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Healthcare providers rely on complex biomedical devices to assess, treat, and monitor patients. Ongoing research efforts are attempting to generate and implement better algorithms and mechanisms to ensure the early, accurate, automated, and clinically meaningful recognition of patterns and changes in patient health and pathology. Effecting such evolutionary advances in patient monitoring will likely require large collections of high-resolution physiologic parameter datasets from a broad spectrum of patients. As part of a research program to scientifically improve patient monitoring (with a focus on alarm fatigue mitigation), investigators developed the Medical Technology Interface-Open/Research toolkit with modular conduit components that provide the following capabilities: 1) access to select bedside monitor physiologic signals in real-world clinical settings for near-real-time acquisition, storage, and export of high-resolution patient datastreams in a portable format (.json); 2) establishment of a safe, parallel test environment at the bedside for experimental datastream analyses in a research framework. Deployment and interfacing of toolkit elements with off-the-shelf software solutions in a live emergency department setting enabled the construction of a bedside clinical informatics (BCI) research pipeline infrastructure that featured 1) indexing, search/query, and retrieval of datastreams for sophisticated analyses, experimental processing, and algorithm development; and 2) dataset visualization for expert adjudication of datastream interpretability, alarm clinical significance and severity, and experimental algorithm performance. In order to help institute a collaborative biomedical engineering research resource, this article shares details of the active ED BCI data pipeline and presents preliminary examples of ongoing multimodal data fusion applications.

© 2019 The Author(s) Recent improvements in the speed and sensitivity of fMRI acquisition techniques suggest that fast fMRI can be used to detect and precisely localize sub-second neural dynamics. This enhanced temporal resolution has enormous potential for neuroscientists. However, physiological noise poses a major challenge for the analysis of fast fMRI data. Physiological noise scales with sensitivity, and its autocorrelation structure is altered in rapidly sampled data, suggesting that new approaches are needed for physiological noise removal in fast fMRI. Existing strategies either rely on external physiological recordings, which can be noisy or difficult to collect, or employ data-driven approaches which make assumptions that may not hold true in fast fMRI. We created a statistical model of harmonic regression with autoregressive noise (HRAN) to estimate and remove cardiac and respiratory noise from the fMRI signal directly. This technique exploits the fact that cardiac and respiratory noise signals are fully sampled (rather than aliasing) when imaging at fast rates, allowing us to track and model physiology over time without requiring external physiological measurements. We then created a joint model of neural hemodynamics, and physiological and autocorrelated noise to more accurately remove noise. We first verified that HRAN accurately estimates cardiac and respiratory dynamics and that our model demonstrates goodness-of-fit in fast fMRI data. In task-driven data, we then demonstrated that HRAN is able to remove physiological noise while leaving the neural signal intact, thereby increasing detection of task-driven voxels. Finally, we established that in both simulations and fast fMRI data HRAN is able to improve statistical inferences as compared with gold-standard physiological noise removal techniques. In conclusion, we created a tool that harnesses the novel information in fast fMRI to remove physiological noise, enabling broader use of the technology to study human brain function.

BackgroundDiscontinuous electroencephalographic activity in children is thought to reflect brain inactivation. Discontinuity has been observed in states of pathology, where it is predictive of adverse neurological outcome, as well as under general anesthesia. Though in preterm-infants discontinuity reflects normal brain development, less is known regarding its role in term children, particularly in the setting of general anesthesia. Here, we conduct a post-hoc exploratory analysis to investigate the spectral features of discontinuous activity in children under general anesthesia.MethodsWe previously recorded electroencephalography in children less than forty months of age under general anesthesia (n = 65). We characterized the relationship between age, anesthetic depth, and discontinuous activity, and used multitaper spectral methods to compare the power spectra of subjects with (n = 35) and without (n = 30) discontinuous activity. In the subjects with discontinuous activity, we examined the amplitude and power spectra associated with the discontinuities and analyzed how these variables varied with age.ResultsCumulative time of discontinuity was associated with increased anesthetic depth and younger age. In particular, age-matched children with discontinuity received higher doses of propofol during induction as compared with children without discontinuity. In the tens of seconds preceding the onset of discontinuous activity, there was a decrease in high-frequency power in children four months and older that could be visually observed with spectrograms. During discontinuous activity, there were distinctive patterns of amplitude, spectral edge, and power in canonical frequency bands that varied with age. Notably, there was a decline in spectral edge in the seconds immediately following each discontinuity.ConclusionDiscontinuous activity in children reflects a state of a younger or more deeply anesthetized brain, and characteristic features of discontinuous activity evolve with age and may reflect neurodevelopment.

Tai Chi (TC) is a slow-motion contemplative exercise that is associated with improvements in sensorimotor measures, including decreased force variability, enhanced tactile acuity, and improved proprioception, especially in elderly populations. Here, we carried out two studies evaluating the effect of TC practice on measures associated with sensorimotor processing. In study 1, we evaluated TC's effects on an oscillatory parameter associated with motor function, beta rhythm (15-30 Hz) coherence, focusing specifically on beta rhythm intermuscular coherence (IMC), which is tightly coupled to beta corticomuscular coherence (CMC). We utilized electromyography (EMG) to compare beta IMC in older TC practitioners with age-matched controls, as well as novices with advanced TC practitioners. Given previous findings of elevated, maladaptive beta coherence in older subjects, we hypothesized that increased TC practice would be associated with a monotonic decrease in beta IMC, but rather discovered that novice practitioners manifested higher beta IMC than both controls and advanced practitioners, forming an inverted U-shaped practice curve. This finding suggests that TC practice elicits complex changes in sensory and motor processes over the developmental lifespan of TC training. In study 2, we focused on somatosensory (e.g., tactile and proprioceptive) responses to the rubber hand illusion (RHI) in a middle-aged TC group, assessing whether responses to the illusion became dampened with greater cumulative practice. As hypothesized, TC practice was associated with decreased likelihood to misattribute tactile stimulation during the RHI to the rubber hand, although there was no effect of TC practice on measures of proprioception or on subjective reports of ownership. These studies provide preliminary evidence that TC practice both modulates beta network coherence in a non-linear fashion, perhaps as a result of the focus on not only efferent motor but also afferent sensory activity, and alters tactile sensations during the RHI. This work is the first to show the effects of TC on low level sensorimotor processing and integrated body awareness, and this multi-scale finding may help to provide a mechanistic explanation for the widespread sensorimotor benefits observed with TC practice in symptoms associated with aging and difficult illnesses such as Parkinson's disease.

Electroencephalography (EEG) offers a unique opportunity to study human neural activity non-invasively with millisecond resolution using minimal equipment in or outside of a lab setting. EEG can be combined with a number of techniques for closed-loop experiments, where external devices are driven by specific neural signals. For example, EEG signals have been used to regulate anesthetic delivery, to control brain-computer interfaces, and to drive transcranial alternating current stimulation for the treatment of psychiatric illness. However, reliable, commercially available EEG systems are expensive, often making them impractical for individual use and research development. Moreover, by design, a majority of these systems cannot be easily altered to the specification needed by the end user. This rigidity makes it extremely difficult or infeasible to adapt EEG to novel closed-loop experiments. For instance, many current systems are not able to communicate with software and hardware from other vendors, nor are they able to achieve low-latency timescales (100 ms) necessary to operate on the fast patterns of neural activities. Recently, open-source alternatives to commercial systems have been developed that can eliminate these problems, driving down research costs and promoting collaborations and innovations. Here, we present methods to expand the use of a commercially available, open-source electrophysiology system, Open Ephys (www.openephys.org), to include human EEG recordings providing a novel technique for low-cost, easily-adaptable EEG recording. We describe the equipment and protocol necessary to interface various EEG caps with the Open Ephys acquisition board, and detail methods for processing data. We present applications of Open Ephys + EEG as a research tool and discuss how this innovative EEG technology lays a framework for improved closed-loop paradigms and novel brain-computer interface experiments.