Neuroinformatics最新文献_第9页

BrainLine: An Open Pipeline for Connectivity Analysis of Heterogeneous Whole-Brain Fluorescence Volumes. BrainLine：一个用于异构全脑荧光体积连通性分析的开放管道。

IF 3 4区医学

Neuroinformatics Pub Date : 2023-10-01 Epub Date: 2023-07-03 DOI: 10.1007/s12021-023-09638-2

Thomas L Athey, Matthew A Wright, Marija Pavlovic, Vikram Chandrashekhar, Karl Deisseroth, Michael I Miller, Joshua T Vogelstein

引用次数: 0

Correction to: Geometric Reliability of Super-Resolution Reconstructed Images from Clinical Fetal MRI in the Second Trimester. 更正：第二个月临床胎儿MRI超分辨率重建图像的几何可靠性。

IF 3 4区医学

Neuroinformatics Pub Date : 2023-10-01 DOI: 10.1007/s12021-023-09642-6

Tommaso Ciceri, Letizia Squarcina, Alessandro Pigoni, Adele Ferro, Florian Montano, Alessandra Bertoldo, Nicola Persico, Simona Boito, Fabio Maria Triulzi, Giorgio Conte, Paolo Brambilla, Denis Peruzzo

引用次数: 0

Auto-Segmentation and Classification of Glioma Tumors with the Goals of Treatment Response Assessment Using Deep Learning Based on Magnetic Resonance Imaging. 使用基于磁共振成像的深度学习对胶质瘤肿瘤进行自动分割和分类，以评估治疗效果。

IF 3 4区医学

Neuroinformatics Pub Date : 2023-10-01 Epub Date: 2023-07-17 DOI: 10.1007/s12021-023-09640-8

Zahra Papi, Sina Fathi, Fatemeh Dalvand, Mahsa Vali, Ali Yousefi, Mohammad Hemmatyar Tabatabaei, Alireza Amouheidari, Iraj Abedi

{"title":"Auto-Segmentation and Classification of Glioma Tumors with the Goals of Treatment Response Assessment Using Deep Learning Based on Magnetic Resonance Imaging.","authors":"Zahra Papi, Sina Fathi, Fatemeh Dalvand, Mahsa Vali, Ali Yousefi, Mohammad Hemmatyar Tabatabaei, Alireza Amouheidari, Iraj Abedi","doi":"10.1007/s12021-023-09640-8","DOIUrl":"10.1007/s12021-023-09640-8","url":null,"abstract":"Glioma is the most common primary intracranial neoplasm in adults. Radiotherapy is a treatment approach in glioma patients, and Magnetic Resonance Imaging (MRI) is a beneficial diagnostic tool in treatment planning. Treatment response assessment in glioma patients is usually based on the Response Assessment in Neuro Oncology (RANO) criteria. The limitation of assessment based on RANO is two-dimensional (2D) manual measurements. Deep learning (DL) has great potential in neuro-oncology to improve the accuracy of response assessment. In the current research, firstly, the BraTS 2018 Challenge dataset included 210 HGG and 75 LGG were applied to train a designed U-Net network for automatic tumor and intra-tumoral segmentation, followed by training of the designed classifier with transfer learning for determining grading HGG and LGG. Then, designed networks were employed for the segmentation and classification of local MRI images of 49 glioma patients pre and post-radiotherapy. The results of tumor segmentation and its intra-tumoral regions were utilized to determine the volume of different regions and treatment response assessment. Treatment response assessment demonstrated that radiotherapy is effective on the whole tumor and enhancing region with p-value ≤ 0.05 with a 95% confidence level, while it did not affect necrosis and peri-tumoral edema regions. This work demonstrated the potential of using deep learning in MRI images to provide a beneficial tool in the automated treatment response assessment so that the patient can obtain the best treatment.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"641-650"},"PeriodicalIF":3.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9817237","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

Applying Joint Graph Embedding to Study Alzheimer's Neurodegeneration Patterns in Volumetric Data. 应用联合图嵌入研究容积数据中的阿尔茨海默氏症神经变性模式

IF 2.7 4区医学

Neuroinformatics Pub Date : 2023-07-01 Epub Date: 2023-06-14 DOI: 10.1007/s12021-023-09634-6

Rosemary He, Daniel Tward

{"title":"Applying Joint Graph Embedding to Study Alzheimer's Neurodegeneration Patterns in Volumetric Data.","authors":"Rosemary He, Daniel Tward","doi":"10.1007/s12021-023-09634-6","DOIUrl":"10.1007/s12021-023-09634-6","url":null,"abstract":"Neurodegeneration measured through volumetry in MRI is recognized as a potential Alzheimer's Disease (AD) biomarker, but its utility is limited by lack of specificity. Quantifying spatial patterns of neurodegeneration on a whole brain scale rather than locally may help address this. In this work, we turn to network based analyses and extend a graph embedding algorithm to study morphometric connectivity from volume-change correlations measured with structural MRI on the timescale of years. We model our data with the multiple random eigengraphs framework, as well as modify and implement a multigraph embedding algorithm proposed earlier to estimate a low dimensional embedding of the networks. Our version of the algorithm guarantees meaningful finite-sample results and estimates maximum likelihood edge probabilities from population-specific network modes and subject-specific loadings. Furthermore, we propose and implement a novel statistical testing procedure to analyze group differences after accounting for confounders and locate significant structures during AD neurodegeneration. Family-wise error rate is controlled at 5% using permutation testing on the maximum statistic. We show that results from our analysis reveal networks dominated by known structures associated to AD neurodegeneration, indicating the framework has promise for studying AD. Furthermore, we find network-structure tuples that are not found with traditional methods in the field.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 3","pages":"601-614"},"PeriodicalIF":2.7,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406695/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10371867","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

Correction to: Building FAIR Functionality: Annotating Events in Time Series Data Using Hierarchical Event Descriptors (HED). 更正:构建FAIR功能:使用分层事件描述符(HED)在时间序列数据中注释事件。

IF 3 4区医学

Neuroinformatics Pub Date : 2023-07-01 DOI: 10.1007/s12021-023-09628-4

Kay Robbins, Dung Truong, Alexander Jones, Ian Callanan, Scott Makeig

引用次数: 0

Geometric Reliability of Super-Resolution Reconstructed Images from Clinical Fetal MRI in the Second Trimester. 第二个月临床胎儿MRI超分辨率重建图像的几何可靠性。

IF 3 4区医学

Neuroinformatics Pub Date : 2023-07-01 Epub Date: 2023-06-07 DOI: 10.1007/s12021-023-09635-5

Tommaso Ciceri, Letizia Squarcina, Alessandro Pigoni, Adele Ferro, Florian Montano, Alessandra Bertoldo, Nicola Persico, Simona Boito, Fabio Maria Triulzi, Giorgio Conte, Paolo Brambilla, Denis Peruzzo

{"title":"Geometric Reliability of Super-Resolution Reconstructed Images from Clinical Fetal MRI in the Second Trimester.","authors":"Tommaso Ciceri, Letizia Squarcina, Alessandro Pigoni, Adele Ferro, Florian Montano, Alessandra Bertoldo, Nicola Persico, Simona Boito, Fabio Maria Triulzi, Giorgio Conte, Paolo Brambilla, Denis Peruzzo","doi":"10.1007/s12021-023-09635-5","DOIUrl":"10.1007/s12021-023-09635-5","url":null,"abstract":"Fetal Magnetic Resonance Imaging (MRI) is an important noninvasive diagnostic tool to characterize the central nervous system (CNS) development, significantly contributing to pregnancy management. In clinical practice, fetal MRI of the brain includes the acquisition of fast anatomical sequences over different planes on which several biometric measurements are manually extracted. Recently, modern toolkits use the acquired two-dimensional (2D) images to reconstruct a Super-Resolution (SR) isotropic volume of the brain, enabling three-dimensional (3D) analysis of the fetal CNS.We analyzed 17 fetal MR exams performed in the second trimester, including orthogonal T2-weighted (T2w) Turbo Spin Echo (TSE) and balanced Fast Field Echo (b-FFE) sequences. For each subject and type of sequence, three distinct high-resolution volumes were reconstructed via NiftyMIC, MIALSRTK, and SVRTK toolkits. Fifteen biometric measurements were assessed both on the acquired 2D images and SR reconstructed volumes, and compared using Passing-Bablok regression, Bland-Altman plot analysis, and statistical tests.Results indicate that NiftyMIC and MIALSRTK provide reliable SR reconstructed volumes, suitable for biometric assessments. NiftyMIC also improves the operator intraclass correlation coefficient on the quantitative biometric measures with respect to the acquired 2D images. In addition, TSE sequences lead to more robust fetal brain reconstructions against intensity artifacts compared to b-FFE sequences, despite the latter exhibiting more defined anatomical details.Our findings strengthen the adoption of automatic toolkits for fetal brain reconstructions to perform biometry evaluations of fetal brain development over common clinical MR at an early pregnancy stage.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 3","pages":"549-563"},"PeriodicalIF":3.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10298897","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

A Method for In-Vivo Mapping of Axonal Diameter Distributions in the Human Brain Using Diffusion-Based Axonal Spectrum Imaging (AxSI). 一种基于扩散的轴突频谱成像(AxSI)在体内绘制人脑轴突直径分布的方法。

IF 3 4区医学

Neuroinformatics Pub Date : 2023-07-01 DOI: 10.1007/s12021-023-09630-w

Hila Gast, Assaf Horowitz, Ronnie Krupnik, Daniel Barazany, Shlomi Lifshits, Shani Ben-Amitay, Yaniv Assaf

{"title":"A Method for In-Vivo Mapping of Axonal Diameter Distributions in the Human Brain Using Diffusion-Based Axonal Spectrum Imaging (AxSI).","authors":"Hila Gast, Assaf Horowitz, Ronnie Krupnik, Daniel Barazany, Shlomi Lifshits, Shani Ben-Amitay, Yaniv Assaf","doi":"10.1007/s12021-023-09630-w","DOIUrl":"https://doi.org/10.1007/s12021-023-09630-w","url":null,"abstract":"In this paper we demonstrate a generalized and simplified pipeline called axonal spectrum imaging (AxSI) for in-vivo estimation of axonal characteristics in the human brain. Whole-brain estimation of the axon diameter, in-vivo and non-invasively, across all fiber systems will allow exploring uncharted aspects of brain structure and function relations with emphasis on connectivity and connectome analysis. While axon diameter mapping is important in and of itself, its correlation with conduction velocity will allow, for the first time, the explorations of information transfer mechanisms within the brain. We demonstrate various well-known aspects of axonal morphometry (e.g., the corpus callosum axon diameter variation) as well as other aspects that are less explored (e.g., axon diameter-based separation of the superior longitudinal fasciculus into segments). Moreover, we have created an MNI based mean axon diameter map over the entire brain for a large cohort of subjects providing the reference basis for future studies exploring relation between axon properties, its connectome representation, and other functional and behavioral aspects of the brain.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 3","pages":"469-482"},"PeriodicalIF":3.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10392489","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

Deep Learning Methods for Identification of White Matter Fiber Tracts: Review of State-of-the-Art and Future Prospective. 识别白质纤维束的深度学习方法:最新进展和未来展望。

IF 3 4区医学

Neuroinformatics Pub Date : 2023-07-01 DOI: 10.1007/s12021-023-09636-4

Nayereh Ghazi, Mohammad Hadi Aarabi, Hamid Soltanian-Zadeh

{"title":"Deep Learning Methods for Identification of White Matter Fiber Tracts: Review of State-of-the-Art and Future Prospective.","authors":"Nayereh Ghazi, Mohammad Hadi Aarabi, Hamid Soltanian-Zadeh","doi":"10.1007/s12021-023-09636-4","DOIUrl":"https://doi.org/10.1007/s12021-023-09636-4","url":null,"abstract":"Quantitative analysis of white matter fiber tracts from diffusion Magnetic Resonance Imaging (dMRI) data is of great significance in health and disease. For example, analysis of fiber tracts related to anatomically meaningful fiber bundles is highly demanded in pre-surgical and treatment planning, and the surgery outcome depends on accurate segmentation of the desired tracts. Currently, this process is mainly done through time-consuming manual identification performed by neuro-anatomical experts. However, there is a broad interest in automating the pipeline such that it is fast, accurate, and easy to apply in clinical settings and also eliminates the intra-reader variabilities. Following the advancements in medical image analysis using deep learning techniques, there has been a growing interest in using these techniques for the task of tract identification as well. Recent reports on this application show that deep learning-based tract identification approaches outperform existing state-of-the-art methods. This paper presents a review of current tract identification approaches based on deep neural networks. First, we review the recent deep learning methods for tract identification. Next, we compare them with respect to their performance, training process, and network properties. Finally, we end with a critical discussion of open challenges and possible directions for future works.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 3","pages":"517-548"},"PeriodicalIF":3.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10018299","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

Big Brain Data Initiatives in Universiti Sains Malaysia: Data Stewardship to Data Repository and Data Sharing. 马来西亚大学的大大脑数据计划:数据管理到数据存储库和数据共享。

IF 3 4区医学

Neuroinformatics Pub Date : 2023-07-01 DOI: 10.1007/s12021-023-09637-3

Nurfaten Hamzah, Nurul Hashimah Ahamed Hassain Malim, Jafri Malin Abdullah, Putra Sumari, Ariffin Marzuki Mokhtar, Siti Nur Syamila Rosli, Sharifah Aida Shekh Ibrahim, Zamzuri Idris

{"title":"Big Brain Data Initiatives in Universiti Sains Malaysia: Data Stewardship to Data Repository and Data Sharing.","authors":"Nurfaten Hamzah, Nurul Hashimah Ahamed Hassain Malim, Jafri Malin Abdullah, Putra Sumari, Ariffin Marzuki Mokhtar, Siti Nur Syamila Rosli, Sharifah Aida Shekh Ibrahim, Zamzuri Idris","doi":"10.1007/s12021-023-09637-3","DOIUrl":"https://doi.org/10.1007/s12021-023-09637-3","url":null,"abstract":"The sharing of open-access neuroimaging data has increased significantly during the last few years. Sharing neuroimaging data is crucial to accelerating scientific advancement, particularly in the field of neuroscience. A number of big initiatives that will increase the amount of available neuroimaging data are currently in development. The Big Brain Data Initiative project was started by Universiti Sains Malaysia as the first neuroimaging data repository platform in Malaysia for the purpose of data sharing. In order to ensure that the neuroimaging data in this project is accessible, usable, and secure, as well as to offer users high-quality data that can be consistently accessed, we first came up with good data stewardship practices. Then, we developed MyneuroDB, an online repository database system for data sharing purposes. Here, we describe the Big Brain Data Initiative and MyneuroDB, a data repository that provides the ability to openly share neuroimaging data, currently including magnetic resonance imaging (MRI), electroencephalography (EEG), and magnetoencephalography (MEG), following the FAIR principles for data sharing.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 3","pages":"589-600"},"PeriodicalIF":3.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10371870","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

Single Neuron Modeling Identifies Potassium Channel Modulation as Potential Target for Repetitive Head Impacts. 单神经元建模确定钾通道调制是头部重复性撞击的潜在目标

IF 2.7 4区医学

Neuroinformatics Pub Date : 2023-07-01 Epub Date: 2023-06-09 DOI: 10.1007/s12021-023-09633-7

Daniel P Chapman, Stefano Vicini, Mark P Burns, Rebekah Evans

{"title":"Single Neuron Modeling Identifies Potassium Channel Modulation as Potential Target for Repetitive Head Impacts.","authors":"Daniel P Chapman, Stefano Vicini, Mark P Burns, Rebekah Evans","doi":"10.1007/s12021-023-09633-7","DOIUrl":"10.1007/s12021-023-09633-7","url":null,"abstract":"Traumatic brain injury (TBI) and repetitive head impacts can result in a wide range of neurological symptoms. Despite being the most common neurological disorder in the world, repeat head impacts and TBI do not have any FDA-approved treatments. Single neuron modeling allows researchers to extrapolate cellular changes in individual neurons based on experimental data. We recently characterized a model of high frequency head impact (HFHI) with a phenotype of cognitive deficits associated with decreases in neuronal excitability of CA1 neurons and synaptic changes. While the synaptic changes have been interrogated in vivo, the cause and potential therapeutic targets of hypoexcitability following repetitive head impacts are unknown. Here, we generated in silico models of CA1 pyramidal neurons from current clamp data of control mice and mice that sustained HFHI. We use a directed evolution algorithm with a crowding penalty to generate a large and unbiased population of plausible models for each group that approximated the experimental features. The HFHI neuron model population showed decreased voltage gated sodium conductance and a general increase in potassium channel conductance. We used partial least squares regression analysis to identify combinations of channels that may account for CA1 hypoexcitability after HFHI. The hypoexcitability phenotype in models was linked to A- and M-type potassium channels in combination, but not by any single channel correlations. We provide an open access set of CA1 pyramidal neuron models for both control and HFHI conditions that can be used to predict the effects of pharmacological interventions in TBI models.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 3","pages":"501-516"},"PeriodicalIF":2.7,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10833395/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10281744","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