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Bayesian Tensor Modeling for Image-based Classification of Alzheimer's Disease. 基于图像的阿尔茨海默病分类贝叶斯张量模型
IF 3 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-07 DOI: 10.1007/s12021-024-09669-3
Rongke Lyu, Marina Vannucci, Suprateek Kundu

Tensor-based representations are being increasingly used to represent complex data types such as imaging data, due to their appealing properties such as dimension reduction and the preservation of spatial information. Recently, there is a growing literature on using Bayesian scalar-on-tensor regression techniques that use tensor-based representations for high-dimensional and spatially distributed covariates to predict continuous outcomes. However surprisingly, there is limited development on corresponding Bayesian classification methods relying on tensor-valued covariates. Standard approaches that vectorize the image are not desirable due to the loss of spatial structure, and alternate methods that use extracted features from the image in the predictive model may suffer from information loss. We propose a novel data augmentation-based Bayesian classification approach relying on tensor-valued covariates, with a focus on imaging predictors. We propose two data augmentation schemes, one resulting in a support vector machine (SVM) type of classifier, and another yielding a logistic regression classifier. While both types of classifiers have been proposed independently in literature, our contribution is to extend such existing methodology to accommodate high-dimensional tensor valued predictors that involve low rank decompositions of the coefficient matrix while preserving the spatial information in the image. An efficient Markov chain Monte Carlo (MCMC) algorithm is developed for implementing these methods. Simulation studies show significant improvements in classification accuracy and parameter estimation compared to routinely used classification methods. We further illustrate our method in a neuroimaging application using cortical thickness MRI data from Alzheimer's Disease Neuroimaging Initiative, with results displaying better classification accuracy throughout several classification tasks, including classification on pairs of the three diagnostic groups: normal control, AD patients, and MCI patients; gender classification (males vs females); and cognitive performance based on high and low levels of MMSE scores.

基于张量的表示法因其降维和保留空间信息等吸引人的特性,正越来越多地被用于表示成像数据等复杂数据类型。最近,关于使用贝叶斯标量-张量回归技术的文献越来越多,这些技术使用基于张量的表示来表示高维和空间分布的协变量,从而预测连续结果。然而,令人惊讶的是,依赖于张量值协变量的相应贝叶斯分类方法的发展却很有限。将图像矢量化的标准方法由于会损失空间结构而不可取,而在预测模型中使用从图像中提取的特征的替代方法可能会造成信息损失。我们提出了一种新颖的基于数据增强的贝叶斯分类方法,该方法依赖于张量值协变量,重点关注成像预测因子。我们提出了两种数据增强方案,一种是支持向量机(SVM)类型的分类器,另一种是逻辑回归分类器。虽然这两种分类器都已在文献中独立提出,但我们的贡献在于扩展了现有的方法,以适应涉及系数矩阵低秩分解的高维张量值预测器,同时保留图像中的空间信息。为实现这些方法,开发了一种高效的马尔科夫链蒙特卡罗(MCMC)算法。模拟研究表明,与常规分类方法相比,我们的分类准确率和参数估计都有了显著提高。我们还利用阿尔茨海默病神经成像计划(Alzheimer's Disease Neuroimaging Initiative)提供的皮层厚度 MRI 数据,在神经成像应用中进一步说明了我们的方法,结果显示我们在多个分类任务中的分类准确性都有所提高,包括正常对照组、AD 患者和 MCI 患者三个诊断组的分类;性别分类(男性 vs 女性);以及基于 MMSE 分数高低的认知表现。
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引用次数: 0
Utilizing fMRI to Guide TMS Targets: the Reliability and Sensitivity of fMRI Metrics at 3 T and 1.5 T. 利用 fMRI 引导 TMS 目标:3 T 和 1.5 T fMRI 指标的可靠性和灵敏度。
IF 3 4区 医学 Q2 Computer Science Pub Date : 2024-05-23 DOI: 10.1007/s12021-024-09667-5
Qiu Ge, Matthew Lock, Xue Yang, Yuejiao Ding, Juan Yue, Na Zhao, Yun-Song Hu, Yong Zhang, Minliang Yao, Yu-Feng Zang

US Food and Drug Administration (FDA) cleared a Transcranial Magnetic Stimulation (TMS) system with functional Magnetic Resonance Imaging-guided (fMRI) individualized treatment protocol for major depressive disorder, which employs resting state-fMRI (RS-fMRI) functional connectivity (FC) to pinpoint the target individually to increase the accuracy and effeteness of the stimulation. Furthermore, task activation-guided TMS, as well as the use of RS-fMRI local metrics for targeted the specific abnormal brain regions, are considered a precise scheme for TMS targeting. Since 1.5 T MRI is more available in hospitals, systematic evaluation of the test-retest reliability and sensitivity of fMRI metrics on 1.5 T and 3 T MRI may provide reference for the application of fMRI-guided individualized-precise TMS stimulation. Twenty participants underwent three RS-fMRI scans and one scan of finger-tapping task fMRI with self-initiated (SI) and visual-guided (VG) conditions at both 3 T and 1.5 T. Then the location reliability derived by FC (with three seed regions) and peak activation were assessed by intra-individual distance. The test-retest reliability and sensitivity of five RS-fMRI local metrics were evaluated using intra-class correlation and effect size, separately. The intra-individual distance of peak activation location between 1.5 T and 3 T was 15.8 mm and 19 mm for two conditions, respectively. The intra-individual distance for the FC derived targets at 1.5 T was 9.6-31.2 mm, compared to that of 3 T (7.6-31.1 mm). The test-retest reliability and sensitivity of RS-fMRI local metrics showed similar trends on 1.5 T and 3 T. These findings hasten the application of fMRI-guided individualized TMS treatment in clinical practice.

美国食品和药物管理局(FDA)批准了一项经颅磁刺激(TMS)系统与功能磁共振成像(fMRI)引导的重度抑郁障碍个体化治疗方案,该方案采用静息状态-fMRI(RS-fMRI)功能连接(FC)来单独定位目标,以提高刺激的准确性和有效性。此外,任务激活引导的 TMS 以及使用 RS-fMRI 局部指标来锁定特定的异常脑区,被认为是 TMS 靶向的精确方案。由于 1.5 T 核磁共振成像在医院较为普及,因此系统评估 1.5 T 和 3 T 核磁共振成像上的 fMRI 指标的测试-重复可靠性和灵敏度,可为应用 fMRI 引导的个体化精确 TMS 刺激提供参考。20名参与者在3 T和1.5 T条件下接受了3次RS-fMRI扫描和1次自发(SI)和视觉引导(VG)条件下的手指敲击任务fMRI扫描。利用类内相关性和效应大小分别评估了五个 RS-fMRI 局部指标的测试-重复可靠性和敏感性。在两种情况下,1.5 T 和 3 T 之间峰值激活位置的个体内距离分别为 15.8 毫米和 19 毫米。在 1.5 T 条件下,FC 导出目标的个体内距离为 9.6-31.2 mm,而在 3 T 条件下为 7.6-31.1 mm。在 1.5 T 和 3 T 条件下,RS-fMRI 局部指标的测试-重复可靠性和灵敏度显示出相似的趋势。
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引用次数: 0
Hands-On Neuroinformatics Education at the Crossroads of Online and In-Person: Lessons Learned from NeuroHackademy. 在线与面授交汇处的神经信息学实践教育:从 NeuroHackademy 学到的经验。
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-20 DOI: 10.1007/s12021-024-09666-6
Ariel Rokem, Noah C Benson

NeuroHackademy ( https://neurohackademy.org ) is a two-week event designed to train early-career neuroscience researchers in data science methods and their application to neuroimaging. The event seeks to bridge the big data skills gap by introducing participants to data science methods and skills that are often ignored in traditional curricula. Such skills are needed for the analysis and interpretation of the kinds of large and complex datasets that have become increasingly important to neuroimaging research due to concerted data collection efforts. In 2020, the event rapidly pivoted from an in-person event to an online event that included hundreds of participants from all over the world. This experience and those of the participants substantially changed our valuation of large online-accessible events. In subsequent events held in 2022 and 2023, we have developed a "hybrid" format that includes both online and in-person participants. We discuss the technical and sociotechnical elements of hybrid events and discuss some of the lessons we have learned while organizing them. We emphasize in particular the role that these events can play in creating a global and inclusive community of practice in the intersection of neuroimaging and data science.

NeuroHackademy ( https://neurohackademy.org ) 是一项为期两周的活动,旨在培训早期神经科学研究人员掌握数据科学方法及其在神经成像中的应用。该活动旨在通过向学员介绍传统课程中经常忽略的数据科学方法和技能,弥补大数据技能方面的差距。这些技能是分析和解释大型复杂数据集所必需的,而随着数据收集工作的开展,这些数据集在神经成像研究中变得越来越重要。2020 年,该活动迅速从现场活动转变为在线活动,包括来自世界各地的数百名参与者。这次经历和参与者的经历大大改变了我们对大型在线活动的评价。在 2022 年和 2023 年举办的后续活动中,我们开发了一种 "混合 "形式,既包括在线参与者,也包括现场参与者。我们讨论了混合活动的技术和社会技术要素,并讨论了我们在组织这些活动时吸取的一些经验教训。我们特别强调了这些活动在神经成像和数据科学交叉领域创建全球性和包容性实践社区方面所能发挥的作用。
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引用次数: 0
Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort. 使用 DataLad 教授研究数据管理:一项多年期、多领域的努力。
IF 3 4区 医学 Q2 Computer Science Pub Date : 2024-05-07 DOI: 10.1007/s12021-024-09665-7
Michał Szczepanik, Adina S Wagner, Stephan Heunis, Laura K Waite, Simon B Eickhoff, Michael Hanke

Research data management has become an indispensable skill in modern neuroscience. Researchers can benefit from following good practices as well as from having proficiency in using particular software solutions. But as these domain-agnostic skills are commonly not included in domain-specific graduate education, community efforts increasingly provide early career scientists with opportunities for organised training and materials for self-study. Investing effort in user documentation and interacting with the user base can, in turn, help developers improve quality of their software. In this work, we detail and evaluate our multi-modal teaching approach to research data management in the DataLad ecosystem, both in general and with concrete software use. Spanning an online and printed handbook, a modular course suitable for in-person and virtual teaching, and a flexible collection of research data management tips in a knowledge base, our free and open source collection of training material has made research data management and software training available to various different stakeholders over the past five years.

研究数据管理已成为现代神经科学不可或缺的技能。研究人员可以从遵循良好实践和熟练使用特定软件解决方案中获益。但是,由于这些与领域无关的技能通常不包括在特定领域的研究生教育中,因此社区的努力越来越多地为早期职业科学家提供有组织的培训机会和自学材料。在用户文档和与用户群互动方面投入精力,反过来也能帮助开发人员提高软件质量。在这项工作中,我们详细介绍并评估了 DataLad 生态系统中研究数据管理的多模式教学方法,包括一般教学方法和具体的软件使用方法。在过去的五年中,我们的免费开源培训材料集为不同的利益相关者提供了研究数据管理和软件培训,其中包括在线和印刷手册、适合现场和虚拟教学的模块化课程以及知识库中灵活的研究数据管理技巧。
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引用次数: 0
Enhanced Spatial Fuzzy C-Means Algorithm for Brain Tissue Segmentation in T1 Images. 用于 T1 图像脑组织分割的增强型空间模糊 C-Means 算法
IF 3 4区 医学 Q2 Computer Science Pub Date : 2024-04-24 DOI: 10.1007/s12021-024-09661-x
B. Jafrasteh, M. Lubián-Gutiérrez, S. Lubián-López, Isabel Benavente-Fernández
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引用次数: 0
Growth-Associated Protein 43 and Tensor-Based Morphometry Indices in Mild Cognitive Impairment 轻度认知障碍患者的生长相关蛋白 43 和基于张量的形态测量指数
IF 3 4区 医学 Q2 Computer Science Pub Date : 2024-04-17 DOI: 10.1007/s12021-024-09663-9
Homa Seyedmirzaei, Amirhossein Salmannezhad, Hamidreza Ashayeri, Ali Shushtari, Bita Farazinia, Mohammad Mahdi Heidari, Amirali Momayezi, Sara Shaki Baher

Growth-associated protein 43 (GAP-43) is found in the axonal terminal of neurons in the limbic system, which is affected in people with Alzheimer’s disease (AD). We assumed GAP-43 may contribute to AD progression and serve as a biomarker. So, in a two-year follow-up study, we assessed GAP-43 changes and whether they are correlated with tensor-based morphometry (TBM) findings in patients with mild cognitive impairment (MCI). We included MCI and cognitively normal (CN) people with available baseline and follow-up cerebrospinal fluid (CSF) GAP-43 and TBM findings from the ADNI database. We assessed the difference between the two groups and correlations in each group at each time point. CSF GAP-43 and TBM measures were similar in the two study groups in all time points, except for the accelerated anatomical region of interest (ROI) of CN subjects that were significantly greater than those of MCI. The only significant correlations with GAP-43 observed were those inverse correlations with accelerated and non-accelerated anatomical ROI in MCI subjects at baseline. Plus, all TBM metrics decreased significantly in all study groups during the follow-up in contrast to CSF GAP-43 levels. Our study revealed significant associations between CSF GAP-43 levels and TBM indices among people of the AD spectrum.

生长相关蛋白 43(GAP-43)存在于边缘系统神经元的轴突末端,阿尔茨海默病(AD)患者的边缘系统会受到影响。我们认为 GAP-43 可能会导致阿尔茨海默病的发展,并可作为一种生物标记物。因此,在一项为期两年的随访研究中,我们评估了轻度认知障碍(MCI)患者的 GAP-43 变化及其是否与张量形态测量(TBM)结果相关。我们从 ADNI 数据库中纳入了有基线和随访脑脊液(CSF)GAP-43 和 TBM 结果的 MCI 和认知正常(CN)患者。我们评估了两组之间的差异以及每组在每个时间点的相关性。除了 CN 受试者的加速解剖感兴趣区(ROI)明显大于 MCI 受试者外,两个研究组在所有时间点的 CSF GAP-43 和 TBM 测量结果均相似。唯一观察到的与GAP-43的显着相关性是基线时与MCI受试者加速和非加速解剖ROI的反相关性。此外,与 CSF GAP-43 水平相比,所有研究组的所有 TBM 指标在随访期间均显著下降。我们的研究表明,在AD谱系人群中,CSF GAP-43水平与TBM指数之间存在明显的关联。
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引用次数: 0
Gradients of Brain Organization: Smooth Sailing from Methods Development to User Community 大脑组织的梯度:从方法开发到用户社区的顺利进行
IF 3 4区 医学 Q2 Computer Science Pub Date : 2024-04-03 DOI: 10.1007/s12021-024-09660-y

Abstract

Multimodal neuroimaging grants a powerful in vivo window into the structure and function of the human brain. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends – or gradients – in brain structure and function, offering a framework to unify principles of brain organization across multiple scales. Strong community enthusiasm for these techniques has been instrumental in their widespread adoption and implementation to answer key questions in neuroscience. Following a brief review of current literature on this framework, this perspective paper will highlight how pragmatic steps aiming to make gradient methods more accessible to the community propelled these techniques to the forefront of neuroscientific inquiry. More specifically, we will emphasize how interest for gradient methods was catalyzed by data sharing, open-source software development, as well as the organization of dedicated workshops led by a diverse team of early career researchers. To this end, we argue that the growing excitement for brain gradients is the result of coordinated and consistent efforts to build an inclusive community and can serve as a case in point for future innovations and conceptual advances in neuroinformatics. We close this perspective paper by discussing challenges for the continuous refinement of neuroscientific theory, methodological innovation, and real-world translation to maintain our collective progress towards integrated models of brain organization.

摘要 多模态神经成像为了解人类大脑的结构和功能提供了一个强大的活体窗口。最近在方法论和概念上的进步使人们能够研究大脑结构和功能的大尺度空间趋势(或梯度)之间的相互作用,从而提供了一个统一多尺度大脑组织原理的框架。社会各界对这些技术的强烈热情有助于它们被广泛采用和实施,以回答神经科学中的关键问题。在简要回顾了当前有关该框架的文献之后,这篇视角论文将着重介绍旨在使梯度方法更易为社区所用的务实步骤是如何将这些技术推向神经科学探索的前沿的。更具体地说,我们将强调数据共享、开源软件开发以及由不同的早期职业研究人员团队领导的专门研讨会的组织是如何促进人们对梯度方法的兴趣的。为此,我们认为,脑梯度研究的日益兴盛是建立一个包容性社区的协调和持续努力的结果,可以作为神经信息学未来创新和概念进步的范例。在本视角论文的最后,我们讨论了神经科学理论的不断完善、方法创新和现实世界的转化所面临的挑战,以保持我们在大脑组织综合模型方面的集体进步。
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引用次数: 0
Visual Prompting Based Incremental Learning for Semantic Segmentation of Multiplex Immuno-Flourescence Microscopy Imagery. 基于视觉提示的增量学习,实现多重免疫荧光显微图像的语义分割
IF 3 4区 医学 Q2 Computer Science Pub Date : 2024-04-01 Epub Date: 2024-02-23 DOI: 10.1007/s12021-024-09651-z
Ryan Faulkenberry, Saurabh Prasad, Dragan Maric, Badrinath Roysam

Deep learning approaches are state-of-the-art for semantic segmentation of medical images, but unlike many deep learning applications, medical segmentation is characterized by small amounts of annotated training data. Thus, while mainstream deep learning approaches focus on performance in domains with large training sets, researchers in the medical imaging field must apply new methods in creative ways to meet the more constrained requirements of medical datasets. We propose a framework for incrementally fine-tuning a multi-class segmentation of a high-resolution multiplex (multi-channel) immuno-flourescence image of a rat brain section, using a minimal amount of labelling from a human expert. Our framework begins with a modified Swin-UNet architecture that treats each biomarker in the multiplex image separately and learns an initial "global" segmentation (pre-training). This is followed by incremental learning and refinement of each class using a very limited amount of additional labeled data provided by a human expert for each region and its surroundings. This incremental learning utilizes the multi-class weights as an initialization and uses the additional labels to steer the network and optimize it for each region in the image. In this way, an expert can identify errors in the multi-class segmentation and rapidly correct them by supplying the model with additional annotations hand-picked from the region. In addition to increasing the speed of annotation and reducing the amount of labelling, we show that our proposed method outperforms a traditional multi-class segmentation by a large margin.

深度学习方法是最先进的医学图像语义分割方法,但与许多深度学习应用不同,医学分割的特点是注释训练数据量小。因此,主流的深度学习方法侧重于在具有大量训练集的领域中的性能,而医学影像领域的研究人员则必须以创造性的方式应用新方法,以满足医学数据集更为严格的要求。我们提出了一个框架,利用人类专家提供的极少量标记,对大鼠大脑切片的高分辨率多重(多通道)免疫荧光图像的多类分割进行增量微调。我们的框架从改进的 Swin-UNet 架构开始,该架构分别处理多重图像中的每个生物标记,并学习初始 "全局 "分割(预训练)。随后,利用人类专家为每个区域及其周围环境提供的非常有限的额外标记数据,对每个类别进行增量学习和完善。这种增量学习利用多类权重作为初始化,并利用额外的标签来引导网络,针对图像中的每个区域进行优化。通过这种方式,专家可以识别多类分割中的错误,并通过向模型提供从该区域手工挑选的附加注释来快速纠正错误。除了提高标注速度和减少标注量之外,我们还展示了我们提出的方法在很大程度上优于传统的多类分割方法。
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引用次数: 0
Updates to the Melbourne Children's Regional Infant Brain Software Package (M-CRIB-S). 更新墨尔本儿童地区婴儿脑软件包(M-CRIB-S)。
IF 3 4区 医学 Q2 Computer Science Pub Date : 2024-04-01 Epub Date: 2024-03-16 DOI: 10.1007/s12021-024-09656-8
Chris L Adamson, Bonnie Alexander, Claire E Kelly, Gareth Ball, Richard Beare, Jeanie L Y Cheong, Alicia J Spittle, Lex W Doyle, Peter J Anderson, Marc L Seal, Deanne K Thompson

The delineation of cortical areas on magnetic resonance images (MRI) is important for understanding the complexities of the developing human brain. The previous version of the Melbourne Children's Regional Infant Brain (M-CRIB-S) (Adamson et al. Scientific Reports, 10(1), 10, 2020) is a software package that performs whole-brain segmentation, cortical surface extraction and parcellation of the neonatal brain. Available cortical parcellation schemes in the M-CRIB-S are the adult-compatible 34- and 31-region per hemisphere Desikan-Killiany (DK) and Desikan-Killiany-Tourville (DKT), respectively. We present a major update to the software package which achieves two aims: 1) to make the voxel-based segmentation outputs derived from the Freesurfer-compatible M-CRIB scheme, and 2) to improve the accuracy of whole-brain segmentation and cortical surface extraction. Cortical surface extraction has been improved with additional steps to improve penetration of the inner surface into thin gyri. The improved cortical surface extraction is shown to increase the robustness of measures such as surface area, cortical thickness, and cortical volume.

磁共振成像(MRI)上皮层区域的划分对于了解人类大脑发育的复杂性非常重要。墨尔本儿童区域婴儿脑(M-CRIB-S)的前一版本(Adamson 等人,《科学报告》,10(1), 10, 2020 年)是一个软件包,可对新生儿大脑进行全脑分割、皮质表面提取和划分。M-CRIB-S 中可用的皮层划分方案分别是与成人兼容的每半球 34 个区域的 Desikan-Killiany(DK)方案和每半球 31 个区域的 Desikan-Killiany-Tourville(DKT)方案。我们对软件包进行了重大更新,以实现两个目标:1)使基于体素的分割输出与 Freesurfer 的 M-CRIB 方案兼容;2)提高全脑分割和皮层表面提取的准确性。皮质表面提取已得到改进,增加了额外的步骤,以提高内表面对薄回旋的穿透力。结果表明,改进后的皮质表面提取提高了表面积、皮质厚度和皮质体积等测量指标的稳健性。
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引用次数: 0
Decentralized Mixed Effects Modeling in COINSTAC. COINSTAC 中的分散混合效应建模。
IF 3 4区 医学 Q2 Computer Science Pub Date : 2024-04-01 Epub Date: 2024-03-01 DOI: 10.1007/s12021-024-09657-7
Sunitha Basodi, Rajikha Raja, Harshvardhan Gazula, Javier Tomas Romero, Sandeep Panta, Thomas Maullin-Sapey, Thomas E Nichols, Vince D Calhoun

Performing group analysis on magnetic resonance imaging (MRI) data with linear mixed-effects (LME) models is challenging due to its large dimensionality and inherent multi-level covariance structure. In addition, as large-scale collaborative projects become commonplace in neuroimaging, data must increasingly be stored and analyzed from different locations. In such settings, substantial overhead can occur in terms of data transfer and coordination between participating research groups. In some cases, data cannot be pooled together due to privacy or regulatory concerns. In this work, we propose a decentralized LME model to perform a large-scale analysis of data from different collaborations without data pooling. This method is efficient as it overcomes the hurdles of data sharing and has lower bandwidth and memory requirements for analysis than the centralized modeling approach. We evaluate our model using features extracted from structural magnetic resonance imaging (sMRI) data. Results highlight gray matter reductions in the temporal lobe/insula and medial frontal regions in schizophrenia, consistent with prior studies. Our analysis also demonstrates that decentralized LME models achieve similar performance compared to the models trained with all the data in one location. We also implement the decentralized LME approach in COINSTAC, an open source, decentralized platform for federating neuroimaging analysis, providing an easy to use tool for dissemination to the neuroimaging community.

使用线性混合效应(LME)模型对磁共振成像(MRI)数据进行分组分析具有很大的挑战性,因为它的维度很大,而且具有固有的多级协方差结构。此外,随着大规模合作项目在神经成像领域的普及,数据必须越来越多地从不同地点存储和分析。在这种情况下,参与研究小组之间的数据传输和协调可能会产生大量开销。在某些情况下,出于隐私或监管方面的考虑,数据不能集中在一起。在这项工作中,我们提出了一种分散式 LME 模型,可以在不进行数据汇集的情况下对来自不同合作机构的数据进行大规模分析。与集中式建模方法相比,这种方法克服了数据共享的障碍,分析所需的带宽和内存也更低,因此非常高效。我们使用从结构性磁共振成像(sMRI)数据中提取的特征来评估我们的模型。结果显示,精神分裂症患者的颞叶/半岛和内侧额叶区域灰质减少,这与之前的研究结果一致。我们的分析还表明,分散式 LME 模型与使用一个位置的所有数据训练的模型相比,具有相似的性能。我们还在 COINSTAC 中实现了分散式 LME 方法,COINSTAC 是一个开源、分散的神经影像分析联合平台,为神经影像社区的传播提供了一个易于使用的工具。
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引用次数: 0
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