METEOR – Mastering the oppressive number of forking paths unfolded by noisy and complex neural data

METEOR â 掌握由嘈杂且复杂的神经数据展开的大量分叉路径

• 批准号: 464552782
• 负责人: Professor Dr. Stefan Debener
• 金额: --
• 依托单位: Abteilung Biologische Psychologie
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/464552782?language=en
• 关键词: METEOR; Mastering; oppressive; number; forking

There is a replication crisis and a “real-world or the lab” dilemma in psychology and cognitive neuroscience. Solving the dilemma and overcoming the crisis at the same time is arguably a serious challenge. One of the main aims in cognitive neuroscience is to discover brain-cognition associations which are replicable across laboratories. A precondition for replicability of individual differences findings in terms of brain-cognition associations obtained inside or outside of the laboratory is rank order stability of neural parameters derived from noisy and complex signal recordings. However, to date we do not know well enough how much hitherto unsuccessful replications are due to the oppressive number of methodological decisions researchers have to make á priori to testing a brain-cognition association. Moreover, we do not yet have standards with respect to the unit of analysis at which replications should be considered successful. We also lack a knowledge app containing a systematic and exhaustive overview of potential methodological choices that are defensible in a typical individual differences analysis workflow for mobile EEG or fMRI, as well as multivariate behavioral data. Thus, laboratories still stick to their customized choices which are sometimes passed over through many generations of young scientist. But – as described in the very recent literature – variability of workflows and of associated substantial findings is huge across laboratories. Finally, hitherto proposed statistical approaches for analyzing the multiverse of potentially constructed datasets for noisy and highly complex multidimensional neural data need extensions through tools available for big data analysis. Such approaches would allow learning about influential decisions and would predict potential heterogeneity of future findings. To take a large step toward filling these gaps, METEOR aims to bring together a larger group of scientists with different and complementary expertise (cognitive neuroscientists using mobile EEG methodology, network neuroscientists working with fMRI data and statisticians experienced with big data analyses tools). By joining forces and a fruitful environment of a collaborative research programme, METEOR will provide standards on a replication success definition for cognitive neuroscience applicable across neuroimaging modalities. Furthermore, it will deliver systematized knowledge and analytic solutions for the multiverse in two neuroimaging modalities – mobile EEG and resting state fMRI – applied to the realm of assessiong individual differences and brain cognition associations. Proposed solutions will be discussed with respect to their applicability to further research questions in the future.

在心理学和认知神经科学中，存在复制危机和“真实世界或实验室”的两难境地。可以说，在解决困境的同时克服危机是一项严峻的挑战。认知神经科学的主要目标之一是发现可在不同实验室复制的大脑-认知关联。在实验室内外获得的大脑-认知关联方面的个体差异研究结果可复制的一个先决条件是，从嘈杂和复杂的信号记录中获得的神经参数的等级顺序稳定性。然而，到目前为止，我们还不太清楚，到目前为止，有多少不成功的复制是由于研究人员在测试大脑认知关联之前不得不做出的大量方法决定造成的。此外，我们还没有关于复制应被视为成功的分析单位的标准。我们也缺乏一个知识应用程序，其中包含对潜在方法选择的系统和详尽的概述，这些方法选择在移动EEG或fMRI的典型个体差异分析工作流程中是可以辩护的，以及多变量行为数据。因此，实验室仍然坚持他们定制的选择，而这些选择有时会被许多代年轻科学家忽略。但正如最近的文献所描述的那样，工作流程和相关实质性发现的可变性在不同实验室之间是巨大的。最后，迄今为止提出的用于分析噪声和高度复杂的多维神经数据的潜在构造数据集的多重宇宙的统计方法需要通过可用于大数据分析的工具进行扩展。这种方法将使人们能够了解有影响力的决策，并预测未来研究结果的潜在异质性。为了在填补这些空白方面迈出一大步，Meteor的目标是将更多具有不同和互补专业知识的科学家聚集在一起(使用移动EEG方法的认知神经科学家，使用功能磁共振数据的网络神经科学家，以及经验丰富的大数据分析工具的统计学家)。通过合作研究方案的力量和富有成效的环境，Meteor将为适用于各种神经成像方式的认知神经科学的复制成功定义提供标准。此外，它将通过两种神经成像方式-移动EEG和静息状态fMRI-为多元宇宙提供系统化的知识和分析解决方案，应用于评估个体差异和大脑认知关联的领域。将讨论提出的解决方案对今后进一步研究问题的适用性。