COINSTAC 中的分散混合效应建模。

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics 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
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引用次数: 0

摘要

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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Decentralized Mixed Effects Modeling in COINSTAC.

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.

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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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