Chapmandelaney5801

Robotic exoskeletons are developed with the aim of enhancing convenience and physical possibilities in daily life. However, at present, these devices lack sufficient synchronization with human movements. To optimize human-exoskeleton interaction, this article proposes a gait recognition and prediction model, called the gait neural network (GNN), which is based on the temporal convolutional network. It consists of an intermediate network, a target network, and a recognition and prediction model. The novel structure of the algorithm can make full use of the historical information from sensors. The performance of the GNN is evaluated based on the publicly available HuGaDB dataset, as well as on data collected by an inertial-based wearable motion capture device. The results show that the proposed approach is highly effective and achieves superior performance compared with existing methods.The paper puts forward an on-board strategy for a training model and develops a real-time human locomotion mode recognition study based on a trained model utilizing two inertial measurement units (IMUs) of robotic transtibial prosthesis. Three transtibial amputees were recruited as subjects in this study to finish five locomotion modes (level ground walking, stair ascending, stair descending, ramp ascending, and ramp descending) with robotic prostheses. An interaction interface was designed to collect sensors' data and instruct to train model and recognition. In this study, the analysis of variance ratio (no more than 0.05) reflects the good repeatability of gait. The on-board training time for SVM (Support Vector Machines), QDA (Quadratic Discriminant Analysis), and LDA (Linear discriminant analysis) are 89, 25, and 10 s based on a 10,000 × 80 training data set, respectively. It costs about 13.4, 5.36, and 0.067 ms for SVM, QDA, and LDA for each recognition process. #link# Taking the recognition accuracy of some previous studies and time consumption into consideration, we choose QDA for real-time recognition study. The real-time recognition accuracies are 97.19 ± 0.36% based on QDA, and we can achieve more than 95% recognition accuracy for each locomotion mode. The receiver operating characteristic also shows the good quality of QDA classifiers. This study provides a preliminary interaction design for human-machine prosthetics in future clinical application. This study just adopts two IMUs not multi-type sensors fusion to improve the integration and wearing convenience, and it maintains comparable recognition accuracy with multi-type sensors fusion at the same time.[This corrects the article DOI 10.3389/fncom.2019.00049.].Subthalamic nucleus deep brain stimulation (STN-DBS) is an effective invasive treatment for advanced Parkinson's disease (PD) at present. Due to the invasiveness and cost of operations, a reliable tool is required to predict the outcome of therapy in the clinical decision-making process. This work aims to investigate whether the topological network of functional connectivity states can predict the outcome of DBS without medication. Fifty patients were recruited to extract the features of the brain related to the improvement rate of PD after STN-DBS and to train the machine learning model that can predict the therapy's effect. The functional connectivity analyses suggested that the GBRT model performed best with Pearson's correlations of r = 0.65, p = 2.58E-07 in medication-off condition. The connections between middle frontal gyrus (MFG) and inferior temporal gyrus (ITG) contribute most in the GBRT model.Oscillations in the beta/low gamma range (10-45 Hz) are recorded in diverse neural structures. They have successfully been modeled as sparsely synchronized oscillations arising from reciprocal interactions between randomly connected excitatory (E) pyramidal cells and local interneurons (I). The synchronization of spatially distant oscillatory spiking E-I modules has been well-studied in the rate model framework but less so for modules of spiking neurons. Here, we first show that previously proposed modifications of rate models provide a quantitative description of spiking E-I modules of Exponential Integrate-and-Fire (EIF) neurons. This allows us to analyze the dynamical regimes of sparsely synchronized oscillatory E-I modules connected by long-range excitatory interactions, for two modules, as well as for a chain of such modules. For modules with a large number of neurons (> 105), we obtain results similar to previously obtained ones based on the classic deterministic Wilson-Cowan rate model, with the added s of traveling waves in the cortex during beta oscillations.Despite the recent progress in AI powered by deep learning in solving narrow tasks, we are not close to human intelligence in its flexibility, versatility, and efficiency. Efficient learning and effective generalization come from inductive biases, and building Artificial General Intelligence (AGI) is an exercise in finding the right set of inductive biases that make fast learning possible while being general enough to be widely applicable in tasks that humans excel at. To make progress in AGI, we argue that we can look at the human brain for such inductive biases and principles of generalization. To that effect, we propose a strategy to gain insights from the brain by simultaneously looking at the world it acts upon and the computational framework to support efficient learning and generalization. We present a neuroscience-inspired generative model of vision as a case study for such approach and discuss some open problems about the path to AGI.Hemiparetic stroke in adulthood often results in the grouped movement pattern of the upper extremity flexion synergy thought to arise from an increased reliance on cortico-reticulospinal pathways due to a loss of lateral corticospinal projections. It is well established that the flexion synergy induces reaching constraints in individuals with adult-onset hemiplegia. The expression of the flexion synergy in individuals with brain injuries onset earlier in the lifespan is currently unknown. An early unilateral brain injury occurring prior to six months post full-term may preserve corticospinal projections which can be used for independent joint control and thus minimizing the expression of the flexion synergy. This study uses kinematics of a ballistic reaching task to evaluate the expression of the flexion synergy in individuals with pediatric hemiplegia (PH) ages six to seventeen years. Fifteen individuals with brain injuries before birth (n = 8) and around full-term (n = 7) and nine age-matched controls with no known neurological impairment completed a set of reaches in an admittance controlled robotic device. Descending drive, and the possible expression of the upper extremity flexion synergy, was modulated by increasing shoulder abduction loading. Individuals with early-onset PH achieved lower peak velocities when reaching with the paretic arm compared to controls; however, no differences in reaching distance were found between groups. Relative maintenance in reaching seen in individuals with early brain injuries highlights minimal expression of the flexion synergy. We interpret this conservation of independent control of the paretic shoulder and elbow as the use of more direct corticospinal projections instead of indirect cortico-reticulospinal pathways used in individuals with adult-onset hemiplegia.Background Multiple sclerosis (MS) may cause variable functional impairment. The discrepancy between functional impairment and brain imaging findings in patients with MS (PwMS) might be attributed to differential adaptive and consolidation capacities. Modulating those abilities could contribute to a favorable clinical course of the disease. Objectives We examined the effect of cerebellar transcranial direct current stimulation (c-tDCS) on locomotor adaptation and consolidation in PwMS using a split-belt treadmill (SBT) paradigm. Methods 40 PwMS and 30 matched healthy controls performed a locomotor adaptation task on a SBT. First, we assessed locomotor adaptation in PwMS. In a second investigation, this training was followed by cerebellar anodal tDCS applied immediately after the task ipsilateral to the fast leg (T0). link2 The SBT paradigm was repeated 24 h (T1) and 78 h (T2) post-stimulation to evaluate consolidation. Results The gait dynamics and adaptation on the SBT were comparable between PwMS and controls. We found no effects of offline cerebellar anodal tDCS on locomotor adaptation and consolidation. Participants who received the active stimulation showed the same retention index than sham-stimulated subjects at T1 (p = 0.33) and T2 (p = 0.46). Selleck Reparixin is preserved in people with mild-to-moderate MS. However, cerebellar anodal tDCS applied immediately post-training does not further enhance this ability. Future studies should define the neurobiological substrates of maintained plasticity in PwMS and how these substrates can be manipulated to improve compensation. Systematic assessments of methodological variables for cerebellar tDCS are urgently needed to increase the consistency and replicability of the results across experiments in various settings.[This corrects the article DOI 10.3389/fnhum.2019.00460.].This article provides an overview of the study protocol for the Teen Inflammation Glutamate Emotion Research (TIGER) project, a longitudinal study in which we plan to recruit 60 depressed adolescents (ages 13-18 years) and 30 psychiatrically healthy controls in order to examine the inflammatory and glutamatergic pathways that contribute to the recurrence of depression in adolescents. TIGER is the first study to examine the effects of peripheral inflammation on neurodevelopmental trajectories by assessing changes in cortical glutamate in depressed adolescents. Here, we describe the scientific rationale, design, and methods for the TIGER project. This article is intended to serve as an introduction to this project and to provide details for investigators who may be seeking to replicate or extend these methods for other related research endeavors.Impaired decision-making is well documented in obsessive-compulsive disorder (OCD) and a range of electrophysiological and functional neuroimaging measures have begun to reveal the pathological mechanisms that underlie the decision-making process. Obsessive-compulsive personality disorder (OCPD) has core symptoms that often overlap with OCD, but similarities between these disorders at the behavioral and neurological levels are often unclear, including whether OCPD exhibits similar decision-making deficits and shared neurological dysfunction. To address these issues, we examined 24 cases of OCD, 19 cases of OCPD, and 26 matched normal control (NC) subjects during the revised Iowa Gambling Task (IGT) using event-related potentials (ERPs). The net IGT scores were lower for OCD subjects than for OCPD or NC subjects, thus indicating that OCD subjects chose more disadvantageous options and were "short-sighted" with regards to information. The feedback-related negativity (FRN) waveform (lose-win) was larger in both OCD and OCPD subjects, which suggested that obstacles exist in the feedback process. link3 Consequently, these subjects might share similar neural mechanisms under ambiguous decision-making circumstances. Furthermore, IGT net scores were significantly and negatively correlated with Hamilton Anxiety Rating Scale (HAMA) and Hamilton Depression Rating Scale (HAMD) scales. This implies that more severe obsessive-compulsive symptoms inspired more negative emotions that led to worse decision-making ability. Therefore, although similar neural mechanisms might exist, this led to different behaviors in which OCPD is associated with better behavioral performance compared to OCD patients.