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A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer's Disease using MRI Images. 基于深度学习的MRI图像早期诊断阿尔茨海默病集成方法。
IF 3 4区 医学 Q2 Computer Science Pub Date : 2024-01-01 Epub Date: 2023-12-02 DOI: 10.1007/s12021-023-09646-2
Sina Fathi, Ali Ahmadi, Afsaneh Dehnad, Mostafa Almasi-Dooghaee, Melika Sadegh

Recently, the early diagnosis of Alzheimer's disease has gained major attention due to the growing prevalence of the disease and the resulting costs imposed on individuals and society. The main objective of this study was to propose an ensemble method based on deep learning for the early diagnosis of AD using MRI images. The methodology of this study consisted of collecting the dataset, preprocessing, creating the individual and ensemble models, evaluating the models based on ADNI data, and validating the trained model based on the local dataset. The proposed method was an ensemble approach selected through a comparative analysis of various ensemble scenarios. Finally, the six best individual CNN-based classifiers were selected to combine and constitute the ensemble model. The evaluation showed an accuracy rate of 98.57, 96.37, 94.22, 99.83, 93.88, and 93.92 for NC/AD, NC/EMCI, EMCI/LMCI, LMCI/AD, four-way and three-way classification groups, respectively. The validation results on the local dataset revealed an accuracy of 88.46 for three-way classification. Our performance results were higher than most reviewed studies and comparable with others. Although comparative analysis showed superior results of ensemble methods against individual architectures, there were no significant differences among various ensemble approaches. The validation results revealed the low performance of individual models in practice. In contrast, the ensemble method showed promising results. However, further studies on various and larger datasets are required to validate the generalizability of the model.

最近,由于阿尔茨海默病的发病率越来越高,以及由此给个人和社会带来的成本,早期诊断得到了广泛关注。本研究的主要目的是提出一种基于深度学习的集成方法,用于MRI图像对AD的早期诊断。本研究的方法包括收集数据集,预处理,创建单个和集成模型,基于ADNI数据评估模型,以及基于局部数据集验证训练模型。所提出的方法是通过对各种集成场景的比较分析选择的集成方法。最后,选取6个最佳的基于cnn的分类器进行组合,构成集成模型。NC/AD、NC/EMCI、EMCI/LMCI、LMCI/AD、四向分类和三向分类的准确率分别为98.57、96.37、94.22、99.83、93.88和93.92。在本地数据集上的验证结果显示,三向分类的准确率为88.46。我们的表现结果高于大多数被审查的研究,并与其他研究相当。虽然对比分析显示了集成方法对单个体系结构的优越结果,但各种集成方法之间没有显着差异。验证结果表明,个别模型在实际应用中表现不佳。相比之下,集合方法显示出令人满意的结果。然而,需要对各种更大的数据集进行进一步的研究来验证模型的泛化性。
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引用次数: 0
Topological Data Analysis Captures Task-Driven fMRI Profiles in Individual Participants: A Classification Pipeline Based on Persistence. 拓扑数据分析捕捉个体参与者的任务驱动功能磁共振成像档案:基于持久性的分类管道。
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-01-01 Epub Date: 2023-11-04 DOI: 10.1007/s12021-023-09645-3
Michael J Catanzaro, Sam Rizzo, John Kopchick, Asadur Chowdury, David R Rosenberg, Peter Bubenik, Vaibhav A Diwadkar

BOLD-based fMRI is the most widely used method for studying brain function. The BOLD signal while valuable, is beset with unique vulnerabilities. The most notable of these is the modest signal to noise ratio, and the relatively low temporal and spatial resolution. However, the high dimensional complexity of the BOLD signal also presents unique opportunities for functional discovery. Topological Data Analyses (TDA), a branch of mathematics optimized to search for specific classes of structure within high dimensional data may provide particularly valuable applications. In this investigation, we acquired fMRI data in the anterior cingulate cortex (ACC) using a basic motor control paradigm. Then, for each participant and each of three task conditions, fMRI signals in the ACC were summarized using two methods: a) TDA based methods of persistent homology and persistence landscapes and b) non-TDA based methods using a standard vectorization scheme. Finally, using machine learning (with support vector classifiers), classification accuracy of TDA and non-TDA vectorized data was tested across participants. In each participant, TDA-based classification out-performed the non-TDA based counterpart, suggesting that our TDA analytic pipeline better characterized task- and condition-induced structure in fMRI data in the ACC. Our results emphasize the value of TDA in characterizing task- and condition-induced structure in regional fMRI signals. In addition to providing our analytical tools for other users to emulate, we also discuss the unique role that TDA-based methods can play in the study of individual differences in the structure of functional brain signals in the healthy and the clinical brain.

基于BOLD的fMRI是研究大脑功能最广泛使用的方法。BOLD信号虽然很有价值,但却充满了独特的漏洞。其中最值得注意的是适度的信噪比,以及相对较低的时间和空间分辨率。然而,BOLD信号的高维复杂性也为功能发现提供了独特的机会。拓扑数据分析(TDA)是数学的一个分支,它被优化为在高维数据中搜索特定类别的结构,可以提供特别有价值的应用。在这项研究中,我们使用基本的运动控制范式获得了前扣带皮层(ACC)的fMRI数据。然后,对于每个参与者和三种任务条件中的每一种,使用两种方法总结ACC中的fMRI信号:a)基于TDA的持久同源性和持久性景观的方法,以及b)使用标准矢量化方案的基于非TDA的方法。最后,使用机器学习(使用支持向量分类器),在参与者中测试TDA和非TDA矢量化数据的分类准确性。在每个参与者中,基于TDA的分类优于基于非TDA的对应分类,这表明我们的TDA分析管道更好地描述了ACC中fMRI数据中任务和条件诱导的结构。我们的结果强调了TDA在表征区域fMRI信号中任务和情况诱导的结构方面的价值。除了为其他用户提供可供效仿的分析工具外,我们还讨论了基于TDA的方法在研究健康和临床大脑中功能性脑信号结构的个体差异中可以发挥的独特作用。
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引用次数: 0
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 Q2 Computer Science Pub Date : 2023-10-01 DOI: 10.1007/s12021-023-09641-7
John Darrell Van Horn, Zachary Jacokes, Benjamin Newman, Teague Henry
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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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-10-01 Epub Date: 2023-08-15 DOI: 10.1007/s12021-023-09639-1
Oswaldo Artiles, Zeina Al Masry, Fahad Saeed

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.

静息状态功能性磁共振成像(rs-fMRI)是一种非侵入性成像技术,广泛应用于神经科学,以了解人脑的功能连接。虽然rs功能磁共振成像多部位数据有助于了解大脑的内部工作,但这些数据的数据采集和处理有许多挑战。其中一个挑战是与不同的收购地点和不同的MRI机器供应商相关的数据的可变性。还必须考虑其他因素,如不同地点之间的人口异质性,以及受试者的年龄和性别等变量。鉴于大多数机器学习模型都是使用这些rs-fMRI多站点数据集开发的,固有的混杂效应可能会对这些计算方法的可推广性和可靠性产生不利影响,并对分类分数施加上限。这项工作旨在确定产生混杂效应的表型和成像变量,并控制这些效应。我们的目标是最大化从自闭症脑成像数据交换(ABIDE)的fMRI多站点数据的机器学习分析中获得的分类分数。为了实现这一目标,我们提出了新的分层方法来产生17个ABIDE位点的同质子样本,并使用多元线性回归模型、Compat协调模型和归一化方法从静态功能连接值生成新特征。使用我们的统计模型和方法获得的主要结果是76.4%的准确率、82.9%的灵敏度和77.0%的特异性,比根据ABIDE的fMRI多位点数据计算的静态功能连接的机器学习分析获得的基线分类得分高8.8%、20.5%和7.5%。
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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 Q2 Computer Science 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
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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 Q2 Computer Science 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
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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 Q2 Computer Science 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

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.

胶质瘤是成人最常见的原发性颅内肿瘤。放射治疗是神经胶质瘤患者的一种治疗方法,磁共振成像(MRI)是治疗计划中一种有益的诊断工具。神经胶质瘤患者的治疗反应评估通常基于神经肿瘤反应评估(RANO)标准。基于RANO的评估局限于二维(2D)手动测量。深度学习(DL)在神经肿瘤学中具有提高反应评估准确性的巨大潜力。在当前的研究中,首先,BraTS 2018 Challenge数据集包括210个HGG和75个LGG,用于训练设计的U-Net网络,用于自动肿瘤和肿瘤内分割,然后使用迁移学习对设计的分类器进行训练,以确定HGG和LGG的分级。然后,采用设计的网络对49例胶质瘤患者放疗前后的局部MRI图像进行分割和分类。利用肿瘤分割的结果及其肿瘤内区域来确定不同区域的体积和治疗反应评估。治疗反应评估表明,放疗对整个肿瘤有效,并增强p值区域 ≤ 0.05,置信水平为95%,而它不影响坏死和肿瘤周围水肿区域。这项工作证明了在MRI图像中使用深度学习的潜力,为自动化治疗反应评估提供了一个有益的工具,使患者能够获得最佳治疗。
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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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-07-01 Epub Date: 2023-06-14 DOI: 10.1007/s12021-023-09634-6
Rosemary He, Daniel Tward

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.

通过核磁共振成像体积测量法测量的神经变性被认为是一种潜在的阿尔茨海默病(AD)生物标志物,但由于缺乏特异性,其效用受到了限制。量化全脑而非局部神经变性的空间模式可能有助于解决这一问题。在这项研究中,我们转而采用基于网络的分析方法,并扩展了图嵌入算法,以研究用结构性核磁共振成像测量的体积变化相关性在数年时间尺度上的形态连接性。我们用多重随机秭归图框架建立数据模型,并修改和实施了早先提出的多重图嵌入算法,以估算网络的低维嵌入。我们的算法版本保证了有意义的有限样本结果,并能根据特定人群的网络模式和特定受试者的负载估计最大似然边缘概率。此外,我们还提出并实施了一种新颖的统计测试程序,用于在考虑混杂因素后分析组间差异,并定位 AD 神经变性过程中的重要结构。通过对最大统计量进行置换检验,将族内误差率控制在 5%。我们的分析结果表明,我们发现的网络以与 AD 神经变性相关的已知结构为主,这表明该框架有望用于研究 AD。此外,我们还发现了该领域传统方法无法发现的网络结构图元。
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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 Q2 Computer Science 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

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.

胎儿磁共振成像(MRI)是表征中枢神经系统(CNS)发育的一种重要的非侵入性诊断工具,对妊娠管理有重要贡献。在临床实践中,胎儿大脑MRI包括在不同平面上采集快速解剖序列,在这些序列上手动提取几个生物特征测量值。最近,现代工具包使用采集的二维(2D)图像重建大脑的超分辨率(SR)各向同性体积,从而能够对胎儿中枢神经系统进行三维(3D)分析。我们分析了妊娠中期进行的17次胎儿MR检查,包括正交T2加权(T2w)涡轮自旋回波(TSE)和平衡快速场回波(b-FFE)序列。对于每种受试者和序列类型,通过NiftyMIC、MIALSRTK和SVRTK工具包重建三个不同的高分辨率体积。在采集的2D图像和SR重建体积上评估了15个生物特征测量,并使用Passing Bablok回归、Bland-Altman图分析和统计检验进行了比较。结果表明,NiftyMIC和MIALSRTK提供了可靠的SR重建体积,适用于生物特征评估。NiftyMIC还提高了操作员关于所采集的2D图像的定量生物特征测量的组内相关系数。此外,与b-FFE序列相比,TSE序列能够针对强度伪影进行更稳健的胎儿大脑重建,尽管后者表现出更明确的解剖细节。我们的研究结果加强了胎儿大脑重建自动化工具包的采用,以在妊娠早期对胎儿大脑发育进行生物测量评估,而不是常规临床MR。
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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 Q2 Computer Science Pub Date : 2023-07-01 DOI: 10.1007/s12021-023-09628-4
Kay Robbins, Dung Truong, Alexander Jones, Ian Callanan, Scott Makeig
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引用次数: 0
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