80.9% and 86.3% when using the EMG and its deconvoluted signal, respectively. CONCLUSION: Even considering the limited dataset and range of classification approaches investigated, our preliminary results indicate the potential usefulness of the deconvolution pre-processing. SIGNIFICANCE: Deconvolution of EMG is a fast pre-processing that could be easily embedded in different myoelectric control applications.

Evidence that large vessels do affect near infrared spectroscopy.

The influence of large vessels on near infrared spectroscopy (NIRS) measurement is generally considered negligible. Aim of this study is to test the hypothesis that changes in the vessel size, by varying the amount of absorbed NIR light, could profoundly affect NIRS blood volume indexes. Changes in haemoglobin concentration (tHb) and in tissue haemoglobin index (THI) were monitored over the basilic vein (BV) and over the biceps muscle belly, in 11 subjects (7 M - 4 F; age 31 ± 8 year) with simultaneous ultrasound monitoring of BV size. The arm was subjected to venous occlusion, according to two pressure profiles: slow (from 0 to 60 mmHg in 135 s) and rapid (0 to 40 mmHg maintained for 30 s). Both tHb and THI detected a larger blood volume increase (1.7 to 4 fold; p < 0.01) and exhibited a faster increase and a greater convexity on the BV than on the muscle. In addition, NIRS signals from BV exhibited higher correlation with changes in BV size than from muscle (r = 0.91 vs 0.55, p < 0.001 for THI). A collection of individual relevant recordings is also included. These results challenge the long-standing belief that the NIRS measurement is unaffected by large vessels and support the concept that large veins may be a major determinant of blood volume changes in multiple experimental conditions.

Non-Invasive Estimation of Right Atrial Pressure Using a Semi-Automated Echocardiographic Tool for Inferior Vena Cava Edge-Tracking.

The non-invasive estimation of right atrial pressure (RAP) would be a key advancement in several clinical scenarios, in which the knowledge of central venous filling pressure is vital for patients' management. The echocardiographic estimation of RAP proposed by Guidelines, based on inferior vena cava (IVC) size and respirophasic collapsibility, is exposed to operator and patient dependent variability. We propose novel methods, based on semi-automated edge-tracking of IVC size and cardiac collapsibility (cardiac caval index­CCI), tested in a monocentric retrospective cohort of patients undergoing echocardiography and right heart catheterization (RHC) within 24 h in condition of clinical and therapeutic stability (170 patients, age 64 ± 14, male 45%, with pulmonary arterial hypertension, heart failure, valvular heart disease, dyspnea, or other pathologies). IVC size and CCI were integrated with other standard echocardiographic features, selected by backward feature selection and included in a linear model (LM) and a support vector machine (SVM), which were cross-validated. Three RAP classes (low < 5 mmHg, intermediate 5−10 mmHg and high > 10 mmHg) were generated and RHC values used as comparator. LM and SVM showed a higher accuracy than Guidelines (63%, 71%, and 61% for LM, SVM, and Guidelines, respectively), promoting the integration of IVC and echocardiographic features for an improved non-invasive estimation of RAP.

Estimation of Aortic Stiffness with Bramwell-Hill Equation: A Comparative Analysis with Carotid-Femoral Pulse Wave Velocity.

Aortic stiffness is an important clinical parameter for predicting cardiovascular events. Carotid-femoral pulse wave velocity (cf-PWV) has been proposed for performing this evaluation non-invasively; however, it requires dedicated equipment and experienced operators. We explored the possibility of measuring aortic stiffness using ultrasound scans of the abdominal aorta coupled with the Bramwell-Hill equation. Healthy subjects were investigated; measurements of cf-PWV were taken by arterial tonometry and aortic systo-diastolic pressure difference was estimated using a validated model. Pulsatility of an abdominal tract of aorta was assessed by automated processing of ultrasound scans. Through a Bland-Altmann analysis, we found large biases when estimating each parameter by applying the Bramwell-Hill equation to the measured values of the other two paramters (bias, ± 1.96 SD; PWV, about 2.1 ± 2.5 m/s; pulsatility, 12 ± 14%; pressure jump, 47 ± 55 mmHg). These results indicate that the two measures are not interchangeable, and that a large part of the bias is attributable to blood pressure estimation. Further studies are needed to identify the possible sources of bias between cf-PWV and aortic pulsatility.

Inferior Vena Cava Edge Tracking Echocardiography: A Promising Tool with Applications in Multiple Clinical Settings.

Ultrasound (US)-based measurements of the inferior vena cava (IVC) diameter are widely used to estimate right atrial pressure (RAP) in a variety of clinical settings. However, the correlation with invasively measured RAP along with the reproducibility of US-based IVC measurements is modest at best. In the present manuscript, we discuss the limitations of the current technique to estimate RAP through IVC US assessment and present a new promising tool developed by our research group, the automated IVC edge-to-edge tracking system, which has the potential to improve RAP assessment by transforming the current categorical classification (low, normal, high RAP) in a continuous and precise RAP estimation technique. Finally, we critically evaluate all the clinical settings in which this new tool could improve current practice.

Assessment of Phasic Changes of Vascular Size by Automated Edge Tracking-State of the Art and Clinical Perspectives.

Assessment of vascular size and of its phasic changes by ultrasound is important for the management of many clinical conditions. For example, a dilated and stiff inferior vena cava reflects increased intravascular volume and identifies patients with heart failure at greater risk of an early death. However, lack of standardization and sub-optimal intra- and inter- operator reproducibility limit the use of these techniques. To overcome these limitations, we developed two image-processing algorithms that quantify phasic vascular deformation by tracking wall movements, either in long or in short axis. Prospective studies will verify the clinical applicability and utility of these methods in different settings, vessels and medical conditions.