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AngoraPy: A Python toolkit for modeling anthropomorphic goal-driven sensorimotor systems AngoraPy:用于模拟拟人目标驱动传感器运动系统的 Python 工具包
IF 3.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2023-12-22 DOI: 10.3389/fninf.2023.1223687
Tonio Weidler, Rainer Goebel, M. Senden
{"title":"AngoraPy: A Python toolkit for modeling anthropomorphic goal-driven sensorimotor systems","authors":"Tonio Weidler, Rainer Goebel, M. Senden","doi":"10.3389/fninf.2023.1223687","DOIUrl":"https://doi.org/10.3389/fninf.2023.1223687","url":null,"abstract":"Goal-driven deep learning increasingly supplements classical modeling approaches in computational neuroscience. The strength of deep neural networks as models of the brain lies in their ability to autonomously learn the connectivity required to solve complex and ecologically valid tasks, obviating the need for hand-engineered or hypothesis-driven connectivity patterns. Consequently, goal-driven models can generate hypotheses about the neurocomputations underlying cortical processing that are grounded in macro- and mesoscopic anatomical properties of the network's biological counterpart. Whereas, goal-driven modeling is already becoming prevalent in the neuroscience of perception, its application to the sensorimotor domain is currently hampered by the complexity of the methods required to train models comprising the closed sensation-action loop. This paper describes AngoraPy, a Python library that mitigates this obstacle by providing researchers with the tools necessary to train complex recurrent convolutional neural networks that model the human sensorimotor system. To make the technical details of this toolkit more approachable, an illustrative example that trains a recurrent toy model on in-hand object manipulation accompanies the theoretical remarks. An extensive benchmark on various classical, 3D robotic, and anthropomorphic control tasks demonstrates AngoraPy's general applicability to a wide range of tasks. Together with its ability to adaptively handle custom architectures, the flexibility of this toolkit demonstrates its power for goal-driven sensorimotor modeling.","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"8 7","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138944101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The hemodynamic response function as a type 2 diabetes biomarker: a data-driven approach 作为 2 型糖尿病生物标志物的血液动力学响应函数:一种数据驱动方法
IF 3.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2023-12-14 DOI: 10.3389/fninf.2023.1321178
Pedro Guimarães, Pedro Serranho, João V. Duarte, Joana Crisóstomo, Carolina Moreno, Leonor Gomes, Rui Bernardes, Miguel Castelo-Branco
{"title":"The hemodynamic response function as a type 2 diabetes biomarker: a data-driven approach","authors":"Pedro Guimarães, Pedro Serranho, João V. Duarte, Joana Crisóstomo, Carolina Moreno, Leonor Gomes, Rui Bernardes, Miguel Castelo-Branco","doi":"10.3389/fninf.2023.1321178","DOIUrl":"https://doi.org/10.3389/fninf.2023.1321178","url":null,"abstract":"<sec><title>Introduction</title><p>There is a need to better understand the neurophysiological changes associated with early brain dysfunction in Type 2 diabetes mellitus (T2DM) before vascular or structural lesions. Our aim was to use a novel unbiased data-driven approach to detect and characterize hemodynamic response function (HRF) alterations in T2DM patients, focusing on their potential as biomarkers.</p></sec><sec><title>Methods</title><p>We meshed task-based event-related (visual speed discrimination) functional magnetic resonance imaging with DL to show, from an unbiased perspective, that T2DM patients’ blood-oxygen-level dependent response is altered. Relevance analysis determined which brain regions were more important for discrimination. We combined explainability with deconvolution generalized linear model to provide a more accurate picture of the nature of the neural changes.</p></sec><sec><title>Results</title><p>The proposed approach to discriminate T2DM patients achieved up to 95% accuracy. Higher performance was achieved at higher stimulus (speed) contrast, showing a direct relationship with stimulus properties, and in the hemispherically dominant left visual hemifield, demonstrating biological interpretability. Differences are explained by physiological asymmetries in cortical spatial processing (right hemisphere dominance) and larger neural signal-to-noise ratios related to stimulus contrast. Relevance analysis revealed the most important regions for discrimination, such as extrastriate visual cortex, parietal cortex, and insula. These are disease/task related, providing additional evidence for pathophysiological significance. Our data-driven design allowed us to compute the unbiased HRF without assumptions.</p></sec><sec><title>Conclusion</title><p>We can accurately differentiate T2DM patients using a data-driven classification of the HRF. HRF differences hold promise as biomarkers and could contribute to a deeper understanding of neurophysiological changes associated with T2DM.</p></sec>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"9 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139102168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Introducing Region Based Pooling for handling a varied number of EEG channels for deep learning models 引入基于区域的池化技术,为深度学习模型处理不同数量的脑电图通道
IF 3.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2023-12-07 DOI: 10.3389/fninf.2023.1272791
Thomas Tveitstøl, Mats Tveter, Ana S. Pérez T., Christoffer Hatlestad-Hall, Anis Yazidi, Hugo L. Hammer, Ira R. J. Hebold Haraldsen
{"title":"Introducing Region Based Pooling for handling a varied number of EEG channels for deep learning models","authors":"Thomas Tveitstøl, Mats Tveter, Ana S. Pérez T., Christoffer Hatlestad-Hall, Anis Yazidi, Hugo L. Hammer, Ira R. J. Hebold Haraldsen","doi":"10.3389/fninf.2023.1272791","DOIUrl":"https://doi.org/10.3389/fninf.2023.1272791","url":null,"abstract":"<sec><title>Introduction</title><p>A challenge when applying an artificial intelligence (AI) deep learning (DL) approach to novel electroencephalography (EEG) data, is the DL architecture's lack of adaptability to changing numbers of EEG channels. That is, the number of channels cannot vary neither in the training data, nor upon deployment. Such highly specific hardware constraints put major limitations on the clinical usability and scalability of the DL models.</p></sec><sec><title>Methods</title><p>In this work, we propose a technique for handling such varied numbers of EEG channels by splitting the EEG montages into distinct regions and merge the channels within the same region to a region representation. The solution is termed <italic>Region Based Pooling</italic> (RBP). The procedure of splitting the montage into regions is performed repeatedly with different region configurations, to minimize potential loss of information. As RBP maps a varied number of EEG channels to a fixed number of region representations, both current and future DL architectures may apply RBP with ease. To demonstrate and evaluate the adequacy of RBP to handle a varied number of EEG channels, sex classification based solely on EEG was used as a test example. The DL models were trained on 129 channels, and tested on 32, 65, and 129-channels versions of the data using the same channel positions scheme. The baselines for comparison were zero-filling the missing channels and applying spherical spline interpolation. The performances were estimated using 5-fold cross validation.</p></sec><sec><title>Results</title><p>For the 32-channel system version, the mean AUC values across the folds were: RBP (93.34%), spherical spline interpolation (93.36%), and zero-filling (76.82%). Similarly, on the 65-channel system version, the performances were: RBP (93.66%), spherical spline interpolation (93.50%), and zero-filling (85.58%). Finally, the 129-channel system version produced the following results: RBP (94.68%), spherical spline interpolation (93.86%), and zero-filling (91.92%).</p></sec><sec><title>Conclusion</title><p>In conclusion, RBP obtained similar results to spherical spline interpolation, and superior results to zero-filling. We encourage further research and development of DL models in the cross-dataset setting, including the use of methods such as RBP and spherical spline interpolation to handle a varied number of EEG channels.</p></sec>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"9 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139584847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Establishing a nomogram to predict refracture after percutaneous kyphoplasty by logistic regression 通过逻辑回归建立预测经皮椎体后凸成形术后骨折的提名图
IF 3.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2023-12-07 DOI: 10.3389/fninf.2023.1304248
Aiqi Zhang, Hongye Fu, Junjie Wang, Zhe Chen, Jiajun Fan
{"title":"Establishing a nomogram to predict refracture after percutaneous kyphoplasty by logistic regression","authors":"Aiqi Zhang, Hongye Fu, Junjie Wang, Zhe Chen, Jiajun Fan","doi":"10.3389/fninf.2023.1304248","DOIUrl":"https://doi.org/10.3389/fninf.2023.1304248","url":null,"abstract":"<sec><title>Introduction</title><p>Several studies have examined the risk factors for post-percutaneous kyphoplasty (PKP) refractures and developed many clinical prognostic models. However, no prior research exists using the Random Forest (RF) model, a favored tool for model development, to predict the occurrence of new vertebral compression fractures (NVCFs). Therefore, this study aimed to investigate the risk factors for the occurrence of post-PKP fractures, compare the predictive performance of logistic regression and RF models in forecasting post-PKP fractures, and visualize the logistic regression model.</p></sec><sec><title>Methods</title><p>We collected clinical data from 349 patients who underwent PKP treatment at our institution from January 2018 to December 2021. Lasso regression was employed to select risk factors associated with the occurrence of NVCFs. Subsequently, logistic regression and RF models were established, and their predictive capabilities were compared. Finally, a nomogram was created.</p></sec><sec><title>Results</title><p>The variables selected using Lasso regression, including bone density, cement distribution, vertebral fracture location, preoperative vertebral height, and vertebral height restoration rate, were included in both the logistic regression and RF models. The area under the curves of the logistic regression and RF models were 0.868 and 0.786, respectively, in the training set and 0.786 and 0.599, respectively, in the validation set. Furthermore, the calibration curve of the logistic regression model also outperformed that of the RF model.</p></sec><sec><title>Conclusion</title><p>The logistic regression model provided better predictive capabilities for identifying patients at risk for post-PKP vertebral fractures than the RF model.</p></sec>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"34 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138824811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Systematic bibliometric and visualized analysis of research hotspots and trends in artificial intelligence in autism spectrum disorder 人工智能在自闭症谱系障碍中的研究热点与趋势的系统文献计量与可视化分析
IF 3.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2023-12-06 DOI: 10.3389/fninf.2023.1310400
Qianfang Jia, Xiaofang Wang, Rongyi Zhou, Bingxiang Ma, Fangqin Fei, Hui Han
{"title":"Systematic bibliometric and visualized analysis of research hotspots and trends in artificial intelligence in autism spectrum disorder","authors":"Qianfang Jia, Xiaofang Wang, Rongyi Zhou, Bingxiang Ma, Fangqin Fei, Hui Han","doi":"10.3389/fninf.2023.1310400","DOIUrl":"https://doi.org/10.3389/fninf.2023.1310400","url":null,"abstract":"BackgroundArtificial intelligence (AI) has been the subject of studies in autism spectrum disorder (ASD) and may affect its identification, diagnosis, intervention, and other medical practices in the future. Although previous studies have used bibliometric techniques to analyze and investigate AI, there has been little research on the adoption of AI in ASD. This study aimed to explore the broad applications and research frontiers of AI used in ASD.MethodsCitation data were retrieved from the Web of Science Core Collection (WoSCC) database to assess the extent to which AI is used in ASD. CiteSpace.5.8. R3 and VOSviewer, two online tools for literature metrology analysis, were used to analyze the data.ResultsA total of 776 publications from 291 countries and regions were analyzed; of these, 256 publications were from the United States and 173 publications were from China, and England had the largest centrality of 0.33; Stanford University had the highest H-index of 17; and the largest cluster label of co-cited references was machine learning. In addition, keywords with a high number of occurrences in this field were autism spectrum disorder (295), children (255), classification (156) and diagnosis (77). The burst keywords from 2021 to 2023 were infants and feature selection, and from 2022 to 2023, the burst keyword was corpus callosum.ConclusionThis research provides a systematic analysis of the literature concerning AI used in ASD, presenting an overall demonstration in this field. In this area, the United States and China have the largest number of publications, England has the greatest influence, and Stanford University is the most influential. In addition, the research on AI used in ASD mostly focuses on classification and diagnosis, and “infants, feature selection, and corpus callosum are at the forefront, providing directions for future research. However, the use of AI technologies to identify ASD will require further research.","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"123 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138520040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Few-shot EEG sleep staging based on transductive prototype optimization network 基于换能化原型优化网络的少次脑电睡眠分期
IF 3.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2023-12-06 DOI: 10.3389/fninf.2023.1297874
Jingcong Li, Chaohuang Wu, Jiahui Pan, Fei Wang
{"title":"Few-shot EEG sleep staging based on transductive prototype optimization network","authors":"Jingcong Li, Chaohuang Wu, Jiahui Pan, Fei Wang","doi":"10.3389/fninf.2023.1297874","DOIUrl":"https://doi.org/10.3389/fninf.2023.1297874","url":null,"abstract":"Electroencephalography (EEG) is a commonly used technology for monitoring brain activities and diagnosing sleep disorders. Clinically, doctors need to manually stage sleep based on EEG signals, which is a time-consuming and laborious task. In this study, we propose a few-shot EEG sleep staging termed transductive prototype optimization network (TPON) method, which aims to improve the performance of EEG sleep staging. Compared with traditional deep learning methods, TPON uses a meta-learning algorithm, which generalizes the classifier to new classes that are not visible in the training set, and only have a few examples for each new class. We learn the prototypes of existing objects through meta-training, and capture the sleep features of new objects through the “learn to learn” method of meta-learning. The prototype distribution of the class is optimized and captured by using support set and unlabeled high confidence samples to increase the authenticity of the prototype. Compared with traditional prototype networks, TPON can effectively solve too few samples in few-shot learning and improve the matching degree of prototypes in prototype network. The experimental results on the public SleepEDF-2013 dataset show that the proposed algorithm outperform than most advanced algorithms in the overall performance. In addition, we experimentally demonstrate the feasibility of cross-channel recognition, which indicates that there are many similar sleep EEG features between different channels. In future research, we can further explore the common features among different channels and investigate the combination of universal features in sleep EEG. Overall, our method achieves high accuracy in sleep stage classification, demonstrating the effectiveness of this approach and its potential applications in other medical fields.","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"26 3","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138520025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Translating single-neuron axonal reconstructions into meso-scale connectivity statistics in the mouse somatosensory thalamus 将单个神经元轴突重建转化为小鼠体感丘脑中尺度连通性统计
IF 3.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2023-12-01 DOI: 10.3389/fninf.2023.1272243
Nestor Timonidis, Rembrandt Bakker, Mario Rubio-Teves, Carmen Alonso-Martínez, Maria Garcia-Amado, Francisco Clascá, Paul H. E. Tiesinga
{"title":"Translating single-neuron axonal reconstructions into meso-scale connectivity statistics in the mouse somatosensory thalamus","authors":"Nestor Timonidis, Rembrandt Bakker, Mario Rubio-Teves, Carmen Alonso-Martínez, Maria Garcia-Amado, Francisco Clascá, Paul H. E. Tiesinga","doi":"10.3389/fninf.2023.1272243","DOIUrl":"https://doi.org/10.3389/fninf.2023.1272243","url":null,"abstract":"Characterizing the connectomic and morphological diversity of thalamic neurons is key for better understanding how the thalamus relays sensory inputs to the cortex. The recent public release of complete single-neuron morphological reconstructions enables the analysis of previously inaccessible connectivity patterns from individual neurons. Here we focus on the Ventral Posteromedial (VPM) nucleus and characterize the full diversity of 257 VPM neurons, obtained by combining data from the MouseLight and Braintell projects. Neurons were clustered according to their most dominantly targeted cortical area and further subdivided by their jointly targeted areas. We obtained a 2D embedding of morphological diversity using the dissimilarity between all pairs of axonal trees. The curved shape of the embedding allowed us to characterize neurons by a 1-dimensional coordinate. The coordinate values were aligned both with the progression of soma position along the dorsal-ventral and lateral-medial axes and with that of axonal terminals along the posterior-anterior and medial-lateral axes, as well as with an increase in the number of branching points, distance from soma and branching width. Taken together, we have developed a novel workflow for linking three challenging aspects of connectomics, namely the topography, higher order connectivity patterns and morphological diversity, with VPM as a test-case. The workflow is linked to a unified access portal that contains the morphologies and integrated with 2D cortical flatmap and subcortical visualization tools. The workflow and resulting processed data have been made available in Python, and can thus be used for modeling and experimentally validating new hypotheses on thalamocortical connectivity.","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"22 7","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138520024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Evaluation of an English language phoneme-based imagined speech brain computer interface with low-cost electroencephalography 利用低成本脑电图对基于音素的英语想象语音脑计算机接口进行评估
IF 3.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2023-11-30 DOI: 10.3389/fninf.2023.1306277
John LaRocco, Qudsia Tahmina, Sam Lecian, Jason Moore, Cole Helbig, Surya Gupta
{"title":"Evaluation of an English language phoneme-based imagined speech brain computer interface with low-cost electroencephalography","authors":"John LaRocco, Qudsia Tahmina, Sam Lecian, Jason Moore, Cole Helbig, Surya Gupta","doi":"10.3389/fninf.2023.1306277","DOIUrl":"https://doi.org/10.3389/fninf.2023.1306277","url":null,"abstract":"<sec><title>Introduction</title><p>Paralyzed and physically impaired patients face communication difficulties, even when they are mentally coherent and aware. Electroencephalographic (EEG) brain–computer interfaces (BCIs) offer a potential communication method for these people without invasive surgery or physical device controls.</p></sec><sec><title>Methods</title><p>Although virtual keyboard protocols are well documented in EEG BCI paradigms, these implementations are visually taxing and fatiguing. All English words combine 44 unique phonemes, each corresponding to a unique EEG pattern. In this study, a complete phoneme-based imagined speech EEG BCI was developed and tested on 16 subjects.</p></sec><sec><title>Results</title><p>Using open-source hardware and software, machine learning models, such as k-nearest neighbor (KNN), reliably achieved a mean accuracy of 97 ± 0.001%, a mean F1 of 0.55 ± 0.01, and a mean AUC-ROC of 0.68 ± 0.002 in a modified one-versus-rest configuration, resulting in an information transfer rate of 304.15 bits per minute. In line with prior literature, the distinguishing feature between phonemes was the gamma power on channels F3 and F7.</p></sec><sec><title>Discussion</title><p>However, adjustments to feature selection, trial window length, and classifier algorithms may improve performance. In summary, these are iterative changes to a viable method directly deployable in current, commercially available systems and software. The development of an intuitive phoneme-based EEG BCI with open-source hardware and software demonstrates the potential ease with which the technology could be deployed in real-world applications.</p></sec>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"104 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138714833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Corrigendum: Learning the heterogeneous representation of brain's structure from serial SEM images using a masked autoencoder. 更正:利用遮蔽式自动编码器从序列 SEM 图像中学习大脑结构的异质表示。
IF 3.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2023-11-27 eCollection Date: 2023-01-01 DOI: 10.3389/fninf.2023.1337766
Ao Cheng, Jiahao Shi, Lirong Wang, Ruobing Zhang
{"title":"Corrigendum: Learning the heterogeneous representation of brain's structure from serial SEM images using a masked autoencoder.","authors":"Ao Cheng, Jiahao Shi, Lirong Wang, Ruobing Zhang","doi":"10.3389/fninf.2023.1337766","DOIUrl":"https://doi.org/10.3389/fninf.2023.1337766","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fninf.2023.1118419.].</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"17 ","pages":"1337766"},"PeriodicalIF":3.5,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10712309/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138801396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Tissue Oxygen Depth Explorer: an interactive database for microscopic oxygen imaging data. 组织氧深度资源管理器:显微氧成像数据交互式数据库。
IF 3.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2023-11-27 eCollection Date: 2023-01-01 DOI: 10.3389/fninf.2023.1278787
Layth N Amra, Philipp Mächler, Natalie Fomin-Thunemann, Kıvılcım Kılıç, Payam Saisan, Anna Devor, Martin Thunemann
{"title":"Tissue Oxygen Depth Explorer: an interactive database for microscopic oxygen imaging data.","authors":"Layth N Amra, Philipp Mächler, Natalie Fomin-Thunemann, Kıvılcım Kılıç, Payam Saisan, Anna Devor, Martin Thunemann","doi":"10.3389/fninf.2023.1278787","DOIUrl":"10.3389/fninf.2023.1278787","url":null,"abstract":"","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"17 ","pages":"1278787"},"PeriodicalIF":3.5,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10711099/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138801400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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