Most cited article - PubMed ID 27243208
SignalPlant: an open signal processing software platform
BACKGROUND: Temporal interference stimulation (TIS) is a novel noninvasive electrical stimulation technique to focally modulate deep brain regions; a minimum of two high-frequency signals (f1 and f2 > 1 kHz) interfere to create an envelope-modulated signal at a deep brain target with the frequency of modulation equal to the difference frequency: Δf = |f2 - f1|. OBJECTIVE: The goals of this study were to verify the capability of TIS to modulate the subthalamic nucleus (STN) with Δf and to compare the effect of TIS and conventional deep brain stimulation (DBS) on the STN beta oscillations in patients with Parkinson's disease (PD). METHODS: DBS leads remained externalized after implantation, allowing local field potentials (LFPs) recordings in eight patients with PD. TIS was performed initially by two pairs (f1 = 9.00 kHz; f2 = 9.13 kHz, 4 mA peak-peak per pair maximum) of scalp electrodes placed in temporoparietal regions to focus the envelope signal maximum (Δf = 130 Hz) at the motor part of the STN target. RESULTS: The comparison between the baseline LFPs and recordings after TIS and conventional DBS sessions showed substantial suppression of high beta power peak after both types of stimulation in all patients. CONCLUSIONS: TIS has the potential to effectively modulate the STN and reduce the beta oscillatory activity in a completely noninvasive manner, as is traditionally possible only with intracranial DBS. Future studies should confirm the clinical effectiveness of TIS and determine whether TIS could be used to identify optimal DBS candidates and individualize DBS targets. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
- Keywords
- Parkinson's disease, beta power, deep brain stimulation, local field potentials, subthalamic nucleus, temporal interference stimulation,
- MeSH
- Beta Rhythm * physiology MeSH
- Deep Brain Stimulation * methods MeSH
- Middle Aged MeSH
- Humans MeSH
- Subthalamic Nucleus * physiopathology MeSH
- Parkinson Disease * therapy physiopathology MeSH
- Aged MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
Intracranial human brain recordings from multiple implanted depth electrodes using stereo-EEG (sEEG) technology for seizure localization provide unique local field potential signals (LFP) sampled with standard macro- and special micro-electrode contacts. Over one hundred macro- and micro-contact LFP signals localized in particular brain regions were recorded from each sEEG monitoring case as patients engaged in an automated battery of verbal memory and non-verbal gaze movement tasks. Subject eye and vocal responses in both visual and auditory task versions were automatically detected in Polish, Czech, and Slovak languages with accurate timing of the correct and incorrect verbal responses using our web-based transcription tool. The behavioral events, LFP and pupillometric signals were synchronized and stored in a standard BIDS data structure with corresponding metadata. Each dataset contains recordings from at least one battery task performed over at least one day. The same set of 180 common nouns in the three languages was used across different battery tasks and recording days to enable the analysis of selective responses to specific word stimuli.
- MeSH
- Electroencephalography MeSH
- Language MeSH
- Cognition * MeSH
- Humans MeSH
- Brain * physiology MeSH
- Eye Movements MeSH
- Eye-Tracking Technology MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Dataset MeSH
The aim of this work was to study the effect of deep brain stimulation of the subthalamic nucleus (STN-DBS) on the subnetwork of subcortical and cortical motor regions and on the whole brain connectivity using the functional connectivity analysis in Parkinson's disease (PD). The high-density source space EEG was acquired and analyzed in 43 PD subjects in DBS on and DBS off stimulation states (off medication) during a cognitive-motor task. Increased high gamma band (50-100 Hz) connectivity within subcortical regions and between subcortical and cortical motor regions was significantly associated with the Movement Disorders Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III improvement after DBS. Whole brain neural correlates of cognitive performance were also detected in the high gamma (50-100 Hz) band. A whole brain multifrequency connectivity profile was found to classify optimal and suboptimal responders to DBS with a positive predictive value of 0.77, negative predictive value of 0.55, specificity of 0.73, and sensitivity of 0.60. Specific connectivity patterns related to PD, motor symptoms improvement after DBS, and therapy responsiveness predictive connectivity profiles were uncovered.
- Keywords
- Connectivity patterns, Deep brain stimulation, EEG, Functional connectivity, Parkinson’s disease, Subthalamic nucleus,
- MeSH
- Electroencephalography methods MeSH
- Deep Brain Stimulation * methods MeSH
- Middle Aged MeSH
- Humans MeSH
- Brain physiopathology diagnostic imaging MeSH
- Subthalamic Nucleus * physiopathology MeSH
- Parkinson Disease * therapy physiopathology MeSH
- Aged MeSH
- Treatment Outcome MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Early identification of cognitive or physical overload is critical in fields where human decision making matters when preventing threats to safety and property. Pilots, drivers, surgeons, and operators of nuclear plants are among those affected by this challenge, as acute stress can impair their cognition. In this context, the significance of paralinguistic automatic speech processing increases for early stress detection. The intensity, intonation, and cadence of an utterance are examples of paralinguistic traits that determine the meaning of a sentence and are often lost in the verbatim transcript. To address this issue, tools are being developed to recognize paralinguistic traits effectively. However, a data bottleneck still exists in the training of paralinguistic speech traits, and the lack of high-quality reference data for the training of artificial systems persists. Regarding this, we present an original empirical dataset collected using the BESST experimental protocol for capturing speech signals under induced stress. With this data, our aim is to promote the development of pre-emptive intervention systems based on stress estimation from speech.
- MeSH
- Humans MeSH
- Stress, Psychological MeSH
- Speech * MeSH
- Machine Learning * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Dataset MeSH
The combination of aminophylline and salbutamol is frequently used in clinical practice in the treatment of obstructive lung diseases. While the side effects (including arrhythmias) of the individual bronchodilator drugs were well described previously, the side effects of combined treatment are almost unknown. We aimed to study the arrhythmogenic potential of combined aminophylline and salbutamol treatment in vitro. For this purpose, we used the established atomic force microscopy (AFM) model coupled with cardiac organoids derived from human pluripotent stem cells (hPSC-CMs). We focused on the chronotropic, inotropic, and arrhythmogenic effects of salbutamol alone and aminophylline and salbutamol combined treatment. We used a method based on heart rate/beat rate variability (HRV/BRV) analysis to detect arrhythmic events in the hPSC-CM based AFM recordings. Salbutamol and aminophylline had a synergistic chronotropic and inotropic effect compared to the effects of monotherapy. Our main finding was that salbutamol reduced the arrhythmogenic effect of aminophylline, most likely mediated by endothelial nitric oxide synthase activated by beta-2 adrenergic receptors. These findings were replicated and confirmed using hPSC-CM derived from two cell lines (CCTL4 and CCTL12). Data suggest that salbutamol as an add-on therapy may not only deliver a bronchodilator effect but also increase the cardiovascular safety of aminophylline, as salbutamol reduces its arrhythmogenic potential.
- Keywords
- Aminophylline, Arrhythmogenic effects, Atomic force microscopy, iPSC, Biomechanical properties, Cardiomyocytes, Drug cardiotoxicity, HESC, Pulmonary drug screening, Salbutamol,
- MeSH
- Albuterol * pharmacology MeSH
- Aminophylline * pharmacology MeSH
- Bronchodilator Agents pharmacology MeSH
- Cell Line MeSH
- Myocytes, Cardiac drug effects metabolism MeSH
- Humans MeSH
- Microscopy, Atomic Force MeSH
- Pluripotent Stem Cells drug effects cytology MeSH
- Arrhythmias, Cardiac * drug therapy MeSH
- Heart Rate drug effects MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Albuterol * MeSH
- Aminophylline * MeSH
- Bronchodilator Agents MeSH
Identifying electrical dyssynchrony is crucial for cardiac pacing and cardiac resynchronization therapy (CRT). The ultra-high-frequency electrocardiography (UHF-ECG) technique allows instantaneous dyssynchrony analyses with real-time visualization. This review explores the physiological background of higher frequencies in ventricular conduction and the translational evolution of UHF-ECG in cardiac pacing and CRT. Although high-frequency components were studied half a century ago, their exploration in the dyssynchrony context is rare. UHF-ECG records ECG signals from eight precordial leads over multiple beats in time. After initial conceptual studies, the implementation of an instant visualization of ventricular activation led to clinical implementation with minimal patient burden. UHF-ECG aids patient selection in biventricular CRT and evaluates ventricular activation during various forms of conduction system pacing (CSP). UHF-ECG ventricular electrical dyssynchrony has been associated with clinical outcomes in a large retrospective CRT cohort and has been used to study the electrophysiological differences between CSP methods, including His bundle pacing, left bundle branch (area) pacing, left ventricular septal pacing and conventional biventricular pacing. UHF-ECG can potentially be used to determine a tailored resynchronization approach (CRT through biventricular pacing or CSP) based on the electrical substrate (true LBBB vs. non-specified intraventricular conduction delay with more distal left ventricular conduction disease), for the optimization of CRT and holds promise beyond CRT for the risk stratification of ventricular arrhythmias.
- Keywords
- cardiac resynchronization therapy, conduction system pacing, electrical dyssynchrony, electrocardiography, ultra-high frequency,
- Publication type
- Journal Article MeSH
- Review MeSH
Beta hypersynchrony was recently introduced into clinical practice in Parkinson's disease (PD) to identify the best stimulation contacts and for adaptive deep brain stimulation (aDBS) sensing. However, many other oscillopathies accompany the disease, and beta power sensing may not be optimal for all patients. The aim of this work was to study the potential clinical usefulness of beta power phase-amplitude coupling (PAC) with high frequency oscillations (HFOs). Subthalamic nucleus (STN) local field potentials (LFPs) from externalized DBS electrodes were recorded and analyzed in PD patients (n = 19). Beta power and HFOs were evaluated in a resting-state condition; PAC was then studied and compared with the electrode contact positions, structural connectivity, and medication state. Beta-HFO PAC (mainly in the 200-500 Hz range) was observed in all subjects. PAC was detectable more specifically in the motor part of the STN compared to beta power and HFOs. Moreover, the presence of PAC better corresponds to the stimulation setup based on the clinical effect. PAC is also sensitive to the laterality of symptoms and dopaminergic therapy, where the greater PAC cluster reflects the more affected side and medication "off" state. Coupling between beta power and HFOs is known to be a correlate of the PD "off" state. Beta-HFO PAC seems to be more sensitive than beta power itself and could be more helpful in the selection of the best clinical stimulation contact and probably also as a potential future input signal for aDBS.
- Publication type
- Journal Article MeSH
Interictal very high-frequency oscillations (VHFOs, 500-2000 Hz) in a resting awake state seem to be, according to a precedent study of our team, a more specific predictor of a good outcome of the epilepsy surgery compared to traditional interictal high-frequency oscillations (HFOs, 80-500 Hz). In this study, we retested this hypothesis on a larger cohort of patients. In addition, we also collected patients' sleep data and hypothesized that the occurrence of VHFOs in sleep will be greater than in resting state. We recorded interictal invasive electroencephalographic (iEEG) oscillations in 104 patients with drug-resistant epilepsy in a resting state and in 35 patients during sleep. 21 patients in the rest study and 11 patients in the sleep study met the inclusion criteria (interictal HFOs and VHFOs present in iEEG recordings, a surgical intervention and a postoperative follow-up of at least 1 year) for further evaluation of iEEG data. In the rest study, patients with good postoperative outcomes had significantly higher ratio of resected contacts with VHFOs compared to HFOs. In sleep, VHFOs were more abundant than in rest and the percentage of resected contacts in patients with good and poor outcomes did not considerably differ in any type of oscillations. In conclusion, (1) our results confirm, in a larger patient cohort, our previous work about VHFOs being a specific predictor of the area which needs to be resected; and (2) that more frequent sleep VHFOs do not further improve the results.
- MeSH
- Wakefulness MeSH
- Electroencephalography methods MeSH
- Epilepsy * MeSH
- Humans MeSH
- Drug Resistant Epilepsy * surgery MeSH
- Sleep MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Very high-frequency oscillations (VHFOs, > 500 Hz) are more specific in localizing the epileptogenic zone (EZ) than high-frequency oscillations (HFOs, < 500 Hz). Unfortunately, VHFOs are not visible in standard clinical stereo-EEG (SEEG) recordings with sampling rates of 1 kHz or lower. Here we show that "shadows" of VHFOs can be found in frequencies below 500 Hz and can help us to identify SEEG channels with a higher probability of increased VHFO rates. Subsequent analysis of Logistic regression models on 141 SEEG channels from thirteen patients shows that VHFO "shadows" provide additional information to gold standard HFO analysis and can potentially help in precise EZ delineation in standard clinical recordings.
- MeSH
- Electroencephalography * MeSH
- Humans MeSH
- Stereotaxic Techniques MeSH
- Blood Coagulation Tests MeSH
- High-Frequency Ventilation * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Manual visual review, annotation and categorization of electroencephalography (EEG) is a time-consuming task that is often associated with human bias and requires trained electrophysiology experts with specific domain knowledge. This challenge is now compounded by development of measurement technologies and devices allowing large-scale heterogeneous, multi-channel recordings spanning multiple brain regions over days, weeks. Currently, supervised deep-learning techniques were shown to be an effective tool for analyzing big data sets, including EEG. However, the most significant caveat in training the supervised deep-learning models in a clinical research setting is the lack of adequate gold-standard annotations created by electrophysiology experts. Here, we propose a semi-supervised machine learning technique that utilizes deep-learning methods with a minimal amount of gold-standard labels. The method utilizes a temporal autoencoder for dimensionality reduction and a small number of the expert-provided gold-standard labels used for kernel density estimating (KDE) maps. We used data from electrophysiological intracranial EEG (iEEG) recordings acquired in two hospitals with different recording systems across 39 patients to validate the method. The method achieved iEEG classification (Pathologic vs. Normal vs. Artifacts) results with an area under the receiver operating characteristic (AUROC) scores of 0.862 ± 0.037, 0.879 ± 0.042, and area under the precision-recall curve (AUPRC) scores of 0.740 ± 0.740, 0.714 ± 0.042. This demonstrates that semi-supervised methods can provide acceptable results while requiring only 100 gold-standard data samples in each classification category. Subsequently, we deployed the technique to 12 novel patients in a pseudo-prospective framework for detecting Interictal epileptiform discharges (IEDs). We show that the proposed temporal autoencoder was able to generalize to novel patients while achieving AUROC of 0.877 ± 0.067 and AUPRC of 0.705 ± 0.154.
