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Towards Automated Brain Aneurysm Detection in TOF-MRA: Open Data, Weak Labels, and Anatomical Knowledge. 在TOF-MRA中实现脑动脉瘤自动检测:开放数据,弱标签和解剖学知识。
IF 3 4区 医学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1007/s12021-022-09597-0
Tommaso Di Noto, Guillaume Marie, Sebastien Tourbier, Yasser Alemán-Gómez, Oscar Esteban, Guillaume Saliou, Meritxell Bach Cuadra, Patric Hagmann, Jonas Richiardi

Brain aneurysm detection in Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) has undergone drastic improvements with the advent of Deep Learning (DL). However, performances of supervised DL models heavily rely on the quantity of labeled samples, which are extremely costly to obtain. Here, we present a DL model for aneurysm detection that overcomes the issue with "weak" labels: oversized annotations which are considerably faster to create. Our weak labels resulted to be four times faster to generate than their voxel-wise counterparts. In addition, our model leverages prior anatomical knowledge by focusing only on plausible locations for aneurysm occurrence. We first train and evaluate our model through cross-validation on an in-house TOF-MRA dataset comprising 284 subjects (170 females / 127 healthy controls / 157 patients with 198 aneurysms). On this dataset, our best model achieved a sensitivity of 83%, with False Positive (FP) rate of 0.8 per patient. To assess model generalizability, we then participated in a challenge for aneurysm detection with TOF-MRA data (93 patients, 20 controls, 125 aneurysms). On the public challenge, sensitivity was 68% (FP rate = 2.5), ranking 4th/18 on the open leaderboard. We found no significant difference in sensitivity between aneurysm risk-of-rupture groups (p = 0.75), locations (p = 0.72), or sizes (p = 0.15). Data, code and model weights are released under permissive licenses. We demonstrate that weak labels and anatomical knowledge can alleviate the necessity for prohibitively expensive voxel-wise annotations.

随着深度学习(DL)的出现,飞行时间磁共振血管造影(TOF-MRA)中的脑动脉瘤检测得到了极大的改进。然而,有监督深度学习模型的性能严重依赖于标记样本的数量,而这些样本的获取成本非常高。在这里,我们提出了一个用于动脉瘤检测的深度学习模型,该模型克服了“弱”标签的问题:超大的注释,创建速度快得多。我们的弱标签生成速度是体素标签生成速度的四倍。此外,我们的模型通过只关注动脉瘤发生的可能位置来利用先前的解剖学知识。我们首先通过内部TOF-MRA数据集的交叉验证来训练和评估我们的模型,该数据集包括284名受试者(170名女性/ 127名健康对照/ 157名患有198个动脉瘤的患者)。在这个数据集上,我们的最佳模型达到了83%的灵敏度,每名患者的假阳性(FP)率为0.8。为了评估模型的普遍性,我们随后参与了一项利用TOF-MRA数据检测动脉瘤的挑战(93名患者,20名对照组,125个动脉瘤)。对于公众挑战,敏感性为68% (FP率= 2.5),在公开排行榜上排名第4 /18。我们发现动脉瘤破裂风险组(p = 0.75)、位置(p = 0.72)和大小(p = 0.15)之间的敏感性无显著差异。数据、代码和模型权重在许可许可下发布。我们证明了弱标签和解剖知识可以减轻昂贵的体素注释的必要性。
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引用次数: 7
ABLE: Automated Brain Lines Extraction Based on Laplacian Surface Collapse. ABLE:基于拉普拉斯表面塌陷的自动脑线提取。
IF 3 4区 医学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1007/s12021-022-09601-7
Alberto Fernández-Pena, Daniel Martín de Blas, Francisco J Navas-Sánchez, Luis Marcos-Vidal, Pedro M Gordaliza, Javier Santonja, Joost Janssen, Susanna Carmona, Manuel Desco, Yasser Alemán-Gómez

The archetypical folded shape of the human cortex has been a long-standing topic for neuroscientific research. Nevertheless, the accurate neuroanatomical segmentation of sulci remains a challenge. Part of the problem is the uncertainty of where a sulcus transitions into a gyrus and vice versa. This problem can be avoided by focusing on sulcal fundi and gyral crowns, which represent the topological opposites of cortical folding. We present Automated Brain Lines Extraction (ABLE), a method based on Laplacian surface collapse to reliably segment sulcal fundi and gyral crown lines. ABLE is built to work on standard FreeSurfer outputs and eludes the delineation of anastomotic sulci while maintaining sulcal fundi lines that traverse the regions with the highest depth and curvature. First, it segments the cortex into gyral and sulcal surfaces; then, each surface is spatially filtered. A Laplacian-collapse-based algorithm is applied to obtain a thinned representation of the surfaces. This surface is then used for careful detection of the endpoints of the lines. Finally, sulcal fundi and gyral crown lines are obtained by eroding the surfaces while preserving the connectivity between the endpoints. The method is validated by comparing ABLE with three other sulcal extraction methods using the Human Connectome Project (HCP) test-retest database to assess the reproducibility of the different tools. The results confirm ABLE as a reliable method for obtaining sulcal lines with an accurate representation of the sulcal topology while ignoring anastomotic branches and the overestimation of the sulcal fundi lines. ABLE is publicly available via https://github.com/HGGM-LIM/ABLE .

人类皮层的典型折叠形状一直是神经科学研究的一个长期课题。然而,准确的神经解剖学分割沟仍然是一个挑战。这个问题的部分原因是不确定脑沟和脑回在哪里过渡,反之亦然。这个问题可以通过关注沟底和回冠来避免,它们代表皮层折叠的拓扑相反。我们提出了一种基于拉普拉斯表面塌陷的自动脑线提取(ABLE)方法,以可靠地分割沟底和脑回冠线。ABLE是建立在标准的FreeSurfer输出上,避免了吻合沟的划定,同时保持吻合沟底线穿过具有最高深度和曲率的区域。首先,它将皮层分割成脑回和脑沟表面;然后,对每个表面进行空间滤波。采用基于拉普拉斯坍缩的算法来获得曲面的稀疏表示。然后,这个表面用于仔细检测线的端点。最后,通过侵蚀表面获得沟底和回冠线,同时保持端点之间的连通性。通过将ABLE与其他三种沟提取方法进行比较,验证了该方法的有效性,并使用人类连接组计划(HCP)测试-重测数据库来评估不同工具的可重复性。结果证实了ABLE是一种可靠的方法,可以准确地表示沟的拓扑结构,同时忽略吻合分支和对沟底线的高估。ABLE可通过https://github.com/HGGM-LIM/ABLE公开获取。
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引用次数: 0
Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher's Choice Paths. 评估多频多层脑网络拓扑结构在不同研究人员选择路径中的可重复性。
IF 3 4区 医学 Q2 Computer Science Pub Date : 2023-01-01 Epub Date: 2022-11-14 DOI: 10.1007/s12021-022-09610-6
Stavros I Dimitriadis

There is a growing interest in the neuroscience community on the advantages of multilayer functional brain networks. Researchers usually treated different frequencies separately at distinct functional brain networks. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. Here, Ι will focus on the multi-frequency multilayer functional connectivity analysis on resting-state fMRI (rs-fMRI) recordings. However, constructing a multilayer network depends on selecting multiple pre-processing steps that can affect the final network topology. Here, I analyzed the rs-fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total), and the rs-fMRI dataset containing 25 subjects with 3 repeat scans. I focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, I untangled specific combinations of researchers' choices that yield consistently brain networks with repeatable topologies, giving me the chance to recommend best practices over consistent topologies.

神经科学界对多层大脑功能网络的优势越来越感兴趣。研究人员通常在不同的大脑功能网络中分别处理不同的频率。然而,有强有力的证据表明,这些网络可以共享互补信息,而它们之间的相互依存关系则可以揭示新的发现。为此,神经科学家们采用了多层网络,它可以在数学上被描述为琐碎单层网络的扩展。由于多层网络具有整合不同信息源的优势,因此在神经科学领域很受欢迎。在这里,Ι 将重点讨论静息态 fMRI(rs-fMRI)记录的多频率多层功能连接分析。然而,构建多层网络取决于选择多个预处理步骤,这些步骤会影响最终的网络拓扑结构。在这里,我分析了一个人在18个月内进行扫描的rs-fMRI数据集(共84次扫描),以及包含25个受试者的3次重复扫描的rs-fMRI数据集。我重点评估了多频率多层拓扑结构的可重复性,探索了两种从 BOLD 活动中提取频率的过滤方法、三种连通性估计器(有无拓扑过滤方案)和两种空间尺度的影响。最后,我解开了研究人员选择的特定组合,这些组合产生了具有可重复拓扑结构的一致的大脑网络,使我有机会就一致拓扑结构推荐最佳实践。
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引用次数: 0
Semi-Automated Quantitative Evaluation of Neuron Developmental Morphology In Vitro Using the Change-Point Test. 利用变化点试验对体外神经元发育形态进行半自动定量评价。
IF 3 4区 医学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1007/s12021-022-09600-8
Ashlee S Liao, Wenxin Cui, Yongjie Jessica Zhang, Victoria A Webster-Wood

Neuron morphology gives rise to distinct axons and dendrites and plays an essential role in neuronal functionality and circuit dynamics. In rat hippocampal neurons, morphological development occurs over roughly one week in vitro. This development has been qualitatively described as occurring in 5 stages. Still, there is a need to quantify cell growth to monitor cell culture health, understand cell responses to sensory cues, and compare experimental results and computational growth model predictions. To address this need, embryonic rat hippocampal neurons were observed in vitro over six days, and their processes were quantified using both standard morphometrics (degree, number of neurites, total length, and tortuosity) and new metrics (distance between change points, relative turning angle, and the number of change points) based on the Change-Point Test to track changes in path trajectories. Of the standard morphometrics, the total length of neurites per cell and the number of endpoints were significantly different between 0.5, 1.5, and 4 days in vitro, which are typically associated with Stages 2-4. Using the Change-Point Test, the number of change points and the average distance between change points per cell were also significantly different between those key time points. This work highlights key quantitative characteristics, both among common and novel morphometrics, that can describe neuron development in vitro and provides a foundation for analyzing directional changes in neurite growth for future studies.

神经元形态产生不同的轴突和树突,在神经元功能和电路动力学中起着至关重要的作用。大鼠海马神经元在体外培养约一周后发生形态发育。这种发展被定性地描述为发生在5个阶段。尽管如此,仍有必要量化细胞生长以监测细胞培养健康,了解细胞对感官线索的反应,并比较实验结果和计算生长模型预测。为了满足这一需求,我们在体外对胚胎大鼠海马神经元进行了为期6天的观察,并使用标准形态计量学(神经突的程度、数量、总长度和弯曲度)和基于变化点测试的新指标(变化点之间的距离、相对转角和变化点的数量)来量化它们的过程,以跟踪路径轨迹的变化。在标准形态计量学中,每个细胞的神经突总长度和终点数量在体外0.5、1.5和4天之间存在显著差异,这通常与2-4期有关。使用变化点测试,这些关键时间点之间的变化点数量和每个单元之间的平均变化点距离也有显著差异。这项工作突出了常见的和新颖的形态计量学中的关键定量特征,这些特征可以描述体外神经元发育,并为未来研究分析神经突生长的方向性变化提供基础。
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引用次数: 2
Review Paper: Reporting Practices for Task fMRI Studies. 综述论文:任务功能磁共振成像研究的报告实践。
IF 3 4区 医学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1007/s12021-022-09606-2
Freya Acar, Camille Maumet, Talia Heuten, Maya Vervoort, Han Bossier, Ruth Seurinck, Beatrijs Moerkerke

What are the standards for the reporting methods and results of fMRI studies, and how have they evolved over the years? To answer this question we reviewed 160 papers published between 2004 and 2019. Reporting styles for methods and results of fMRI studies can differ greatly between published studies. However, adequate reporting is essential for the comprehension, replication and reuse of the study (for instance in a meta-analysis). To aid authors in reporting the methods and results of their task-based fMRI study the COBIDAS report was published in 2016, which provides researchers with clear guidelines on how to report the design, acquisition, preprocessing, statistical analysis and results (including data sharing) of fMRI studies (Nichols et al. in Best Practices in Data Analysis and Sharing in Neuroimaging using fMRI, 2016). In the past reviews have been published that evaluate how fMRI methods are reported based on the 2008 guidelines, but they did not focus on how task based fMRI results are reported. This review updates reporting practices of fMRI methods, and adds an extra focus on how fMRI results are reported. We discuss reporting practices about the design stage, specific participant characteristics, scanner characteristics, data processing methods, data analysis methods and reported results.

功能磁共振成像研究的报告方法和结果的标准是什么,这些年来它们是如何演变的?为了回答这个问题,我们回顾了2004年至2019年间发表的160篇论文。fMRI研究方法和结果的报告风格在已发表的研究之间可能存在很大差异。然而,充分的报告对于研究的理解、复制和重用是必不可少的(例如在荟萃分析中)。为了帮助作者报告基于任务的fMRI研究的方法和结果,COBIDAS报告于2016年发表,该报告为研究人员提供了关于如何报告fMRI研究的设计、获取、预处理、统计分析和结果(包括数据共享)的明确指导方针(Nichols等人在《使用fMRI的神经成像数据分析和共享的最佳实践》,2016)。在过去已经发表的评论中,评估了如何根据2008年指南报告功能磁共振成像方法,但他们没有关注如何报告基于任务的功能磁共振成像结果。这篇综述更新了功能磁共振成像方法的报告实践,并增加了对功能磁共振成像结果如何报告的额外关注。我们讨论了关于设计阶段、具体参与者特征、扫描仪特征、数据处理方法、数据分析方法和报告结果的报告实践。
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引用次数: 1
Deep Learning Classification of Treatment Response in Diabetic Painful Neuropathy: A Combined Machine Learning and Magnetic Resonance Neuroimaging Methodological Study. 糖尿病疼痛性神经病变治疗反应的深度学习分类:机器学习与磁共振神经成像的联合方法学研究。
IF 3 4区 医学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1007/s12021-022-09603-5
Kevin Teh, Paul Armitage, Solomon Tesfaye, Dinesh Selvarajah

Functional magnetic resonance imaging (fMRI) has been shown successfully to assess and stratify patients with painful diabetic peripheral neuropathy (pDPN). This supports the idea of using neuroimaging as a mechanism-based technique to individualise therapy for patients with painful DPN. The aim of this study was to use deep learning to predict treatment response in patients with pDPN using resting state functional imaging (rs-fMRI). We divided 43 painful pDPN patients into responders and non-responders to lidocaine treatment (responders n = 29 and non-responders n = 14). We used rs-fMRI to extract functional connectivity features, using group independent component analysis (gICA), and performed automated treatment response deep learning classification with three-dimensional convolutional neural networks (3D-CNN). Using gICA we achieved an area under the receiver operating characteristic curve (AUC) of 96.60% and F1-Score of 95% in a ten-fold cross validation (CV) experiment using our described 3D-CNN algorithm. To our knowledge, this is the first study utilising deep learning methods to classify treatment response in pDPN.

功能磁共振成像(fMRI)已被证明成功地评估和分层疼痛性糖尿病周围神经病变(pDPN)患者。这支持了使用神经影像学作为一种基于机制的技术来个体化治疗疼痛DPN患者的想法。本研究的目的是利用静息状态功能成像(rs-fMRI),利用深度学习预测pDPN患者的治疗反应。我们将43例疼痛性pDPN患者分为对利多卡因治疗有反应和无反应两组(有反应者29例,无反应者14例)。我们使用rs-fMRI提取功能连接特征,使用组独立分量分析(gICA),并使用三维卷积神经网络(3D-CNN)进行自动治疗响应深度学习分类。在十倍交叉验证(CV)实验中,使用我们描述的3D-CNN算法,使用gICA实现了接收器工作特征曲线下面积(AUC)为96.60%,F1-Score为95%。据我们所知,这是第一个利用深度学习方法对pDPN治疗反应进行分类的研究。
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引用次数: 2
Brain Age Prediction in Developing Childhood with Multimodal Magnetic Resonance Images. 用多模态磁共振图像预测儿童发育中的脑年龄。
IF 3 4区 医学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1007/s12021-022-09596-1
Hongjie Cai, Aojie Li, Guangjun Yu, Xiujun Yang, Manhua Liu

It is well known that brain development is very fast and complex in the early childhood with age-based neurological and physiological changes of brain structure and function. The brain maturity is an important indicator for evaluating the normal development of children. In this paper, we propose a multimodal regression framework to combine the features from structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI) data for age prediction of children. First, three types of features are extracted from sMRI and DTI data. Second, we propose to combine the sparse coding and Q-Learning for feature selection from each modality. Finally, the ensemble regression is performed by random forest based on proximity measures to fuse multimodal features for age prediction. The proposed method is evaluated on 212 participants, including 76 young children less than 2 years old and 136 children aged from 2-15 years old recruited from Shanghai Children's Hospital. The results show that integrating multimodal features has achieved the highest accuracies with the root mean squared error (RMSE) of 0.208 years and mean absolute error (MAE) of 0.150 years for age prediction of young children (0-2), and RMSE of 1.666 years and MAE of 1.087 years for older children (2-15). We have shown that the selected features by Q-Learning can consistently improve the prediction accuracy. The comparison of prediction results demonstrates that the proposed method performs better than other competing methods.

众所周知,儿童早期的大脑发育非常迅速和复杂,大脑结构和功能的神经和生理变化是基于年龄的。脑成熟度是评价儿童正常发育的重要指标。在本文中,我们提出了一个多模态回归框架,结合结构磁共振成像(sMRI)和扩散张量成像(DTI)数据的特征进行儿童年龄预测。首先,从sMRI和DTI数据中提取三种类型的特征。其次,我们提出将稀疏编码和Q-Learning相结合,从每个模态中进行特征选择。最后,采用基于接近度的随机森林进行集合回归,融合多模态特征进行年龄预测。该方法在上海儿童医院招募了76名2岁以下幼儿和136名2 ~ 15岁儿童,共212名被试进行了评价。结果表明,综合多模态特征预测幼儿(0-2岁)年龄的准确率最高,均方根误差(RMSE)为0.208年,平均绝对误差(MAE)为0.150年;较大儿童(2-15岁)年龄预测的RMSE为1.666年,平均绝对误差(MAE)为1.087年。我们已经证明,通过Q-Learning选择的特征可以持续提高预测精度。预测结果的对比表明,该方法的预测效果优于其他方法。
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引用次数: 1
EBRAINS Live Papers - Interactive Resource Sheets for Computational Studies in Neuroscience. EBRAINS Live论文-神经科学计算研究的交互式资源表。
IF 3 4区 医学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1007/s12021-022-09598-z
Shailesh Appukuttan, Luca L Bologna, Felix Schürmann, Michele Migliore, Andrew P Davison

We present here an online platform for sharing resources underlying publications in neuroscience. It enables authors to easily upload and distribute digital resources, such as data, code, and notebooks, in a structured and systematic way. Interactivity is a prominent feature of the Live Papers, with features to download, visualise or simulate data, models and results presented in the corresponding publications. The resources are hosted on reliable data storage servers to ensure long term availability and easy accessibility. All data are managed via the EBRAINS Knowledge Graph, thereby helping maintain data provenance, and enabling tight integration with tools and services offered under the EBRAINS ecosystem.

我们在这里提出了一个在线平台,用于共享神经科学出版物的资源。它使作者能够以结构化和系统化的方式轻松上传和分发数字资源,如数据、代码和笔记本。互动性是Live Papers的一个突出特点,具有下载、可视化或模拟相应出版物中呈现的数据、模型和结果的功能。这些资源托管在可靠的数据存储服务器上,以确保长期可用性和易于访问。所有数据都通过EBRAINS知识图谱进行管理,从而帮助维护数据来源,并实现与EBRAINS生态系统下提供的工具和服务的紧密集成。
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引用次数: 8
Dementia in Convolutional Neural Networks: Using Deep Learning Models to Simulate Neurodegeneration of the Visual System. 卷积神经网络中的痴呆:使用深度学习模型模拟视觉系统的神经变性。
IF 3 4区 医学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1007/s12021-022-09602-6
Jasmine A Moore, Anup Tuladhar, Zahinoor Ismail, Pauline Mouches, Matthias Wilms, Nils D Forkert

Although current research aims to improve deep learning networks by applying knowledge about the healthy human brain and vice versa, the potential of using such networks to model and study neurodegenerative diseases remains largely unexplored. In this work, we present an in-depth feasibility study modeling progressive dementia in silico with deep convolutional neural networks. Therefore, networks were trained to perform visual object recognition and then progressively injured by applying neuronal as well as synaptic injury. After each iteration of injury, network object recognition accuracy, saliency map similarity between the intact and injured networks, and internal activations of the degenerating models were evaluated. The evaluation revealed that cognitive function of the network progressively decreased with increasing injury load whereas this effect was much more pronounced for synaptic damage. The effects of neurodegeneration found for the in silico model are especially similar to the loss of visual cognition seen in patients with posterior cortical atrophy.

尽管目前的研究旨在通过应用关于健康人脑的知识来改进深度学习网络,反之亦然,但使用这种网络来建模和研究神经退行性疾病的潜力在很大程度上仍未被探索。在这项工作中,我们提出了一项深入的可行性研究,用深度卷积神经网络在计算机上模拟进行性痴呆。因此,神经网络被训练来进行视觉物体识别,然后通过神经元和突触损伤来逐步损伤。在每次损伤迭代后,评估网络目标识别精度、完整和损伤网络之间的显著性图相似性以及退化模型的内部激活情况。结果表明,随着损伤负荷的增加,神经网络的认知功能逐渐下降,而突触损伤的认知功能下降更为明显。在计算机模型中发现的神经退行性变的影响与后皮层萎缩患者的视觉认知丧失特别相似。
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引用次数: 1
An Algorithm Based on a Cable-Nernst Planck Model Predicting Synaptic Activity throughout the Dendritic Arbor with Micron Specificity. 基于Cable-Nernst Planck模型预测树突乔木突触活动的微米特异性算法。
IF 3 4区 医学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1007/s12021-022-09609-z
Claire Guerrier, Tristan Dellazizzo Toth, Nicolas Galtier, Kurt Haas

Recent technological advances have enabled the recording of neurons in intact circuits with a high spatial and temporal resolution, creating the need for modeling with the same precision. In particular, the development of ultra-fast two-photon microscopy combined with fluorescence-based genetically-encoded Ca2+-indicators allows capture of full-dendritic arbor and somatic responses associated with synaptic input and action potential output. The complexity of dendritic arbor structures and distributed patterns of activity over time results in the generation of incredibly rich 4D datasets that are challenging to analyze (Sakaki et al. in Frontiers in Neural Circuits 14:33, 2020). Interpreting neural activity from fluorescence-based Ca2+ biosensors is challenging due to non-linear interactions between several factors influencing intracellular calcium ion concentration and its binding to sensors, including the ionic dynamics driven by diffusion, electrical gradients and voltage-gated conductances. To investigate those dynamics, we designed a model based on a Cable-like equation coupled to the Nernst-Planck equations for ionic fluxes in electrolytes. We employ this model to simulate signal propagation and ionic electrodiffusion across a dendritic arbor. Using these simulation results, we then designed an algorithm to detect synapses from Ca2+ imaging datasets. We finally apply this algorithm to experimental Ca2+-indicator datasets from neurons expressing jGCaMP7s (Dana et al. in Nature Methods 16:649-657, 2019), using full-dendritic arbor sampling in vivo in the Xenopus laevis optic tectum using fast random-access two-photon microscopy. Our model reproduces the dynamics of visual stimulus-evoked jGCaMP7s-mediated calcium signals observed experimentally, and the resulting algorithm allows prediction of the location of synapses across the dendritic arbor. Our study provides a way to predict synaptic activity and location on dendritic arbors, from fluorescence data in the full dendritic arbor of a neuron recorded in the intact and awake developing vertebrate brain.

最近的技术进步使完整的神经元电路的记录具有高的空间和时间分辨率,创造了对相同精度的建模的需求。特别是,超快速双光子显微镜结合基于荧光的遗传编码Ca2+指示器的发展,可以捕获与突触输入和动作电位输出相关的全树突乔木和体细胞反应。随着时间的推移,树突树梢结构的复杂性和活动的分布模式导致生成非常丰富的4D数据集,这些数据集具有挑战性(Sakaki等人在Frontiers in Neural Circuits 14:33, 2020)。由于影响细胞内钙离子浓度及其与传感器结合的几个因素之间的非线性相互作用,包括由扩散、电梯度和电压门控电导驱动的离子动力学,从基于荧光的Ca2+生物传感器解释神经活动具有挑战性。为了研究这些动力学,我们设计了一个基于Cable-like方程与电解质中离子通量的能思-普朗克方程耦合的模型。我们使用这个模型来模拟信号的传播和离子的电扩散。利用这些模拟结果,我们设计了一种从Ca2+成像数据集检测突触的算法。最后,我们将该算法应用于来自表达jGCaMP7s的神经元的实验Ca2+指标数据集(Dana等人在Nature Methods 16:649-657, 2019),使用快速随机访问双光子显微镜在非洲爪猴的光学顶部进行全树突乔木采样。我们的模型再现了视觉刺激引起的jgcamp7s介导的钙信号的动态,实验观察到,所得算法可以预测树突乔木上突触的位置。我们的研究提供了一种预测突触活动和树突树突上的位置的方法,从完整的和清醒的发育中的脊椎动物大脑中记录的神经元的完整树突树突的荧光数据。
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