Neuroinformatics最新文献_第8页

Editorial: Is Now the Time for Foundational Theory of Brain Connectivity? 社论：现在是建立大脑连通性基础理论的时候了吗？

IF 3 4区医学

Neuroinformatics Pub Date : 2023-10-01 DOI: 10.1007/s12021-023-09641-7

John Darrell Van Horn, Zachary Jacokes, Benjamin Newman, Teague Henry

引用次数: 1

Confounding Effects on the Performance of Machine Learning Analysis of Static Functional Connectivity Computed from rs-fMRI Multi-site Data. 对机器学习性能的混淆影响从rs fMRI多站点数据计算的静态功能连接分析。

IF 2.7 4区医学

Neuroinformatics Pub Date : 2023-10-01 Epub Date: 2023-08-15 DOI: 10.1007/s12021-023-09639-1

Oswaldo Artiles, Zeina Al Masry, Fahad Saeed

{"title":"Confounding Effects on the Performance of Machine Learning Analysis of Static Functional Connectivity Computed from rs-fMRI Multi-site Data.","authors":"Oswaldo Artiles, Zeina Al Masry, Fahad Saeed","doi":"10.1007/s12021-023-09639-1","DOIUrl":"10.1007/s12021-023-09639-1","url":null,"abstract":"Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive imaging technique widely used in neuroscience to understand the functional connectivity of the human brain. While rs-fMRI multi-site data can help to understand the inner working of the brain, the data acquisition and processing of this data has many challenges. One of the challenges is the variability of the data associated with different acquisitions sites, and different MRI machines vendors. Other factors such as population heterogeneity among different sites, with variables such as age and gender of the subjects, must also be considered. Given that most of the machine-learning models are developed using these rs-fMRI multi-site data sets, the intrinsic confounding effects can adversely affect the generalizability and reliability of these computational methods, as well as the imposition of upper limits on the classification scores. This work aims to identify the phenotypic and imaging variables producing the confounding effects, as well as to control these effects. Our goal is to maximize the classification scores obtained from the machine learning analysis of the Autism Brain Imaging Data Exchange (ABIDE) rs-fMRI multi-site data. To achieve this goal, we propose novel methods of stratification to produce homogeneous sub-samples of the 17 ABIDE sites, as well as the generation of new features from the static functional connectivity values, using multiple linear regression models, ComBat harmonization models, and normalization methods. The main results obtained with our statistical models and methods are an accuracy of 76.4%, sensitivity of 82.9%, and specificity of 77.0%, which are 8.8%, 20.5%, and 7.5% above the baseline classification scores obtained from the machine learning analysis of the static functional connectivity computed from the ABIDE rs-fMRI multi-site data.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"651-668"},"PeriodicalIF":2.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10054936","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

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