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Aims: The Covid-19 pandemic has had a substantial impact on the mental health of the general public and high-risk groups across the globe. Southeast Asia, one of the first regions to be affected by the outbreak, is of particular interest given its proximity and close links to China, experience with recent epidemics (i.e. SARS), and the variable course of the outbreak in the region thus far. The aim of this study was to systemically review and assess the prevalence of anxiety, depression and insomnia symptoms in the general adult population, frontline and general healthcare workers (HCWs), and adult students in Southeast Asia during the course of the first year of the Covid-19 pandemic. Methods: Several literature databases (PubMed, Embase, PsycINFO, Web of Science, and medRxiv) were systemically searched for articles published up to February 2021. Two reviewers independently evaluated all the relevant studies using pre-determined criteria and assessed the risk of bias for each included study. The prevalence rates of mental health symptoms were estimated using a random-effect meta-analysis model. Results: In total, 32 samples from 25 studies with 20,352 participants were included. Anxiety symptoms was assessed in all 25 studies and depressive symptoms in 15 studies with pooled prevalence rates of 22% and 16% respectively. Only two studies evaluated insomnia, whose prevalence was estimated to be 19%. The overall prevalence of mental health disorders was similar amongst frontline HCWs (18%), general HCWs (17%), and students (20%) whilst being noticeably higher in the general population (27%). Conclusions: The results indicate that a considerable proportion of participants report the presence of anxiety, depression and insomnia symptoms. However, the pooled prevalence rates in Southeast Asia are significantly lower than those reported in other meta-analyses from other areas such as China and Europe. This meta-analysis may provide valuable evidence for more targeted identification of mental health needs and guide future research of long-term outcomes. Keywords: meta-analysis; Covid-19; mental health; pandemic; general population; healthcare workers; students; anxiety; depression; insomnia

This systematic review aims to 1) summarize the prevalence of anxiety, depression, distress, insomnia, and PTSD in the adult population during the first year of the COVID pandemic in developing countries and 2) uncover and highlight the uneven distribution of research on mental health in all developing countries across regions.Several literature databases were systemically searched for meta-analyses published by September 22, 2021, on the prevalence rates of mental health symptoms in developing countries worldwide. We meta-analysed the raw data of the individual empirical results from the previous meta-analysis papers in developing countries in different regions.The prevalence rates of mental health symptoms were summarized based on 341 empirical studies with a total of 1 704 072 participants from 40 out of 167 developing countries in Africa, Asia (East, Southeast, South, and West), Europe, and Latin America. Comparatively, Africa (39%) and West Asia (35%) had the worse overall mental health symptoms, followed by Latin America (32%). The prevalence rates of overall mental health symptoms of medical students (38%), general adult students (30%), and frontline health care workers (HCWs) (27%) were higher than those of general HCWs (25%) and general populations (23%). Among five mental health symptoms, distress (29%) and depression (27%) were the most prevalent. Interestingly, people in the least developing countries suffered less than those in emergent and other developing countries. The various instruments employed lead to result heterogeneity, demonstrating the importance of using the well-established instruments with the standard cut-off points (eg, GAD-7, GAD-2, and DASS-21 for anxiety, PHQ-9 and DASS-21 for depression, and ISI for insomnia).The research effort on mental health in developing countries during COVID-19 has been highly uneven in the scope of countries and mental health outcomes. This meta-analysis, the largest on this topic to date, shows that the mental health symptoms are highly prevalent yet differ across regions. The accumulated systematic evidence from this study can help enable the prioritization of mental health assistance efforts to allocate attention and resources across countries and regions.

Aims There is a lack of evidence related to the prevalence of mental health symptoms as well as their heterogeneities during the coronavirus disease 2019 (COVID-19) pandemic in Latin America, a large area spanning the equator. The current study aims to provide meta-analytical evidence on mental health symptoms during COVID-19 among frontline healthcare workers, general healthcare workers, the general population and university students in Latin America. Methods Bibliographical databases, such as PubMed, Embase, Web of Science, PsycINFO and medRxiv, were systematically searched to identify pertinent studies up to August 13, 2021. Two coders performed the screening using predefined eligibility criteria. Studies were assigned quality scores using the Mixed Methods Appraisal Tool. The double data extraction method was used to minimise data entry errors. Results A total of 62 studies with 196 950 participants in Latin America were identified. The pooled prevalence of anxiety, depression, distress and insomnia was 35%, 35%, 32% and 35%, respectively. There was a higher prevalence of mental health symptoms in South America compared to Central America (36% v. 28%, p < 0.001), in countries speaking Portuguese (40%) v. Spanish (30%). The pooled prevalence of mental health symptoms in the general population, general healthcare workers, frontline healthcare workers and students in Latin America was 37%, 34%, 33% and 45%, respectively. Conclusions The high yet heterogenous level of prevalence of mental health symptoms emphasises the need for appropriate identification of psychological interventions in Latin America.

Background: General population, frontline healthcare workers (HCWs), and adult students in Spain are at risk of anxiety, depression, and insomnia symptoms during the COVID-19 crisis. A meta-analysis of the individual studies on these symptoms would provide systematic evidence to aid policymakers and researchers in focusing on prevalence, risk, and best interventions. Objective: This paper aims to be the first meta-analysis and systematic review to calculate the prevalence of anxiety, depression, and insomnia symptoms in Spain’s adult population (general population, frontline healthcare workers (HCWs), and adult students) during the Covid-19 epidemic. Method: Random-effect meta-analysis was used to estimate the prevalence of anxiety, depression, and insomnia. Results: The meta-analysis includes 28 studies with 38 individual samples in Spain. The pooled prevalence of anxiety symptoms in 22 studies comprising a sample population of 82,024 was 20% (95% CI: 15–25%), that of depression symptoms in 22 articles with a total sample comprising 82,890 individuals was 22% (95% CI: 18–28%), and that of insomnia symptoms in three articles with a sample population of 745 was 57% (95% CI: 48–66%. Conclusions: The accumulative evidence reveals that adults in Spain suffered higher prevalence rates of mental symptoms during the COVID-19 crisis, with a significantly higher rate relative to other countries such as China. Our synthesis also reveals a relative lack of studies on frontline and general HCWs in Spain Refereed/Peer-reviewed

Motor brain machine interfaces (BMIs) directly link the brain to artificial actuators and have the potential to mitigate severe body paralysis caused by neurological injury or disease. Most BMI systems involve a decoder that analyzes neural spike counts to infer movement intent. However, many classical BMI decoders (1) fail to take advantage of temporal patterns of spike trains, possibly over long time horizons; (2) are insufficient to achieve good BMI performance at high temporal resolution, as the underlying Gaussian assumption of decoders based on spike counts is violated. Here, we propose a new statistical feature that represents temporal patterns or temporal codes of spike events with richer description—wavelet average coefficients (WAC)—to be used as decoder input instead of spike counts. We constructed a wavelet decoder framework by using WAC features with a sliding-window approach, and compared the resulting decoder against classical decoders (Wiener and Kalman family) and new deep learning based decoders ( Long Short-Term Memory) using spike count features. We found that the sliding-window approach boosts decoding temporal resolution, and using WAC features significantly improves decoding performance over using spike count features.

ObjectiveIn this paper, we aim to provide a systematic review and meta-analysis on the prevalence rates of mental health symptoms of anxiety, depression, and insomnia among the major populations during the COVID-19 pandemic in Africa.DesignA systematic review and meta-analysis.Data sourcesWe search and include articles using PubMed, Embase, Web of Science, PsycINFO, and medRxiv databases between Feb 202 and Feb 6th, 2021.Eligibility criteria and data analysisThe meta-analysis targets the prevalence rates of mental health symptoms of major populations including frontline/general healthcare workers (HCWs), the general adult population, and medical students during the COVID-19 pandemic in Africa. To estimate the prevalence rates of anxiety, depression, and insomnia, we pooled data using random-effects meta-analyses.ResultsIn this meta-analysis, we identify and include 28 studies and 32 independent samples from 12 countries with a total of 15,072 participants in Africa. Ethiopia (7) and Egypt (6) had the largest number of studies. While many countries including, but not limited to, Algeria, Kenya, and Ghana had a high number of COVID-19 cases, as many as three quarters of African countries have no studies. The pooled prevalence of anxiety in 27 studies was 37% (95%CI: 31-43%,I2= 99.0%) and that of depression in 24 studies was 45% (95%CI: 36-51%,I2= 99.5%) and that of insomnia in 9 studies was 28% (95%CI: 20-41%,I2= 99.2%). The pooled prevalence rates of anxiety, depression, and insomnia in North Africa (44%, 55%, and 31%, respectively) are higher than the rates in Sub-Saharan Africa (31%, 30%, and 24%, respectively). Our analysis indicated high heterogeneity and varying prevalence rates of mental health symptoms during the COVID-19 pandemic in Africa.DiscussionWe discuss our findings that a) a scarcity of studies in several African countries with a high number of COVID-19 cases, b) high heterogeneity among the studies, c) the extent of prevalence of mental health symptoms in Africa to be high, and d) the pattern of mental health symptoms in Africa differs from elsewhere, i.e., more African adults suffer from depression rather than anxiety and insomnia during COVID 19 compared to adult population in other countries or regions. Hence, our findings carry crucial implications for healthcare organizations and future research to enable evidence-based medicine in Africa. Our findings also call for increased scholarly attention on Africa, the least studied continent with a limited amount of research on mental health symptoms under the COVID 19 pandemic.Trial registrationCRD42020224458

Motor brain machine interfaces (BMI) directly link the brain to artificial actuators and have the potential to mitigate severe body paralysis caused by neurological injury or disease. Most BMI systems involve a decoder that analyzes neural spike counts to infer movement intent. However, many classical BMI decoders 1) fail to take advantage of temporal patterns of spike trains, possibly over long time horizons; 2) are insufficient to achieve good BMI performance at high temporal resolution, as the underlying Gaussian assumption of decoders based on spike counts is violated. Here, we propose a new statistical feature that represents temporal patterns or temporal codes of spike events with richer description - wavelet average coefficients (WAC) - to be used as decoder input instead of spike counts. We constructed a wavelet decoder framework by using WAC features with a sliding-window approach, and compared the resulting decoder against classical decoders (Wiener and Kalman family) using spike count features. We found that the sliding-window approach boosts decoding temporal resolution, and using WAC features significantly improves decoding performance over using spike count features.

Background: There is a considerable increase in psychological morbidities during COVID-19 pandemic among several demographic groups, including healthcare workers, the general population, and students. Regional disparities across the globe do exist, but evidence on the prevalence of mental disorder symptoms as well as their heterogeneities in Latin America during the COVID-19 pandemic is lacking. The current study aims to provide meta-analytical evidence on mental disorder symptoms during COVID-19 among frontline healthcare workers, general healthcare workers, the general population, and university students in Latin America. Methods: Bibliographical databases, such as PubMed, Embase, Web of Sciences, PsycINFO, and medRxiv, were systematically searched to identify pertinent studies up to February 6, 2021. Two coders performed the screening using predefined eligibility criteria. Studies were assigned quality scores using the Mixed Methods Appraisal Tool. The double data extraction method was used to minimize data entry errors. The pooled prevalence of anxiety, depression, distress, and insomnia was calculated using a random-effects model. Doi plot and the Luis Furuya–Kanamori (LFK) index were used to quantify publication bias. Findings: A total of 33 studies with 101,772 participants in Latin America were identified. The pooled prevalence of anxiety, depression, distress, and insomnia was 32%, 27%, 32%, and 35%, respectively. There was a higher prevalence of mental health symptoms in South America compared to Central America (33% vs. 27%, p

For brain-computer interfaces (BCIs), obtaining sufficient training data for algorithms that map neural signals onto actions can be difficult, expensive or even impossible. Here we report the development and use of a generative model-a model that synthesizes a virtually unlimited number of new data distributions from a learned data distribution-that learns mappings between hand kinematics and the associated neural spike trains. The generative spike-train synthesizer is trained on data from one recording session with a monkey performing a reaching task and can be rapidly adapted to new sessions or monkeys by using limited additional neural data. We show that the model can be adapted to synthesize new spike trains, accelerating the training and improving the generalization of BCI decoders. The approach is fully data-driven, and hence, applicable to applications of BCIs beyond motor control.

Learning a map from movement to neural data (Encoding Problem) and vice versa (Decoding Problem) are crucial to understanding motor control. A principled encoding model that understands underlying neural dynamics can help better solve the decoding problem. Here, we develop a new generative encoding model leveraging deep learning that autonomously captures neural dynamics. After training, the model can synthesize spike trains given any observed kinematics, under the guidance of the learned neural dynamics. When neural data from other sessions or subjects are limited, synthesized spike trains can improve cross-session and cross-subject decoding performance of a Brain Computer Interface decoder. For cross-subject, even with ample data for both subjects, neural dynamics learned from a previous subject can transfer useful knowledge that improves the best achievable decoding performance for the new subject. The approach is general and fully data-driven, and hence could apply to neuroscience encoding and decoding problems beyond motor control.

Primary motor (M1), primary somatosensory (S1) and dorsal premotor (PMd) cortical areas of rhesus monkeys previously have been associated only with sensorimotor control of limb movements. Here we show that a significant number of neurons in these areas also represent body position and orientation in space. Two rhesus monkeys (K and M) used a wheelchair controlled by a brain-machine interface (BMI) to navigate in a room. During this whole-body navigation, the discharge rates of M1, S1, and PMd neurons correlated with the two-dimensional (2D) room position and the direction of the wheelchair and the monkey head. This place cell-like activity was observed in both monkeys, with 44.6% and 33.3% of neurons encoding room position in monkeys K and M, respectively, and the overlapping populations of 41.0% and 16.0% neurons encoding head direction. These observations suggest that primary sensorimotor and premotor cortical areas in primates are likely involved in allocentrically representing body position in space during whole-body navigation, which is an unexpected finding given the classical hierarchical model of cortical processing that attributes functional specialization for spatial processing to the hippocampal formation.

Lower limb paralysis from spinal cord injury (SCI) or neurological disease carries a poor prognosis for recovery and remains a large societal burden. Neurophysiological and neuroprosthetic research have the potential to improve quality of life for these patients; however, the lack of an ethical and sustainable nonhuman primate model for paraplegia hinders their advancement. Therefore, our multidisciplinary team developed a way to induce temporary paralysis in awake behaving macaques by creating a fully implantable lumbar epidural catheter-subcutaneous port system that enables easy and reliable targeted drug delivery for sensorimotor blockade. During treadmill walking, aliquots of 1.5% lidocaine with 1:200,000 epinephrine were percutaneously injected into the ports of three rhesus macaques while surface electromyography (EMG) recorded muscle activity from their quadriceps and gastrocnemii. Diminution of EMG amplitude, loss of voluntary leg movement, and inability to bear weight were achieved for 60–90 min in each animal, followed by a complete recovery of function. The monkeys remained alert and cooperative during the paralysis trials and continued to take food rewards, and the ports remained functional after several months. This technique will enable recording from the cortex and/or spinal cord in awake behaving nonhuman primates during the onset, maintenance, and resolution of paraplegia for the first time, thus opening the door to answering basic neurophysiological questions about the acute neurological response to spinal cord injury and recovery. It will also negate the need to permanently injure otherwise high-value research animals for certain experimental paradigms aimed at developing and testing neural interface decoding algorithms for patients with lower extremity dysfunction. NEW & NOTEWORTHY A novel implantable lumbar epidural catheter-subcutaneous port system enables targeted drug delivery and induction of temporary paraplegia in awake, behaving nonhuman primates. Three macaques displayed loss of voluntary leg movement for 60–90 min after injection of lidocaine with epinephrine, followed by a full recovery. This technique for the first time will enable ethical live recording from the proximal central nervous system during the acute onset, maintenance, and resolution of paraplegia.

Several groups have developed brain-machine-interfaces (BMIs) that allow primates to use cortical activity to control artificial limbs. Yet, it remains unknown whether cortical ensembles could represent the kinematics of whole-body navigation and be used to operate a BMI that moves a wheelchair continuously in space. Here we show that rhesus monkeys can learn to navigate a robotic wheelchair, using their cortical activity as the main control signal. Two monkeys were chronically implanted with multichannel microelectrode arrays that allowed wireless recordings from ensembles of premotor and sensorimotor cortical neurons. Initially, while monkeys remained seated in the robotic wheelchair, passive navigation was employed to train a linear decoder to extract 2D wheelchair kinematics from cortical activity. Next, monkeys employed the wireless BMI to translate their cortical activity into the robotic wheelchair’s translational and rotational velocities. Over time, monkeys improved their ability to navigate the wheelchair toward the location of a grape reward. The navigation was enacted by populations of cortical neurons tuned to whole-body displacement. During practice with the apparatus, we also noticed the presence of a cortical representation of the distance to reward location. These results demonstrate that intracranial BMIs could restore whole-body mobility to severely paralyzed patients in the future.

Several species of the New World monkeys have been used as experimental models in biomedical and neurophysiological research. However, a method for controlled arm reaching tasks has not been developed for these species.We have developed a fully automated, pneumatically driven, portable, and reconfigurable experimental apparatus for arm-reaching tasks suitable for these small primates.We have utilized the apparatus to train two owl monkeys in a visually-cued arm-reaching task. Analysis of neural recordings demonstrates directional tuning of the M1 neurons.Our apparatus allows automated control, freeing the experimenter from manual experiments.The presented apparatus provides a valuable tool for conducting neurophysiological research on New World monkeys.