Estimating the dimensionality of the manifold underlying multi-electrode neural recordings.

It is generally accepted that the number of neurons in a given brain area far exceeds the number of neurons needed to carry any specific function controlled by that area. For example, motor areas of the human brain contain tens of millions of neurons that control the activation of tens or at most hundreds of muscles. This massive redundancy implies the covariation of many neurons, which constrains the population activity to a low-dimensional manifold within the space of all possible patterns of neural activity. To gain a conceptual understanding of the complexity of the neural activity within a manifold, it is useful to estimate its dimensionality, which quantifies the number of degrees of freedom required to describe the observed population activity without significant information loss. While there are many algorithms for dimensionality estimation, we do not know which are well suited for analyzing neural activity. The objective of this study was to evaluate the efficacy of several representative algorithms for estimating the dimensionality of linearly and nonlinearly embedded data. We generated synthetic neural recordings with known intrinsic dimensionality and used them to test the algorithms' accuracy and robustness. We emulated some of the important challenges associated with experimental data by adding noise, altering the nature of the embedding of the low-dimensional manifold within the high-dimensional recordings, varying the dimensionality of the manifold, and limiting the amount of available data. We demonstrated that linear algorithms overestimate the dimensionality of nonlinear, noise-free data. In cases of high noise, most algorithms overestimated the dimensionality. We thus developed a denoising algorithm based on deep learning, the "Joint Autoencoder", which significantly improved subsequent dimensionality estimation. Critically, we found that all algorithms failed when the intrinsic dimensionality was high (above 20) or when the amount of data used for estimation was low. Based on the challenges we observed, we formulated a pipeline for estimating the dimensionality of experimental neural data.

Mortality Factors in Crush Syndrome.

BACKGROUND: Crush Syndrome is a major cause of morbidity and mortality following large-scale catastrophic earthquakes. Since there are no randomized controlled studies on Crush Syndrome, knowledge on this subject is limited to expert experience. The primary objective is to analyze the epidemiological and demographic characteristics, clinical outcomes, and mortality factors of earthquake victims after the Pazarcik and Elbistan earthquakes on February 6, 2023. METHODS: This cross-sectional and observational retrospective study evaluated 610 earthquake victims who presented to our center between February 6 and April 30, 2023. Among these patients, 128 with Crush Syndrome were included in the study. Patient information was gathered from hospital records during their stay and from national registries upon referral. The primary outcome was to identify risk factors for mortality. Demographic and laboratory data were analyzed by acute kidney injury (AKI) stages; mortality-affecting factors were identified through regression analysis. RESULTS: Of the 128 Crush Syndrome patients (100 adults, 28 children), 64 were female. The AKI rate was 32.8%. Among patients with AKI, the frequency of hemodialysis requirement was 69%, and the mortality rate was 14.2%. The overall mortality rate for patients with Crush Syndrome was 4.6%, compared to 3.9% (19/482) in earthquake victims without Crush Syndrome (p=0.705). Notably, low systolic blood pressure at admission was the only factor significantly affecting mortality in Crush Syndrome patients (Hazard Ratio [HR]: 1.088, p=0.021, 95% Confidence Interval [CI]). CONCLUSION: Our study highlights low systolic blood pressure upon admission as a significant risk factor for increased mortality in Crush Syndrome patients. This finding may contribute to the literature by emphasizing the importance of monitoring blood pressure under rubble and administering more aggressive fluid therapy to patients with low systolic blood pressure.

From monkeys to humans: observation-basedEMGbrain-computer interface decoders for humans with paralysis.

Objective. Intracortical brain-computer interfaces (iBCIs) aim to enable individuals with paralysis to control the movement of virtual limbs and robotic arms. Because patients' paralysis prevents training a direct neural activity to limb movement decoder, most iBCIs rely on 'observation-based' decoding in which the patient watches a moving cursor while mentally envisioning making the movement. However, this reliance on observed target motion for decoder development precludes its application to the prediction of unobservable motor output like muscle activity. Here, we ask whether recordings of muscle activity from a surrogate individual performing the same movement as the iBCI patient can be used as target for an iBCI decoder.Approach. We test two possible approaches, each using data from a human iBCI user and a monkey, both performing similar motor actions. In one approach, we trained a decoder to predict the electromyographic (EMG) activity of a monkey from neural signals recorded from a human. We then contrast this to a second approach, based on the hypothesis that the low-dimensional 'latent' neural representations of motor behavior, known to be preserved across time for a given behavior, might also be preserved across individuals. We 'transferred' an EMG decoder trained solely on monkey data to the human iBCI user after using Canonical Correlation Analysis to align the human latent signals to those of the monkey.Main results. We found that both direct and transfer decoding approaches allowed accurate EMG predictions between two monkeys and from a monkey to a human.Significance. Our findings suggest that these latent representations of behavior are consistent across animals and even primate species. These methods are an important initial step in the development of iBCI decoders that generate EMG predictions that could serve as signals for a biomimetic decoder controlling motion and impedance of a prosthetic arm, or even muscle force directly through functional electrical stimulation.

Evolving pharmacological approaches in gastroesophageal reflux disease.

INTRODUCTION: Proton pump inhibitors (PPIs) have considerably improved quality of life in patients with gastroesophageal reflux disease (GERD). However, many patients remain symptomatic despite standard PPI therapy. AREAS COVERED: This review focuses on evolving therapeutic strategies related to the pathophysiological processes of GERD and insufficient response to PPIs. Several clinical trials evaluated new PPI formulations and newer types of acid-suppressive drugs. These studies have evaluated traditional end points in GERD, but have not shown clinical superiority to current PPIs. Novel therapeutic strategies targeting underlying mechanisms of GERD, such as transient lower esophageal sphincter relaxations (TLESRs) and esophageal hypersensitivity, are being developed for add-on therapy to PPIs. Prokinetic drugs may also have some potential in the add-on treatment of GERD with insufficient response to PPIs. Add-on studies are hampered by insufficient information on optimal patient selection and lack of established end points. EXPERT OPINION: Newer drugs for symptomatic control in GERD have largely focused on improved acid suppression, without evidence of clinical superiority. Drugs targeting esophageal motility and sensitivity to be used as add-onc therapy in PPI insufficient responders have not reached Phase III trials to date, due to difficulties with patient selection, tolerability and end points.

Engaging and disengaging recurrent inhibition coincides with sensing and unsensing of a sensory stimulus.

Even simple sensory stimuli evoke neural responses that are dynamic and complex. Are the temporally patterned neural activities important for controlling the behavioral output? Here, we investigated this issue. Our results reveal that in the insect antennal lobe, due to circuit interactions, distinct neural ensembles are activated during and immediately following the termination of every odorant. Such non-overlapping response patterns are not observed even when the stimulus intensity or identities were changed. In addition, we find that ON and OFF ensemble neural activities differ in their ability to recruit recurrent inhibition, entrain field-potential oscillations and more importantly in their relevance to behaviour (initiate versus reset conditioned responses). Notably, we find that a strikingly similar strategy is also used for encoding sound onsets and offsets in the marmoset auditory cortex. In sum, our results suggest a general approach where recurrent inhibition is associated with stimulus 'recognition' and 'derecognition'.

Acotiamide, a novel gastroprokinetic for the treatment of patients with functional dyspepsia: postprandial distress syndrome.

Functional dyspepsia (FD) is a highly prevalent condition with major socioeconomic and healthcare impact. To date, no pharmacological treatment for FD has been approved. The Rome consensus proposed to subdivide FD into postprandial distress syndrome (PDS), characterized by meal-related symptoms and epigastric pain syndrome, characterized by pain and burning. Acotiamide (Z-338 or YM443) is a new drug, developed for the treatment of FD. Acotiamide enhances acetylcholine release from enteric neurons through muscarinic receptor antagonism and acetycholinesterase inhibition, thereby enhancing gastric emptying and gastric accommodation. Acotiamide was evaluated in FD in clinical studies in Europe, Japan and the USA, beneficial effects were observed for the PDS symptoms of postprandial fullness and early satiation, with a dose of 100 mg three-times a day. A 4-week placebo-controlled Phase III study in PDS patients in Japan confirmed efficacy of acotiamide in relieving postprandial fullness, early satiation and upper abdominal bloating.