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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 : 2024-11-06 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
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 : 2024-11-06 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
Improved ADHD Diagnosis Using EEG Connectivity and Deep Learning through Combining Pearson Correlation Coefficient and Phase-Locking Value. 通过结合皮尔逊相关系数和锁相值,利用脑电图连接性和深度学习改进多动症诊断。
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-18 DOI: 10.1007/s12021-024-09685-3
Elham Ahmadi Moghadam, Farhad Abedinzadeh Torghabeh, Seyyed Abed Hosseini, Mohammad Hossein Moattar

Attention Deficit Hyperactivity Disorder (ADHD) is a widespread neurobehavioral disorder affecting children and adolescents, requiring early detection for effective treatment. EEG connectivity measures can reveal the interdependencies between EEG recordings, highlighting brain network patterns and functional behavior that improve diagnostic accuracy. This study introduces a novel ADHD diagnostic method by combining linear and nonlinear brain connectivity maps with an attention-based convolutional neural network (Att-CNN). Pearson Correlation Coefficient (PCC) and Phase-Locking Value (PLV) are used to create fused connectivity maps (FCMs) from various EEG frequency subbands, which are then inputted into the Att-CNN. The attention module is strategically placed after the latest convolutional layer in the CNN. The performance of different optimizers (Adam and SGD) and learning rates are assessed. The suggested model obtained 98.88%, 98.41%, 98.19%, and 98.30% for accuracy, precision, recall, and F1 Score, respectively, using the SGD optimizer in the FCM of the theta band with a learning rate of 1e-1. With the use of FCM, Att-CNN, and advanced optimizers, the proposed technique has the potential to produce trustworthy instruments for the early diagnosis of ADHD, greatly enhancing both patient outcomes and diagnostic accuracy.

注意力缺陷多动障碍(ADHD)是一种广泛影响儿童和青少年的神经行为障碍,需要及早发现才能有效治疗。脑电连接测量可以揭示脑电记录之间的相互依存关系,突出大脑网络模式和功能行为,从而提高诊断的准确性。本研究通过将线性和非线性脑连接图与基于注意力的卷积神经网络(Att-CNN)相结合,介绍了一种新型多动症诊断方法。利用皮尔逊相关系数(PCC)和锁相值(PLV)从不同的脑电图频率子带创建融合连接图(FCM),然后将其输入 Att-CNN。注意力模块被战略性地置于 CNN 最新卷积层之后。对不同优化器(Adam 和 SGD)的性能和学习率进行了评估。在θ波段的 FCM 中使用 SGD 优化器,学习率为 1e-1,建议模型的准确率、精确率、召回率和 F1 分数分别达到 98.88%、98.41%、98.19% 和 98.30%。通过使用 FCM、Att-CNN 和高级优化器,所提出的技术有望为多动症的早期诊断提供值得信赖的工具,从而大大提高患者的治疗效果和诊断准确性。
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引用次数: 0
A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images. 基于深度学习的管道,用于分割组织学图像中的大脑皮层层状结构。
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-17 DOI: 10.1007/s12021-024-09688-0
Jiaxuan Wang, Rui Gong, Shahrokh Heidari, Mitchell Rogers, Toshiki Tani, Hiroshi Abe, Noritaka Ichinohe, Alexander Woodward, Patrice J Delmas

Characterizing the anatomical structure and connectivity between cortical regions is a critical step towards understanding the information processing properties of the brain and will help provide insight into the nature of neurological disorders. A key feature of the mammalian cerebral cortex is its laminar structure. Identifying these layers in neuroimaging data is important for understanding their global structure and to help understand the connectivity patterns of neurons in the brain. We studied Nissl-stained and myelin-stained slice images of the brain of the common marmoset (Callithrix jacchus), which is a new world monkey that is becoming increasingly popular in the neuroscience community as an object of study. We present a novel computational framework that first acquired the cortical labels using AI-based tools followed by a trained deep learning model to segment cerebral cortical layers. We obtained a Euclidean distance of 1274.750 ± 156.400 μ m for the cortical labels acquisition, which was in the acceptable range by computing the half Euclidean distance of the average cortex thickness ( 1800.630 μ m ). We compared our cortical layer segmentation pipeline with the pipeline proposed by Wagstyl et al. (PLoS biology, 18(4), e3000678 2020) adapted to 2D data. We obtained a better mean 95 th percentile Hausdorff distance (95HD) of  92.150 μ m . Whereas a mean 95HD of  94.170 μ m was obtained from Wagstyl et al. We also compared our pipeline's performance against theirs using their dataset (the BigBrain dataset). The results also showed better segmentation quality, 85.318 % Jaccard Index acquired from our pipeline, while 83.000 % was stated in their paper.

描述大脑皮层区域之间的解剖结构和连通性是了解大脑信息处理特性的关键一步,有助于深入了解神经系统疾病的本质。哺乳动物大脑皮层的一个主要特征是层状结构。从神经影像数据中识别这些层对于了解其整体结构和帮助理解大脑神经元的连接模式非常重要。我们研究了普通狨猴(Callithrix jacchus)大脑的尼氏染色和髓鞘染色切片图像。我们提出了一个新颖的计算框架,首先使用基于人工智能的工具获取皮层标签,然后使用训练有素的深度学习模型分割大脑皮层。通过计算平均皮层厚度的一半欧氏距离(1800.630 μ m),我们得出皮层标签获取的欧氏距离为 1274.750 ± 156.400 μ m,在可接受范围内。我们将皮质层分割管道与 Wagstyl 等人提出的适用于二维数据的管道(PLoS biology, 18(4), e3000678 2020)进行了比较。我们获得了更好的平均 95th 百分位数豪斯多夫距离(95HD),为 92.150 μ m。我们还使用 Wagstyl 等人的数据集(BigBrain 数据集)与他们的数据集进行了比较。结果也显示了更好的分割质量,我们的管道获得了 85.318 % 的 Jaccard 指数,而他们的论文中提到的是 83.000 %。
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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 : 2024-10-15 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
Stitcher: A Surface Reconstruction Tool for Highly Gyrified Brains. Stitcher:高度回旋大脑的表面重建工具
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-10 DOI: 10.1007/s12021-024-09678-2
Heitor Mynssen, Kamilla Avelino-de-Souza, Khallil Chaim, Vanessa Lanes Ribeiro, Nina Patzke, Bruno Mota

Brain reconstruction, specially of the cerebral cortex, is a challenging task and even more so when it comes to highly gyrified brained animals. Here, we present Stitcher, a novel tool capable of generating such surfaces utilizing MRI data and manual segmentation. Stitcher makes a triangulation between consecutive brain slice segmentations by recursively adding edges that minimize the total length and simultaneously avoid self-intersection. We applied this new method to build the cortical surfaces of two dolphins: Guiana dolphin (Sotalia guianensis), Franciscana dolphin (Pontoporia blainvillei); and one pinniped: Steller sea lion (Eumetopias jubatus). Specifically in the case of P. blainvillei, two reconstructions at two different resolutions were made. Additionally, we also performed reconstructions for sub and non-cortical structures of Guiana dolphin. All our cortical mesh results show remarkable resemblance with the real anatomy of the brains, except P. blainvillei with low-resolution data. Sub and non-cortical meshes were also properly reconstructed and the spatial positioning of structures was preserved with respect to S. guianensis cerebral cortex. In a comparative perspective between methods, Stitcher presents compatible results for volumetric measurements when contrasted with other anatomical standard tools. In this way, Stitcher seems to be a viable pipeline for new neuroanatomical analysis, enhancing visualization and descriptions of non-primates species, and broadening the scope of compared neuroanatomy.

大脑重建,尤其是大脑皮层的重建,是一项极具挑战性的任务,而对于高度回旋的大脑动物来说更是如此。在这里,我们展示了 Stitcher,一种能够利用核磁共振成像数据和手动分割生成此类曲面的新型工具。Stitcher 通过递归添加边缘,使总长度最小化,同时避免自交,从而在连续的大脑切片分割之间形成三角剖面。我们应用这种新方法构建了两种海豚的皮层表面:Guiana dolphin (Sotalia guianensis) 和 Franciscana dolphin (Pontoporia blainvillei):斯特勒海狮(Eumetopias jubatus)。特别是对于 P. blainvillei,我们以两种不同的分辨率进行了两次重建。此外,我们还对圭亚那海豚的皮层下和非皮层结构进行了重建。除了低分辨率数据的 P. blainvillei 外,我们所有的皮层网格结果都与大脑的真实解剖结构非常相似。皮质下和非皮质网状结构也得到了正确的重建,而且与圭亚那豚大脑皮质相比,结构的空间定位得到了保留。从各种方法的比较角度来看,Stitcher 与其他解剖标准工具相比,在体积测量方面取得了一致的结果。因此,Stitcher 似乎是进行新的神经解剖分析的可行管道,它增强了非原生物种的可视化和描述,并拓宽了比较神经解剖学的范围。
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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 : 2024-10-09 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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引用次数: 0
Neuroinformatics and Analysis of Traumatic Brain Injury and Related Conditions. 创伤性脑损伤及相关疾病的神经信息学与分析。
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-27 DOI: 10.1007/s12021-024-09691-5
Andrei Irimia
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引用次数: 0
Neuroinformatics Applications of Data Science and Artificial Intelligence. 数据科学和人工智能的神经信息学应用。
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-24 DOI: 10.1007/s12021-024-09692-4
Ivo D Dinov

Leveraging vast neuroimaging and electrophysiological datasets, AI algorithms are uncovering patterns that offer unprecedented insights into brain structure and function. Neuroinformatics, the fusion of neuroscience and AI, is advancing technologies like brain-computer interfaces, AI-driven cognitive enhancement, and personalized neuromodulation for treating neurological disorders. These developments hold potential to improve cognitive functions, restore motor abilities, and create human-machine collaborative systems. Looking ahead, the convergence of neuroscience and AI is set to transform cognitive modeling, decision-making, and mental health interventions. This fusion mirrors the quest for nuclear fusion energy, both driven by the need to unlock profound sources of understanding. As STEM disciplines continue to drive core developments of foundational models of the brain, neuroinformatics promises to lead innovations in augmented intelligence, personalized healthcare, and effective decision-making systems.

利用庞大的神经成像和电生理学数据集,人工智能算法正在揭示各种模式,为了解大脑结构和功能提供前所未有的洞察力。神经信息学是神经科学与人工智能的融合,正在推动脑机接口、人工智能驱动的认知增强以及用于治疗神经系统疾病的个性化神经调控等技术的发展。这些发展为改善认知功能、恢复运动能力和创建人机协作系统带来了潜力。展望未来,神经科学与人工智能的融合必将改变认知建模、决策和心理健康干预。这种融合与对核聚变能源的追求如出一辙,都是出于开启深刻理解源泉的需要。随着 STEM 学科继续推动大脑基础模型的核心发展,神经信息学有望引领增强智能、个性化医疗保健和有效决策系统方面的创新。
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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 : 2024-09-23 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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