Background and objective

In medical imaging, population studies have to overcome the differences that exist between individuals to identify invariant image features that can be used for diagnosis purposes. In functional neuroimaging, an appealing solution to identify neural coding principles that hold at the population level is inter-subject pattern analysis, i.e. to learn a predictive model on data from multiple subjects and evaluate its generalization performance on new subjects. Although it has gained popularity in recent years, its widespread adoption is still hampered by the blatant lack of a formal definition in the literature. In this paper, we precisely introduce the first principled formalization of inter-subject pattern analysis targeted at multivariate group analysis of functional neuroimaging.

Methods

We propose to frame inter-subject pattern analysis as a multi-source transductive transfer question, thus grounding it within several well defined machine learning settings and broadening the spectrum of usable algorithms. We describe two sets of inter-subject brain decoding experiments that use several open datasets: a magneto-encephalography study with 16 subjects and a functional magnetic resonance imaging paradigm with 100 subjects. We assess the relevance of our framework by performing model comparisons, where one brain decoding model exploits our formalization while others do not.

Results

The first set of experiments demonstrates the superiority of a brain decoder that uses subject-by-subject standardization compared to state of the art models that use other standardization schemes, making the case for the interest of the transductive and the multi-source components of our formalization The second set of experiments quantitatively shows that, even after such transformation, it is more difficult for a brain decoder to generalize to new participants rather than to new data from participants available in the training phase, thus highlighting the transfer gap that needs to be overcome.

Conclusion

This paper describes the first formalization of inter-subject pattern analysis as a multi-source transductive transfer learning problem. We demonstrate the added value of this formalization using proof-of-concept experiments on several complementary functional neuroimaging datasets. This work should contribute to popularize inter-subject pattern analysis for functional neuroimaging population studies and pave the road for future methodological innovations.

中文翻译：

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受试者间模式分析用于功能性神经影像的多变量组分析。统一的形式化

背景和目标

在医学成像中，人群研究必须克服个体之间存在的差异，以识别可用于诊断目的的不变图像特征。在功能性神经影像学中，识别人群总体中的神经编码原理的一种有吸引力的解决方案是受试者间模式分析，即学习来自多个受试者的数据的预测模型并评估其在新受试者上的泛化性能。尽管近年来它已变得流行，但由于在文献中公然缺乏正式定义，它的广泛采用仍然受到阻碍。在本文中，我们精确地介绍了针对功能性神经影像的多组分析的受试者间模式分析的第一个原则形式化。

方法

我们建议将主题间模式分析框架化为多源转导传递问题，从而将其基于几个定义明确的机器学习设置中，并拓宽可用算法的范围。我们描述了使用几个开放数据集的两组受试者间脑部解码实验：包含16位受试者的磁脑图研究和包含100位受试者的功能性磁共振成像范例。我们通过执行模型比较来评估我们框架的相关性，其中一种大脑解码模型利用了我们的形式化，而另一种则没有。

结果

第一组实验证明，与使用其他标准化方案的最新模型相比，使用逐项标准化的大脑解码器具有优越性，这为我们的转导性和多源组分的利益提供了理由形式化第二组实验定量地表明，即使经过这样的转换，大脑解码器也很难推广到新的参与者，而不是训练阶段可用的参与者的新数据，因此突出了需要克服的转移差距。克服。

结论

本文将主体间模式分析的首次形式化描述为一种多源转导转移学习问题。我们在几个互补的功能性神经影像数据集上使用概念验证实验证明了这种形式化的附加价值。这项工作应有助于普及功能性神经影像人群研究的受试者间模式分析，并为未来的方法学创新铺平道路。