Improved neuroimaging meta analyses with modern text based processing

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。个别研究会受到偏见、报告限制或缺乏力量的影响，所有这些都难以评估。荟萃分析是从生命科学和神经科学文献中达成共识和提取可靠信息的关键。随着科学出版物的数量迅速增加，假阳性结果也在增加。由于神经影像学实验的成本很高，研究中参与者的数量往往提供有限的力量。目前的元分析工具-如Neurosynth或NeuroQuery-能够自动处理大量（约14，000）神经成像出版物的语料库，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地图的方法。这些工具并没有解释手稿中使用的方法，尽管已经证明分析管道对神经成像结果有重大影响。在文本分析和自然语言处理的先进方法，不仅允许更好的文献计量学，但也提高了自动化分析的文献。我们建议探讨如何提高荟萃分析在神经影像学与先进的文本挖掘分析的文献。我们的第一个任务是自动提取标签或特征向量表示的神经成像领域和数据处理和分析方法，为每个出版物在一个大的语料库。一旦文章被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这种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，簇大小测试、用于功能性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，并为那些在荟萃分析中引入偏倚和/或方差的神经影像学方法提出纠正策略。最后，我们将使用荟萃分析结果来增强和改进样本量有限的新研究的分析，并采用非常大的神经影像数据集来验证荟萃分析图作为先验的使用，以规范统计和预测结果。

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.