scholarly journals Social-affective features drive human representations of observed actions

2021 ◽  
Author(s):  
Diana C Dima ◽  
Tyler M Tomita ◽  
Christopher J Honey ◽  
Leyla Isik
Keyword(s):  
Large Scale ◽  
Time Window ◽  
Variance Partitioning ◽  
Neural Responses ◽  
Behavioral Experiments ◽  
Social Settings ◽  
Complex Scenes ◽  
Late Stages ◽  
The Brain ◽  
Better Than

Humans observe actions performed by others in many different visual and social settings. What features do we extract and attend when we view such complex scenes, and how are they processed in the brain? To answer these questions, we curated two large-scale sets of naturalistic videos of everyday actions and estimated their perceived similarity in two behavioral experiments. We normed and quantified a large range of visual, action-related and social-affective features across the stimulus sets. Using a cross-validated variance partitioning analysis, we found that social-affective features predicted similarity judgments better than, and independently of, visual and action features in both behavioral experiments. Next, we conducted an electroencephalography (EEG) experiment, which revealed a sustained correlation between neural responses to videos and their behavioral similarity. Visual, action, and social-affective features predicted neural patterns at early, intermediate and late stages respectively during this behaviorally relevant time window. Together, these findings show that social-affective features are important for perceiving naturalistic actions, and are extracted at the final stage of a temporal gradient in the brain.

scholarly journals Less Is Not More: Neural Responses to Missing and Superfluous Accents in Context

2012 ◽  
Vol 24 (12) ◽  
pp. 2400-2418 ◽  
Author(s):  
Diana V. Dimitrova ◽  
Laurie A. Stowe ◽  
Gisela Redeker ◽  
John C. J. Hoeks
Keyword(s):  
Semantic Processing ◽  
Time Window ◽  
Neural System ◽  
Judgment Task ◽  
Neural Systems ◽  
Neural Responses ◽  
Making Sense ◽  
Negative Effect ◽  
The Brain ◽  
Preceding Question

Prosody, particularly accent, aids comprehension by drawing attention to important elements such as the information that answers a question. A study using ERP registration investigated how the brain deals with the interpretation of prosodic prominence. Sentences were embedded in short dialogues and contained accented elements that were congruous or incongruous with respect to a preceding question. In contrast to previous studies, no explicit prosodic judgment task was added. Robust effects of accentuation were evident in the form of an “accent positivity” (200–500 msec) for accented elements irrespective of their congruity. Our results show that incongruously accented elements, that is, superfluous accents, activate a specific set of neural systems that is inactive in case of incongruously unaccented elements, that is, missing accents. Superfluous accents triggered an early positivity around 100 msec poststimulus, followed by a right-lateralized negative effect (N400). This response suggests that redundant information is identified immediately and leads to the activation of a neural system that is associated with semantic processing (N400). No such effects were found when contextually expected accents were missing. In a later time window, both missing and superfluous accents triggered a late positivity on midline electrodes, presumably related to making sense of both kinds of mismatching stimuli. These results challenge previous findings of greater processing for missing accents and suggest that the natural processing of prosody involves a set of distinct, temporally organized neural systems.

PENGEMBANGAN MEDIA BIG BOOK UNTUK MENINGKATKAN HASIL BELAJAR PECAHAN SENILAI SISWA SD

2018 ◽  
Vol 16 (1) ◽  
pp. 67-76
Author(s):  
Disyacitta Neolia Firdana ◽  
Trimurtini Trimurtini
Keyword(s):  
Learning Outcomes ◽  
Large Scale ◽  
Fourth Grade ◽  
Learning Activities ◽  
Equivalent Fractions ◽  
Fourth Grade Students ◽  
Teacher Needs ◽  
The Media ◽  
Test Result ◽  
Better Than

This research aimed to determine the properness and effectiveness of the big book media on learning equivalent fractions of fourth grade students. The method of research is Research and Development  (R&D). This study was conducted in fourth grade of SDN Karanganyar 02 Kota Semarang. Data sources from media validation, material validation, learning outcomes, and teacher and students responses on developed media. Pre-experimental research design with one group pretest-posttest design. Big book developed consist of equivalent fractions material, students learning activities sheets with rectangle and circle shape pictures, and questions about equivalent fractions. Big book was developed based on students and teacher needs. This big book fulfill the media validity of 3,75 with very good criteria and scored 3 by material experts with good criteria. In large-scale trial, the result of students posttest have learning outcomes completness 82,14%. The result of N-gain calculation with result 0,55 indicates the criterion “medium”. The t-test result 9,6320 > 2,0484 which means the average of posttest outcomes is better than the average of pretest outcomes. Based on that data, this study has produced big book media which proper and effective as a media of learning equivalent fractions of fourth grade elementary school.

Implantable neural interfaces

2018 ◽  
Author(s):  
Stefano Vassanelli
Keyword(s):  
Brain Function ◽  
Large Scale ◽  
Ionic Channels ◽  
Patch Clamp Technique ◽  
Advantages And Disadvantages ◽  
Optogenetic Stimulation ◽  
Physical Interfaces ◽  
The Brain

Establishing direct communication with the brain through physical interfaces is a fundamental strategy to investigate brain function. Starting with the patch-clamp technique in the seventies, neuroscience has moved from detailed characterization of ionic channels to the analysis of single neurons and, more recently, microcircuits in brain neuronal networks. Development of new biohybrid probes with electrodes for recording and stimulating neurons in the living animal is a natural consequence of this trend. The recent introduction of optogenetic stimulation and advanced high-resolution large-scale electrical recording approaches demonstrates this need. Brain implants for real-time neurophysiology are also opening new avenues for neuroprosthetics to restore brain function after injury or in neurological disorders. This chapter provides an overview on existing and emergent neurophysiology technologies with particular focus on those intended to interface neuronal microcircuits in vivo. Chemical, electrical, and optogenetic-based interfaces are presented, with an analysis of advantages and disadvantages of the different technical approaches.

What Has Direct Cortical and Subcortical Electrostimulation Taught Us about Neurolinguistics?

2019 ◽  
pp. 185-211
Author(s):  
Hugues Duffau
Keyword(s):  
White Matter ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Great Accuracy ◽  
Subcortical White Matter ◽  
Cerebral Lesions ◽  
Physiological Basis ◽  
Postoperative Mri ◽  
The Brain

Investigating the neural and physiological basis of language is one of the most important challenges in neurosciences. Direct electrical stimulation (DES), usually performed in awake patients during surgery for cerebral lesions, is a reliable tool for detecting both cortical and subcortical (white matter and deep grey nuclei) regions crucial for cognitive functions, especially language. DES transiently interacts locally with a small cortical or axonal site, but also nonlocally, as the focal perturbation will disrupt the entire subnetwork sustaining a given function. Thus, in contrast to functional neuroimaging, DES represents a unique opportunity to identify with great accuracy and reproducibility, in vivo in humans, the structures that are actually indispensable to the function, by inducing a transient virtual lesion based on the inhibition of a subcircuit lasting a few seconds. Currently, this is the sole technique that is able to directly investigate the functional role of white matter tracts in humans. Thus, combining transient disturbances elicited by DES with the anatomical data provided by pre- and postoperative MRI enables to achieve reliable anatomo-functional correlations, supporting a network organization of the brain, and leading to the reappraisal of models of language representation. Finally, combining serial peri-operative functional neuroimaging and online intraoperative DES allows the study of mechanisms underlying neuroplasticity. This chapter critically reviews the basic principles of DES, its advantages and limitations, and what DES can reveal about the neural foundations of language, that is, the large-scale distribution of language areas in the brain, their connectivity, and their ability to reorganize.

scholarly journals Content-Based Estimation of Brain MRI Tilt in Three Orthogonal Directions

2021 ◽  
Author(s):  
Pooja Prabhu ◽  
A. K. Karunakar ◽  
Sanjib Sinha ◽  
N. Mariyappa ◽  
G. K. Bhargava ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Mri ◽  
Similarity Measures ◽  
Principal Component ◽  
Brain Anatomy ◽  
Morphological Operations ◽  
Mr Images ◽  
Tilt Correction ◽  
Brain Mr Images ◽  
The Brain

AbstractIn a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.

A new method to predict anomaly in brain network based on graph deep learning

2020 ◽  
Vol 31 (6) ◽  
pp. 681-689
Author(s):  
Jalal Mirakhorli ◽  
Hamidreza Amindavar ◽  
Mojgan Mirakhorli
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Brain Plasticity ◽  
Brain Network ◽  
High Order ◽  
Brain Diseases ◽  
Simultaneous Occurrence ◽  
Generative Adversarial Network ◽  
The Brain ◽  
Brain Connections

AbstractFunctional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.

scholarly journals Engineered glycomaterial implants orchestrate large-scale functional repair of brain tissue chronically after severe traumatic brain injury

2021 ◽  
Vol 7 (10) ◽  
pp. eabe0207
Author(s):  
Charles-Francois V. Latchoumane ◽  
Martha I. Betancur ◽  
Gregory A. Simchick ◽  
Min Kyoung Sun ◽  
Rameen Forghani ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Severe Traumatic Brain Injury ◽  
Large Scale ◽  
Growth Factor Signaling ◽  
Neuronal Circuit ◽  
Reach To Grasp ◽  
Function Recovery ◽  
Cellular Repair ◽  
The Brain

Severe traumatic brain injury (sTBI) survivors experience permanent functional disabilities due to significant volume loss and the brain’s poor capacity to regenerate. Chondroitin sulfate glycosaminoglycans (CS-GAGs) are key regulators of growth factor signaling and neural stem cell homeostasis in the brain. However, the efficacy of engineered CS (eCS) matrices in mediating structural and functional recovery chronically after sTBI has not been investigated. We report that neurotrophic factor functionalized acellular eCS matrices implanted into the rat M1 region acutely after sTBI significantly enhanced cellular repair and gross motor function recovery when compared to controls 20 weeks after sTBI. Animals subjected to M2 region injuries followed by eCS matrix implantations demonstrated the significant recovery of “reach-to-grasp” function. This was attributed to enhanced volumetric vascularization, activity-regulated cytoskeleton (Arc) protein expression, and perilesional sensorimotor connectivity. These findings indicate that eCS matrices implanted acutely after sTBI can support complex cellular, vascular, and neuronal circuit repair chronically after sTBI.

scholarly journals Classification of Recreation Opportunity Spectrum Using Night Lights for Evidence of Humans and POI Data for Social Setting

2021 ◽  
Vol 13 (14) ◽  
pp. 7782
Author(s):  
Wenjing Zeng ◽  
Yongde Zhong ◽  
Dali Li ◽  
Jinyang Deng
Keyword(s):  
Large Scale ◽  
Regional Scale ◽  
Hunan Province ◽  
Social Settings ◽  
Recreation Opportunity Spectrum ◽  
City District ◽  
Night Lights ◽  
Recreational Resources ◽  
Metropolitan Counties ◽  
Land Types

The recreation opportunity spectrum (ROS) has been widely recognized as an effective tool for the inventory and planning of outdoor recreational resources. However, its applications have been primarily focused on forest-dominated settings with few studies being conducted on all land types at a regional scale. The creation of a ROS is based on physical, social, and managerial settings, with the physical setting being measured by three criteria: remoteness, size, and evidence of humans. One challenge to extending the ROS to all land types on a large scale is the difficulty of quantifying the evidence of humans and social settings. Thus, this study, for the first time, developed an innovative approach that used night lights as a proxy for evidence of humans and points of interest (POI) for social settings to generate an automatic ROS for Hunan Province using Geographic Information System (GIS) spatial analysis. The whole province was classified as primitive (2.51%), semi-primitive non-motorized (21.33%), semi-primitive motorized (38.60%), semi-developed natural (30.99%), developed natural (5.61%), and highly developed (0.96%), which was further divided into three subclasses: large-natural (0.63%), small natural (0.27%), and facilities (0.06%). In order to implement the management and utilization of natural recreational resources in Hunan Province at the county (city, district) level, the province’s 122 counties (cities, districts) were categorized into five levels based on the ROS factor dominance calculated at the county and provincial levels. These five levels include key natural recreational counties (cities, districts), general natural recreational counties (cities, districts), rural counties (cities, districts), general metropolitan counties (cities, districts), and key metropolitan counties (cities, districts), with the corresponding numbers being 8, 21, 50, 24, and 19, respectively.

scholarly journals Assessment of Numerical Methods for Plunging Breaking Wave Predictions

2021 ◽  
Vol 9 (3) ◽  
pp. 264
Author(s):  
Shanti Bhushan ◽  
Oumnia El Fajri ◽  
Graham Hubbard ◽  
Bradley Chambers ◽  
Christopher Kees
Keyword(s):  
Solitary Wave ◽  
Wave Breaking ◽  
Large Scale ◽  
Turbulence Models ◽  
Navier Stokes ◽  
Wave Crest ◽  
Breaking Wave ◽  
Rans Turbulence Models ◽  
Run Up ◽  
Better Than

This study evaluates the capability of Navier–Stokes solvers in predicting forward and backward plunging breaking, including assessment of the effect of grid resolution, turbulence model, and VoF, CLSVoF interface models on predictions. For this purpose, 2D simulations are performed for four test cases: dam break, solitary wave run up on a slope, flow over a submerged bump, and solitary wave over a submerged rectangular obstacle. Plunging wave breaking involves high wave crest, plunger formation, and splash up, followed by second plunger, and chaotic water motions. Coarser grids reasonably predict the wave breaking features, but finer grids are required for accurate prediction of the splash up events. However, instabilities are triggered at the air–water interface (primarily for the air flow) on very fine grids, which induces surface peel-off or kinks and roll-up of the plunger tips. Reynolds averaged Navier–Stokes (RANS) turbulence models result in high eddy-viscosity in the air–water region which decays the fluid momentum and adversely affects the predictions. Both VoF and CLSVoF methods predict the large-scale plunging breaking characteristics well; however, they vary in the prediction of the finer details. The CLSVoF solver predicts the splash-up event and secondary plunger better than the VoF solver; however, the latter predicts the plunger shape better than the former for the solitary wave run-up on a slope case.

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