Visual features are processed before navigational affordances in the human brain.

To navigate through their immediate environment humans process scene information rapidly. How does the cascade of neural processing elicited by scene viewing to facilitate navigational planning unfold over time? To investigate, we recorded human brain responses to visual scenes with electroencephalography and related those to computational models that operationalize three aspects of scene processing (2D, 3D, and semantic information), as well as to a behavioral model capturing navigational affordances. We found a temporal processing hierarchy: navigational affordance is processed later than the other scene features (2D, 3D, and semantic) investigated. This reveals the temporal order with which the human brain computes complex scene information and suggests that the brain leverages these pieces of information to plan navigation.

Pain neuroscience education in patients with chronic musculoskeletal pain: an umbrella review.

Introduction: In recent years, pain neuroscience education (PNE) has been the focus of extensive research in the scientific literature in the field of physical therapy, but the results obtained are controversial and its clinical application remains unclear. The main aim of this umbrella review was to assess the effectiveness of PNE in patients with chronic musculoskeletal pain (CMP). Methods: We searched systematically in PubMed (Medline), PEDro, EMBASE, CINAHL and PsycINFO. Methodological quality was analyzed using AMSTAR-2 scale and overlapping analysis using GROOVE tool. Results: 16 systematic reviews were included. A qualitative synthesis was performed for the following sets of patients with CMP: overall CMP, chronic spinal pain, patients with fibromyalgia and patients with osteoarthritis. In general terms, it seems that the addition of the PNE-based intervention to other treatments, mostly exercise-based interventions although we might refer to it in terms of a multimodal approach, leads to greater clinical improvements than the multimodal approach alone. We have found this especially in the reduction of the influence of psychosocial variables. However, it seems that studies testing the effectiveness of PNE in isolation, systematic reviews with or without meta-analysis did not show statistically significant improvements overall in terms of pain intensity, disability levels or psychosocial variables. Discussion: There is a great heterogeneity in the results obtained and the PNE protocols used, a critically low quality in the reviews included and a very high overlap, so there is a need to improve the studies in this field before clinical application. Systematic review registration: PROSPERO (CRD42022355634).

LLM Multimodal Traffic Accident Forecasting.

With the rise in traffic congestion in urban centers, predicting accidents has become paramount for city planning and public safety. This work comprehensively studied the efficacy of modern deep learning (DL) methods in forecasting traffic accidents and enhancing Level-4 and Level-5 (L-4 and L-5) driving assistants with actionable visual and language cues. Using a rich dataset detailing accident occurrences, we juxtaposed the Transformer model against traditional time series models like ARIMA and the more recent Prophet model. Additionally, through detailed analysis, we delved deep into feature importance using principal component analysis (PCA) loadings, uncovering key factors contributing to accidents. We introduce the idea of using real-time interventions with large language models (LLMs) in autonomous driving with the use of lightweight compact LLMs like LLaMA-2 and Zephyr-7b-α. Our exploration extends to the realm of multimodality, through the use of Large Language-and-Vision Assistant (LLaVA)-a bridge between visual and linguistic cues by means of a Visual Language Model (VLM)-in conjunction with deep probabilistic reasoning, enhancing the real-time responsiveness of autonomous driving systems. In this study, we elucidate the advantages of employing large multimodal models within DL and deep probabilistic programming for enhancing the performance and usability of time series forecasting and feature weight importance, particularly in a self-driving scenario. This work paves the way for safer, smarter cities, underpinned by data-driven decision making.

LLM Adaptive PID Control for B5G Truck Platooning Systems.

This paper presents an exploration into the capabilities of an adaptive PID controller within the realm of truck platooning operations, situating the inquiry within the context of Cognitive Radio and AI-enhanced 5G and Beyond 5G (B5G) networks. We developed a Deep Learning (DL) model that emulates an adaptive PID controller, taking into account the implications of factors such as communication latency, packet loss, and communication range, alongside considerations of reliability, robustness, and security. Furthermore, we harnessed a Large Language Model (LLM), GPT-3.5-turbo, to deliver instantaneous performance updates to the PID system, thereby elucidating its potential for incorporation into AI-enabled radio and networks. This research unveils crucial insights for augmenting the performance and safety parameters of vehicle platooning systems within B5G networks, concurrently underlining the prospective applications of LLMs within such technologically advanced communication environments.

Biomechanical analysis of breastfeeding positions and their effects on lumbopelvic curvatures and lumbar muscle responses.

This study aimed to analyze the position of the lumbopelvic region and lumbar muscle activity in the most common breastfeeding positions. We recorded the curvatures of the lumbar spine and pelvis by means of an electrogoniometer, and the muscle activation levels of the erector spinae with electromyography, in 34 women in erect standing and breastfeeding their children in several positions. Both side lying and clutch-hold positions showed a greater degree of lumbar spine flexion compared to standing. In all sitting postures it was observed that the pelvis was placed in retroversion when compared to standing and side lying. In muscle activity, it was observed that the activation intensity of the right erector in the right side-supported side lying position was significantly lower compared to the rest of breastfeeding postures and standing. Side lying may be a better position to avoid muscle fatigue.

The dose-dependent effects of transcutaneous electrical nerve stimulation for pain relief in individuals with fibromyalgia: a systematic review and meta-analysis.

ABSTRACT: Transcutaneous electrical nerve stimulation (TENS) is a nonpharmacological modality widely used to manage pain; however, its effectiveness for individuals with fibromyalgia (FM) has been questioned. In previous studies and systematic reviews, variables related to dose of TENS application have not been considered. The objectives of this meta-analysis were (1) to determine the effect of TENS on pain in individuals with FM and (2) determine the dose-dependent effect of TENS dose parameters on pain relief in individuals with FM. We searched the PubMed, PEDro, Cochrane, and EMBASE databases for relevant manuscripts. Data were extracted from 11 of the 1575 studies. The quality of the studies was assessed using the PEDro scale and RoB-2 assessment. This meta-analysis was performed using a random-effects model that, when not considering the TENS dosage applied, showed that the treatment had no overall effect on pain (d+ = 0.51, P > 0.050, k = 14). However, the moderator analyses, which were performed assuming a mixed-effect model, revealed that 3 of the categorical variables were significantly associated with effect sizes: the number of sessions ( P = 0.005), the frequency ( P = 0.014), and the intensity ( P = 0.047). The electrode placement was not significantly associated with any effect sizes. Thus, there is evidence that TENS can effectively reduce pain in individuals with FM when applied at high or at mixed frequencies, a high intensity, or in long-term interventions involving 10 or more sessions. This review protocol was registered at PROSPERO (CRD42021252113).

Effectiveness of Virtual Reality on Balance and Risk of Falls in People with Multiple Sclerosis: A Systematic Review and Meta-Analysis.

The aim of this study was to systematically review the scientific evidence related to the physiotherapy interventions in neurorehabilitation that utilize virtual reality (VR) for balance training and risk of falls in people with multiple sclerosis (MS). A search was conducted in Medline (PubMed), PEDro, and Google Scholar to identify all the relevant studies. Clinical trials assessing the effects of VR in people with MS were included. Risk of bias was evaluated using the Cochrane Risk of Bias Tool and PEDro scale. Qualitative analysis was performed according to the GRADE. In total, 16 studies (n = 663) were included. The meta-analysis showed statistically significant differences for the VR intervention in comparison with conventional treatment for balance, with a moderate clinical effect in eight studies (SMD: 0.63; 95% CI 0.34-0.92; p < 0.05). In addition, the meta-analysis showed statistically significant differences for the VR intervention in comparison with conventional treatment for risk of falls, with a small clinical effect in six studies (SMD: -0.55; 95% CI -1.07-0.04; p < 0.05). VR-based treatments are more effective than non-intervention in improving balance and fall risk in people with MS, with a very low certainty of evidence. In addition, they also show to be more effective than conventional rehabilitation, with a very low certainty of evidence.

A large and rich EEG dataset for modeling human visual object recognition.

The human brain achieves visual object recognition through multiple stages of linear and nonlinear transformations operating at a millisecond scale. To predict and explain these rapid transformations, computational neuroscientists employ machine learning modeling techniques. However, state-of-the-art models require massive amounts of data to properly train, and to the present day there is a lack of vast brain datasets which extensively sample the temporal dynamics of visual object recognition. Here we collected a large and rich dataset of high temporal resolution EEG responses to images of objects on a natural background. This dataset includes 10 participants, each with 82,160 trials spanning 16,740 image conditions. Through computational modeling we established the quality of this dataset in five ways. First, we trained linearizing encoding models that successfully synthesized the EEG responses to arbitrary images. Second, we correctly identified the recorded EEG data image conditions in a zero-shot fashion, using EEG synthesized responses to hundreds of thousands of candidate image conditions. Third, we show that both the high number of conditions as well as the trial repetitions of the EEG dataset contribute to the trained models' prediction accuracy. Fourth, we built encoding models whose predictions well generalize to novel participants. Fifth, we demonstrate full end-to-end training of randomly initialized DNNs that output EEG responses for arbitrary input images. We release this dataset as a tool to foster research in visual neuroscience and computer vision.

Single Image Video Prediction with Auto-Regressive GANs.

In this paper, we introduce an approach for future frames prediction based on a single input image. Our method is able to generate an entire video sequence based on the information contained in the input frame. We adopt an autoregressive approach in our generation process, i.e., the output from each time step is fed as the input to the next step. Unlike other video prediction methods that use "one shot" generation, our method is able to preserve much more details from the input image, while also capturing the critical pixel-level changes between the frames. We overcome the problem of generation quality degradation by introducing a "complementary mask" module in our architecture, and we show that this allows the model to only focus on the generation of the pixels that need to be changed, and to reuse those that should remain static from its previous frame. We empirically validate our methods against various video prediction models on the UT Dallas Dataset, and show that our approach is able to generate high quality realistic video sequences from one static input image. In addition, we also validate the robustness of our method by testing a pre-trained model on the unseen ADFES facial expression dataset. We also provide qualitative results of our model tested on a human action dataset: The Weizmann Action database.

The spatiotemporal neural dynamics of object location representations in the human brain.

To interact with objects in complex environments, we must know what they are and where they are in spite of challenging viewing conditions. Here, we investigated where, how and when representations of object location and category emerge in the human brain when objects appear on cluttered natural scene images using a combination of functional magnetic resonance imaging, electroencephalography and computational models. We found location representations to emerge along the ventral visual stream towards lateral occipital complex, mirrored by gradual emergence in deep neural networks. Time-resolved analysis suggested that computing object location representations involves recurrent processing in high-level visual cortex. Object category representations also emerged gradually along the ventral visual stream, with evidence for recurrent computations. These results resolve the spatiotemporal dynamics of the ventral visual stream that give rise to representations of where and what objects are present in a scene under challenging viewing conditions.

The Impact of the COVID-19 Pandemic on the Lifestyles and Levels of Anxiety and Depression of Patients with Schizophrenia: A Retrospective Observational Study.

The movement restrictions put in place as a result of the COVID-19 pandemic required modification of the population's usual routines, including those of the most vulnerable groups such as patients with schizophrenia. This was a retrospective observational study. We used an online survey to collect information on patient adherence to the Mediterranean diet (Mediterranean Diet Adherence Screener questionnaire), physical exercise (International Physical Activity Questionnaire Short Form), and tobacco consumption and levels of anxiety and depression (Hospital Anxiety and Depression Scale) before and during the movement restrictions. A total of 102 people with schizophrenia participated in this study. During the COVID-19 pandemic lockdown the participants significantly increased the number of minutes spent sitting per day (z = -6.73; p < 0.001), decreased the time they spent walking (z = -6.32; p < 0.001), and increased their tobacco consumption (X2 = 156.90; p < 0.001). These results were also accompanied by a significant increase in their reported levels of anxiety (z = -7.45; p < 0.001) and depression (z = -7.03, p < 0.001). No significant differences in patient diets during the pandemic compared to before the movement restrictions were reported. These results suggest the need to implement specific programs to improve lifestyle and reduce anxiety and depression during possible future pandemic situations.

The short and long-term effects of aerobic, strength, or mixed exercise programs on schizophrenia symptomatology.

The purpose of this study was to compare the effects of three different physical exercise programs on the symptomatology, body composition, physical activity, physical fitness, and quality of life of individuals with schizophrenia. A total of 432 patients were assessed for eligibility and 86 were randomized into the aerobic (n = 28), strength (n = 29) or mixed (n = 29) groups. Positive, negative, and general symptoms of psychosis, body mass index (BMI), physical activity (IPAQ-SF), physical fitness (6-min walk test [6MWT] and hand-grip strength [HGS]), and quality of life (WHOQUOL-BREF) were assessed at baseline, post-intervention (16 weeks), and at 10-months. Our results at 16 weeks showed significant improvements in all three groups in the negative, general, and total symptoms with moderate to large effect sizes (P < 0.01, ηp2 > 0.11), no change in the BMI, 6MWT or IPAQ-SF, and a significant improvement in the HGS test in the strength and mixed groups (P ≤ 0.05, ηp2 > 0.08). Nonetheless, all the improvements had disappeared at 10 months. We concluded that 3 weekly sessions of a moderate to vigorous progressive exercise program for 16 weeks improved the symptomatology of individuals with schizophrenia in all three groups, with no differences between them. However, the effects had declined to baseline levels by the 10-month follow-up, suggesting that exercise interventions should be maintained over time.

AttendAffectNet-Emotion Prediction of Movie Viewers Using Multimodal Fusion with Self-Attention.

In this paper, we tackle the problem of predicting the affective responses of movie viewers, based on the content of the movies. Current studies on this topic focus on video representation learning and fusion techniques to combine the extracted features for predicting affect. Yet, these typically, while ignoring the correlation between multiple modality inputs, ignore the correlation between temporal inputs (i.e., sequential features). To explore these correlations, a neural network architecture-namely AttendAffectNet (AAN)-uses the self-attention mechanism for predicting the emotions of movie viewers from different input modalities. Particularly, visual, audio, and text features are considered for predicting emotions (and expressed in terms of valence and arousal). We analyze three variants of our proposed AAN: Feature AAN, Temporal AAN, and Mixed AAN. The Feature AAN applies the self-attention mechanism in an innovative way on the features extracted from the different modalities (including video, audio, and movie subtitles) of a whole movie to, thereby, capture the relationships between them. The Temporal AAN takes the time domain of the movies and the sequential dependency of affective responses into account. In the Temporal AAN, self-attention is applied on the concatenated (multimodal) feature vectors representing different subsequent movie segments. In the Mixed AAN, we combine the strong points of the Feature AAN and the Temporal AAN, by applying self-attention first on vectors of features obtained from different modalities in each movie segment and then on the feature representations of all subsequent (temporal) movie segments. We extensively trained and validated our proposed AAN on both the MediaEval 2016 dataset for the Emotional Impact of Movies Task and the extended COGNIMUSE dataset. Our experiments demonstrate that audio features play a more influential role than those extracted from video and movie subtitles when predicting the emotions of movie viewers on these datasets. The models that use all visual, audio, and text features simultaneously as their inputs performed better than those using features extracted from each modality separately. In addition, the Feature AAN outperformed other AAN variants on the above-mentioned datasets, highlighting the importance of taking different features as context to one another when fusing them. The Feature AAN also performed better than the baseline models when predicting the valence dimension.

Impact of the COVID-19 Pandemic on the Lifestyles and Quality of Life of Women With Fertility Problems: A Cross-Sectional Study.

Background: The COVID-19 pandemic has implied worldwide the imposition of confinement measures and mobility restrictions, to a greater or lesser extent. It has also meant the closure of some public medical services such as reproductive care. This situation may have impacted the health-related behaviour and quality of life of women with fertility problems. Objective: The objective of this study was to analyse the effects of confinement and the suspension of reproductive medical care on the lifestyle (diet, physical exercise, and smoking habits), anxiety and depression, and quality of life of infertile women by comparing their pre- and post-confinement situations. Methods: We carried out a cross-sectional, internet-based study. Information was collected on these women's adherence to the Mediterranean diet (MEDAS questionnaire), physical exercise (IPAQ-SF), anxiety and depression (HADS), and quality of life related to fertility (FertiQol) before, during, and after confinement. The survey was conducted between 1 September and 28 October 2020. Results: A total of 85 women participated. There had been a significant increase in anxiety and depression levels (P < 0.001) and an increase in tobacco consumption among female smokers during confinement vs. pre-confinement (62.5% had increased their consumption). The participants had also increased the mean number of hours they spent sitting (P < 0.001). There had also been an increase in vigorous and moderate exercise levels by 40 and 30%, respectively (P = 0.004). However, no differences were observed in these patients' eating habits as a result of confinement (P = 0.416). When the reproduction service was resumed, the participants showed higher anxiety level scores (P = 0.001) with respect to the pre-confinement situation as well as lower mean FertiQol scale score (P = 0.008). Conclusions: Confinement had increased anxiety and depression levels among these infertile women as well as tobacco use among the participants who were smokers. The prolonged closure of reproductive care units decreased the quality of life of the participants of this study. These results suggest the need to implement online programs to improve healthy habits and quality of life of this population group.

Unveiling functions of the visual cortex using task-specific deep neural networks.

The human visual cortex enables visual perception through a cascade of hierarchical computations in cortical regions with distinct functionalities. Here, we introduce an AI-driven approach to discover the functional mapping of the visual cortex. We related human brain responses to scene images measured with functional MRI (fMRI) systematically to a diverse set of deep neural networks (DNNs) optimized to perform different scene perception tasks. We found a structured mapping between DNN tasks and brain regions along the ventral and dorsal visual streams. Low-level visual tasks mapped onto early brain regions, 3-dimensional scene perception tasks mapped onto the dorsal stream, and semantic tasks mapped onto the ventral stream. This mapping was of high fidelity, with more than 60% of the explainable variance in nine key regions being explained. Together, our results provide a novel functional mapping of the human visual cortex and demonstrate the power of the computational approach.

Unraveling Representations in Scene-selective Brain Regions Using Scene-Parsing Deep Neural Networks.

Visual scene perception is mediated by a set of cortical regions that respond preferentially to images of scenes, including the occipital place area (OPA) and parahippocampal place area (PPA). However, the differential contribution of OPA and PPA to scene perception remains an open research question. In this study, we take a deep neural network (DNN)-based computational approach to investigate the differences in OPA and PPA function. In a first step, we search for a computational model that predicts fMRI responses to scenes in OPA and PPA well. We find that DNNs trained to predict scene components (e.g., wall, ceiling, floor) explain higher variance uniquely in OPA and PPA than a DNN trained to predict scene category (e.g., bathroom, kitchen, office). This result is robust across several DNN architectures. On this basis, we then determine whether particular scene components predicted by DNNs differentially account for unique variance in OPA and PPA. We find that variance in OPA responses uniquely explained by the navigation-related floor component is higher compared to the variance explained by the wall and ceiling components. In contrast, PPA responses are better explained by the combination of wall and floor, that is, scene components that together contain the structure and texture of the scene. This differential sensitivity to scene components suggests differential functions of OPA and PPA in scene processing. Moreover, our results further highlight the potential of the proposed computational approach as a general tool in the investigation of the neural basis of human scene perception.

Effects of neuromuscular training compared to classic strength-resistance training in patients with acute coronary syndrome: A study protocol for a randomized controlled trial.

The aim of the present clinical trial is to evaluate the effectiveness of neuromuscular versus classical strength-resistance training as part of a cardiac rehabilitation programme in patients following acute coronary syndrome. The study is designed as a double-blinded, randomised, and controlled clinical trial. Thirty participants suffering from acute coronary syndrome who meet our inclusion criteria will be recruited by a private tertiary hospital. The intervention group will follow 20 sessions of a cardiac rehabilitation programme divided into two parts: aerobic training and neuromuscular strength-resistance training. The control group will complete the same aerobic training as well as a classical strength-resistance training workout programme. The primary outcome of the study will be the mean difference in change from baseline in the Incremental Shuttle Walking Test. The secondary outcomes will be the cardiorespiratory fitness of the patients (assessed by means of the Chester Step Test), lower-limb performance (assessed with the 30-Second Chair Stand Test and Single-Leg Squat Test), lower-limb strength (hip flexor handheld dynamometry), sexual dysfunction assessment (Sex Health Inventory for Men) and quality of life (EQ-5D-5L). This work will provide evidence for the effectiveness of a neuromuscular versus a classic strength-training programme in terms of cardiorespiratory fitness, lower-limb performance capacities and quality of life, in cardiac patients. The data obtained could lead to more effective and functional workouts which, in turn, may enhance the speed at which these patients can return to their everyday activities of life and improve the efficiency of their movement patterns and heart responses. Furthermore, patients may find neuromuscular workout routines more motivating and engaging, thus encouraging them to adopt healthier lifestyle patterns.

Analysis of the Impact of the Confinement Resulting from COVID-19 on the Lifestyle and Psychological Wellbeing of Spanish Pregnant Women: An Internet-Based Cross-Sectional Survey.

(1) Background: This study aimed to analyze the impact of the confinement due to the COVID-19 pandemics on the eating, exercise, and quality-of-life habits of pregnant women. (2) Methods: This was an internet-based cross-sectional survey which collected information about adherence to the Mediterranean diet, physical exercise, health-related quality of life (HRQoL), and perceived obstacles (in terms of exercise, preparation for delivery, and medical appointments) of pregnant women before and after the confinement. The survey was conducted in 18-31 May 2020. (3) Results: A total of 90 pregnant women participated in this study. There was a significant decrease in the levels of physical activity (p < 0.01) as well as in HRQoL (p < 0.005). The number of hours spent sitting increased by 50% (p < 0.001), 52.2% were unable to attend delivery preparation sessions because these had been cancelled. However, there were no significant differences in the eating pattern of these women (p = 0.672). Conclusions: These results suggest the need to implement specific online programs to promote exercise and reduce stress, thus improving the HRQoL in this population, should similar confinements need to occur again for any reason in the future.

Scale and translation-invariance for novel objects in human vision.

Though the range of invariance in recognition of novel objects is a basic aspect of human vision, its characterization has remained surprisingly elusive. Here we report tolerance to scale and position changes in one-shot learning by measuring recognition accuracy of Korean letters presented in a flash to non-Korean subjects who had no previous experience with Korean letters. We found that humans have significant scale-invariance after only a single exposure to a novel object. The range of translation-invariance is limited, depending on the size and position of presented objects. To understand the underlying brain computation associated with the invariance properties, we compared experimental data with computational modeling results. Our results suggest that to explain invariant recognition of objects by humans, neural network models should explicitly incorporate built-in scale-invariance, by encoding different scale channels as well as eccentricity-dependent representations captured by neurons' receptive field sizes and sampling density that change with eccentricity. Our psychophysical experiments and related simulations strongly suggest that the human visual system uses a computational strategy that differs in some key aspects from current deep learning architectures, being more data efficient and relying more critically on eye-movements.