Identifying Naturalistic Movies from Human Brain Activity with High-Density Diffuse Optical Tomography.

Modern neuroimaging modalities, particularly functional MRI (fMRI), can decode detailed human experiences. Thousands of viewed images can be identified or classified, and sentences can be reconstructed. Decoding paradigms often leverage encoding models that reduce the stimulus space into a smaller yet generalizable feature set. However, the neuroimaging devices used for detailed decoding are non-portable, like fMRI, or invasive, like electrocorticography, excluding application in naturalistic use. Wearable, non-invasive, but lower-resolution devices such as electroencephalography and functional near-infrared spectroscopy (fNIRS) have been limited to decoding between stimuli used during training. Herein we develop and evaluate model-based decoding with high-density diffuse optical tomography (HD-DOT), a higher-resolution expansion of fNIRS with demonstrated promise as a surrogate for fMRI. Using a motion energy model of visual content, we decoded the identities of novel movie clips outside the training set with accuracy far above chance for single-trial decoding. Decoding was robust to modulations of testing time window, different training and test imaging sessions, hemodynamic contrast, and optode array density. Our results suggest that HD-DOT can translate detailed decoding into naturalistic use.

Scalable, modular continuous wave functional near-infrared spectroscopy system (Spotlight).

Significance: We present a fiberless, portable, and modular continuous wave-functional near-infrared spectroscopy system, Spotlight, consisting of multiple palm-sized modules-each containing high-density light-emitting diode and silicon photomultiplier detector arrays embedded in a flexible membrane that facilitates optode coupling to scalp curvature. Aim: Spotlight's goal is to be a more portable, accessible, and powerful functional near-infrared spectroscopy (fNIRS) device for neuroscience and brain-computer interface (BCI) applications. We hope that the Spotlight designs we share here can spur more advances in fNIRS technology and better enable future non-invasive neuroscience and BCI research. Approach: We report sensor characteristics in system validation on phantoms and motor cortical hemodynamic responses in a human finger-tapping experiment, where subjects wore custom 3D-printed caps with two sensor modules. Results: The task conditions can be decoded offline with a median accuracy of 69.6%, reaching 94.7% for the best subject, and at a comparable accuracy in real time for a subset of subjects. We quantified how well the custom caps fitted to each subject and observed that better fit leads to more observed task-dependent hemodynamic response and better decoding accuracy. Conclusions: The advances presented here should serve to make fNIRS more accessible for BCI applications.

Rapid adaptation of brain-computer interfaces to new neuronal ensembles or participants via generative modelling.

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.

Mental health symptoms during the COVID-19 pandemic in developing countries: A systematic review and meta-analysis.

Background: 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. Methods: 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. Results: 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). Conclusions: 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.

Mental disorder symptoms during the COVID-19 pandemic in Latin America - a systematic review and meta-analysis.

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.

Meta-analytic evidence of depression and anxiety in Eastern Europe during the COVID-19 pandemic.

Objective: To perform a systematic and meta-analysis on the prevalence rates of mental health symptoms including anxiety and depression during the COVID-19 pandemic in the general population in Eastern Europe, as well as three select sub-populations: students, general healthcare workers, and frontline healthcare workers. Data sources: Studies in PubMed, Embase, Web of Science, PsycINFO, and medRxiv up to 6 February 2021. Eligibility criteria and data analysis: Prevalence rates of mental health symptoms in the general population and key sub-populations during the COVID-19 pandemic in Eastern Europe. Data were pooled using a random-effects meta-analysis to estimate the prevalence rates of anxiety and depression. Results: The meta-analysis identifies and includes 21 studies and 26 independent samples in Eastern Europe. Poland (n = 4), Serbia (n = 4), Russia (n = 3), and Croatia (n = 3) had the greatest number of studies. To our knowledge, no studies have been conducted in eleven Eastern European countries including Hungary, Slovakia, and Slovenia. The pooled prevalence of anxiety in 18 studies with 22 samples was 30% (95% CI: 24-37%) pooled prevalence of depression in 18 studies with 23 samples was 27% (95% CI: 21-34%). Implications: The cumulative evidence from the meta-analysis reveals high prevalence rates of clinically significant symptoms during the COVID-19 pandemic in Eastern Europe. The findings suggest evidence of a potential mental health crisis in Eastern Europe during the ongoing COVID-19 pandemic. Our synthesis also reveals a relative lack of studies in certain Eastern European countries as well as high heterogeneities among the existing studies, calling for more effort to achieve evidence-based mental healthcare in Eastern Europe.

Objetivo: Realizar un metanálisis sistemático sobre las tasas de prevalencia de síntomas de salud mental, incluidos ansiedad y depresión durante la pandemia de COVID-19 en la población general de Europa del Este, así como en tres subpoblaciones seleccionadas: estudiantes, trabajadores sanitarios generales y trabajadores sanitarios de primera línea.Fuentes de datos: Estudios en PubMed, Embase, Web of Science, PsycINFO y medRxiv hasta el 6 de febrero de 2021.Criterios de elegibilidad y análisis de datos: Tasas de prevalencia de síntomas de salud mental en la población general y subpoblaciones claves durante la pandemia de COVID-19 en Europa del Este. Los datos se combinaron mediante un metanálisis de efectos aleatorios para estimar las tasas de prevalencia de ansiedad y depresión.Resultados: El metanálisis identifica e incluye 21 estudios y 26 muestras independientes en Europa del Este. Polonia (n = 4), Serbia (n = 4), Rusia (n = 3) y Croacia (n = 3) tuvieron el mayor número de estudios. Hasta donde sabemos, no se han realizado estudios en once países de Europa del Este, incluidos Hungría, Eslovaquia y Eslovenia. La prevalencia combinada de ansiedad en 18 estudios con 22 muestras fue de 30% (IC del 95%: 24­37%) y la prevalencia combinada de depresión en 18 estudios con 23 muestras fue de 27% (IC del 95%: 21­34%).Implicaciones: La evidencia acumulada del metanálisis revela altas tasas de prevalencia de síntomas clínicamente significativos durante la pandemia de COVID-19 en Europa del Este. Los hallazgos sugieren evidencia de una posible crisis de salud mental en Europa del Este durante la pandemia de COVID-19 en curso. Nuestra síntesis también revela una relativa falta de estudios en ciertos países de Europa del Este, así como una gran heterogeneidad entre los estudios existentes, lo que exige un mayor esfuerzo para lograr una atención de la salud mental basada en la evidencia en Europa del Este.

A Systematic Review and Meta-Analysis of Symptoms of Anxiety, Depression, and Insomnia in Spain in the COVID-19 Crisis.

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.

A systematic review and meta-analysis of the mental health symptoms during the Covid-19 pandemic in Southeast Asia.

AIMS: The Covid-19 pandemic has had a substantial impact on the mental health of the general public and high-risk groups worldwide. Due to its proximity and close links to China, Southeast Asia was one of the first regions to be affected by the outbreak. The aim of this systematic review was to evaluate the prevalence of anxiety, depression and insomnia in the general adult population and healthcare workers (HCWs) in Southeast Asia during the course of the first year of the pandemic. METHODS: Several literature databases were systemically searched for articles published up to February 2021 and two reviewers independently evaluated all relevant studies using pre-determined criteria. The prevalence rates of mental health symptoms were calculated using a random-effect meta-analysis model. RESULTS: In total, 32 samples from 25 studies with 20 352 participants were included. Anxiety was assessed in all 25 studies and depression in 15 studies with pooled prevalence rates of 22% and 16%, respectively. Only two studies assessed insomnia, which was estimated at 19%. The prevalence of anxiety and depression was similar among frontline HCWs (18%), general HCWs (17%), and students (20%) while being noticeably higher in the general population (27%). CONCLUSIONS: This is the first systematic review to investigate the mental health impact of the Covid-19 pandemic in Southeast Asia. A considerable proportion of the general population and HCWs reported mild to moderate symptoms of anxiety and depression; the pooled prevalence rater, however, remain significantly lower than those reported in other areas such as China and Europe.

Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface.

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.

A novel paraplegia model in awake behaving macaques.

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.

An automatic experimental apparatus to study arm reaching in New World monkeys.

BACKGROUND: 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. NEW METHOD: We have developed a fully automated, pneumatically driven, portable, and reconfigurable experimental apparatus for arm-reaching tasks suitable for these small primates. RESULTS: 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. COMPARISON WITH EXISTING METHOD(S): Our apparatus allows automated control, freeing the experimenter from manual experiments. CONCLUSION: The presented apparatus provides a valuable tool for conducting neurophysiological research on New World monkeys.

Wireless Cortical Brain-Machine Interface for Whole-Body Navigation in Primates.

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.

Dry lithography of large-area, thin-film organic semiconductors using frozen CO(2) resists.

To address the incompatibility of organic semiconductors with traditional photolithography, an inert, frozen CO(2) resist is demonstrated that forms an in situ shadow mask. Contact with a room-temperature micro-featured stamp is used to pattern the resist. After thin film deposition, the remaining CO(2) is sublimed to lift off unwanted material. Pixel densities of 325 pixels-per-inch are shown.