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. During normal pregnancy the intrauterine environment attenuates external auditory stimuli while exposing the fetus to filtered maternal voice, intra-abdominal sounds, and external stimuli. In contrast, during the third trimester of development, preterm infants are exposed to a vastly different soundscape including non-attenuated auditory sounds and a lack of womb related stimuli, both of which may affect postnatal brain maturation. Therefore, fostering a nurturing postnatal auditory environment during hospitalization may have a significant impact on related outcomes of preterm infants. Studies using a range of postnatal auditory stimulations have suggested that exposure to sounds or lack thereof can have a significant impact on outcomes. However, studies are inconsistent with sound levels, duration of exposure to auditory stimuli, and the gestational age at which infants are exposed. IMPACT: Auditory stimulation can provide a low cost and low risk intervention to stabilize respiration, improve neuronal maturation and reduce long-term sequelae in preterm infants. The potential benefits of auditory stimulation are dependent on the type of sound, the duration of exposure and age at time of exposure. Future studies should focus on the optimal type and duration of sound exposure and postnatal developmental window to improve outcomes.

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. Previous research has discussed various data collection methods, but a comprehensive solution for bedside data acquisition to analysis has not been achieved. In this paper, we proposed a multimodal data acquisition and analysis system called INSMA, with the ability to acquire, store, process, and visualize multiple types of data from the Philips IntelliVue patient monitor. We also discuss how the acquired data can be used for patient state tracking. INSMA is being tested in the ICU at University Hospitals Cleveland Medical Center.

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. METHODS: IH frequency, duration, and nadirs were assessed using oxygen saturation (SpO2) waveforms in a retrospective cohort of 137 extremely premature newborns (<28 weeks gestation). Daily levels of inspired oxygen and mean airway pressure exposures were also recorded. RESULTS: Diagnosis of BPD at 36 weeks postmenstrual age was associated with increased daily IH, longer IH duration, and a higher IH nadir. Significant differences were detected through day 7 to day 26 of life. Infants who developed BPD had lower mean SpO2 despite their exposure to increased inspired oxygen and increased mean airway pressure. CONCLUSIONS: BPD was associated with more frequent, longer, and less severe IH events in addition to increased oxygen and pressure exposure within the first 26 days of life. Early IH patterns may contribute to the development of BPD or aid in identification of neonates at high risk.

Left-insular damage, autonomic instability, and sudden unexpected death in epilepsy.

We analyzed the only two sudden unexpected death in epilepsy (SUDEP) cases from 320 prospectively recruited patients in the three-year Prevention and Risk Identification of SUDEP Mortality (PRISM) project. Both patients had surgically refractory epilepsy, evidence of left insular damage following previous temporal/temporo-insular resections, and progressive changes in heart rate variability (HRV) in monitored evaluations prior to death. Insular damage is known to cause autonomic dysfunction and increased mortality in acute stroke. This report suggests a possible role for the insula in the pathogenesis of SUDEP. The presence of intrinsic insular lesions or acquired insular damage in patients with refractory epilepsy may be an additional risk factor for SUDEP.

A neural mass model of basal ganglia nuclei simulates pathological beta rhythm in Parkinson's disease.

An increase in beta oscillations within the basal ganglia nuclei has been shown to be associated with movement disorder, such as Parkinson's disease. The motor cortex and an excitatory-inhibitory neuronal network composed of the subthalamic nucleus (STN) and the external globus pallidus (GPe) are thought to play an important role in the generation of these oscillations. In this paper, we propose a neuron mass model of the basal ganglia on the population level that reproduces the Parkinsonian oscillations in a reciprocal excitatory-inhibitory network. Moreover, it is shown that the generation and frequency of these pathological beta oscillations are varied by the coupling strength and the intrinsic characteristics of the basal ganglia. Simulation results reveal that increase of the coupling strength induces the generation of the beta oscillation, as well as enhances the oscillation frequency. However, for the intrinsic properties of each nucleus in the excitatory-inhibitory network, the STN primarily influences the generation of the beta oscillation while the GPe mainly determines its frequency. Interestingly, describing function analysis applied on this model theoretically explains the mechanism of pathological beta oscillations.

Design and Development of a Smart Exercise Bike for Motor Rehabilitation in Individuals with Parkinson's Disease.

Recent studies in rehabilitation of Parkinson's disease (PD) have shown that cycling on a tandem bike at a high pedaling rate can reduce the symptoms of the disease. In this research, a smart motorized bicycle has been designed and built for assisting Parkinson's patients with exercise to improve motor function. The exercise bike can accurately control the rider's experience at an accelerated pedaling rate while capturing real-time test data. Here, the design and development of the electronics and hardware as well as the software and control algorithms are presented. Two control algorithms have been developed for the bike; one that implements an inertia load (static mode) and one that implements a speed reference (dynamic mode). In static mode the bike operates as a regular exercise bike with programmable resistance (load) that captures and records the required signals such as heart rate, cadence and power. In dynamic mode the bike operates at a user-selected speed (cadence) with programmable variability in speed that has been shown to be essential to achieving the desired motor performance benefits for PD patients. In addition, the flexible and extensible design of the bike permits readily changing the control algorithm and incorporating additional I/O as needed to provide a wide range of riding experiences. Furthermore, the network-enabled controller provides remote access to bike data during a riding session.

Information technology in critical care: review of monitoring and data acquisition systems for patient care and research.

There is a broad consensus that 21st century health care will require intensive use of information technology to acquire and analyze data and then manage and disseminate information extracted from the data. No area is more data intensive than the intensive care unit. While there have been major improvements in intensive care monitoring, the medical industry, for the most part, has not incorporated many of the advances in computer science, biomedical engineering, signal processing, and mathematics that many other industries have embraced. Acquiring, synchronizing, integrating, and analyzing patient data remain frustratingly difficult because of incompatibilities among monitoring equipment, proprietary limitations from industry, and the absence of standard data formatting. In this paper, we will review the history of computers in the intensive care unit along with commonly used monitoring and data acquisition systems, both those commercially available and those being developed for research purposes.

Effects of environment light during sleep on autonomic functions of heart rate and breathing.

PURPOSE: Poor sleep hygiene including sleeping in the daytime or with the lights on at night is discovered during the assessment of many sleep disorders including sleep apnea. The aim of this study was to investigate whether environmental light affected autonomic control of heart rate, sleep-disordered breathing (SDB), and/or breathing patterning. METHODS: Seventeen non-obese healthy volunteers without witnessed snoring and apneas were recruited. Studies were performed at home using a type 3 portable monitor combined with actigraphy for sleep-wake timing, using a randomly assigned, crossover between dark, or 1,000 lx of fluorescent lighting environment. The outcomes were low-frequency power divided by high-frequency power (LF/HF ratio) in the analysis of heart rate variability, the apnea-hypopnea index (AHI), and ventilatory pattern variability before and after sleep onset between environments. RESULTS: The LF/HF ratio and AHI were both significantly higher in light as compared to dark. Before sleep onset, the coefficient of variation (CV) for breath-to-breath tidal volume representing breathing irregularity tended to be higher in light than in dark environment. The CV values for tidal volume after sleep onset were significantly decreased compared with before sleep onset in both sleep environments. Mutual information of the ventilatory pattern was significantly lower before sleep onset than after sleep onset, only in the light environment. CONCLUSIONS: Sleeping in the light has effects like that of a stressor as it is associated with neuroexcitation, SDB, and resting breathing irregularity in healthy volunteers. These findings may be relevant to many sleep disorders associated with poor sleep hygiene.

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. We used computational models and optimization approaches to develop a novel biomimetic PNS paradigm that uses interleaved multi-contact (IMC) PNS to approximate the critical neural coding properties underlying touch. The IMC paradigm combines field shaping, in which two contacts are active simultaneously, with pulse-by-pulse contact and parameter variations throughout the touch stimulus. We show in simulation that IMC PNS results in better neural code mimicry than single contact PNS created with the same optimization techniques, and that field steering via two-contact IMC PNS results in better neural code mimicry than one-contact IMC PNS. We also show that IMC PNS results in better neural code mimicry than existing PNS paradigms, including prior biomimetic PNS. Future clinical studies will determine if the IMC paradigm can improve the naturalness and usefulness of sensory feedback for those with neurological disorders.

Significant postictal hypotension: expanding the spectrum of seizure-induced autonomic dysregulation.

Periictal autonomic dysregulation is best studied using a "polygraphic" approach: electroencephalography ([EEG]), 3-channel electrocardiography [ECG], pulse oximetry, respiration, and continuous noninvasive blood pressure [BP]), which may help elucidate agonal pathophysiologic mechanisms leading to sudden unexpected death in epilepsy (SUDEP). A number of autonomic phenomena have been described in generalized tonic-clonic seizures (GTCS), the most common seizure type associated with SUDEP, including decreased heart rate variability, cardiac arrhythmias, and changes in skin conductance. Postictal generalized EEG suppression (PGES) has been identified as a potential risk marker of SUDEP, and PGES has been found to correlate with post-GTCS autonomic dysregulation in some patients. Herein, we describe a patient with a GTCS in whom polygraphic measurements were obtained, including continuous noninvasive blood pressure recordings. Significant postictal hypotension lasting >60 s was found, which closely correlated with PGES duration. Similar EEG changes are well described in hypotensive patients with vasovagal syncope and a similar vasodepressor phenomenon, and consequent cerebral hypoperfusion may account for the PGES observed in some patients after a GTCS. This further raises the possibility that profound, prolonged, and irrecoverable hypotension may comprise one potential SUDEP mechanism.

Age-specific periictal electroclinical features of generalized tonic-clonic seizures and potential risk of sudden unexpected death in epilepsy (SUDEP).

Generalized tonic-clonic seizure (GTCS) is the commonest seizure type associated with sudden unexpected death in epilepsy (SUDEP). This study examined the semiological and electroencephalographic differences (EEG) in the GTCSs of adults as compared with those of children. The rationale lies on epidemiological observations that have noted a tenfold higher incidence of SUDEP in adults. We analyzed the video-EEG data of 105 GTCS events in 61 consecutive patients (12 children, 23 seizure events and 49 adults, 82 seizure events) recruited from the Epilepsy Monitoring Unit. Semiological, EEG, and 3-channel EKG features were studied. Periictal seizure phase durations were analyzed including tonic, clonic, total seizure, postictal EEG suppression (PGES), and recovery phases. Heart rate variability (HRV) measures including RMSSD (root mean square successive difference of RR intervals), SDNN (standard deviation of NN intervals), and SDSD (standard deviation of differences) were analyzed (including low frequency/high frequency power ratios) during preictal baseline and ictal and postictal phases. Generalized estimating equations (GEEs) were used to find associations between electroclinical features. Separate subgroup analyses were carried out on adult and pediatric age groups as well as medication groups (no antiepileptic medication cessation versus unchanged or reduced medication) during admission. Major differences were seen in adult and pediatric seizures with total seizure duration, tonic phase, PGES, and recovery phases being significantly shorter in children (p<0.01). Generalized estimating equation analysis, using tonic phase duration as the dependent variable, found age to correlate significantly (p<0.001), and this remained significant during subgroup analysis (adults and children) such that each 0.12-second increase in tonic phase duration correlated with a 1-second increase in PGES duration. Postictal EEG suppression durations were on average 28s shorter in children. With cessation of medication, total seizure duration was significantly increased by a mean value of 8s in children and 11s in adults (p<0.05). Tonic phase duration also significantly increased with medication cessation, and although PGES durations increased, this was not significant. Root mean square successive difference was negatively correlated with PGES duration (longer PGES durations were associated with decreased vagally mediated heart rate variability; p<0.05) but not with tonic phase duration. This study clearly points out identifiable electroclinical differences between adult and pediatric GTCSs that may be relevant in explaining lower SUDEP risk in children. The findings suggest that some prolonged seizure phases and prolonged PGES duration may be electroclinical markers of SUDEP risk and merit further study.

Breathing irregularity during wakefulness associates with CPAP acceptance in sleep apnea.

PURPOSE: Individuals have different breathing patterns at rest, during wakefulness, and during sleep, and patients with sleep apnea are no different. The hypothesis for this study was that breathing irregularity during wakefulness associates with CPAP acceptance in obstructive sleep apnea (OSA). METHODS: From a 2007-2010-database of patients with a diagnostic polysomnography (PSG) and prescribed CPAP (n = 380), retrospectively, 66 patients who quit CPAP treatment at 6 months were identified. Among them, 27 OSA patients quit despite having no side effects for discontinuing CPAP (Group A) and were compared to a matched group (age, body mass index, and apnea-hypopnea index) with good 6-month CPAP adherence (Group B; n = 21). Five minutes of respiratory signal during wakefulness at the initial PSG were extracted from respiratory inductance plethysmography recordings, and measured in a blinded fashion. The coefficients of variation (CV) for the breath-to-breath inspiration time (T i), expiration time (T e), T i + T e (T tot), and relative tidal volume, as well as an independent information theory-based metric of signal pattern variability (mutual information) were compared between groups. RESULTS: The CV for tidal volume was significantly greater (p = 0.001), and mutual information was significantly lower (p = 0.041) in Group A as compared to Group B. CONCLUSIONS: Differences in two independent measures of breathing irregularity correlated with CPAP rejection in OSA patients without nasal symptoms or comorbidity. Prospective studies of adherence should examine traits of breathing stability.

The use of heart rate variability for the early detection of treatable complications after aneurysmal subarachnoid hemorrhage.

High-grade aneurysmal subarachnoid hemorrhage patients are monitored in the ICU for up to 21 days, as they are at risk for complications such as vasospasm of cerebral arteries, cardiac arrhythmias and neurogenic stress cardiomyopathy. The diagnosis of these treatable complications is often delayed by the limitations of monitoring capabilities. We applied computational analysis to a cohort of 24 aneurysmal subarachnoid hemorrhage patients, to identify heart rate variability and ECG frequency profiles that may be potential biomarkers of severe vasospasm, reversible cardiomyopathy and death.

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

Introduction: 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. Methods: We implanted Sprague Dawley male rats with BP transducers to monitor arterial blood pressure (BP) and recorded ventilatory waveforms and BP simultaneously using whole-body plethysmography in conjunction with BP transducer receivers. After baseline (BSLN) recordings, we injected lipopolysaccharide (LPS, n = 8) or phosphate buffered saline (PBS, n =3) intraperitoneally on 3 consecutive days. We recorded for 4-6 h after the injection, chose 3 epochs from each hour and analyzed VPV and CVC as well as heart rate variability (HRV). Results: First, the responses to sepsis varied across rats, but within rats the repeated measures of NLCI, CVC, as well as respiratory frequency (fR), HR, BP and HRV had a low coefficient of variation, (<0.2) at each time point. Second, HR, fR, and NLCI increased from BSLN on Days 1-3; whereas CVC decreased on Days 2 and 3. In contrast, changes in BP and the relative low-(LF) and high-frequency (HF) of HRV were not significant. The coefficient of variation decreased from BSLN to Day 3, except for CVC. Interestingly, NLCI increased before fR in LPS-treated rats. Finally, we histologically confirmed lung injury, systemic inflammation via ELISA and the presence of the proinflammatory cytokine, IL-1ß, with immunohistochemistry in the ponto-medullary respiratory nuclei. Discussion: Our findings support that NLCI reflects changes in the rat's health induced by systemic injection of LPS and reflected in increases in HR and fR. CVC decreased over the course to the experiment. We conclude that NLCI reflected the increase in predictability of the ventilatory waveform and (together with our previous work) may reflect action of inflammatory cytokines on the network generating respiration.

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. A scalable hierarchical heterogeneous multicore architecture is presented, and a synergistic routing scheme for hybrid neural information is proposed. The BiCoSS system can accommodate different levels of GRs and biological plausibility of SNN models in an efficient and scalable manner. Over four million neurons can be realized on BiCoSS with a power efficiency of 2.8k larger than the GPU platform, and the average latency of BiCoSS is 3.62 and 2.49 times higher than conventional architectures of digital neuromorphic systems. For the verification, BiCoSS is used to replicate various biological cognitive activities, including motor learning, action selection, context-dependent learning, and movement disorders. Comprehensively considering the programmability, biological plausibility, learning capability, computational power, and scalability, BiCoSS is shown to outperform the alternative state-of-the-art works for large-scale SNN, while its real-time computational capability enables a wide range of potential applications.

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. As an application to early Parkinson's disease (PD), we propose an enhanced EEG microstate recognition framework based on deep neural networks, which yields recognition rates from 90% to 99%, as accompanied by a strong anti-artifact property. Additionally, gradient-weighted class activation mapping, as a visualization technique, is employed to locate the activated functional brain regions of each microstate class. We find that each microstate class corresponds to a particular activated brain region. Finally, based on the improved identification of microstate sequences, we explore the EEG microstate characteristics and their clinical associations. We show that the decreased occurrences of a particular microstate class reflect the degree of cognitive decline in early PD, and reduced transitions between certain microstates suggest injury in motor-related brain regions. The novel EEG microstate recognition framework paves the way to revealing more effective biomarkers for early PD.

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. In most delayed feedback neuromodulation strategies, the stimulation signal is obtained from the local field potential (LFP) of the excitatory subthalamic nucleus (STN) neurons and then is administered back to STN itself only. The inhibitory external globus pallidus (GPe) nucleus in the excitatory-inhibitory STN-GPe reciprocal network has not been involved in the design of the delayed feedback control strategies. Thus, considering the role of GPe, this paper proposes three schemes involving GPe in the design of the delayed feedback strategies and compared their effectiveness to the traditional paradigm using STN only. Based on a neural mass model of STN-GPe network having capability of simulating the LFP directly, the proposed stimulation strategies are tested and compared. Our simulation results show that the four types of delayed feedback control schemes are all effective, even if with a simple linear delayed feedback algorithm. But the three new control strategies we propose here further improve the control performance by enlarging the oscillatory suppression space and reducing the energy expenditure, suggesting that they may be more effective in applications. This paper may guide a new approach to optimize the closed-loop deep brain stimulation treatment to alleviate the Parkinsonian state by retargeting the measurement and stimulation nucleus.

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. Thus, here we propose to close the loop of DBS based on the beta oscillations in cortex. By modify the cortico-basal ganglia-thalamic neural loop model, more biologically realistic underlying the Parkinsonian phenomenon is approached. Stimulation results show the proposed closed-loop DBS strategy using cortical beta oscillation as feedback information has more profound roles in alleviating the pathological neural abnormality than the traditional open-loop DBS. Additionally, we compare the stimulation effects with subthalamic nucleus feedback strategy. It is shown that using cortical beta information as the feedback signals can further enlarge the control parameter space based on proportional-integral control structure with a lower energy expenditure. This work may pave the way to optimizing the DBS effects in a closed-loop arrangement.

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 (ß) 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. We found that the increased intra-coupling in the basal ganglia-thalamic (BG-Th) oscillator contributes to increased oscillations in the lower ß frequency band (12-25 Hz), while increased intra-coupling in the cortical oscillator mainly contributes to increased oscillations in the upper ß frequency band (26-35 Hz). Interestingly, pathological upper ß oscillations in the cortical oscillator may be another origin of the lower ß oscillations in the BG-Th oscillator, in addition to increased intra-coupling strength within the BG-Th network. Lower ß oscillations in the BG-Th oscillator can also change the dominant oscillation frequency of a cortical nucleus from the upper to the lower ß band. Thus, this work may pave the way towards revealing a possible neural basis underlying the Parkinsonian state.