Computer vision for automated seizure detection and classification: A systematic review.

Computer vision (CV) shows increasing promise as an efficient, low-cost tool for video seizure detection and classification. Here, we provide an overview of the fundamental concepts needed to understand CV and summarize the structure and performance of various model architectures used in video seizure analysis. We conduct a systematic literature review of the PubMed, Embase, and Web of Science databases from January 1, 2000 to September 15, 2023, to identify the strengths and limitations of CV seizure analysis methods and discuss the utility of these models when applied to different clinical seizure phenotypes. Reviews, nonhuman studies, and those with insufficient or poor quality data are excluded from the review. Of the 1942 records identified, 45 meet inclusion criteria and are analyzed. We conclude that the field has shown tremendous growth over the past 2 decades, leading to several model architectures with impressive accuracy and efficiency. The rapid and scalable detection offered by CV models holds the potential to reduce sudden unexpected death in epilepsy and help alleviate resource limitations in epilepsy monitoring units. However, a lack of standardized, thorough validation measures and concerns about patient privacy remain important obstacles for widespread acceptance and adoption. Investigation into the performance of models across varied datasets from clinical and nonclinical environments is an essential area for further research.

Effects of atipamezole on selected physiologic parameters in cynomolgus macaques (Macaca fascicularis).

BACKGROUND: Atipamezole, an α-2 adrenergic receptor antagonist, reverses the α-2 agonist anesthetic effects. There is a dearth of information on the physiological effects of these drugs in cynomolgus macaques (Macaca fascicularis). We assessed atipamezole's physiologic effects. We hypothesized atipamezole administration would alter anesthetic parameters. METHODS: Five cynomolgus macaques were sedated with ketamine/dexmedetomidine intramuscularly, followed 45 min later with atipamezole (0.5 mg/kg). Anesthetic parameters (heart rate, blood pressure [systolic (SAP), diastolic (DAP), and mean (MAP) blood pressure], body temperature, respiratory rate, and %SpO2) were monitored prior to and every 10 min (through 60 min) post atipamezole injection. RESULTS: While heart rate was significantly increased for 60 min; SAP, DAP, MAP, and temperature were significantly decreased at 10 min. CONCLUSIONS: This study indicates subcutaneous atipamezole results in increased heart rate and transient blood pressure decrease. These findings are clinically important to ensure anesthetist awareness to properly support and treat patients as needed.

Detecting the symptoms of Parkinson's disease with non-standard video.

BACKGROUND: Neurodegenerative diseases, such as Parkinson's disease (PD), necessitate frequent clinical visits and monitoring to identify changes in motor symptoms and provide appropriate care. By applying machine learning techniques to video data, automated video analysis has emerged as a promising approach to track and analyze motor symptoms, which could facilitate more timely intervention. However, existing solutions often rely on specialized equipment and recording procedures, which limits their usability in unstructured settings like the home. In this study, we developed a method to detect PD symptoms from unstructured videos of clinical assessments, without the need for specialized equipment or recording procedures. METHODS: Twenty-eight individuals with Parkinson's disease completed a video-recorded motor examination that included the finger-to-nose and hand pronation-supination tasks. Clinical staff provided ground truth scores for the level of Parkinsonian symptoms present. For each video, we used a pre-existing model called PIXIE to measure the location of several joints on the person's body and quantify how they were moving. Features derived from the joint angles and trajectories, designed to be robust to recording angle, were then used to train two types of machine-learning classifiers (random forests and support vector machines) to detect the presence of PD symptoms. RESULTS: The support vector machine trained on the finger-to-nose task had an F1 score of 0.93 while the random forest trained on the same task yielded an F1 score of 0.85. The support vector machine and random forest trained on the hand pronation-supination task had F1 scores of 0.20 and 0.33, respectively. CONCLUSION: These results demonstrate the feasibility of developing video analysis tools to track motor symptoms across variable perspectives. These tools do not work equally well for all tasks, however. This technology has the potential to overcome barriers to access for many individuals with degenerative neurological diseases like PD, providing them with a more convenient and timely method to monitor symptom progression, without requiring a structured video recording procedure. Ultimately, more frequent and objective home assessments of motor function could enable more precise telehealth optimization of interventions to improve clinical outcomes inside and outside of the clinic.

Transforming modeling in neurorehabilitation: clinical insights for personalized rehabilitation.

Practicing clinicians in neurorehabilitation continue to lack a systematic evidence base to personalize rehabilitation therapies to individual patients and thereby maximize outcomes. Computational modeling- collecting, analyzing, and modeling neurorehabilitation data- holds great promise. A key question is how can computational modeling contribute to the evidence base for personalized rehabilitation? As representatives of the clinicians and clinician-scientists who attended the 2023 NSF DARE conference at USC, here we offer our perspectives and discussion on this topic. Our overarching thesis is that clinical insight should inform all steps of modeling, from construction to output, in neurorehabilitation and that this process requires close collaboration between researchers and the clinical community. We start with two clinical case examples focused on motor rehabilitation after stroke which provide context to the heterogeneity of neurologic injury, the complexity of post-acute neurologic care, the neuroscience of recovery, and the current state of outcome assessment in rehabilitation clinical care. Do we provide different therapies to these two different patients to maximize outcomes? Asking this question leads to a corollary: how do we build the evidence base to support the use of different therapies for individual patients? We discuss seven points critical to clinical translation of computational modeling research in neurorehabilitation- (i) clinical endpoints, (ii) hypothesis- versus data-driven models, (iii) biological processes, (iv) contextualizing outcome measures, (v) clinical collaboration for device translation, (vi) modeling in the real world and (vii) clinical touchpoints across all stages of research. We conclude with our views on key avenues for future investment (clinical-research collaboration, new educational pathways, interdisciplinary engagement) to enable maximal translational value of computational modeling research in neurorehabilitation.

The distribution of acquired peripheral nerve injuries associated with severe COVID-19 implicate a mechanism of entrapment neuropathy: a multicenter case series and clinical feasibility study of a wearable, wireless pressure sensor.

We diagnosed 66 peripheral nerve injuries in 34 patients who survived severe coronavirus disease 2019 (COVID-19). We combine this new data with published case series re-analyzed here (117 nerve injuries; 58 patients) to provide a comprehensive accounting of lesion sites. The most common are ulnar (25.1%), common fibular (15.8%), sciatic (13.1%), median (9.8%), brachial plexus (8.7%) and radial (8.2%) nerves at sites known to be vulnerable to mechanical loading. Protection of peripheral nerves should be prioritized in the care of COVID-19 patients. To this end, we report proof of concept data of the feasibility for a wearable, wireless pressure sensor to provide real time monitoring in the intensive care unit setting.

Physiological roles and molecular mechanisms of K+ -Cl- cotransport in the mammalian kidney and cardiovascular system: where are we?

In the early 80s, renal microperfusion studies led to the identification of a basolateral K+ -Cl- cotransport mechanism in the proximal tubule, thick ascending limb of Henle and collecting duct. More than ten years later, this mechanism was found to be accounted for by three different K+ -Cl- cotransporters (KCC1, KCC3 and KCC4) that are differentially distributed along the renal epithelium. Two of these isoforms (KCC1 and KCC3) were also found to be expressed in arterial walls, the myocardium and a variety of neurons. Subsequently, valuable insights have been gained into the molecular and physiological properties of the KCCs in both the mammalian kidney and cardiovascular system. There is now robust evidence indicating that KCC4 sustains distal renal acidification and that KCC3 regulates myogenic tone in resistance vessels. However, progress in understanding the functional significance of these transporters has been slow, probably because each of the KCC isoforms is not identically distributed among species and some of them share common subcellular localizations with other KCC isoforms or sizeable conductive Cl- pathways. In addition, the mechanisms underlying the process of K+ -Cl- cotransport are still ill defined. The present review focuses on the knowledge gained regarding the roles and properties of KCCs in renal and cardiovascular tissues.

Faster processing of moving compared with flashed bars in awake macaque V1 provides a neural correlate of the flash lag illusion.

When the brain has determined the position of a moving object, because of anatomical and processing delays the object will have already moved to a new location. Given the statistical regularities present in natural motion, the brain may have acquired compensatory mechanisms to minimize the mismatch between the perceived and real positions of moving objects. A well-known visual illusion-the flash lag effect-points toward such a possibility. Although many psychophysical models have been suggested to explain this illusion, their predictions have not been tested at the neural level, particularly in a species of animal known to perceive the illusion. To this end, we recorded neural responses to flashed and moving bars from primary visual cortex (V1) of awake, fixating macaque monkeys. We found that the response latency to moving bars of varying speed, motion direction, and luminance was shorter than that to flashes, in a manner that is consistent with psychophysical results. At the level of V1, our results support the differential latency model positing that flashed and moving bars have different latencies. As we found a neural correlate of the illusion in passively fixating monkeys, our results also suggest that judging the instantaneous position of the moving bar at the time of flash-as required by the postdiction/motion-biasing model-may not be necessary for observing a neural correlate of the illusion. Our results also suggest that the brain may have evolved mechanisms to process moving stimuli faster and closer to real time compared with briefly appearing stationary stimuli. NEW & NOTEWORTHY We report several observations in awake macaque V1 that provide support for the differential latency model of the flash lag illusion. We find that the equal latency of flash and moving stimuli as assumed by motion integration/postdiction models does not hold in V1. We show that in macaque V1, motion processing latency depends on stimulus luminance, speed and motion direction in a manner consistent with several psychophysical properties of the flash lag illusion.

Molecular insights into the normal operation, regulation, and multisystemic roles of K+-Cl- cotransporter 3 (KCC3).

Long before the molecular identity of the Na+-dependent K+-Cl- cotransporters was uncovered in the mid-nineties, a Na+-independent K+-Cl- cotransport system was also known to exist. It was initially observed in sheep and goat red blood cells where it was shown to be ouabain-insensitive and to increase in the presence of N-ethylmaleimide (NEM). After it was established between the early and mid-nineties, the expressed sequence tag (EST) databank was found to include a sequence that was highly homologous to those of the Na+-dependent K+-Cl- cotransporters. This sequence was eventually found to code for the Na+-independent K+-Cl- cotransport function that was described in red blood cells several years before. It was termed KCC1 and led to the discovery of three isoforms called KCC2, KCC3, and KCC4. Since then, it has become obvious that each one of these isoforms exhibits unique patterns of distribution and fulfills distinct physiological roles. Among them, KCC3 has been the subject of great attention in view of its important role in the nervous system and its association with a rare hereditary sensorimotor neuropathy (called Andermann syndrome) that affects many individuals in Quebec province (Canada). It was also found to play important roles in the cardiovascular system, the organ of Corti, and circulating blood cells. As will be seen in this review, however, there are still a number of uncertainties regarding the transport properties, structural organization, and regulation of KCC3. The same is true regarding the mechanisms by which KCC3 accomplishes its numerous functions in animal cells.

Molecular features and physiological roles of K+-Cl- cotransporter 4 (KCC4).

A K+-Cl- cotransport system was documented for the first time during the mid-seventies in sheep and goat red blood cells. It was then described as a Na+-independent and ouabain-insensitive ion carrier that could be stimulated by cell swelling and N-ethylmaleimide (NEM), a thiol-reacting agent. Twenty years later, this system was found to be dispensed by four different isoforms in animal cells. The first one was identified in the expressed sequence tag (EST) database by Gillen et al. based on the assumption that it would be homologous to the Na+-dependent K+-Cl- cotransport system for which the molecular identity had already been uncovered. Not long after, the three other isoforms were once again identified in the EST databank. Among those, KCC4 has generated much interest a few years ago when it was shown to sustain distal renal acidification and hearing development in mouse. As will be seen in this review, many additional roles were ascribed to this isoform, in keeping with its wide distribution in animal species. However, some of them have still not been confirmed through animal models of gene inactivation or overexpression. Along the same line, considerable knowledge has been acquired on the mechanisms by which KCC4 is regulated and the environmental cues to which it is sensitive. Yet, it is inferred to some extent from historical views and extrapolations.

Improved estimation and interpretation of correlations in neural circuits.

Ambitious projects aim to record the activity of ever larger and denser neuronal populations in vivo. Correlations in neural activity measured in such recordings can reveal important aspects of neural circuit organization. However, estimating and interpreting large correlation matrices is statistically challenging. Estimation can be improved by regularization, i.e. by imposing a structure on the estimate. The amount of improvement depends on how closely the assumed structure represents dependencies in the data. Therefore, the selection of the most efficient correlation matrix estimator for a given neural circuit must be determined empirically. Importantly, the identity and structure of the most efficient estimator informs about the types of dominant dependencies governing the system. We sought statistically efficient estimators of neural correlation matrices in recordings from large, dense groups of cortical neurons. Using fast 3D random-access laser scanning microscopy of calcium signals, we recorded the activity of nearly every neuron in volumes 200 µm wide and 100 µm deep (150-350 cells) in mouse visual cortex. We hypothesized that in these densely sampled recordings, the correlation matrix should be best modeled as the combination of a sparse graph of pairwise partial correlations representing local interactions and a low-rank component representing common fluctuations and external inputs. Indeed, in cross-validation tests, the covariance matrix estimator with this structure consistently outperformed other regularized estimators. The sparse component of the estimate defined a graph of interactions. These interactions reflected the physical distances and orientation tuning properties of cells: The density of positive 'excitatory' interactions decreased rapidly with geometric distances and with differences in orientation preference whereas negative 'inhibitory' interactions were less selective. Because of its superior performance, this 'sparse+latent' estimator likely provides a more physiologically relevant representation of the functional connectivity in densely sampled recordings than the sample correlation matrix.

Trial-to-trial, uncertainty-based adjustment of decision boundaries in visual categorization.

Categorization is a cornerstone of perception and cognition. Computationally, categorization amounts to applying decision boundaries in the space of stimulus features. We designed a visual categorization task in which optimal performance requires observers to incorporate trial-to-trial knowledge of the level of sensory uncertainty when setting their decision boundaries. We found that humans and monkeys did adjust their decision boundaries from trial to trial as the level of sensory noise varied, with some subjects performing near optimally. We constructed a neural network that implements uncertainty-based, near-optimal adjustment of decision boundaries. Divisive normalization emerges automatically as a key neural operation in this network. Our results offer an integrated computational and mechanistic framework for categorization under uncertainty.

Survival Outcomes Following Pediatric Liver Transplantation (Pedi-SOFT) Score: A Novel Predictive Index.

A prognostic index to predict survival after liver transplantation could address several clinical needs. Here, we devised a scoring system that predicts recipient survival after pediatric liver transplantation. We used univariate and multivariate analysis on 4565 pediatric liver transplant recipients data and identified independent recipient and donor risk factors for posttransplant mortality at 3 months. Multiple imputation was used to account for missing variables. We identified five factors as significant predictors of recipient mortality after pediatric liver transplantation: two previous transplants (OR 5.88, CI 2.88-12.01), one previous transplant (OR 2.54, CI 1.75-3.68), life support (OR 3.68, CI 2.39-5.67), renal insufficiency (OR 2.66, CI 1.84-3.84), recipient weight under 6 kilograms (OR 1.67, CI 1.12-2.36) and cadaveric technical variant allograft (OR 1.38, CI 1.03-1.83). The Survival Outcomes Following Pediatric Liver Transplant score assigns weighted risk points to each of these factors in a scoring system to predict 3-month recipient survival after liver transplantation with a C-statistic of 0.74. Although quite accurate when compared with other posttransplant survival models, we would not advocate individual clinical application of the index.

Brugia malayi infective larvae fail to activate Langerhans cells and dermal dendritic cells in human skin.

Filarial infection in humans is initiated when a mosquito deposits third-stage parasite larvae (L3) in the skin. Langerhans cells (LCs) and dermal dendritic cells (DDCs) are the first cells that the parasite encounters, and L3s must evade these highly effective antigen-presenting cells to establish infection. To assess LC and DDC responses to L3 in human skin, we employed three models of increasing physiologic relevance: in vitro-generated LCs, epidermal blister explants and full-thickness human skin sections. In vitro-generated LCs expressed TLR1-10 and robustly produced IL-6 and TNF-α in response to PolyI:C, but pre-exposure to L3s did not alter inflammatory cytokine production or TLR expression. L3s did not modulate expression of LC markers CDH1, CD207, or CD1a, or the regulatory products TSLP or IDO in epidermal explants or in vitro-generated LC. LC, CD14+ DDC, CD1c+ DC and CD141+ DC from human skin sections were analysed by flow cytometry. While PolyI:C potently induced CCL22 production in LC, CD1c+ DC, and CD141+ DC, and IL-10 production in LC, L3s did not modulate the numbers of or cytokine production by any skin DC subset. L3s broadly failed to activate or modulate LCs or DDCs, suggesting filarial larvae expertly evade APC detection in human skin.

Validation of portable in-clinic video-based gait analysis for prosthesis users.

Despite the common focus of gait in rehabilitation, there are few tools that allow quantitatively characterizing gait in the clinic. We recently described an algorithm, trained on a large dataset from our clinical gait analysis laboratory, which produces accurate cycle-by-cycle estimates of spatiotemporal gait parameters including step timing and walking velocity. Here, we demonstrate this system generalizes well to clinical care with a validation study on prosthetic users seen in therapy and outpatient clinics. Specifically, estimated walking velocity was similar to annotated 10-m walking velocities, and cadence and foot contact times closely mirrored our wearable sensor measurements. Additionally, we found that a 2D keypoint detector pretrained on largely able-bodied individuals struggles to localize prosthetic joints, particularly for those individuals with more proximal or bilateral amputations, but after training a prosthetic-specific joint detector video-based gait analysis also works on these individuals. Further work is required to validate the other outputs from our algorithm including sagittal plane joint angles and step length. Code for the gait transformer and the trained weights are available at https://github.com/peabody124/GaitTransformer .

Stable, chronic in-vivo recordings from a fully wireless subdural-contained 65,536-electrode brain-computer interface device.

Minimally invasive, high-bandwidth brain-computer-interface (BCI) devices can revolutionize human applications. With orders-of-magnitude improvements in volumetric efficiency over other BCI technologies, we developed a 50-µm-thick, mechanically flexible micro-electrocorticography (µECoG) BCI, integrating 256×256 electrodes, signal processing, data telemetry, and wireless powering on a single complementary metal-oxide-semiconductor (CMOS) substrate containing 65,536 recording and 16,384 stimulation channels, from which we can simultaneously record up to 1024 channels at a given time. Fully implanted below the dura, our chip is wirelessly powered, communicating bi-directionally with an external relay station outside the body. We demonstrated chronic, reliable recordings for up to two weeks in pigs and up to two months in behaving non-human primates from somatosensory, motor, and visual cortices, decoding brain signals at high spatiotemporal resolution.

A fast and simple population code for orientation in primate V1.

Orientation tuning has been a classic model for understanding single-neuron computation in the neocortex. However, little is known about how orientation can be read out from the activity of neural populations, in particular in alert animals. Our study is a first step toward that goal. We recorded from up to 20 well isolated single neurons in the primary visual cortex of alert macaques simultaneously and applied a simple, neurally plausible decoder to read out the population code. We focus on two questions: First, what are the time course and the timescale at which orientation can be read out from the population response? Second, how complex does the decoding mechanism in a downstream neuron have to be to reliably discriminate between visual stimuli with different orientations? We show that the neural ensembles in primary visual cortex of awake macaques represent orientation in a way that facilitates a fast and simple readout mechanism: With an average latency of 30-80 ms, the population code can be read out instantaneously with a short integration time of only tens of milliseconds, and neither stimulus contrast nor correlations need to be taken into account to compute the optimal synaptic weight pattern. Our study shows that-similar to the case of single-neuron computation-the representation of orientation in the spike patterns of neural populations can serve as an exemplary case for understanding the computations performed by neural ensembles underlying visual processing during behavior.

Leishmania major inhibits IL-12 in macrophages by signalling through CR3 (CD11b/CD18) and down-regulation of ETS-mediated transcription.

Leishmania major is an aetiological agent of cutaneous leishmaniasis. The parasite primarily infects immune sentinel cells, specifically macrophages and dendritic cells, in the mammalian host. Infection is receptor mediated and is known to involve parasite binding to cell surface protein complement receptor 3 (CR3, Mac-1, CD11b/CD18). Engagement of CR3 by various ligands inhibits production of interleukin-12 (IL-12), the cytokine that drives antileishmanial T helper 1-type immune responses. Likewise, L. major infection inhibits IL-12 production and activation of host macrophages. Our data indicate that in the absence of CR3, L. major-infected bone marrow-derived macrophages produce more IL-12 and nitric oxide compared with WT cells upon lipopolysaccharide (LPS) stimulation. We therefore investigated multiple signalling pathways by which L. major may inhibit IL-12 transcription through CR3 ligation. We demonstrate that L. major infection does not elicit significant NFκB p65, MAPK, IRF-1 or IRF-8 activation in WT or CD11b-deficient macrophages. Furthermore, infection neither inhibits LPS-induced MAPK or NFκB activation nor blocks IFN-γ-activated IRF-1 and IRF-8. ETS-mediated transcription, however, is inhibited by L. major infection independently of CR3. Our data indicate that L. major-mediated inhibition of IL-12 occurs through CR3 engagement; however, the mechanism of inhibition is independent of NFκB, MAPK, IRF and ETS.

Powered knee and ankle prosthesis use with a K2 level ambulator: a case report.

Powered prosthetic knees and ankles have the capability of restoring power to the missing joints and potential to provide increased functional mobility to users. Nearly all development with these advanced prostheses is with individuals who are high functioning community level ambulators even though limited community ambulators may also receive great benefit from these devices. We trained a 70 year old male participant with a unilateral transfemoral amputation to use a powered knee and powered ankle prosthesis. He participated in eight hours of therapist led in-lab training (two hours per week for four weeks). Sessions included static and dynamic balance activities for improved stability and comfort with the powered prosthesis and ambulation training on level ground, inclines, and stairs. Assessments were taken with both the powered prosthesis and his prescribed, passive prosthesis post-training. Outcome measures showed similarities in velocity between devices for level-ground walking and ascending a ramp. During ramp descent, the participant had a slightly faster velocity and more symmetrical stance and step times with the powered prosthesis compared to his prescribed prosthesis. For stairs, he was able to climb with reciprocal stepping for both ascent and descent, a stepping strategy he is unable to do with his prescribed prosthesis. More research with limited community ambulators is necessary to understand if further functional improvements are possible with either additional training, longer accommodation periods, and/or changes in powered prosthesis control strategies.