Neuroinformatics最新文献_第9页

On the Long-term Archiving of Research Data. 研究数据的长期存档。

IF 3 4区医学

Neuroinformatics Pub Date : 2023-04-01 DOI: 10.1007/s12021-023-09621-x

Cyril Pernet, Claus Svarer, Ross Blair, John D Van Horn, Russell A Poldrack

引用次数: 2

Correction to: Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG Data. 更正:通过脑磁图数据张量分解发现脑发育模式的多主体分析。

IF 3 4区医学

Neuroinformatics Pub Date : 2023-01-01 DOI: 10.1007/s12021-023-09620-y

Irina Belyaeva, Ben Gabrielson, Yu-Ping Wang, Tony W Wilson, Vince D Calhoun, Julia M Stephen, Tülay Adali

引用次数: 0

Polymer Physics-Based Classification of Neurons. 基于聚合物物理的神经元分类。

IF 3 4区医学

Neuroinformatics Pub Date : 2023-01-01 DOI: 10.1007/s12021-022-09605-3

Kiri Choi, Won Kyu Kim, Changbong Hyeon

{"title":"Polymer Physics-Based Classification of Neurons.","authors":"Kiri Choi, Won Kyu Kim, Changbong Hyeon","doi":"10.1007/s12021-022-09605-3","DOIUrl":"https://doi.org/10.1007/s12021-022-09605-3","url":null,"abstract":"Recognizing that diverse morphologies of neurons are reminiscent of structures of branched polymers, we put forward a principled and systematic way of classifying neurons that employs the ideas of polymer physics. In particular, we use 3D coordinates of individual neurons, which are accessible in recent neuron reconstruction datasets from electron microscope images. We numerically calculate the form factor, F(q), a Fourier transform of the distance distribution of particles comprising an object of interest, which is routinely measured in scattering experiments to quantitatively characterize the structure of materials. For a polymer-like object consisting of n monomers spanning over a length scale of r, F(q) scales with the wavenumber [Formula: see text] as [Formula: see text] at an intermediate range of q, where [Formula: see text] is the fractal dimension or the inverse scaling exponent ([Formula: see text]) characterizing the geometrical feature ([Formula: see text]) of the object. F(q) can be used to describe a neuron morphology in terms of its size ([Formula: see text]) and the extent of branching quantified by [Formula: see text]. By defining the distance between F(q)s as a measure of similarity between two neuronal morphologies, we tackle the neuron classification problem. In comparison with other existing classification methods for neuronal morphologies, our F(q)-based classification rests solely on 3D coordinates of neurons with no prior knowledge of morphological features. When applied to publicly available neuron datasets from three different organisms, our method not only complements other methods but also offers a physical picture of how the dendritic and axonal branches of an individual neuron fill the space of dense neural networks inside the brain.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 1","pages":"177-193"},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10776582","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: 2

Bayesian Coherence Analysis for Microcircuit Structure Learning. 微电路结构学习的贝叶斯相干分析。

IF 3 4区医学

Neuroinformatics Pub Date : 2023-01-01 DOI: 10.1007/s12021-022-09608-0

Rong Chen

引用次数: 0

Correction to: Bayesian Coherence Analysis for Microcircuit Structure Learning. 修正:微电路结构学习的贝叶斯相干分析。

IF 3 4区医学

Neuroinformatics Pub Date : 2023-01-01 DOI: 10.1007/s12021-022-09611-5

Rong Chen

引用次数: 0

Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG Data. 基于脑磁图数据张量分解的脑发育模式发现多主体分析。

IF 3 4区医学

Neuroinformatics Pub Date : 2023-01-01 DOI: 10.1007/s12021-022-09599-y

Irina Belyaeva, Ben Gabrielson, Yu-Ping Wang, Tony W Wilson, Vince D Calhoun, Julia M Stephen, Tülay Adali

{"title":"Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG Data.","authors":"Irina Belyaeva, Ben Gabrielson, Yu-Ping Wang, Tony W Wilson, Vince D Calhoun, Julia M Stephen, Tülay Adali","doi":"10.1007/s12021-022-09599-y","DOIUrl":"https://doi.org/10.1007/s12021-022-09599-y","url":null,"abstract":"Identification of informative signatures from electrophysiological signals is important for understanding brain developmental patterns, where techniques such as magnetoencephalography (MEG) are particularly useful. However, less attention has been given to fully utilizing the multidimensional nature of MEG data for extracting components that describe these patterns. Tensor factorizations of MEG yield components that encapsulate the data's multidimensional nature, providing parsimonious models identifying latent brain patterns for meaningful summarization of neural processes. To address the need for meaningful MEG signatures for studies of pediatric cohorts, we propose a tensor-based approach for extracting developmental signatures of multi-subject MEG data. We employ the canonical polyadic (CP) decomposition for estimating latent spatiotemporal components of the data, and use these components for group level statistical inference. Using CP decomposition along with hierarchical clustering, we were able to extract typical early and late latency event-related field (ERF) components that were discriminative of high and low performance groups ([Formula: see text]) and significantly correlated with major cognitive domains such as attention, episodic memory, executive function, and language comprehension. We demonstrate that tensor-based group level statistical inference of MEG can produce signatures descriptive of the multidimensional MEG data. Furthermore, these features can be used to study group differences in brain patterns and cognitive function of healthy children. We provide an effective tool that may be useful for assessing child developmental status and brain function directly from electrophysiological measurements and facilitate the prospective assessment of cognitive processes.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 1","pages":"115-141"},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10776082","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: 1

Automatic Cerebral Hemisphere Segmentation in Rat MRI with Ischemic Lesions via Attention-based Convolutional Neural Networks. 基于注意的卷积神经网络在脑缺血大鼠MRI中的脑半球自动分割。

IF 3 4区医学

Neuroinformatics Pub Date : 2023-01-01 DOI: 10.1007/s12021-022-09607-1

Juan Miguel Valverde, Artem Shatillo, Riccardo De Feo, Jussi Tohka

{"title":"Automatic Cerebral Hemisphere Segmentation in Rat MRI with Ischemic Lesions via Attention-based Convolutional Neural Networks.","authors":"Juan Miguel Valverde, Artem Shatillo, Riccardo De Feo, Jussi Tohka","doi":"10.1007/s12021-022-09607-1","DOIUrl":"https://doi.org/10.1007/s12021-022-09607-1","url":null,"abstract":"We present MedicDeepLabv3+, a convolutional neural network that is the first completely automatic method to segment cerebral hemispheres in magnetic resonance (MR) volumes of rats with ischemic lesions. MedicDeepLabv3+ improves the state-of-the-art DeepLabv3+ with an advanced decoder, incorporating spatial attention layers and additional skip connections that, as we show in our experiments, lead to more precise segmentations. MedicDeepLabv3+ requires no MR image preprocessing, such as bias-field correction or registration to a template, produces segmentations in less than a second, and its GPU memory requirements can be adjusted based on the available resources. We optimized MedicDeepLabv3+ and six other state-of-the-art convolutional neural networks (DeepLabv3+, UNet, HighRes3DNet, V-Net, VoxResNet, Demon) on a heterogeneous training set comprised by MR volumes from 11 cohorts acquired at different lesion stages. Then, we evaluated the trained models and two approaches specifically designed for rodent MRI skull stripping (RATS and RBET) on a large dataset of 655 MR rat brain volumes. In our experiments, MedicDeepLabv3+ outperformed the other methods, yielding an average Dice coefficient of 0.952 and 0.944 in the brain and contralateral hemisphere regions. Additionally, we show that despite limiting the GPU memory and the training data, our MedicDeepLabv3+ also provided satisfactory segmentations. In conclusion, our method, publicly available at https://github.com/jmlipman/MedicDeepLabv3Plus , yielded excellent results in multiple scenarios, demonstrating its capability to reduce human workload in rat neuroimaging studies.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 1","pages":"57-70"},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931784/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10772112","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: 3

Editorial: What the New White House Rules on Equitable Access Mean for the Neurosciences. 社论:白宫关于公平准入的新规定对神经科学意味着什么?

IF 3 4区医学

Neuroinformatics Pub Date : 2023-01-01 DOI: 10.1007/s12021-022-09618-y

John Darrell Van Horn

引用次数: 0

Consent Codes: Maintaining Consent in an Ever-expanding Open Science Ecosystem. 同意代码:在不断扩大的开放科学生态系统中维护同意。

IF 3 4区医学

Neuroinformatics Pub Date : 2023-01-01 DOI: 10.1007/s12021-022-09577-4

Stephanie O M Dyke, Kathleen Connor, Victoria Nembaware, Nchangwi S Munung, Kathy Reinold, Giselle Kerry, Mamana Mbiyavanga, Lyndon Zass, Mauricio Moldes, Samir Das, John M Davis, Jordi Rambla De Argila, J Dylan Spalding, Alan C Evans, Nicola Mulder, Jason Karamchandani

引用次数: 0

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

Neuroinformatics 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

{"title":"Towards Automated Brain Aneurysm Detection in TOF-MRA: Open Data, Weak Labels, and Anatomical Knowledge.","authors":"Tommaso Di Noto, Guillaume Marie, Sebastien Tourbier, Yasser Alemán-Gómez, Oscar Esteban, Guillaume Saliou, Meritxell Bach Cuadra, Patric Hagmann, Jonas Richiardi","doi":"10.1007/s12021-022-09597-0","DOIUrl":"https://doi.org/10.1007/s12021-022-09597-0","url":null,"abstract":"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.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 1","pages":"21-34"},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931814/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9323605","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: 7