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Role of the Thalamic Reticular Nucleus in Social Memory

Neuroscience Bulletin 2025年第2期41卷 355-358页

作者： Xue Liu Jianing Zhu Junqiang Zheng Han Xu Department of Psychiatry of the Second Affiliated Hospital and School of Brain Science and Brain Medicine Zhejiang University School of MedicineHangzhou310058China Nanhu Brain-computer Interface Institute Hangzhou311100China College of Life Sciences Zhejiang UniversityHangzhou310058China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-machine IntegrationState Key Laboratory of Brain-machine IntelligenceZhejiang UniversityHangzhou311121China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang UniversityHangzhou310058China Lingang Laboratory Shanghai200031China

Social behaviors are crucial for gregarious animals,including *** order to exhibit appropriate behaviors in a complex social context,such as mating,aggression,avoidance,and cooperation,individuals need to remember their previous experiences with other members and accurately recognize them when they meet *** ability is called“social memory”[1].Many psychiatric disorders in humans,such as autism spectrum disorder,attention deficit hyperactivity disorder,and schizophrenia,are characterized by social memory *** with these disorders,along with corresponding animal models,often show defects associated with the thalamic reticular nucleus(TRN).The TRN,a thin layer of neurons surrounding the thalamus,mainly regulates and coordinates the transfer of information between the cortex and the thalamus,playing a role in higher brain functions such as consciousness,attention,and sensory ***,whether the TRN is involved in social memory remains unknown.

关键词： exhibit appropriate behaviors social behaviors mating social memory thalamic reticular nucleus gregarious animals autism spectrum disorderattention deficit hyperactivity disorderand psychiatric disorders

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Sex-Differential Neural Circuits and Behavioral Responses for Empathy

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Neuroscience Bulletin 2025年第1期41卷 192-194页

作者： Jingkai Fan Xinrong Wang Han Xu Department of Psychiatry of the Second Affiliated Hospital and School of Brain Science and Brain Medicine Zhejiang University School of MedicineHangzhou310058China Nanhu Brain-computer Interface Institute Hangzhou311100China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-machine IntegrationState Key Laboratory of Brain-machine IntelligenceZhejiang UniversityHangzhou311121China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang UniversityHangzhou310058China Lingang Laboratory Shanghai200031China

Empathy is one of the most important abilities for social *** a world that is largely socially constructed,when encountering altered affective states of others,particularly negative states,individuals often exhibit evolutionarily conserved empathic behaviors,such as mirroring,consolation,and helping[l,2].Appropriate empathic behaviors can foster the consolidation of social *** studies have suggested that both male and female rodents show some empathic behaviors when facing affective conspecifics[1].However,the role of sex in different empathic behaviors remains largely ***,there is a need to probe whether animals show sex-dependent empathic behavior and which neural circuits modulate these distinct behaviors.

关键词： mirror largely behavior

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Attention Detection Using EEG Signals and Machine Learning: A Review

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Machine Intelligence Research 2025年第2期22卷 219-238页

作者： Qianru Sun Yueying Zhou Peiliang Gong Daoqiang Zhang Key Laboratory of Brain-Machine Intelligence Technology Ministry of EducationNanjing University of Aeronautics and AstronauticsNanjing 211106China College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Attention detection using electroencephalogram(EEG)signals has become a popular ***,there seems to be a notable gap in the literature regarding comprehensive and systematic reviews of machine learning methods for attention detection using EEG ***,this survey outlines recent advances in EEG-based attention detection within the past five years,with a primary focus on auditory attention detection(AAD)and attention level ***,we provide a brief overview of commonly used paradigms,preprocessing techniques,and artifact-handling methods,as well as listing accessible datasets used in these ***,we summarize the machine learning methods for classification in this field and divide them into two categories:traditional machine learning methods and deep learning *** also analyse the most frequently used methods and discuss the factors influencing each technique′s performance and ***,we discuss the existing challenges and future trends in this field.

关键词： Attention detection electroencephalogram(EEG) machine learning deep learning brain-computer interface.

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Frontostriatal circuit dysfunction leads to cognitive inflexibility in neuroligin-3 R451C knockin mice (Mar, 10.1038/s41380-024-02505-9, 2024)

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MOLECULAR PSYCHIATRY 2025年第1期30卷 365-366页

作者： Lin, Shen Fan, Cui-ying Wang, Hao-ran Li, Xiao-fan Zeng, Jia-li Lan, Pei-xuan Li, Hui-xian Zhang, Bin Hu, Chun Xu, Junyu Luo, Jian-hong Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital School of Brain Science and Brain Medicine Zhejiang University School of Medicine Hangzhou China Nanhu Brain-Computer Interface Institute Hangzhou China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-Machine Integration State Key Laboratory of Brain-Machine Intelligence Zhejiang University Hangzhou China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang University Hangzhou China Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province School of Medicine Hangzhou City University Hangzhou China Institute for Brain Research and Rehabilitation Key Laboratory of Brain Cognition and Education Sciences of Ministry of Education South China Normal University Guangzhou China

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Machine learning assisted screening of binary alloys for metal-based anode materials

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Journal of Energy Chemistry 2025年第5期104卷 62-68页

作者： Xingyue Shi Linming Zhou Yuhui Huang Chaohui Wu Yongjun Wu Juan Li Zijian Hong State Key Laboratory of Silicon and Advanced Semiconductor Materials School of Materials Science and Engineering Zhejiang University Nanhu Brain-Computer Interface Institute Zhejiang Key Laboratory of Advanced Solid State Energy Storage Technology and Applications Research Institute of Zhejiang University-Taizhou Inner Mongolia Electric Power Economic and Technological Research Institute Institute of Fundamental and Transdisciplinary Research Zhejiang University College of Materials Science and Engineering Zhejiang University of Technology School of Engineering Hangzhou City University

Metal alloy anode materials with high specific capacity and low voltage have recently gained significant attention due to their excellent electrochemical performance and the ability to suppress dendrite ***, experimental investigations of metal alloys can be time-consuming and expensive, often requiring extensive experimental design and effort. In this study, we developed a machine learning model based on the Crystal Graph Convolutional Neural Network(CGCNN) to screen alloy anode materials for seven battery systems, including lithium(Li), sodium(Na), potassium(K), zinc(Zn), magnesium(Mg), calcium(Ca), and aluminum(Al). We utilized data with tens of thousands of alloy materials from the Materials Project(MP) and Automatic FLOW for Materials Discovery(AFLOW) databases. Without any experimental voltage input, we identified over 30 alloy systems that have been experimentally validated with good precision. Additionally, we predicted over 100 alloy anodes with low potential and high specific capacity. We hope this work to spur further interest in employing advanced machine learning models for the design of battery materials.

关键词： Alloy anodes CGCNN Machine learning Metal-based batteries

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ControlVideo: conditional control for one-shot text-driven video editing and beyond

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science China(Information sciences) 2025年第3期68卷 150-162页

作者： Min ZHAO Rongzhen WANG Fan BAO Chongxuan LI Jun ZHU Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML CenterTsinghua Laboratory of Brain and Intelligence Lab Tsinghua University ShengShu Technology Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Pazhou Laboratory (Huangpu)

This paper presents ControlVideo for text-driven video editing — generating a video that aligns with a given text while preserving the structure of the source video. Building on a pre-trained text-to-image diffusion model, ControlVideo enhances the fidelity and temporal consistency by incorporating additional conditions(such as edge maps), and fine-tuning the key-frame and temporal attention on the source video-text pair via an in-depth exploration of the design space. Extensive experimental results demonstrate that ControlVideo outperforms various competitive baselines by delivering videos that exhibit high fidelity w.r.t. the source content, and temporal consistency, all while aligning with the text. By incorporating low-rank adaptation layers into the model before training, ControlVideo is further empowered to generate videos that align seamlessly with reference images. More importantly, ControlVideo can be readily extended to the more challenging task of long video editing(e.g., with hundreds of frames), where maintaining long-range temporal consistency is crucial. To achieve this, we propose to construct a fused ControlVideo by applying basic ControlVideo to overlapping short video segments and key frame videos and then merging them by pre-defined weight functions. Empirical results validate its capability to create videos across 140 frames, which is approximately 5.83 to 17.5 times more than what previous studies achieved. The code is available at https://***/thu-ml/controlvideo.

关键词： diffusion models controllable generation text-driven editing video editing long video editing

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A Wireless Fully Implantable DBS Device for Closed-Loop Neuromodulation Applications

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IEEE Sensors Journal 2025年第9期25卷 15331-15344页

作者： Su, Kangyu Qiu, Zhang Xu, Kedi Xu, Jian Wang, Yueming Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang University MOE Frontier Science Center for Brain Science and Brain-Machine Integration Hangzhou310027 China Nanhu Brain-Computer Interface Institute Hangzhou311100 China Zhejiang University College of Biomedical Engineering and Instrument Science Hangzhou310027 China Zhejiang University School of Medicine Affiliated Mental Health Center Hangzhou Seventh People’s Hospital Hangzhou310013 China Zhejiang University State Key Laboratory of Brain-Machine Intelligence Hangzhou310027 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China

Closed-loop deep brain stimulation (DBS) is an emerging technology in the field of neuromodulation, which can provide effective treatment methods for intractable neurological disorders. To address the faced challenges, this article presents a high-precision closed-loop DBS device based on a custom-designed neuromodulation integrated circuit (CNIC) fabricated in a 180-nm bipolar-CMOS-DMOS (BCD) process. Also, three new techniques have been developed to optimize system performance. First, to improve system stability, a frequency shaping & quick blanking amplifier (FSQBA)-based analog front-end is proposed to extend system dynamic range (DR). Second, both stimulation artifacts (SAs) suppression scheme and closed-loop control algorithm are developed to achieve real simultaneous recording and stimulation. Third, a redundant-crossfire (RXF) technique is designed to enhance current precision without consuming much extra area. Bench-top measurements show that the designed system features a wide DR of 112 dB, a short saturation recovery time of 100 μs, and a high stimulation current resolution of 13.1-bit. Besides, animal experiments demonstrate that the designed system is highly suitable for long-term implantation applications, thus providing accurate and reliable closed-loop control for neuromodulation therapies. © 2001-2012 IEEE.

关键词： CMOS integrated circuits

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Visual numerical cognition in pigeons: conformity to the Weber-Fechner law

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Animal cognition 2025年第1期28卷 39页

作者： Peng Wu Juncai Zhu Qingzhi He Zhizhong Wang Li Shi School of Electrical and Information Engineering Zhengzhou University Zhengzhou 450001 China. Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou 450001 China. School of Electrical and Information Engineering Zhengzhou University Zhengzhou 450001 China. wzz1982@***. Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou 450001 China. wzz1982@***. Department of Automation Tsinghua University Beijing 100084 China. li_shi_edu@***. Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou 450001 China. li_shi_edu@***.

As representatives of a basal bird lineage, pigeons have exhibited remarkable visual numerical cognition, comparable even to that of monkeys. Nevertheless, whether visual numerical cognition in pigeons conforms to the Weber-Fechner law remains unknown. To address this, we designed a fully automated apparatus tailored for pigeons and used it to train them to perform a delayed match-to-numerosity task. The results showed that on a linear scale, pigeons represented smaller numerosities with higher precision and larger numerosities with lower precision, exhibiting a numerical magnitude effect. When the linear scale was compressed into a logarithmic scale, this magnitude effect was offset, resulting in similar representational characteristics across different numerosities. This finding suggests that the mental number line of pigeons is logarithmic rather than linear, consistent with the Weber-Fechner law. While biological brains seek precision in representing numerical information, they must also take computational load into account. This representational strategy may be the optimal outcome of the trade-off between computational precision and computational load that biological brains have achieved through long-term evolution.

关键词： Birds Logarithm Mental number line Numerosity Pigeons

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Electrophysiological Abnormalities Associated with Sustained Attention in Children with Attention Deficit Hyperactivity Disorder and Autism Spectrum Disorder

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IEEE Transactions on Neural Systems and Rehabilitation Engineering 2025年 33卷 1785-1795页

作者： Zhao, Jiajing Bao, Mengyi Ruan, Wencong Kuang, Rui Li, Haifeng Wang, Yueming Yao, Lin Affiliated Mental Health Center Hangzhou Seventh People’s Hospital Zhejiang University School of Medicine Department of Neurobiology China Nanhu Brain-Computer Interface Institute Hangzhou311100 China Zhejiang University College of Computer Science and Technology MOE Frontiers Science Center for Brain and Brain-Machine Integration Zhejiang University China Children’s Hospital of Zhejiang University School of Medicine Rehabilitation Department Hangzhou China Zhejiang University College of Computer Science and Technology China State Key Laboratory of Brain-Machine Intelligence Zhejiang University MOE Frontiers Science Center for Brain and Brain-Machine Integration China and the College of Biomedical Engineering and Instrument Science Zhejiang University College of Computer Science and Technology China

Objective: This study investigates electrophysiological abnormalities in children with Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) during sustained attention tasks, focusing on vigilance and inhibitory control, and explores associations between neural markers and attentional performance. Methods: Children with ADHD (n = 30), ASD (n = 23), and typically developing (TD) children (n = 31) completed a Test of Variables of Attention (TOVA) task while electroencephalography (EEG) was recorded. Event-related potentials (ERPs: P1, N2, P3) and event-related desynchronization/synchronization (ERD/ERS: theta ERS, alpha ERD, beta ERS) were measured and compared across groups. Correlations between electrophysiological features and behavioral performance were analyzed. Results: Both ADHD and ASD groups demonstrated attenuated P1 amplitudes during vigilance task and reduced prefrontal theta ERS during inhibitory control. The ASD group exhibited additional impairments, including attenuated N2 amplitudes in inhibitory control, reduced P3 amplitudes, and weaker alpha ERD across conditions. The ADHD group showed additional deficits in theta ERS. Notably, N2 amplitude and theta ERS during vigilance state significantly correlated with response time measures. Conclusions: Children with ADHD and ASD share deficits in primary visual stimulus processing and inhibitory attention allocation. ASD-specific impairments involve top-down processing and inhibition, while ADHD-specific challenges involve attentional allocation and modulation. Significance: These findings enhance the electrophysiological understanding of sustained attention in ADHD and ASD, offering insights that may inform future diagnostic and intervention strategies. © 2001-2011 IEEE.

关键词： Electroencephalography Variable speed drives Pediatrics brain modeling Biological system modeling Autism Medical diagnostic imaging Resource management Hospitals Neural activity

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Constructing A Theoretical Model to Bridge Neural Transition with a State Switch in Bipolar Disorder

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Neuroscience Bulletin 2025年第1期41卷 181-185页

作者： Xiaonan Guo Lizichen Chen Jianbo Lai Shaohua Hu Department of Psychiatry the First Affiliated HospitalZhejiang University School of MedicineHangzhou310003China Department of Engineering Mechanics Zhejiang UniversityHangzhou310027China Nanhu Brain-computer Interface Institute Hangzhou311100China The Zhejiang Key Laboratory of Precision Psychiatry Hangzhou310003China MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University School of MedicineHangzhou310003China Brain Research Institute of Zhejiang University Hangzhou310003China The State Key Lab of Brain-Machine Intelligence Zhejiang UniversityHangzhouChina Zhejiang Engineering Center for Mathematical Mental Health Hangzhou310003China

Reward or stress,which exists extensively,causes resilient emotional fluctuations under common ***,reward or stress is a typical trigger for manic or depressive episodes of bipolar disorder(BD),which is corroborated by psychological theory,biological findings,and psychosocial treatment approaches[1,2].During an episode of BD,the affective aberration can be persistent and switchable,accompanied by opposite constellations of cognitive and psychomotor *** by uncontrollable mood ranging in severity,duration,and polarity,to disentangle the pathophysiology mechanism of BD is to delineate the mystery of affective fluctuations driven by reward or stress.

关键词： bipolar Transition finding

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