Brain Informatics最新文献_第10页

Hierarchical intrinsically motivated agent planning behavior with dreaming in grid environments 网格环境下做梦的层次内在动机智能体规划行为

Brain Informatics Pub Date : 2022-04-02 DOI: 10.1186/s40708-022-00156-6

Evgenii Dzhivelikian, Artem V. Latyshev, Petr Kuderov, A. Panov

引用次数: 5

Individual differences in skill acquisition and transfer assessed by dual task training performance and brain activity 通过双任务训练表现和大脑活动评估技能习得和迁移的个体差异

Brain Informatics Pub Date : 2022-04-02 DOI: 10.1186/s40708-022-00157-5

Pratusha Reddy, P. Shewokis, K. Izzetoglu

引用次数: 6

A dynamic directed transfer function for brain functional network-based feature extraction 基于脑功能网络的动态定向传递函数特征提取

Brain Informatics Pub Date : 2022-03-18 DOI: 10.1186/s40708-022-00154-8

Mingai Li, Na Zhang

引用次数: 0

Fast cortical surface reconstruction from MRI using deep learning 基于深度学习的MRI快速皮层表面重建

Brain Informatics Pub Date : 2022-03-09 DOI: 10.1186/s40708-022-00155-7

Jianxun Ren, Qingyu Hu, Weiwei Wang, Wei Zhang, C. S. Hubbard, Pingjian Zhang, Ning An, Yingyi Zhou, L. Dahmani, Danhong Wang, Xiaoxuan Fu, Zhenyu Sun, Yezhe Wang, Ruiqi Wang, Luming Li, Hesheng Liu

引用次数: 3

Machine and cognitive intelligence for human health: systematic review. 人类健康的机器和认知智能:系统综述。

Brain Informatics Pub Date : 2022-02-12 DOI: 10.1186/s40708-022-00153-9

Xieling Chen, Gary Cheng, Fu Lee Wang, Xiaohui Tao, Haoran Xie, Lingling Xu

{"title":"Machine and cognitive intelligence for human health: systematic review.","authors":"Xieling Chen, Gary Cheng, Fu Lee Wang, Xiaohui Tao, Haoran Xie, Lingling Xu","doi":"10.1186/s40708-022-00153-9","DOIUrl":"https://doi.org/10.1186/s40708-022-00153-9","url":null,"abstract":"Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service. There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model and system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":"5"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8840949/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39608223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Modeling and predicting individual tacit coordination ability. 个体隐性协调能力的建模与预测。

Brain Informatics Pub Date : 2022-02-04 DOI: 10.1186/s40708-022-00152-w

Dor Mizrahi, Ilan Laufer, Inon Zuckerman

{"title":"Modeling and predicting individual tacit coordination ability.","authors":"Dor Mizrahi, Ilan Laufer, Inon Zuckerman","doi":"10.1186/s40708-022-00152-w","DOIUrl":"https://doi.org/10.1186/s40708-022-00152-w","url":null,"abstract":"Background: Previous experiments in tacit coordination games hinted that some people are more successful in achieving coordination than others, although the variability in this ability has not yet been examined before. With that in mind, the overarching aim of our study is to model and describe the variability in human decision-making behavior in the context of tacit coordination games.Methods: In this study, we conducted a large-scale experiment to collect behavioral data, characterized the distribution of tacit coordination ability, and modeled the decision-making behavior of players. First, we measured the multimodality in the data and described it by using a Gaussian mixture model. Then, using multivariate linear regression and dimensionality reduction (PCA), we have constructed a model linking between individual strategic profiles of players and their coordination ability. Finally, we validated the predictive performance of the model by using external validation.Results: We demonstrated that coordination ability is best described by a multimodal distribution corresponding to the levels of coordination ability and that there is a significant relationship between the player's strategic profile and their coordination ability. External validation determined that our predictive model is robust.Conclusions: The study provides insight into the amount of variability that exists in individual tacit coordination ability as well as in individual strategic profiles and shows that both are quite diverse. Our findings may facilitate the construction of improved algorithms for human-machine interaction in diverse contexts. Additional avenues for future research are discussed.","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8817001/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39890586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

MCGNet⁺: an improved motor imagery classification based on cosine similarity. MCGNet+:基于余弦相似度的改进运动图像分类。

Brain Informatics Pub Date : 2022-02-01 DOI: 10.1186/s40708-021-00151-3

Yan Li, Ning Zhong, David Taniar, Haolan Zhang

{"title":"MCGNet+: an improved motor imagery classification based on cosine similarity.","authors":"Yan Li, Ning Zhong, David Taniar, Haolan Zhang","doi":"10.1186/s40708-021-00151-3","DOIUrl":"https://doi.org/10.1186/s40708-021-00151-3","url":null,"abstract":"It has been a challenge for solving the motor imagery classification problem in the brain informatics area. Accuracy and efficiency are the major obstacles for motor imagery analysis in the past decades since the computational capability and algorithmic availability cannot satisfy complex brain signal analysis. In recent years, the rapid development of machine learning (ML) methods has empowered people to tackle the motor imagery classification problem with more efficient methods. Among various ML methods, the Graph neural networks (GNNs) method has shown its efficiency and accuracy in dealing with inter-related complex networks. The use of GNN provides new possibilities for feature extraction from brain structure connection. In this paper, we proposed a new model called MCGNet+, which improves the performance of our previous model MutualGraphNet. In this latest model, the mutual information of the input columns forms the initial adjacency matrix for the cosine similarity calculation between columns to generate a new adjacency matrix in each iteration. The dynamic adjacency matrix combined with the spatial temporal graph convolution network (ST-GCN) has better performance than the unchanged matrix model. The experimental results indicate that MCGNet+ is robust enough to learn the interpretable features and outperforms the current state-of-the-art methods.","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8807751/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39754836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Resting state fMRI connectivity is sensitive to laminar connectional architecture in the human brain. 静息状态fMRI连接对人脑层流连接结构非常敏感。

Brain Informatics Pub Date : 2022-01-17 DOI: 10.1186/s40708-021-00150-4

Gopikrishna Deshpande, Yun Wang, Jennifer Robinson

{"title":"Resting state fMRI connectivity is sensitive to laminar connectional architecture in the human brain.","authors":"Gopikrishna Deshpande, Yun Wang, Jennifer Robinson","doi":"10.1186/s40708-021-00150-4","DOIUrl":"https://doi.org/10.1186/s40708-021-00150-4","url":null,"abstract":"Previous invasive studies indicate that human neocortical graymatter contains cytoarchitectonically distinct layers, with notable differences in their structural connectivity with the rest of the brain. Given recent improvements in the spatial resolution of anatomical and functional magnetic resonance imaging (fMRI), we hypothesize that resting state functional connectivity (FC) derived from fMRI is sensitive to layer-specific thalamo-cortical and cortico-cortical microcircuits. Using sub-millimeter resting state fMRI data obtained at 7 T, we found that: (1) FC between the entire thalamus and cortical layers I and VI was significantly stronger than between the thalamus and other layers. Furthermore, FC between somatosensory thalamus (ventral posterolateral nucleus, VPL) and layers IV, VI of the primary somatosensory cortex were stronger than with other layers; (2) Inter-hemispheric cortico-cortical FC between homologous regions in superficial layers (layers I-III) was stronger compared to deep layers (layers V-VI). These findings are in agreement with structural connections inferred from previous invasive studies that showed that: (i) M-type neurons in the entire thalamus project to layer-I; (ii) Pyramidal neurons in layer-VI target all thalamic nuclei, (iii) C-type neurons in the VPL project to layer-IV and receive inputs from layer-VI of the primary somatosensory cortex, and (iv) 80% of collosal projecting neurons between homologous cortical regions connect superficial layers. Our results demonstrate for the first time that resting state fMRI is sensitive to structural connections between cortical layers (previously inferred through invasive studies), specifically in thalamo-cortical and cortico-cortical networks.","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2022-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8764001/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39827574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning. 利用机器学习在长期记录的局部场电位中再现与通道无关的伪信号

Brain Informatics Pub Date : 2022-01-07 DOI: 10.1186/s40708-021-00149-x

Marcos Fabietti, Mufti Mahmud, Ahmad Lotfi

{"title":"Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning.","authors":"Marcos Fabietti, Mufti Mahmud, Ahmad Lotfi","doi":"10.1186/s40708-021-00149-x","DOIUrl":"10.1186/s40708-021-00149-x","url":null,"abstract":"Acquisition of neuronal signals involves a wide range of devices with specific electrical properties. Combined with other physiological sources within the body, the signals sensed by the devices are often distorted. Sometimes these distortions are visually identifiable, other times, they overlay with the signal characteristics making them very difficult to detect. To remove these distortions, the recordings are visually inspected and manually processed. However, this manual annotation process is time-consuming and automatic computational methods are needed to identify and remove these artefacts. Most of the existing artefact removal approaches rely on additional information from other recorded channels and fail when global artefacts are present or the affected channels constitute the majority of the recording system. Addressing this issue, this paper reports a novel channel-independent machine learning model to accurately identify and replace the artefactual segments present in the signals. Discarding these artifactual segments by the existing approaches causes discontinuities in the reproduced signals which may introduce errors in subsequent analyses. To avoid this, the proposed method predicts multiple values of the artefactual region using long-short term memory network to recreate the temporal and spectral properties of the recorded signal. The method has been tested on two open-access data sets and incorporated into the open-access SANTIA (SigMate Advanced: a Novel Tool for Identification of Artefacts in Neuronal Signals) toolbox for community use.","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8741911/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39795131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Brain Informatics: 15th International Conference, BI 2022, Padua, Italy, July 15–17, 2022, Proceedings 脑信息学:第15届国际会议，BI 2022，意大利帕多瓦，2022年7月15日至17日，会议录

Brain Informatics Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-15037-1

引用次数: 1