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Generative Modelling of Cortical Receptor Distributions from Cytoarchitectonic Images in the Macaque Brain. 根据猕猴大脑的细胞结构图像建立皮质受体分布生成模型
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-01 Epub Date: 2024-07-08 DOI: 10.1007/s12021-024-09673-7
Ahmed Nebli, Christian Schiffer, Meiqi Niu, Nicola Palomero-Gallagher, Katrin Amunts, Timo Dickscheid

Neurotransmitter receptor densities are relevant for understanding the molecular architecture of brain regions. Quantitative in vitro receptor autoradiography, has been introduced to map neurotransmitter receptor distributions of brain areas. However, it is very time and cost-intensive, which makes it challenging to obtain whole-brain distributions. At the same time, high-throughput light microscopy and 3D reconstructions have enabled high-resolution brain maps capturing measures of cell density across the whole human brain. Aiming to bridge gaps in receptor measurements for building detailed whole-brain atlases, we study the feasibility of predicting realistic neurotransmitter density distributions from cell-body stainings. Specifically, we utilize conditional Generative Adversarial Networks (cGANs) to predict the density distributions of the M2 receptor of acetylcholine and the kainate receptor for glutamate in the macaque monkey's primary visual (V1) and motor cortex (M1), based on light microscopic scans of cell-body stained sections. Our model is trained on corresponding patches from aligned consecutive sections that display cell-body and receptor distributions, ensuring a mapping between the two modalities. Evaluations of our cGANs, both qualitative and quantitative, show their capability to predict receptor densities from cell-body stained sections while maintaining cortical features such as laminar thickness and curvature. Our work underscores the feasibility of cross-modality image translation problems to address data gaps in multi-modal brain atlases.

神经递质受体密度与了解大脑区域的分子结构息息相关。定量体外受体自显影技术已被引入绘制脑区神经递质受体分布图。然而,这种方法非常耗费时间和成本,因此获得全脑分布图具有挑战性。与此同时,高通量光学显微镜和三维重建技术已能绘制高分辨率脑图,测量整个人脑的细胞密度。为了弥补受体测量方面的差距,建立详细的全脑图谱,我们研究了从细胞体染色预测现实神经递质密度分布的可行性。具体来说,我们根据细胞体染色切片的光镜扫描,利用条件生成对抗网络(cGANs)来预测猕猴初级视觉(V1)和运动皮层(M1)中乙酰胆碱的 M2 受体和谷氨酸的 kainate 受体的密度分布。我们的模型是在显示细胞体和受体分布的对齐连续切片的相应斑块上进行训练的,以确保两种模式之间的映射。对我们的 cGANs 进行的定性和定量评估表明,它们有能力从细胞体染色切片中预测受体密度,同时保持皮层特征,如层厚度和曲率。我们的工作强调了跨模态图像转换问题在解决多模态脑图谱数据缺口方面的可行性。
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引用次数: 0
An Automated Tool to Classify and Transform Unstructured MRI Data into BIDS Datasets. 将非结构化核磁共振成像数据分类和转换为 BIDS 数据集的自动工具。
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-01 Epub Date: 2024-03-26 DOI: 10.1007/s12021-024-09659-5
Alexander Bartnik, Sujal Singh, Conan Sum, Mackenzie Smith, Niels Bergsland, Robert Zivadinov, Michael G Dwyer

The increasing use of neuroimaging in clinical research has driven the creation of many large imaging datasets. However, these datasets often rely on inconsistent naming conventions in image file headers to describe acquisition, and time-consuming manual curation is necessary. Therefore, we sought to automate the process of classifying and organizing magnetic resonance imaging (MRI) data according to acquisition types common to the clinical routine, as well as automate the transformation of raw, unstructured images into Brain Imaging Data Structure (BIDS) datasets. To do this, we trained an XGBoost model to classify MRI acquisition types using relatively few acquisition parameters that are automatically stored by the MRI scanner in image file metadata, which are then mapped to the naming conventions prescribed by BIDS to transform the input images to the BIDS structure. The model recognizes MRI types with 99.475% accuracy, as well as a micro/macro-averaged precision of 0.9995/0.994, a micro/macro-averaged recall of 0.9995/0.989, and a micro/macro-averaged F1 of 0.9995/0.991. Our approach accurately and quickly classifies MRI types and transforms unstructured data into standardized structures with little-to-no user intervention, reducing the barrier of entry for clinical scientists and increasing the accessibility of existing neuroimaging data.

随着神经成像技术在临床研究中的应用日益广泛,许多大型成像数据集应运而生。然而,这些数据集往往依赖于图像文件头中不一致的命名约定来描述采集情况,因此必须进行耗时的人工整理。因此,我们试图将磁共振成像(MRI)数据的分类和整理过程自动化,使其符合临床常规的采集类型,并将原始、非结构化图像自动转换为脑成像数据结构(BIDS)数据集。为此,我们训练了一个 XGBoost 模型,利用核磁共振扫描仪自动存储在图像文件元数据中的相对较少的采集参数对核磁共振成像采集类型进行分类,然后将这些参数映射到 BIDS 规定的命名约定,将输入图像转换为 BIDS 结构。该模型识别磁共振成像类型的准确率为 99.475%,微观/宏观平均精确度为 0.9995/0.994,微观/宏观平均召回率为 0.9995/0.989,微观/宏观平均 F1 为 0.9995/0.991。我们的方法能准确、快速地对核磁共振成像类型进行分类,并将非结构化数据转化为标准化结构,几乎不需要用户干预,从而降低了临床科学家的入门门槛,提高了现有神经成像数据的可访问性。
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引用次数: 0
Where Top-Down Meets Bottom-Up: Cell-Type Specific Connectivity Map of the Whisker System. 自上而下与自下而上的结合:胡须系统的细胞类型特异性连接图
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-01 Epub Date: 2024-05-20 DOI: 10.1007/s12021-024-09658-6
Nicolas Rault, Tido Bergmans, Natasja Delfstra, Bisley J Kleijnen, Fleur Zeldenrust, Tansu Celikel

Sensorimotor computation integrates bottom-up world state information with top-down knowledge and task goals to form action plans. In the rodent whisker system, a prime model of active sensing, evidence shows neuromodulatory neurotransmitters shape whisker control, affecting whisking frequency and amplitude. Since neuromodulatory neurotransmitters are mostly released from subcortical nuclei and have long-range projections that reach the rest of the central nervous system, mapping the circuits of top-down neuromodulatory control of sensorimotor nuclei will help to systematically address the mechanisms of active sensing. Therefore, we developed a neuroinformatic target discovery pipeline to mine the Allen Institute's Mouse Brain Connectivity Atlas. Using network connectivity analysis, we identified new putative connections along the whisker system and anatomically confirmed the existence of 42 previously unknown monosynaptic connections. Using this data, we updated the sensorimotor connectivity map of the mouse whisker system and developed the first cell-type-specific map of the network. The map includes 157 projections across 18 principal nuclei of the whisker system and neuromodulatory neurotransmitter-releasing. Performing a graph network analysis of this connectome, we identified cell-type specific hubs, sources, and sinks, provided anatomical evidence for monosynaptic inhibitory projections into all stages of the ascending pathway, and showed that neuromodulatory projections improve network-wide connectivity. These results argue that beyond the modulatory chemical contributions to information processing and transfer in the whisker system, the circuit connectivity features of the neuromodulatory networks position them as nodes of sensory and motor integration.

感觉运动计算将自下而上的世界状态信息与自上而下的知识和任务目标整合在一起,从而形成行动计划。啮齿动物的胡须系统是主动感知的主要模型,有证据表明神经调节神经递质会影响胡须的控制,影响胡须的频率和振幅。由于神经调节神经递质大多从皮层下核团释放,并有长程投射到达中枢神经系统的其他部分,因此绘制自上而下神经调节控制感觉运动核团的回路图将有助于系统地研究主动感知的机制。因此,我们开发了一个神经信息目标发现管道,以挖掘艾伦研究所的小鼠脑连接图谱。通过网络连通性分析,我们确定了胡须系统的新推定连接,并从解剖学角度证实了 42 个以前未知的单突触连接的存在。利用这些数据,我们更新了小鼠胡须系统的感觉运动连接图谱,并绘制了第一张细胞类型特异性网络图。该图谱包括横跨胡须系统 18 个主要神经核和神经调节神经递质释放的 157 个投射。通过对该连接组进行图网络分析,我们确定了细胞类型特异性枢纽、源和汇,提供了单突触抑制性投射进入上升通路所有阶段的解剖学证据,并表明神经调节性投射改善了整个网络的连接性。这些结果表明,除了胡须系统中信息处理和传递的化学调节贡献外,神经调节网络的电路连接特征也使它们成为感觉和运动整合的节点。
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引用次数: 0
CADENCE - Neuroinformatics Tool for Supervised Calcium Events Detection. CADENCE - 用于监督钙事件检测的神经信息学工具。
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-01 DOI: 10.1007/s12021-024-09677-3
Nikolay Aseyev, Anastasia Borodinova, Svetlana Pavlova, Marina Roshchina, Matvey Roshchin, Evgeny Nikitin, Pavel Balaban

CADENCE is an open Python 3-written neuroinformatics tool with Qt6 graphic user interface for supervised calcium events detection. In neuronal ensembles recording during calcium imaging experiments, the output of instruments such as Celena X, Zeiss LSM 5 Live confocal microscope and Miniscope is a movie showing flashing cells somata. There are few pipelines to convert video to relative fluorescence ΔF/F, from simplest ImageJ plugins to sophisticated tools like MiniAn (Dong et al. in Elife 11, https://doi.org/10.7554/eLife.70661 , 2022). Minian, an open-source miniscope analysis pipeline. Elife, 11.). While in some areas of study relative fluorescence ΔF/F may be the desired result in itself, researchers of neuronal ensembles are typically interested in a more detailed analysis of calcium events as indirect proxy of neuronal electrical activity. For such analyses, researchers need a tool to infer calcium events from the continuous ΔF/F curve in order to create a raster representation of calcium events for later use in analysis software, such as Elephant (Denker, M., Yegenoglu, A., & Grün, S. (2018). Collaborative HPC-enabled workflows on the HBP Collaboratory using the Elephant framework. Neuroinformatics, 19.). Here we present such an open tool with supervised calcium events detection.

CADENCE 是一个开放的 Python 3 编写的神经信息学工具,具有 Qt6 图形用户界面,用于监督钙事件检测。在钙成像实验中记录神经元群时,Celena X、Zeiss LSM 5 Live 共聚焦显微镜和 Miniscope 等仪器的输出是显示闪烁细胞体的视频。从最简单的ImageJ插件到像MiniAn(Dong等人,载于Elife 11, https://doi.org/10.7554/eLife.70661, 2022)这样复杂的工具,将视频转换为相对荧光ΔF/F的管道并不多。Minian是一个开源的miniscope分析管道。Elife,11)。虽然在某些研究领域,相对荧光 ΔF/F 本身可能就是所需的结果,但神经元集合的研究人员通常对作为神经元电活动间接替代物的钙事件的更详细分析感兴趣。为了进行此类分析,研究人员需要一种工具来从连续的ΔF/F曲线中推断钙事件,以便创建钙事件的栅格表示,供以后的分析软件使用,如 Elephant(Denker,M.,Yegenoglu,A. & Grün, S. (2018)。使用大象框架的 HBP 协作实验室上的协作式 HPC-enabled 工作流。Neuroinformatics, 19.).在此,我们将介绍这样一款具有监督钙事件检测功能的开放式工具。
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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, INTERDISCIPLINARY APPLICATIONS 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, INTERDISCIPLINARY APPLICATIONS 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, INTERDISCIPLINARY APPLICATIONS 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, INTERDISCIPLINARY APPLICATIONS 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, INTERDISCIPLINARY APPLICATIONS 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
Age Prediction Using Resting-State Functional MRI. 利用静息状态功能磁共振成像预测年龄
IF 3 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-04-01 Epub Date: 2024-02-11 DOI: 10.1007/s12021-024-09653-x
Jose Ramon Chang, Zai-Fu Yao, Shulan Hsieh, Torbjörn E M Nordling

The increasing lifespan and large individual differences in cognitive capability highlight the importance of comprehending the aging process of the brain. Contrary to visible signs of bodily ageing, like greying of hair and loss of muscle mass, the internal changes that occur within our brains remain less apparent until they impair function. Brain age, distinct from chronological age, reflects our brain's health status and may deviate from our actual chronological age. Notably, brain age has been associated with mortality and depression. The brain is plastic and can compensate even for severe structural damage by rewiring. Functional characterization offers insights that structural cannot provide. Contrary to the multitude of studies relying on structural magnetic resonance imaging (MRI), we utilize resting-state functional MRI (rsfMRI). We also address the issue of inclusion of subjects with abnormal brain ageing through outlier removal. In this study, we employ the Least Absolute Shrinkage and Selection Operator (LASSO) to identify the 39 most predictive correlations derived from the rsfMRI data. The data is from a cohort of 176 healthy right-handed volunteers, aged 18-78 years (95/81 male/female, mean age 48, SD 17) collected at the Mind Research Imaging Center at the National Cheng Kung University. We establish a normal reference model by excluding 68 outliers, which achieves a leave-one-out mean absolute error of 2.48 years. By asking which additional features that are needed to predict the chronological age of the outliers with a smaller error, we identify correlations predictive of abnormal aging. These are associated with the Default Mode Network (DMN). Our normal reference model has the lowest prediction error among published models evaluated on adult subjects of almost all ages and is thus a candidate for screening for abnormal brain aging that has not yet manifested in cognitive decline. This study advances our ability to predict brain aging and provides insights into potential biomarkers for assessing brain age, suggesting that the role of DMN in brain aging should be studied further.

寿命的延长和认知能力的巨大个体差异凸显了了解大脑衰老过程的重要性。与头发变白、肌肉减少等身体老化的明显迹象不同,大脑内部发生的变化在损害功能之前并不明显。脑龄与实际年龄不同,它反映了大脑的健康状况,可能与实际年龄有偏差。值得注意的是,脑龄与死亡率和抑郁症有关。大脑具有可塑性,甚至可以通过重新布线来补偿严重的结构性损伤。功能表征提供了结构表征无法提供的洞察力。与众多依赖结构磁共振成像(MRI)的研究相反,我们利用静息态功能磁共振成像(rsfMRI)。我们还通过剔除离群值解决了纳入大脑异常老化受试者的问题。在本研究中,我们采用最小绝对收缩和选择操作器(LASSO)从 rsfMRI 数据中识别出 39 种最具预测性的相关性。数据来自成大心灵研究影像中心收集的 176 名健康右撇子志愿者,年龄在 18-78 岁之间(95/81 男/女,平均年龄 48 岁,SD 17)。通过排除 68 个异常值,我们建立了一个正常参考模型,其平均绝对误差为 2.48 岁。通过询问需要哪些额外特征才能以较小误差预测异常值的年代年龄,我们确定了预测异常衰老的相关性。这些特征与默认模式网络(DMN)有关。在对几乎所有年龄段的成年受试者进行的评估中,我们的正常参考模型的预测误差最小,因此是筛查尚未表现为认知能力衰退的大脑异常衰老的候选模型。这项研究提高了我们预测大脑衰老的能力,并为评估大脑年龄的潜在生物标志物提供了见解,这表明应该进一步研究 DMN 在大脑衰老中的作用。
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Neuroinformatics
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