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Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme. 神经科学跨学科合作培训:人脑项目教育计划的启示。
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-01 Epub Date: 2024-11-06 DOI: 10.1007/s12021-024-09682-6
Alice Geminiani, Judith Kathrein, Alper Yegenoglu, Franziska Vogel, Marcelo Armendariz, Ziv Ben-Zion, Petrut Antoniu Bogdan, Joana Covelo, Marissa Diaz Pier, Karin Grasenick, Vitali Karasenko, Wouter Klijn, Tina Kokan, Carmen Alina Lupascu, Anna Lührs, Tara Mahfoud, Taylan Özden, Jens Egholm Pedersen, Luca Peres, Ingrid Reiten, Nikola Simidjievski, Inga Ulnicane, Michiel van der Vlag, Lyuba Zehl, Alois Saria, Sandra Diaz-Pier, Johannes Passecker

Neuroscience education is challenged by rapidly evolving technology and the development of interdisciplinary approaches for brain research. The Human Brain Project (HBP) Education Programme aimed to address the need for interdisciplinary expertise in brain research by equipping a new generation of researchers with skills across neuroscience, medicine, and information technology. Over its ten year duration, the programme engaged over 1,300 experts and attracted more than 5,500 participants from various scientific disciplines in its blended learning curriculum, specialised schools and workshops, and events fostering dialogue among early-career researchers. Key principles of the programme's approach included fostering interdisciplinarity, adaptability to the evolving research landscape and infrastructure, and a collaborative environment with a focus on empowering early-career researchers. Following the programme's conclusion, we provide here an analysis and in-depth view across a diverse range of educational formats and events. Our results show that the Education Programme achieved success in its wide geographic reach, the diversity of participants, and the establishment of transversal collaborations. Building on these experiences and achievements, we describe how leveraging digital tools and platforms provides accessible and highly specialised training, which can enhance existing education programmes for the next generation of brain researchers working in decentralised European collaborative spaces. Finally, we present the lessons learnt so that similar initiatives may improve upon our experience and incorporate our suggestions into their own programme.

神经科学教育面临着快速发展的技术和跨学科脑研究方法的挑战。人脑项目(HBP)教育计划旨在通过培养具备神经科学、医学和信息技术技能的新一代研究人员,满足脑研究对跨学科专业知识的需求。在十年的时间里,该计划聘请了 1 300 多名专家,吸引了来自不同科学学科的 5 500 多人参加其混合学习课程、专门学校和讲习班,以及促进早期研究人员之间对话的活动。该计划的主要原则包括促进跨学科性、适应不断变化的研究环境和基础设施,以及营造一个以增强早期研究人员能力为重点的合作环境。计划结束后,我们在此对各种教育形式和活动进行了分析和深入探讨。我们的结果表明,教育计划在广泛的地理覆盖范围、参与者的多样性以及横向合作的建立方面取得了成功。在这些经验和成就的基础上,我们介绍了如何利用数字工具和平台提供便捷和高度专业化的培训,从而加强针对在分散的欧洲合作空间工作的下一代脑研究人员的现有教育计划。最后,我们介绍了所吸取的经验教训,以便类似的倡议可以借鉴我们的经验并将我们的建议纳入他们自己的计划中。
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引用次数: 0
A Bayesian Multiplex Graph Classifier of Functional Brain Connectivity Across Diverse Tasks of Cognitive Control. 不同认知控制任务中大脑功能连接性的贝叶斯多重图分类器
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-01 Epub Date: 2024-06-11 DOI: 10.1007/s12021-024-09670-w
Sharmistha Guha, Jose Rodriguez-Acosta, Ivo D Dinov

This article seeks to investigate the impact of aging on functional connectivity across different cognitive control scenarios, particularly emphasizing the identification of brain regions significantly associated with early aging. By conceptualizing functional connectivity within each cognitive control scenario as a graph, with brain regions as nodes, the statistical challenge revolves around devising a regression framework to predict a binary scalar outcome (aging or normal) using multiple graph predictors. Popular regression methods utilizing multiplex graph predictors often face limitations in effectively harnessing information within and across graph layers, leading to potentially less accurate inference and predictive accuracy, especially for smaller sample sizes. To address this challenge, we propose the Bayesian Multiplex Graph Classifier (BMGC). Accounting for multiplex graph topology, our method models edge coefficients at each graph layer using bilinear interactions between the latent effects associated with the two nodes connected by the edge. This approach also employs a variable selection framework on node-specific latent effects from all graph layers to identify influential nodes linked to observed outcomes. Crucially, the proposed framework is computationally efficient and quantifies the uncertainty in node identification, coefficient estimation, and binary outcome prediction. BMGC outperforms alternative methods in terms of the aforementioned metrics in simulation studies. An additional BMGC validation was completed using an fMRI study of brain networks in adults. The proposed BMGC technique identified that sensory motor brain network obeys certain lateral symmetries, whereas the default mode network exhibits significant brain asymmetries associated with early aging.

本文旨在研究不同认知控制情景下衰老对功能连通性的影响,特别强调识别与早期衰老显著相关的脑区。通过将每种认知控制情景中的功能连通性概念化为一个图,以脑区为节点,统计挑战围绕着设计一个回归框架,利用多重图预测因子预测二元标量结果(衰老或正常)。利用多重图预测因子的流行回归方法在有效利用图层内部和图层之间的信息方面往往存在局限性,导致推断和预测的准确性可能较低,尤其是在样本量较小的情况下。为了应对这一挑战,我们提出了贝叶斯多重图分类器(BMGC)。考虑到多重图拓扑结构,我们的方法利用与边缘连接的两个节点相关的潜在效应之间的双线性交互作用,对每个图层的边缘系数进行建模。这种方法还在所有图层的特定节点潜在效应上采用了变量选择框架,以识别与观察结果相关的有影响力的节点。最重要的是,所提出的框架计算效率高,并能量化节点识别、系数估计和二元结果预测中的不确定性。在模拟研究中,BMGC 在上述指标方面优于其他方法。另外,BMGC 还通过对成人大脑网络的 fMRI 研究进行了验证。所提出的 BMGC 技术确定了感官运动脑网络遵循某些横向对称性,而默认模式网络则表现出与早期衰老相关的显著脑不对称。
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引用次数: 0
Classifying Neuronal Cell Types Based on Shared Electrophysiological Information from Humans and Mice. 根据人类和小鼠共享的电生理信息对神经元细胞类型进行分类
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-01 Epub Date: 2024-07-08 DOI: 10.1007/s12021-024-09675-5
Ofek Ophir, Orit Shefi, Ofir Lindenbaum

The brain is an intricate system that controls a variety of functions. It consists of a vast number of cells that exhibit diverse characteristics. To understand brain function in health and disease, it is crucial to classify neurons accurately. Recent advancements in machine learning have provided a way to classify neurons based on their electrophysiological activity. This paper presents a deep-learning framework that classifies neurons solely on this basis. The framework uses data from the Allen Cell Types database, which contains a survey of biological features derived from single-cell recordings from mice and humans. The shared information from both sources is used to classify neurons into their broad types with the help of a joint model. An accurate domain-adaptive model, integrating electrophysiological data from both mice and humans, is implemented. Furthermore, data from mouse neurons, which also includes labels of transgenic mouse lines, is further classified into subtypes using an interpretable neural network model. The framework provides state-of-the-art results in terms of accuracy and precision while also providing explanations for the predictions.

大脑是一个控制各种功能的复杂系统。它由大量细胞组成,这些细胞表现出不同的特征。要了解大脑在健康和疾病中的功能,对神经元进行准确分类至关重要。机器学习领域的最新进展为根据神经元的电生理活动对其进行分类提供了一种方法。本文介绍了一种深度学习框架,该框架可完全在此基础上对神经元进行分类。该框架使用来自艾伦细胞类型数据库的数据,该数据库包含从小鼠和人类单细胞记录中提取的生物特征调查。在联合模型的帮助下,来自这两个来源的共享信息被用于将神经元划分为广泛的类型。我们建立了一个精确的领域自适应模型,整合了小鼠和人类的电生理数据。此外,来自小鼠神经元的数据(也包括转基因小鼠品系的标签)也利用可解释的神经网络模型进一步划分为亚型。该框架在准确度和精确度方面提供了最先进的结果,同时还为预测提供了解释。
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引用次数: 0
Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort. 使用 DataLad 教授研究数据管理:一项多年期、多领域的努力。
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-01 Epub Date: 2024-05-07 DOI: 10.1007/s12021-024-09665-7
Michał Szczepanik, Adina S Wagner, Stephan Heunis, Laura K Waite, Simon B Eickhoff, Michael Hanke

Research data management has become an indispensable skill in modern neuroscience. Researchers can benefit from following good practices as well as from having proficiency in using particular software solutions. But as these domain-agnostic skills are commonly not included in domain-specific graduate education, community efforts increasingly provide early career scientists with opportunities for organised training and materials for self-study. Investing effort in user documentation and interacting with the user base can, in turn, help developers improve quality of their software. In this work, we detail and evaluate our multi-modal teaching approach to research data management in the DataLad ecosystem, both in general and with concrete software use. Spanning an online and printed handbook, a modular course suitable for in-person and virtual teaching, and a flexible collection of research data management tips in a knowledge base, our free and open source collection of training material has made research data management and software training available to various different stakeholders over the past five years.

研究数据管理已成为现代神经科学不可或缺的技能。研究人员可以从遵循良好实践和熟练使用特定软件解决方案中获益。但是,由于这些与领域无关的技能通常不包括在特定领域的研究生教育中,因此社区的努力越来越多地为早期职业科学家提供有组织的培训机会和自学材料。在用户文档和与用户群互动方面投入精力,反过来也能帮助开发人员提高软件质量。在这项工作中,我们详细介绍并评估了 DataLad 生态系统中研究数据管理的多模式教学方法,包括一般教学方法和具体的软件使用方法。在过去的五年中,我们的免费开源培训材料集为不同的利益相关者提供了研究数据管理和软件培训,其中包括在线和印刷手册、适合现场和虚拟教学的模块化课程以及知识库中灵活的研究数据管理技巧。
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引用次数: 0
Bridging the Gap: How Neuroinformatics is Preparing the Next Generation of Neuroscience Researchers. 缩小差距:神经信息学如何培养下一代神经科学研究人员》(Bridging the Gap: How Neuroinformatics is Preparing the Next Generation of Neuroscience Researchers)。
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-01 DOI: 10.1007/s12021-024-09693-3
Mathew Abrams, John Darrell Van Horn

Neurotechnology and big data are two rapidly advancing fields that have the potential to transform our understanding of the brain and its functions. Advancements in neurotechnology have enabled researchers to investigate the function of the brain at unprecedented levels of granularity at the functional, molecular, and anatomical levels. Thus, resulting in the collection of not only more data, but also larger datasets. To fully harness the potential of big data and advancements in neurotechnology to improve our understanding of the nervous system, there is a need to train a new generation of neuroscientists capable of not only domain expertise, but also the computational and data science skills required to interrogate and integrate big data. Importantly, neuroinformatics is the subdiscipline of neuroscience devoted to the development of neuroscience data and knowledge bases together with computational models and analytical tools for sharing, integration and analysis of experimental data, and advancement of theories about the nervous system function. While there are only a few formal training programs in neuroinformatics, and since neuroinformatics is rarely incorporated into traditional neuroscience training programs, the neuroinformatics community has attempted to bridge the gap between the traditional neuroscience education programs and the needs of the next generation of neuroscience researchers through community initiatives and workshops. Thus, the purpose of this special collection is to highlight several such community efforts which span from in-person workshops to large-scale, global virtual training consortiums and from training students to training-the-trainers.

神经技术和大数据是两个快速发展的领域,它们有可能改变我们对大脑及其功能的认识。神经技术的进步使研究人员能够在功能、分子和解剖层面以前所未有的精细程度研究大脑的功能。因此,收集到的数据不仅更多,而且数据集也更大。要充分利用大数据的潜力和神经技术的进步来提高我们对神经系统的认识,就需要培养新一代的神经科学家,他们不仅要具备相关领域的专业知识,还要掌握查询和整合大数据所需的计算和数据科学技能。重要的是,神经信息学是神经科学的一个分支学科,致力于开发神经科学数据和知识库以及计算模型和分析工具,以共享、整合和分析实验数据,并推进有关神经系统功能的理论。虽然目前只有少数几个正规的神经信息学培训项目,而且神经信息学很少被纳入传统的神经科学培训项目,但神经信息学界一直试图通过社区活动和研讨会来弥补传统神经科学教育项目与下一代神经科学研究人员需求之间的差距。因此,本特辑的目的是重点介绍几项此类社区活动,这些活动从面对面的研讨会到大规模的全球虚拟培训联盟,从培训学生到培训培训师,不一而足。
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引用次数: 0
Mesoscale Brain Mapping: Bridging Scales and Modalities in Neuroimaging - A Symposium Review. 中尺度脑图谱:中尺度脑图谱:神经成像中尺度与模式的桥梁--专题讨论会综述。
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-01 Epub Date: 2024-09-23 DOI: 10.1007/s12021-024-09686-2
Joshua K Marchant, Natalie G Ferris, Diana Grass, Magdelena S Allen, Vivek Gopalakrishnan, Mark Olchanyi, Devang Sehgal, Maxina Sheft, Amelia Strom, Berkin Bilgic, Brian Edlow, Elizabeth M C Hillman, Meher R Juttukonda, Laura Lewis, Shahin Nasr, Aapo Nummenmaa, Jonathan R Polimeni, Roger B H Tootell, Lawrence L Wald, Hui Wang, Anastasia Yendiki, Susie Y Huang, Bruce R Rosen, Randy L Gollub

Advances in the spatiotemporal resolution and field-of-view of neuroimaging tools are driving mesoscale studies for translational neuroscience. On October 10, 2023, the Center for Mesoscale Mapping (CMM) at the Massachusetts General Hospital (MGH) Athinoula A. Martinos Center for Biomedical Imaging and the Massachusetts Institute of Technology (MIT) Health Sciences Technology based Neuroimaging Training Program (NTP) hosted a symposium exploring the state-of-the-art in this rapidly growing area of research. "Mesoscale Brain Mapping: Bridging Scales and Modalities in Neuroimaging" brought together researchers who use a broad range of imaging techniques to study brain structure and function at the convergence of the microscopic and macroscopic scales. The day-long event centered on areas in which the CMM has established expertise, including the development of emerging technologies and their application to clinical translational needs and basic neuroscience questions. The in-person symposium welcomed more than 150 attendees, including 57 faculty members, 61 postdoctoral fellows, 35 students, and four industry professionals, who represented institutions at the local, regional, and international levels. The symposium also served the training goals of both the CMM and the NTP. The event content, organization, and format were planned collaboratively by the faculty and trainees. Many CMM faculty presented or participated in a panel discussion, thus contributing to the dissemination of both the technologies they have developed under the auspices of the CMM and the findings they have obtained using those technologies. NTP trainees who benefited from the symposium included those who helped to organize the symposium and/or presented posters and gave "flash" oral presentations. In addition to gaining experience from presenting their work, they had opportunities throughout the day to engage in one-on-one discussions with visiting scientists and other faculty, potentially opening the door to future collaborations. The symposium presentations provided a deep exploration of the many technological advances enabling progress in structural and functional mesoscale brain imaging. Finally, students worked closely with the presenting faculty to develop this report summarizing the content of the symposium and putting it in the broader context of the current state of the field to share with the scientific community. We note that the references cited here include conference abstracts corresponding to the symposium poster presentations.

神经成像工具在时空分辨率和视场方面的进步正在推动神经科学转化的中尺度研究。2023 年 10 月 10 日,麻省总医院(MGH)阿西努拉-马丁诺斯生物医学成像中心(Athinoula A. Martinos Center for Biomedical Imaging)的中尺度绘图中心(CMM)与麻省理工学院(MIT)基于健康科学技术的神经成像培训计划(NTP)共同主办了一场研讨会,探讨这一快速发展的研究领域的最新进展。"中尺度脑图谱:连接神经成像的尺度和模式 "研讨会汇集了使用多种成像技术研究微观和宏观尺度交汇处大脑结构和功能的研究人员。为期一天的活动围绕着CMM已建立的专业领域展开,包括新兴技术的开发及其在临床转化需求和基础神经科学问题上的应用。150多名与会者参加了这次面对面的研讨会,其中包括57名教师、61名博士后研究员、35名学生和4名业界专业人士,他们分别代表地方、地区和国际层面的机构。此次研讨会还实现了坐标测量机和国家热带木材计划的培训目标。活动的内容、组织和形式由教师和学员共同策划。许多坐标测量机教员在会上发言或参加小组讨论,从而促进了他们在坐标测量机支持下开发的技术和利用这些技术取得的研究成果的传播。从研讨会中获益的国家培训计划学员包括那些帮助组织研讨会和/或展示海报以及做 "快闪 "口头报告的学员。除了从展示自己的工作中获得经验外,他们还有机会全天与来访的科学家和其他教师进行一对一的讨论,为今后的合作打开了潜在的大门。专题讨论会的发言深入探讨了促进大脑结构和功能中尺度成像进展的众多技术进步。最后,学生们与主讲教师密切合作,编写了这份报告,总结了研讨会的内容,并将其置于该领域现状的大背景下,与科学界分享。我们注意到,此处引用的参考文献包括与研讨会海报演讲相对应的会议摘要。
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引用次数: 0
Anatomic Interpretability in Neuroimage Deep Learning: Saliency Approaches for Typical Aging and Traumatic Brain Injury. 神经影像深度学习中的解剖可解释性:典型老化和创伤性脑损伤的显著性方法。
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-01 Epub Date: 2024-11-06 DOI: 10.1007/s12021-024-09694-2
Kevin H Guo, Nikhil N Chaudhari, Tamara Jafar, Nahian F Chowdhury, Paul Bogdan, Andrei Irimia

The black box nature of deep neural networks (DNNs) makes researchers and clinicians hesitant to rely on their findings. Saliency maps can enhance DNN explainability by suggesting the anatomic localization of relevant brain features. This study compares seven popular attribution-based saliency approaches to assign neuroanatomic interpretability to DNNs that estimate biological brain age (BA) from magnetic resonance imaging (MRI). Cognitively normal (CN) adults (N = 13,394, 5,900 males; mean age: 65.82 ± 8.89 years) are included for DNN training, testing, validation, and saliency map generation to estimate BA. To study saliency robustness to the presence of anatomic deviations from normality, saliency maps are also generated for adults with mild traumatic brain injury (mTBI, N = 214, 135 males; mean age: 55.3 ± 9.9 years). We assess saliency methods' capacities to capture known anatomic features of brain aging and compare them to a surrogate ground truth whose anatomic saliency is known a priori. Anatomic aging features are identified most reliably by the integrated gradients method, which outperforms all others through its ability to localize relevant anatomic features. Gradient Shapley additive explanations, input × gradient, and masked gradient perform less consistently but still highlight ubiquitous neuroanatomic features of aging (ventricle dilation, hippocampal atrophy, sulcal widening). Saliency methods involving gradient saliency, guided backpropagation, and guided gradient-weight class attribution mapping localize saliency outside the brain, which is undesirable. Our research suggests the relative tradeoffs of saliency methods to interpret DNN findings during BA estimation in typical aging and after mTBI.

深度神经网络(DNN)的黑箱性质使研究人员和临床医生对其研究结果的可靠性犹豫不决。通过提示相关大脑特征的解剖定位,显著性图谱可以增强 DNN 的可解释性。本研究比较了七种流行的基于归因的显著性方法,这些方法可为根据磁共振成像(MRI)估计生物脑年龄(BA)的 DNN 分配神经解剖学可解释性。认知正常(CN)成年人(N = 13,394 人,男性 5,900 人;平均年龄:65.82 ± 8.89 岁)被纳入 DNN 训练、测试、验证和生成显著性图谱以估算 BA。为了研究生理盐水对解剖结构偏离正态的稳健性,我们还为轻度脑损伤(mTBI,N = 214,135 名男性;平均年龄:55.3 ± 9.9 岁)的成人生成了生理盐水图。我们评估了显著性方法捕捉已知脑老化解剖特征的能力,并将其与解剖显著性先验已知的替代地面实况进行比较。综合梯度法能最可靠地识别出解剖学衰老特征,其定位相关解剖学特征的能力优于其他所有方法。梯度沙普利相加解释法、输入×梯度法和掩蔽梯度法的一致性较差,但仍能突出无处不在的衰老神经解剖特征(脑室扩张、海马萎缩、脑沟增宽)。涉及梯度盐度、引导反向传播和引导梯度权重类别归因映射的盐度方法将盐度定位在大脑之外,这是不可取的。我们的研究表明,在典型老龄化和创伤性脑损伤后的BA估计过程中,可以通过解释DNN发现的生理盐水方法进行相对权衡。
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引用次数: 0
Hands-On Neuroinformatics Education at the Crossroads of Online and In-Person: Lessons Learned from NeuroHackademy. 在线与面授交汇处的神经信息学实践教育:从 NeuroHackademy 学到的经验。
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-01 Epub Date: 2024-05-20 DOI: 10.1007/s12021-024-09666-6
Ariel Rokem, Noah C Benson

NeuroHackademy ( https://neurohackademy.org ) is a two-week event designed to train early-career neuroscience researchers in data science methods and their application to neuroimaging. The event seeks to bridge the big data skills gap by introducing participants to data science methods and skills that are often ignored in traditional curricula. Such skills are needed for the analysis and interpretation of the kinds of large and complex datasets that have become increasingly important to neuroimaging research due to concerted data collection efforts. In 2020, the event rapidly pivoted from an in-person event to an online event that included hundreds of participants from all over the world. This experience and those of the participants substantially changed our valuation of large online-accessible events. In subsequent events held in 2022 and 2023, we have developed a "hybrid" format that includes both online and in-person participants. We discuss the technical and sociotechnical elements of hybrid events and discuss some of the lessons we have learned while organizing them. We emphasize in particular the role that these events can play in creating a global and inclusive community of practice in the intersection of neuroimaging and data science.

NeuroHackademy ( https://neurohackademy.org ) 是一项为期两周的活动,旨在培训早期神经科学研究人员掌握数据科学方法及其在神经成像中的应用。该活动旨在通过向学员介绍传统课程中经常忽略的数据科学方法和技能,弥补大数据技能方面的差距。这些技能是分析和解释大型复杂数据集所必需的,而随着数据收集工作的开展,这些数据集在神经成像研究中变得越来越重要。2020 年,该活动迅速从现场活动转变为在线活动,包括来自世界各地的数百名参与者。这次经历和参与者的经历大大改变了我们对大型在线活动的评价。在 2022 年和 2023 年举办的后续活动中,我们开发了一种 "混合 "形式,既包括在线参与者,也包括现场参与者。我们讨论了混合活动的技术和社会技术要素,并讨论了我们在组织这些活动时吸取的一些经验教训。我们特别强调了这些活动在神经成像和数据科学交叉领域创建全球性和包容性实践社区方面所能发挥的作用。
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引用次数: 0
Characteristics of the Structural Connectivity in Patients with Brain Injury and Chronic Health Symptoms: A Pilot Study. 脑损伤和慢性健康症状患者的结构连通性特征:一项试点研究
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-01 Epub Date: 2024-07-11 DOI: 10.1007/s12021-024-09681-7
Xiaojian Kang, Byung C Yoon, Emily Grossner, Maheen M Adamson

Diffusion properties from diffusion tensor imaging (DTI) are exquisitely sensitive to white matter abnormalities incurred during traumatic brain injury (TBI), especially for those patients with chronic post-TBI symptoms such as headaches, dizziness, fatigue, etc. The evaluation of structural and functional connectivity using DTI has become a promising method for identifying subtle alterations in brain connectivity associated with TBI that are otherwise not visible with conventional imaging. This study assessed whether TBI patients with (n = 17) or without (n = 16) chronic symptoms (TBIcs/TBIncs) exhibit any changes in structural connectivity (SC) and mean fractional anisotropy (mFA) of intra- and inter-hemispheric connections when compared to a control group (CG) (n = 13). Reductions in SC and mFA were observed for TBIcs compared to CG, but not for TBIncs. More connections were found to have mFA reductions than SC reductions. On the whole, SC is dominated by ipsilateral connections for all the groups after the comparison of contralateral and ipsilateral connections. More contra-ipsi reductions of mFA were found for TBIcs than TBIncs compared to CG. These findings suggest that TBI patients with chronic symptoms not only demonstrate decreased global and regional mFA but also reduced structural network connectivity.

通过弥散张量成像(DTI)获得的弥散特性对创伤性脑损伤(TBI)期间出现的白质异常非常敏感,尤其是对那些有头痛、头晕、疲劳等 TBI 后慢性症状的患者。使用 DTI 评估结构和功能连通性已成为一种很有前途的方法,可用于识别与 TBI 相关的大脑连通性的细微改变,而这些改变在传统成像中是看不到的。本研究评估了与对照组(CG)(n = 13)相比,有(n = 17)或无(n = 16)慢性症状(TBIcs/TBIncs)的 TBI 患者在半球内和半球间连接的结构连通性(SC)和平均分数各向异性(mFA)方面是否有任何变化。与对照组相比,观察到 TBIcs 的 SC 和 mFA 下降,但 TBIncs 没有下降。与 SC 的减少相比,发现有更多连接的 mFA 减少。总体而言,在对比对侧和同侧连接后,所有组别的 SC 均以同侧连接为主。与 CG 相比,TBIcs 比 TBIncs 的 mFA 减少更多。这些研究结果表明,有慢性症状的创伤性脑损伤患者不仅表现出整体和区域性 mFA 的减少,而且还表现出结构性网络连接的减少。
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引用次数: 0
Effect of Electrode Distance and Size on Electrocorticographic Recordings in Human Sensorimotor Cortex. 电极距离和大小对人类感觉运动皮层皮层电图记录的影响
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-01 Epub Date: 2024-10-09 DOI: 10.1007/s12021-024-09689-z
Simon H Geukes, Mariana P Branco, Erik J Aarnoutse, Annike Bekius, Julia Berezutskaya, Nick F Ramsey

Subdural electrocorticography (ECoG) is a valuable technique for neuroscientific research and for emerging neurotechnological clinical applications. As ECoG grids accommodate increasing numbers of electrodes and higher densities with new manufacturing methods, the question arises at what point the benefit of higher density ECoG is outweighed by spatial oversampling. To clarify the optimal spacing between ECoG electrodes, in the current study we evaluate how ECoG grid density relates to the amount of non-shared neurophysiological information between electrode pairs, focusing on the sensorimotor cortex. We simultaneously recorded high-density (HD, 3 mm pitch) and ultra-high-density (UHD, 0.9 mm pitch) ECoG, obtained intraoperatively from six participants. We developed a new metric, the normalized differential root mean square (ndRMS), to quantify the information that is not shared between electrode pairs. The ndRMS increases with inter-electrode center-to-center distance up to 15 mm, after which it plateaus. We observed differences in ndRMS between frequency bands, which we interpret in terms of oscillations in frequencies below 32 Hz with phase differences between pairs, versus (un)correlated signal fluctuations in the frequency range above 64 Hz. The finding that UHD recordings yield significantly higher ndRMS than HD recordings is attributed to the amount of tissue sampled by each electrode. These results suggest that ECoG densities with submillimeter electrode distances are likely justified.

硬膜下皮层电图(ECoG)是神经科学研究和新兴神经技术临床应用的重要技术。随着 ECoG 网格在新的制造方法下可容纳越来越多的电极和更高的密度,问题是高密度 ECoG 的优势在什么时候会被空间过采样所抵消。为了明确心电图电极之间的最佳间距,我们在本研究中评估了心电图网格密度与电极对之间非共享神经生理信息量的关系,重点是感觉运动皮层。我们同时记录了六名参与者术中获得的高密度(HD,间距 3 毫米)和超高密度(UHD,间距 0.9 毫米)心电图。我们开发了一种新指标--归一化差分均方根(ndRMS),用于量化电极对之间未共享的信息。ndRMS随电极间中心到中心距离的增加而增加,最高可达15毫米,之后趋于平稳。我们观察到不同频段的 ndRMS 存在差异,我们将其解释为:32 Hz 以下频率的振荡与电极对之间的相位差,以及 64 Hz 以上频率范围的(非)相关信号波动。UHD 记录的 ndRMS 明显高于 HD 记录,这是因为每个电极采样的组织量不同。这些结果表明,采用亚毫米电极距离的心电图密度可能是合理的。
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Neuroinformatics
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