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第四届（2023年）中国安全药理学学术年会

作者：王相冲 Jianjun Wu Heming Wei 许彦芳苏素文河北医科大学药理教研室 Neuroscience & Behavioral Disorders Program Duke-NUS Medical School Singapore Research Laboratory KK Women's & Children's HospitalSingapore

目的:乌头类中药被誉为"回阳救逆"第一要药,其应用历史悠久、临床疗效显著。然而,其主要活性成分乌头碱有明显的毒性,主要表现为中枢神经系统和心血管系统的毒性反应,特别是其心脏毒性,可引起致死性心律失常,严重危害人类健... 详细信息

目的:乌头类中药被誉为"回阳救逆"第一要药,其应用历史悠久、临床疗效显著。然而,其主要活性成分乌头碱有明显的毒性,主要表现为中枢神经系统和心血管系统的毒性反应,特别是其心脏毒性,可引起致死性心律失常,严重危害人类健康。然而,乌头碱致心律失常的机制尚不明确。因此,本研究采用整体动物实验和体外细胞实验相结合的方法,对乌头碱致心律失常的分子细胞学机制进行探讨。

关键词：心律失常浓度依赖性心室肌细胞乌头碱钙电流钠电流动作电位时程分子机制

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Blood tests of brain function: Neuronal extracellular vesicles

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Biomarkers in Neuropsychiatry 2022年 7卷 100058-100058页

作者： Levine, Amir Strawn, Jeffrey R. Department of Psychiatry Columbia University and New York State Psychiatric Institute New York NY United States Anxiety Disorders Research Program College of Medicine University of Cincinnati Cincinnati OH United States Department of Psychiatry and Behavioral Neuroscience College of Medicine University of Cincinnati Cincinnati OH United States

Extracellular vesicles (EVs) are tiny (<1 µm) membrane-bound vesicles released by all tissues (including the brain) that cross the blood-brain barrier and facilitate cell-to-cell communication within and among tissues. They represent the body's “Twitter system,” rapidly disseminating packets of information throughout the brain and body. Not unlike Twitter, EVs convey short messages through their cargos (e.g., RNAs, proteins, lipids, and metabolites), which direct the molecular activity of recipient cells in both health and disease. Extracellular vesicles serve as mediators of brain function and represent a reservoir for CNS-specific biomarkers that can be sequestered from plasma to guide diagnosis and treatment, representing a new frontier in the molecular study of psychiatric illness and in the development of biomarkers. The EV field has immense potential to revolutionize diagnostic approaches in psychiatry, facilitate precision treatment, predict response, and discover much-needed novel therapeutics. © 2022

关键词： Anxiety Biomarker Depression Extracellular vesicles L1CAM

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Depth-targeted intracortical microstroke by two-photon photothrombosis in rodent brain

Depth-targeted intracortical microstroke by two-photon photo...

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Clinical and Translational Biophotonics, Translational 2022

作者： Fukuda, Masahiro Matsumura, Takayoshi Suda, Toshio Hirase, Hajime International Research Center for Medical Sciences Kumamoto University 2-2-1 Honjo Chuo-ku Kumamoto860-0811 Japan Signature Program in Neuroscience and Behavioral Disorders Duke-NUS Medical School 8 college Road Singapore169857 Singapore Division of Inflammation Research Center for Molecular Medicine Jichi Medical University 3311-1 Yakushiji Tochigi Shimotsuke329-0498 Japan Cancer Science Institute of Singapore National University of Singapore 14 Medical Drive #12-01 Singapore117599 Singapore Center for Translational Neuromedicine Faculty of Health and Life Sciences University of Copenhagen Blegdamsvej 3B Copenhagen N2200 Denmark

We have developed depth-targeted Photothrombosis (DTPT) technique that can target a single vessel in the cortical parenchyma by two-photon excitation. Rose Bengal-mediated DTPT could be induced at 720 nm scanning with... 详细信息

ISBN: (纸本)9781557528209

关键词： Photons

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Minimizing Hybrid Dice Loss for Highly Imbalanced 3D Neuroimage Segmentation

Minimizing Hybrid Dice Loss for Highly Imbalanced 3D Neuroim...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yuhao Lu Juan Helen Zhou Cuntai Guan School of Computer Science and Engineering Nanyang Technological University Singapore Neuroscience and Behavioral Disorders Program Duke-National University of Singapore Medical School

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Recent advances in medical image segmentation have largely been driven by the success of deep learning algorithms. However, one main challenge for the training of one- stage segmentation networks is the serious imbalance between the number of examples that are easy and hard to classify or in positive and negative classes. In this paper, we first investigate and compare the strategies that were proposed parallelly to handle one or two of these imbalance problems. And we propose a hybrid loss that addresses these two imbalance problems together by combining the merits of Exponential logarithmic Dice and weighted Cross entropy Loss (EDCL). Without any whistles and bells, the proposed EDC loss with 3D Unet achieves mean dice of 57.38%, which surpasses the other state-of-the- art methods with 5-fold cross-validation on a public dataset for 3D brain lesion segmentation, Anatomical Tracings of Lesions After Stroke (ATLAS) v1.2.

关键词： Entropy Training Three-dimensional displays Lesions Image segmentation Loss measurement Biomedical imaging

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Focal Clusters of Peri-Synaptic Matrix Contribute to Activity-Dependent Plasticity and Memory

SSRN

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SSRN 2023年

作者： Chelini, Gabriele Mirzapourdelavar, Hadi Durning, Peter Baidoe-Ansah, David O’Donovan, Sinead M. Klengel, Torsten Balasco, Luigi Berciu, Cristina Boyer-Boiteau, Anne Smith, Robert Ressler, Kerry J. Bozzi, Yuri Dityatev, Alexander Berretta, Sabina Translational Neuroscience Laboratory McLean Hospital BelmontMA United States Department of Psychiatry Harvard Medical School BostonMA United States DZNE Molecular Neuroplasticity Magdeburg Germany Cognitive Disorders Research Laboratory University of Toledo ToledoOH United States Translational Molecular Genomics Laboratory Mclean Hospital BelmontMA United States University Medical Center Göttingen Department of Psychiatry Göttingen Germany Center for Mind/Brain Science University of Trento Rovereto Italy Program in Neuroscience Harvard Medical School BostonMA United States Neurobiology of Fear Laboratory McLean Hospital BelmontMA United States CNR Neuroscience Institute Pisa Italy Medical Faculty Otto von Guericke University Magdeburg Germany Center for Behavioral Brain Sciences Otto von Guericke University Magdeburg Germany

Experience-dependent learning depends on synaptic plasticity. Recent findings show that effective integration of novel salient information requires coordinated processes of homo- and hetero- synaptic plasticity onto neighboring dendritic stretches. In this work, we hypothesized that activity-dependent remodeling of the peri-synaptic extracellular matrix contributes to this mechanism. We show that clusters of the peri-synaptic matrix, containing the 6-sulfated chondroitin sulfate proteoglycan versican ( Vcan-CSPG ), emerge in response to sensory stimuli, showing temporal and spatial coincidence with the dendritic spine plasticity. Moreover, the depletion of Vcan-CSPG in the CA1 hippocampal region was accompanied by impaired long-term potentiation and object location memory in adult mice. These findings show that a specific CSPG glycoform regulates the molecular mechanisms underlying induction and consolidation of synaptic plasticity, confirming that experience-dependent refinement of the brain extracellular matrix plays a critical role in learning and memory. © 2023, The Authors. All rights reserved.

关键词： Sulfur compounds

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Pediatric anxiety and daily fine particulate matter: A longitudinal study

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Psychiatry Research Communications 2022年第4期2卷 100077页

作者： Vancil, Andrew Strawn, Jeffrey R. Rasnick, Erika Levine, Amir Schroeder, Heidi K. Specht, Ashley M. Turner, Ashley L. Ryan, Patrick H. Brokamp, Cole Cincinnati Children's Hospital Medical Center Division of Biostatistics and Epidemiology United States University of Cincinnati Anxiety Disorders Research Program Department of Psychiatry & Behavioral Neuroscience United States Department of Psychiatry College of Physicians and Surgeons Columbia University United States Division of Child and Adolescent Psychiatry New York State Psychiatric Institute United States University of Cincinnati College of Medicine Department of Pediatrics United States

Daily variations in ambient fine particulate matter (PM2.5) could contribute to the morbidity of anxiety disorders in children and adolescents, but has not yet been studied longitudinally at a daily level. We tested t...

Daily variations in ambient fine particulate matter (PM_2.5) could contribute to the morbidity of anxiety disorders in children and adolescents, but has not yet been studied longitudinally at a daily level. We tested this association using repeated weekly measures of anxiety symptom severity in a group of 23 adolescents with generalized anxiety disorder. After estimating ambient PM_2.5 concentrations using a validated model, we found that increased concentrations were significantly associated with increased anxiety symptom severity and frequency two, three, and four days later. PM_2.5 may be a novel, modifiable exposure that could inform population level interventions to decrease psychiatric morbidity. © 2022 The Authors

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TSception:A Deep Learning Framework for Emotion Detection Using EEG

TSception:A Deep Learning Framework for Emotion Detection Us...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yi Ding Neethu Robinson Qiuhao Zeng Duo Chen Aung Aung Phyo Wai Tih-Shih Lee Cuntai Guan School of Computer Science and Engineering Nanyang Technological University Singapore Neuroscience and Behavioral Disorders Program National University of Singapore Medical School Singapore Singapore Singapore General Hospital Singapore Singapore

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In this paper, we propose a deep learning framework, TSception, for emotion detection from electroencephalogram (EEG). TSception consists of temporal and spatial convolutional layers, which learn discriminative representations in the time and channel domains simultaneously. The temporal learner consists of multi-scale 1D convolutional kernels whose lengths are related to the sampling rate of the EEG signal, which learns multiple temporal and frequency representations. The spatial learner takes advantage of the asymmetry property of emotion responses at the frontal brain area to learn the discriminative representations from the left and right hemispheres of the brain. In our study, a system is designed to study the emotional arousal in an immersive virtual reality (VR) environment. EEG data were collected from 18 healthy subjects using this system to evaluate the performance of the proposed deep learning network for the classification of low and high emotional arousal states. The proposed method is compared with SVM, EEGNet, and LSTM. TSception achieves a high classification accuracy of 86.03%, which outperforms the prior methods significantly (p

关键词： Kernel Electroencephalography Convolution Machine learning Feature extraction Electrodes Convolutional neural networks

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Corrigendum to ’ The role of ultrasonic vocalizations in rat laryngological investigations’ [Physiology & Behavior volume 294 (2025) start page 1 –end page 10 /article number 114887]

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Physiology and Behavior 2025年 295卷 114907-114907页

作者： Shembel, Adrianna C. Johnson, Aaron M. Ciucci, Michelle R. Lunaris, Charlie Lenell Morrison, Robert A. Rudisch, Denis Michael School of Behavioral and Brain Sciences University of Texas at Dallas Dallas TX United States Department of Otolaryngology-Head and Neck Voice Center University of Texas Southwestern Medical Center Dallas TX United States Department of Otolaryngology—Head and Neck Surgery NYU Voice Center NYU Langone Health New York NY United States Department of Surgery Division of Otolaryngology-Head & Neck Surgery UW School of Medicine and Public Health University of Wisconsin-Madison Madison WI United States Department of Communication Sciences and Disorders University of Wisconsin-Madison Madison WI United States Neuroscience Training Program University of Wisconsin-Madison Madison WI United States Communication Sciences and Disorders University of Northern Colorado Greeley CO United States Institute for Clinical and Translational Research UW School of Medicine and Public Health University of Wisconsin-Madison Madison WI United States

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TSception: A deep learning framework for emotion detection using EEG

arXiv

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arXiv 2020年

作者： Ding, Yi Robinson, Neethu Zeng, Qiuhao Chen, Duo Wai, Aung Aung Phyo Lee, Tih-Shih Guan, Cuntai School of Computer Science and Engineering Nanyang Technological University Singapore Neuroscience and Behavioral Disorders Program Duke University National University of Singapore Medical School Singapore Singapore Singapore General Hospital Singapore Singapore

关键词： Electroencephalography

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Neural signatures of emotional biases predict clinical outcomes in difficult-to-treat depression

Research directions. Depression

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Research directions. Depression 2024年 1卷 e21页

作者： Diede Fennema Gareth J Barker Owen O'Daly Beata R Godlewska Ewan Carr Kimberley Goldsmith Allan H Young Jorge Moll Roland Zahn Centre of Affective Disorders Institute of Psychiatry Psychology & Neuroscience King's College London London UK. Department of Neuroimaging Institute of Psychiatry Psychology & Neuroscience King's College London London UK. Psychopharmacology Research Unit University Department of Psychiatry University of Oxford Oxford UK. Oxford Health NHS Foundation Trust Warneford Hospital Oxford UK. Department of Biostatistics and Health Informatics Institute of Psychiatry Psychology & Neuroscience King's College London London UK. National Service for Affective Disorders South London and Maudsley NHS Foundation Trust London UK. Cognitive and Behavioral Neuroscience Unit D'Or Institute for Research and Education (IDOR) Pioneer Science Program Rio de Janeiro Brazil.

Background:Neural predictors underlying variability in depression outcomes are poorly understood. Functional MRI measures of subgenual cortex connectivity, self-blaming and negative perceptual biases have shown prognostic potential in treatment-naïve, medication-free and fully remitting forms of major depressive disorder (MDD). However, their role in more chronic, difficult-to-treat forms of MDD is unknown. Methods:Forty-five participants (n = 38 meeting minimum data quality thresholds) fulfilled criteria for difficult-to-treat MDD. Clinical outcome was determined by computing percentage change at follow-up from baseline (four months) on the self-reported Quick Inventory of Depressive Symptomatology (16-item). Baseline measures included self-blame-selective connectivity of the right superior anterior temporal lobe with an Brodmann Area 25 region-of-interest, blood-oxygen-level-dependent bilateral amygdala activation for subliminal sad vs happy faces, and resting-state connectivity of the subgenual cortex with an defined ventrolateral prefrontal cortex/insula region-of-interest. Findings:A linear regression model showed that baseline severity of depressive symptoms explained 3% of the variance in outcomes at follow-up ([3,34] = .33, = .81). In contrast, our three pre-registered neural measures combined, explained 32% of the variance in clinical outcomes ([4,33] = 3.86, .01). Conclusion:These findings corroborate the pathophysiological relevance of neural signatures of emotional biases and their potential as predictors of outcomes in difficult-to-treat depression.

关键词： depression difficult-to-treat fMRI prediction prognosis

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