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Progress of research in the application of ultrasound technology for the treatment of Alzheimer’s disease

Neural Regeneration Research 2025年第10期20卷 2823-2837页

作者： Qiuquan Cai Lianghui Meng Meina Quan Ling Wang Jing Ren Chenguang Zheng Jiajia Yang Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin UniversityTianjinChina Innovation Center for Neurological Disorders Xuanwu HospitalCapital Medical UniversityBeijingChina Brain-Computer Interaction and Human-Machine Fusion Haihe Laboratory Tianjin UniversityTianjinChina Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin UniversityTianjinChina

Alzheimer’s disease is a common neurodegenerative disorder defined by decreased reasoning abilities,memory loss,and cognitive *** presence of the blood-brain barrier presents a major obstacle to the development of effective drug therapies for Alzheimer’s *** use of ultrasound as a novel physical modulation approach has garnered widespread attention in recent *** a safe and feasible therapeutic and drug-delivery method,ultrasound has shown promise in improving cognitive *** article provides a summary of the application of ultrasound technology for treating Alzheimer’s disease over the past 5 years,including standalone ultrasound treatment,ultrasound combined with microbubbles or drug therapy,and magnetic resonance imaging-guided focused ultrasound *** is placed on the benefits of introducing these treatment methods and their potential *** found that several ultrasound methods can open the blood-brain barrier and effectively alleviate amyloid-βplaque *** believe that ultrasound is an effective therapy for Alzheimer’s disease,and this review provides a theoretical basis for future ultrasound treatment methods.

关键词： Alzheimer’s disease blood-brain barrier drugs magnetic resonance imaging-guided focused ultrasound microbubbles scanning ultrasound ultrasound ultrasound stimulation

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SEED-VII: A Multimodal Dataset of Six Basic Emotions with Continuous Labels for Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第02期16卷 969-985页

作者： Jiang, Wei-Bang Liu, Xuan-Hao Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Shanghai200240 China

Recognizing emotions from physiological signals is a topic that has garnered widespread interest, and research continues to develop novel techniques for perceiving emotions. However, the emergence of deep learning has highlighted the need for comprehensive and high-quality emotional datasets that enable the accurate decoding of human emotions. To systematically explore human emotions, we develop a multimodal dataset consisting of six basic (happiness, sadness, fear, disgust, surprise, and anger) emotions and the neutral emotion, named SEED-VII. This multimodal dataset includes electroencephalography (EEG) and eye movement signals. The seven emotions in SEED-VII are elicited by 80 different videos and fully investigated with continuous labels that indicate the intensity levels of the corresponding emotions. Additionally, we propose a novel Multimodal Adaptive Emotion Transformer (MAET), that can flexibly process both unimodal and multimodal inputs. Adversarial training is utilized in the MAET to mitigate subject discrepancies, which enhances domain generalization. Our extensive experiments, encompassing both subject-dependent and cross-subject conditions, demonstrate the superior performance of the MAET in terms of handling various inputs. Continuous labels are used to filter the data with high emotional intensity, and this strategy is proven to be effective for attaining improved emotion recognition performance. Furthermore, complementary properties between the EEG signals and eye movements and stable neural patterns of the seven emotions are observed. © 2010-2012 IEEE.

关键词： Eye movements

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Artificial Intelligence Driven Predictive Analysis of Acoustic and Linguistic Behaviors for ASD Identification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5709-5719页

作者： Ashwini, B. Deeptanshu Gulati, Sheffali Shukla, Jainendra Human-Machine Interaction Laboratory Delhi New Delhi110020 India All India Institute of Medical Sciences Department of Pediatrics Child Neurology Division Centre of Excellence and Advanced Research for Childhood Neurodevelopmental Disorders New Delhi110029 India Department of Computer Science and Engineering Delhi New Delhi110020 India

The identification of autism spectrum disorder (ASD) faces challenges due to the lack of reliable biomarkers and the subjectivity in diagnostic procedures, necessitating improved tools for objectivity and efficiency. Being a key characteristic of autism, language impairments are regarded as potential markers for identifying ASD. However, current research predominantly focuses on analyzing language characteristics in English, overlooking linguistic and contextual specificities in other resource-constrained languages. Motivated by these, we developed an artificial intelligence (AI)-based system to detect ASD, utilizing a range of acoustic and linguistic features extracted from dyadic conversations between a child and their communication partner. Validating our model on 76 English-speaking children [35 ASD and 41 typically developing (TD)] and 33 Hindi-speaking children (15 ASD and 18 TD), our extensive analysis of a diverse and comprehensive set of acoustic and linguistic speech attributes, including lexical, syntactic, semantic, and pragmatic elements revealed reliable speech attributes as predictors of ASD. This comprehensive analysis achieved a remarkable macro F1-score of approximately ∼91.30%. We further addressed the influence of linguistic diversity on speech-based ASD assessment by examining speech behaviors in both English and the low-resource language, Hindi. Specific features such as adverbs and distinct roots contributed significantly to ASD classification in English, while the proportion of unintelligible utterances and adposition use held greater importance in Hindi. This study underscores the reliability of speech-based biomarkers in ASD assessment, emphasizing their effectiveness across diverse linguistic backgrounds and highlighting the need for language-specific research in this domain. © 2024 IEEE.

关键词： Semantics

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Virtual Displays for Knowledge Work: Extending or Replacing Physical Monitors for More Flexibility and Screen Space

Virtual Displays for Knowledge Work: Extending or Replacing ...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Leonardo Pavanatto Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech USA

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

In this paper, we explore the potential of virtual displays in sup-porting knowledge work, focusing on scenarios where conventional physical monitors fall short. Our research investigates the feasibility and productivity costs of extending or replacing physical monitors with augmented and virtual reality displays. We present three com-pelling use cases, illustrating how virtual displays enhance flexibility, adaptability, and customization in remote and diverse work settings. Lessons learned from user studies highlight hardware and interface design challenges, emphasizing the need for larger resolution and field of view in head-worn displays (HWD). We conclude the paper with research opportunities and a call to address the evolving demands of knowledge work interfaces.

关键词： Productivity Three-dimensional displays Head-mounted displays Focusing Virtual reality User interfaces Hardware

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Enhancing Impulse Response-Based SSVEP Decoding Method by Incorporating Temporal Local Correlation 7

Enhancing Impulse Response-Based SSVEP Decoding Method by In...

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7th International Conference on Biological Information and Biomedical Engineering, BIBE 2024

作者： Huang, Jiayang Wang, Jiaxi Yang, Pengfei Xiong, Bang Zhang, Zhi-Qiang School of Computer Science and Technology The Key Laboratory of Smart Human-Computer Interaction Wearable Technology of Shaanxi Province Xidian University China School of Electronic and Electrical Engineering Institute of Robotics Autonomous Systems and Sensing University of Leeds United Kingdom

ISBN: (纸本)9783800762910

Various training-based spatial filtering methods have been proposed to classify steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, many overlook the temporal instability of SSVEPs, leading to decreased classification performance. Therefore, we propose an SSVEP decoding method that calculates template signals and spatial filters using the subject's impulse responses and periodic impulses, incorporating the temporally local correlation coefficient in the SSVEP decoding process. Then, we employ a combined correlation coefficient for SSVEP classification. The proposed method addresses the temporal instability of SSVEPs, with template signals that exhibit strong individual characteristics. Utilizing a public benchmark dataset, we achieved a classification accuracy of 87.43% and an ITR of 256.1 bits/min at 1s time window, demonstrating the performance of this method. Compared to other classical spatial filtering methods, the proposed method demonstrates superior classification performance, which can enhance the practical usability of SSVEP-BCI systems. © VDE VERLAG GMBH.

关键词： Wiener filtering

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Planet Purifiers: A Collaborative Immersive Experience Proposing New Modifications to HOMER and Fishing Reel interaction Techniques

Planet Purifiers: A Collaborative Immersive Experience Propo...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Alexander Giovannelli Fionn Murphy Trey Davis Chaerin Lee Rehema Abulikemu Matthew Gallagher Sahil Sharma Lee Lisle Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech Blacksburg VA USA

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

This paper presents our solution to the 2025 3DUI Contest challenge. We aimed to develop a collaborative, immersive experience that raises awareness about trash pollution in natural landscapes while enhancing traditional interaction techniques in virtual environments. To achieve these objectives, we created an engaging multiplayer game where one user collects harmful pollutants while the other user provides medication to impacted wildlife using enhancements to traditional interaction techniques: HOMER and Fishing Reel. We enhanced HOMER to use a cone volume to reduce the precise aiming required by a selection raycast to provide a more efficient means to collect pollutants at large distances, coined as FLOW-MATCH. To improve the animal feed distribution to wildlife far away from the user with Fishing Reel, we created RAWR-XD, an asymmetric bi-manual technique to more conveniently adjust the reeling speed using the non-selecting wrist rotation of the user.

关键词： Wrist Three-dimensional displays Pollution Planets Wildlife Collaboration Virtual environments Immersive experience Games Food products

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VTA-NAc neural pathways involved in the mechanism of depression-like behavior induced by chronic sleep deprivation in mice 24

VTA-NAc neural pathways involved in the mechanism of depress...

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11th International Conference on Biomedical and Bioinformatics Engineering, ICBBE 2024

作者： Li, Yaqing Mo, Weiyi Ren, Jing Sun, Tianyi Zheng, Chengguaang Wang, Ling Yang, Jiajia Ming, Dong Medical School Tianjin University Tianjin China ZhejiangLab Zhejiang Hangzhou China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin China Haihe Laboratory of Brian-Computer Interaction and Human-Machine Integration Tianjin China

ISBN: (纸本)9798400718274

Sleep plays a crucial role in individual physical and mental health. Insufficient sleep leads to inattention, memory loss, cognitive dysfunction and emotional disorders. The mesolimbic pathway, ventral tegmental area (VTA) to nucleus accumbens (NAc) dopaminergic neural pathway has been shown to be associated with sleep-wake behavior and depression-like behavior. In this study, we investigated the relation between chronic sleep deprivation (CSD)-induced depression-like behaviors and the activity of VTA-NAc dopaminergic pathway. CSD caused weight loss and depression-like behaviors in the mice, including anhedonia and helplessness. CSD prominently reduced the change of mice' s DA signal when moving freely. Additionally, NAc DA release became less noticeable in rewarding event and retreat behavior after exposing to CSD. In conclusion, our findings indicated that VTA-NAc dopaminergic neural pathway is directly related to depressive-like behavior caused by chronic sleep deprivation. This study provides a theoretical basis for the in-depth understanding of the physiological function of sleep, and provides a potential target for clinical research on improving or treating sleep disorder-related depression. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Sleep research

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SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera Video Tracking and Recognition 8

SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera...

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8th International Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2022

作者： Siahkali, Farbod Alavi, Seyed Amirmahdi Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran Islamic Azad University Human and Robot Interaction Laboratory School of Computer Science Central Tehran Branch Tehran Iran

ISBN: (纸本)9781665476232

In this paper, a new publicly available 1 web-Scraped Iranian Vehicle Dataset (SIVD) for simultaneous real-time vehicle tracking and recognition is proposed. The datasets provided for Iranian cars in the literature have two fundamental problems. First, the lack of images from different angles, and second, the small number of classes compared to the dispersion of car models in the real world. Therefore, for the purposes of this paper, Iranian vehicle images from car sales websites are collected, and the SIVD dataset is proposed which contains 29 classes and 36,705 images. This paper aims at developing a classification network for Iranian vehicle recognition and implement a real-time tracking system using the YOLOv5 network to perform real-time vehicle model recognition and tracking tasks simultaneously. The ResNet50 achieved an accuracy of 99.29%, the highest among the investigated classification networks.1https://***/Farbod-Siahkali/Iranian-Vehicle-Tracking-and-Recognition © 2022 IEEE.

关键词： Model automobiles

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A Neural Architecture Search Method with Multi-Dimensional Correlation Representation for Fault Diagnosis 9

A Neural Architecture Search Method with Multi-Dimensional C...

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9th IEEE Smart World Congress, SWC 2023

作者： Liu, Yumeng Wang, Tingqi Zheng, Xu Tian, Ling Wang, Hongan Institute of Software Chinese Academy of Sciences Beijing Key Laboratory of Human-Computer Interaction Beijing China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China

ISBN: (纸本)9798350319804

As deep learning has become more widely used for fault diagnosis, the shortcomings of model transferability and human model design costs are growing increasingly evident. The current work has tackled each of these two issues. Domain adaptation methods can provide transportability by mapping data to a common domain. For the consumption of manually constructed networks, Neural Architecture Search (NAS) is an intelligent solution that automatically select the ideal network structure. Unfortunately, there is little work to connect these two methodologies in fault diagnosis. Furthermore, these approaches are incapable in extracting internal correlations from instrumental signal data. In this paper, we propose a transferrable NAS fault diagnosis method, which maximizes savings in manual costs. We also integrate graph neural networks (GNN) in a stepwise manner into the NAS and domain adaptation to provide a multi-dimension correlation representation. Firstly, we structure the signal data into graph based on temporal relationships. Then, NAS designs automatically for selecting the optimal network structure and useful features. In NAS, graph encoder of GNN is used to establish internal relationships. Finally, domain adaptation provides transferability. At the same time, GNN's weights in domain adaptation help to further mine data internal associations. We tested our method on two publicly available datasets and performed comparisons. The experimental results show the proposed method has superior performance. © 2023 IEEE.

关键词： Domain Adaptation Fault Diagnosis Graph Neural Network Neural Architecture Search

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Breath Tools: Exploring the Effects on Adherence and User Experience of 4 Sounds Assisting Runners with Coupling Breath to Steps

Breath Tools: Exploring the Effects on Adherence and User Ex...

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Extended Abstracts of the 2023 CHI Conference on human Factors in Computing Systems, CHI EA 2023

作者： Van Rheden, Vincent Harbour, Eric Finkenzeller, Thomas Meschtscherjakov, Alexander Human Computer Interaction Division Department of Artificial Intelligence and Human Interfaces University of Salzburg Salzburg Austria Department of Sport and Exercise Science University of Salzburg Rif Hallein Austria

ISBN: (纸本)9781450394222

Running is a popular sport worldwide. Locomotor-Respiratory Coupling (LRC), a breathing technique in which the breath is coupled to steps, can positively impact running experience and economy. LRC may be difficult to learn and maintain during a run. Sound cues are a promising method to guide LRC while running. We designed four guiding sounds and evaluated these in a preliminary study (N=9). All sounds supported participants with LRC (on average 74.9±16.2% of time attached to the LRC rhythm). Based on our findings we advocate sound guidance for LRC and present directions to improve adherence and run experience. © 2023 Owner/Author.

关键词： Sports

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