Assessing Changes in Motor Function and Mobility in Individuals with Parkinson's Disease After 12 Sessions of Patient-Specific Adaptive Dynamic Cycling.

Background And Purpose: This pilot randomized controlled trial evaluated the effects of 12 sessions of patient-specific adaptive dynamic cycling (PSADC) versus non-adaptive cycling (NA) on motor function and mobility in individuals with Parkinson's disease (PD), using inertial measurement unit (IMU) sensors for objective assessment.

Methods: Twenty-three participants with PD (13 in the PSADC group and 10 in the NA group) completed the study over a 4-week period. Motor function was measured using the Kinesia™ sensors and the MDS-UPDRS Motor III, while mobility was assessed with the TUG test using OPAL IMU sensors.

Bedside monitoring tools and advanced signal processing approaches to monitor critically-ill infants.

There is a substantial body of literature that supports neonatal monitoring and signal analysis of the collected data to provide valuable insights for improving patient clinical care and to inform new research studies. This comprehensive monitoring approach extends beyond the collection of conventional vital signs to include the acquisition of continuous waveform data from patient monitors and other bedside medical devices. This paper discusses the necessary infrastructure for waveform retrieval from bedside monitors, and explores options provided by leading healthcare companies, third-party vendors or academic research teams to implement scalable monitoring systems across entire critical care units.

Utilizing Entropy of Cadence to Optimize Cycling Rehabilitation in Individuals With Parkinson's Disease.

Background: Previous studies have established that increased Sample Entropy (SampEn) of cadence, a measure of non-linear variability, during dynamic cycling leads to greater improvements in motor function for individuals with Parkinson's disease (PD). However, there is significant variability in responses among individuals with PD due to symptoms and disease progression.

Objectives: The aim of this study was to develop and test a paradigm for adapting a cycling exercise intervention using SampEn of cadence and rider effort to improve motor function.

The effect of early postnatal auditory stimulation on outcomes in preterm infants.

Preterm infants are deprived of in utero sensory stimulation during the third trimester, an important period of central nervous system development. As a result, maturational trajectories are often reduced in infants born preterm. One such system affected is the brain including the auditory and respiratory control pathways.

Interleaved Multi-Contact Peripheral Nerve Stimulation to Enhance Reproduction of Tactile Sensation: A Computational Modeling Study.

Peripheral nerve stimulation (PNS) is an effective means to elicit sensation for rehabilitation of people with loss of a limb or limb function. While most current PNS paradigms deliver current through single electrode contacts to elicit each tactile percept, multi-contact extraneural electrodes offer the opportunity to deliver PNS with groups of contacts individually or simultaneously. Multi-contact PNS strategies could be advantageous in developing biomimetic PNS paradigms to recreate the natural neural activity during touch, because they may be able to selectively recruit multiple distinct neural populations.

Dynamics of ventilatory pattern variability and Cardioventilatory Coupling during systemic inflammation in rats.

Biometrics of common physiologic signals can reflect health status. We have developed analytics to measure the predictability of ventilatory pattern variability (VPV, Nonlinear Complexity Index (NLCI) that quantifies the predictability of a continuous waveform associated with inhalation and exhalation) and the cardioventilatory coupling (CVC, the tendency of the last heartbeat in expiration to occur at preferred latency before the next inspiration). We hypothesized that measures of VPV and CVC are sensitive to the development of endotoxemia, which evoke neuroinflammation.

An enhanced EEG microstate recognition framework based on deep neural networks: an application to Parkinson's disease.

Variations in brain activity patterns reveal impairments of motor and cognitive functions in the human brain. Electroencephalogram (EEG) microstates embody brain activity patterns at a microscopic time scale. However, current microstate analysis method can only recognize less than 90% of EEG signals per subject, which severely limits the characterization of dynamic brain activity.

Association between postictal EEG suppression, postictal autonomic dysfunction, and sudden unexpected death in epilepsy: Evidence from intracranial EEG.

Objective: The association between postictal electroencephalogram (EEG) suppression (PES), autonomic dysfunction, and Sudden Unexpected Death in Epilepsy (SUDEP) remains poorly understood. We compared PES on simultaneous intracranial and scalp-EEG and evaluated the association of PES with postictal heart rate variability (HRV) and SUDEP outcome.

Methods: Convulsive seizures were analyzed in patients with drug-resistant epilepsy at 5 centers.

A Predictive Biomarker Model Using Quantitative Electroencephalography in Adolescent Major Depressive Disorder.

With evolving understanding of psychiatric diagnosis and treatment, demand for biomarkers for psychiatric disorders in children and adolescents has grown dramatically. This study utilized quantitative electroencephalography (qEEG) to develop a predictive model for adolescent major depressive disorder (MDD). We hypothesized that youth with MDD compared to healthy controls (HCs) could be differentiated using a singular logistic regression model that utilized qEEG data alone.

Closing the loop of DBS using the beta oscillations in cortex.

Cortical information has great importance to reflect the deep brain stimulation (DBS) effects for Parkinson's disease patients. Using cortical activities to feedback is an available closed-loop idea for DBS. Previous studies have demonstrated the pathological beta (12-35 Hz) cortical oscillations can be suppressed by appropriate DBS settings.

BiCoSS: Toward Large-Scale Cognition Brain With Multigranular Neuromorphic Architecture.

The further exploration of the neural mechanisms underlying the biological activities of the human brain depends on the development of large-scale spiking neural networks (SNNs) with different categories at different levels, as well as the corresponding computing platforms. Neuromorphic engineering provides approaches to high-performance biologically plausible computational paradigms inspired by neural systems. In this article, we present a biological-inspired cognitive supercomputing system (BiCoSS) that integrates multiple granules (GRs) of SNNs to realize a hybrid compatible neuromorphic platform.

α-Satellite: An AI-Driven System and Benchmark Datasets for Dynamic COVID-19 Risk Assessment in the United States.

The fast evolving and deadly outbreak of coronavirus disease (COVID-19) has posed grand challenges to human society. To slow the spread of virus infections and better respond for community mitigation, by advancing capabilities of artificial intelligence (AI) and leveraging the large-scale and up-to-date data generated from heterogeneous sources (e.g.

Hyperventilation Increases the Randomness of Ocular Palatal Tremor Waveforms.

Hyperventilation changes the extracellular pH modulating many central pathologies, such as tremor. The questions that remain unanswered are the following: (1) Hyperventilation modulates which aspects of the oscillations? (2) Whether the effects of hyperventilation are instantaneous and the recovery is rapid and complete? Here we study the effects of hyperventilation on eye oscillations in the syndrome of oculopalatal tremor (OPT), a disease model affecting the inferior olive and cerebellar system. These regions are commonly involved in the pathogenesis of many movement disorders.

Gabapentin and memantine increases randomness of oscillatory waveform in ocular palatal tremor.

Syndrome of oculopalatal tremor (OPT) causes pendular nystagmus of the eyes and its disabling consequence on the visual system. Classic pharmacotherapeutic studies revealed reduction in the eye velocity of the oscillatory waveforms. Subjective improvement in vision, however, remains out of proportionately low.

INSMA: An integrated system for multimodal data acquisition and analysis in the intensive care unit.

Modern intensive care units (ICU) are equipped with a variety of different medical devices to monitor the physiological status of patients. These devices can generate large amounts of multimodal data daily that include physiological waveform signals (arterial blood pressure, electrocardiogram, respiration), patient alarm messages, numeric vitals data, etc. In order to provide opportunities for increasingly improved patient care, it is necessary to develop an effective data acquisition and analysis system that can assist clinicians and provide decision support at the patient bedside.

Comments and Corrections Corrections to "Application of Reinforcement Learning to Deep Brain Stimulation in a Computational Model of Parkinson's Disease".

In the above article [1], financial support was incorrectly published. The correct information is as follows: This work was supported in part by the National Natural Science Foundation of China under Grant 61501330 and Grant 61771330, and in part by the Tianjin Municipal Special Program of Talents Development for Excellent Youth Scholars under Grant TJTZJH-QNBJRC-2-2.

The role of coupling connections in a model of the cortico-basal ganglia-thalamocortical neural loop for the generation of beta oscillations.

Excessive neural synchronization in the cortico-basal ganglia-thalamocortical circuits in the beta (β) frequency range (12-35 Hz) is closely associated with dopamine depletion in Parkinson's disease (PD) and correlated with movement impairments, but the neural basis remains unclear. In this work, we establish a double-oscillator neural mass model for the cortico-basal ganglia-thalamocortical closed-loop system and explore the impacts of dopamine depletion induced changes in coupling connections within or between the two oscillators on neural activities within the loop. Spectral analysis of the neural mass activities revealed that the power and frequency of their principal components are greatly dependent on the coupling strengths between nuclei.

Application of Reinforcement Learning to Deep Brain Stimulation in a Computational Model of Parkinson's Disease.

Deep brain stimulation (DBS) has been proven to be an effective treatment to deal with the symptoms of Parkinson's disease (PD). Currently, the DBS is in an open-loop pattern with which the stimulation parameters remain constant regardless of fluctuations in the disease state, and adjustments of parameters rely mostly on trial and error of experienced clinicians. This could bring adverse effects to patients due to possible overstimulation.

Collection and Analysis of Multimodal Data for SUDEP Biomarker Discovery.

This paper discusses the acquisition and processing of multimodal physiological data from patients with epilepsy in Epilepsy Monitoring Units for the discovery of risk factors for Sudden Expected Death in Epilepsy (SUDEP) that can be combined through integrative analysis for biomarker discovery.

Delayed Feedback-Based Suppression of Pathological Oscillations in a Neural Mass Model.

Suppression of excessively synchronous beta frequency (12-35 Hz) oscillatory activity in the basal ganglia is believed to correlate with the alleviation of hypokinetic motor symptoms of the Parkinson's disease. Delayed feedback is an effective strategy to interrupt the synchronization and has been used in the design of closed-loop neuromodulation methods computationally. Although tremendous efforts in this are being made by optimizing delayed feedback algorithm and stimulation waveforms, there are still remaining problems in the selection of effective parameters in the delayed feedback control schemes.

Resting-State Quantitative Electroencephalography Demonstrates Differential Connectivity in Adolescents with Major Depressive Disorder.

Biomarkers for psychiatric disorders in children and adolescents are urgently needed. This cross-sectional pilot study investigated quantitative electroencephalogram (qEEG), a promising intermediate biomarker, in pediatric patients with major depressive disorder (MDD) compared with healthy controls (HCs). We hypothesized that youth with MDD would have increased coherence (connectivity) and absolute alpha power in the frontal cortex compared with HC.

Ventilatory pattern variability as a biometric for severity of acute lung injury in rats.

We hypothesize that ventilatory pattern variability (VPV) varies with the magnitude of acute lung injury (ALI). In adult male rats, we instilled a low- or high- dose of bleomycin or saline (PBS) intratracheally. While representative samples of pulmonary tissue indicated graded lung injury, coefficient of variation (CV) of TTOT did not differ among the 3 groups.

Scalable Digital Neuromorphic Architecture for Large-Scale Biophysically Meaningful Neural Network With Multi-Compartment Neurons.

Multicompartment emulation is an essential step to enhance the biological realism of neuromorphic systems and to further understand the computational power of neurons. In this paper, we present a hardware efficient, scalable, and real-time computing strategy for the implementation of large-scale biologically meaningful neural networks with one million multi-compartment neurons (CMNs). The hardware platform uses four Altera Stratix III field-programmable gate arrays, and both the cellular and the network levels are considered, which provides an efficient implementation of a large-scale spiking neural network with biophysically plausible dynamics.

Incidence, Recurrence, and Risk Factors for Peri-ictal Central Apnea and Sudden Unexpected Death in Epilepsy.

Peri-ictal breathing dysfunction was proposed as a potential mechanism for SUDEP. We examined the incidence and risk factors for both ictal (ICA) and post-convulsive central apnea (PCCA) and their relationship with potential seizure severity biomarkers (i. e.

Neonatal intermittent hypoxemia events are associated with diagnosis of bronchopulmonary dysplasia at 36 weeks postmenstrual age.

Background: Bronchopulmonary dysplasia (BPD) is a chronic lung disease and major pulmonary complication after premature birth. We have previously shown that increased intermittent hypoxemia (IH) events have been correlated to adverse outcomes and mortality in extremely premature infants. We hypothesize that early IH patterns are associated with the development of BPD.