Improved neuroimaging meta analyses with modern text based processing

通过基于现代文本的处理改进神经影像元分析

• 批准号: RGPIN-2021-03543
• 负责人: Poline, JeanBaptiste
• 金额: $ 1.75万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764429
• 关键词: Improved; neuroimaging; meta; analyses; modern

The scientific community relies heavily on meta-analyses to establish trustworthy results in a rapidly growing body of literature. Individual studies are subject to biases, reporting limitations, or lack of power, all of which are difficult to assess. Meta-analyses are key to reach consensus and extract solid information from the literature in life sciences and neuroscience. As the number of scientific publications is rapidly increasing, false positive findings are increasing as well. Because neuroimaging experiments are costly, the number of participants included in studies often provides limited power. Current meta-analysis tools -such as Neurosynth or NeuroQuery- are able to automatically process a large (~14,000) corpus of neuroimaging publications from which the brain activity coordinates have been extracted, and provide a way to compute brain maps associated with the publication labelled terms. These tools do not account for the methods used in the manuscript, even though it has been demonstrated that analysis pipelines have a major impact on neuroimaging results. Advanced methods in text analysis and natural language processing should not only permit better bibliometrics but also improved automatized analysis of the literature. We propose to explore ways to improve meta-analyses in neuroimaging with advanced text mining analysis of the literature. Our first task is to automatically extract labels or feature vector representations of both the neuroimaging domains and the data processing and analysis methods for each publication in a large corpus. Once articles have been annotated with labels for neuroimaging methods, we propose to analyse the impact of a given method by comparing meta-analysis results from articles using or not this specific method (e.g., cluster size test, specific type of movement correction for functional MRI analysis). We will also study the temporal evolution of results with the year of publication and assess if there is a link with the emergence of new methods, and propose correction strategies for those neuroimaging methods that introduce bias and/or variance in meta-analyses. Finally, we will use the meta-analysis results to robustify and improve analysis of new studies with limited sample sizes, and employ a very large neuroimaging dataset to validate the use of meta-analytic maps as priors to regularize statistical and prediction results.

科学界在很大程度上依赖于荟萃分析，以在快速增长的文献中建立可信的结果。个别研究可能存在偏见、报告局限性或缺乏效力，所有这些都难以评估。荟萃分析是从生命科学和神经科学的文献中达成共识和提取可靠信息的关键。随着科学出版物数量的迅速增加，假阳性结果也在增加。由于神经成像实验成本高昂，研究参与者的数量往往有限。目前的荟萃分析工具，如Neurosynth或NeuroQuery，能够自动处理大量（约14000）神经影像学出版物的语料库，从中提取大脑活动坐标，并提供一种计算与出版物标记术语相关的大脑图的方法。这些工具没有考虑到手稿中使用的方法，尽管已经证明分析管道对神经成像结果有重大影响。文本分析和自然语言处理的先进方法不仅可以实现更好的文献计量学，而且可以改进文献的自动化分析。我们建议通过文献的高级文本挖掘分析来探索改进神经影像学元分析的方法。我们的第一个任务是自动提取神经成像领域的标签或特征向量表示，以及大型语料库中每个出版物的数据处理和分析方法。一旦文章标注了神经成像方法的标签，我们建议通过比较使用或不使用该特定方法的文章的荟萃分析结果来分析给定方法的影响（例如，聚类大小测试，用于功能MRI分析的特定类型的运动校正）。我们还将研究结果随出版年份的时间演变，并评估是否与新方法的出现有关，并对那些在meta分析中引入偏倚和/或方差的神经影像学方法提出纠正策略。最后，我们将使用meta分析结果来巩固和改进有限样本量的新研究分析，并使用一个非常大的神经成像数据集来验证meta分析图作为正则化统计和预测结果的先验使用。