International journal of neural systems最新文献_第6页

Neonatal White Matter Damage Analysis using DTI Super-resolution and Multi-modality Image Registration 使用DTI超分辨率和多模态图像配准分析新生儿白质损伤

International journal of neural systems Pub Date : 2023-10-27 DOI: 10.1142/s0129065724500011

Yi Wang, Yuan Zhang, Chi Ma, Rui Wang, Zhe Guo, Yu Shen, Miaomiao Wang, Hongying Meng

{"title":"Neonatal White Matter Damage Analysis using DTI Super-resolution and Multi-modality Image Registration","authors":"Yi Wang, Yuan Zhang, Chi Ma, Rui Wang, Zhe Guo, Yu Shen, Miaomiao Wang, Hongying Meng","doi":"10.1142/s0129065724500011","DOIUrl":"https://doi.org/10.1142/s0129065724500011","url":null,"abstract":"Punctate White Matter Damage (PWMD) is a common neonatal brain disease, which can easily cause neurological disorder and strongly affect life quality in terms of neuromotor and cognitive performance. Especially, at the neonatal stage, the best cure time can be easily missed because PWMD is not conducive to the diagnosis based on current existing methods. The lesion of PWMD is relatively straightforward on T1-weighted Magnetic Resonance Imaging (T1 MRI), showing semi-oval, cluster or linear high signals. Diffusion Tensor Magnetic Resonance Image (DT-MRI, referred to as DTI) is a noninvasive technique that can be used to study brain microstructures in vivo, and provide information on movement and cognition-related nerve fiber tracts. Therefore, a new method was proposed to use T1 MRI combined with DTI for better neonatal PWMD analysis based on DTI super-resolution and multi-modality image registration. First, after preprocessing, neonatal DTI super-resolution was performed with the three times B-spline interpolation algorithm based on the Log-Euclidean space to improve DTIs' resolution to fit the T1 MRIs and facilitate nerve fiber tractography. Second, the symmetric diffeomorphic registration algorithm and inverse b0 image were selected for multi-modality image registration of DTI and T1 MRI. Finally, the 3D lesion models were combined with fiber tractography results to analyze and predict the degree of PWMD lesions affecting fiber tracts. Extensive experiments demonstrated the effectiveness and super performance of our proposed method. This streamlined technique can play an essential auxiliary role in diagnosing and treating neonatal PWMD.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":"148 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136312439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Level Laser Induced Pain Measurement With Wasserstein Generative Adversarial Network - Gradient Penalty Model 基于Wasserstein生成对抗网络-梯度惩罚模型的多层次激光诱导疼痛测量

International journal of neural systems Pub Date : 2023-10-20 DOI: 10.1142/s0129065723500673

Jiancai Leng, Jianqun Zhu, Yihao Yan, Xin Yu, Ming Liu, Yitai Lou, Yanbing Liu, Licai Gao, Yuan Sun, Tianzheng He, Qingbo Yang, Chao Feng, Dezheng Wang, Yang Zhang, Qing Xu, Fangzhou Xu

引用次数: 0

Effect of Action Units, Viewpoint and Immersion on Emotion Recognition Using Dynamic Virtual Faces. 动作单元、视点和沉浸对动态虚拟人脸情感识别的影响。

International journal of neural systems Pub Date : 2023-10-01 DOI: 10.1142/S0129065723500533

Miguel A Vicente-Querol, Antonio Fernández-Caballero, Pascual González, Luz M González-Gualda, Patricia Fernández-Sotos, José P Molina, Arturo S García

{"title":"Effect of Action Units, Viewpoint and Immersion on Emotion Recognition Using Dynamic Virtual Faces.","authors":"Miguel A Vicente-Querol, Antonio Fernández-Caballero, Pascual González, Luz M González-Gualda, Patricia Fernández-Sotos, José P Molina, Arturo S García","doi":"10.1142/S0129065723500533","DOIUrl":"https://doi.org/10.1142/S0129065723500533","url":null,"abstract":"<p><p>Facial affect recognition is a critical skill in human interactions that is often impaired in psychiatric disorders. To address this challenge, tests have been developed to measure and train this skill. Recently, virtual human (VH) and virtual reality (VR) technologies have emerged as novel tools for this purpose. This study investigates the unique contributions of different factors in the communication and perception of emotions conveyed by VHs. Specifically, it examines the effects of the use of action units (AUs) in virtual faces, the positioning of the VH (frontal or mid-profile), and the level of immersion in the VR environment (desktop screen versus immersive VR). Thirty-six healthy subjects participated in each condition. Dynamic virtual faces (DVFs), VHs with facial animations, were used to represent the six basic emotions and the neutral expression. The results highlight the important role of the accurate implementation of AUs in virtual faces for emotion recognition. Furthermore, it is observed that frontal views outperform mid-profile views in both test conditions, while immersive VR shows a slight improvement in emotion recognition. This study provides novel insights into the influence of these factors on emotion perception and advances the understanding and application of these technologies for effective facial emotion recognition training.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":"33 10","pages":"2350053"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41155867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-supervised eeg representation learning with contrastive predictive coding for post-stroke 基于对比预测编码的脑卒中后自监督脑电表征学习

International journal of neural systems Pub Date : 2023-09-29 DOI: 10.1142/s0129065723500661

Fangzhou Xu, Yihao Yan, Jianqun Zhu, Xinyi Chen, Licai Gao, Yanbing Liu, Weiyou Shi, Yitai Lou, Wei Wang, Jiancai Leng, Yang Zhang

{"title":"Self-supervised eeg representation learning with contrastive predictive coding for post-stroke","authors":"Fangzhou Xu, Yihao Yan, Jianqun Zhu, Xinyi Chen, Licai Gao, Yanbing Liu, Weiyou Shi, Yitai Lou, Wei Wang, Jiancai Leng, Yang Zhang","doi":"10.1142/s0129065723500661","DOIUrl":"https://doi.org/10.1142/s0129065723500661","url":null,"abstract":"Stroke patients are prone to fatigue during the EEG acquisition procedure, and experiments have high requirements on cognition and physical limitations of subjects. Therefore, how to learn effective feature representation is very important. Deep learning networks have been widely used in motor imagery (MI) based brain-computer interface (BCI). This paper proposes a contrast predictive coding (CPC) framework based on the modified s-transform (MST) to generate MST-CPC feature representations. MST is used to acquire the temporal-frequency feature to improve the decoding performance for MI task recognition. EEG2Image is used to convert multi-channel one-dimensional EEG into two-dimensional EEG topography. High-level feature representations are generated by CPC which consists of an encoder and autoregressive model. Finally, the effectiveness of generated features is verified by the k-means clustering algorithm. It can be found that our model generates features with high efficiency and a good clustering effect. After classification performance evaluation, the average classification accuracy of MI tasks is 89% based on 40 subjects. The proposed method can obtain effective feature representations and improve the performance of MI-BCI systems. By comparing several self-supervised methods on the public dataset, it can be concluded that the MST-CPC model has the highest average accuracy. This is a breakthrough in the combination of self-supervised learning and image processing of EEG signals. It is helpful to provide effective rehabilitation training for stroke patients to promote motor function recovery.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135132847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0