Modelling behavioural deficits based on structural lesion imaging is a popular approach to map functions in the human brain, and efforts to translationally apply lesion-behaviour modelling to predict post-stroke outcomes are on the rise. The high-dimensional complexity of lesion data, however, evokes challenges in both lesion behaviour mapping and post stroke outcome prediction. This paper aims to deepen the understanding of this complexity by reframing it from the perspective of causal and non-causal dependencies in the data, and by discussing what this complexity implies for different data modelling approaches. By means of theoretical discussion and empirical examination, several common strategies and views are challenged, and future research perspectives are outlined. A main conclusion is that lesion-behaviour inference is subject to a lesion-anatomical bias that cannot be overcome by using multivariate models or any other algorithm that is blind to causality behind relations in the data. This affects the validity of lesion behaviour mapping and might even wrongfully identify paradoxical effects of lesion-induced functional facilitation - but, as this paper argues, only to a minor degree. Thus, multivariate lesion-brain inference appears to be a valuable tool to deepen our understanding of the human brain, but only because it takes into account the functional relation between brain areas. The perspective of causality and inter-variable dependence is further used to point out challenges in improving lesion behaviour models. Firstly, the dependencies in the data open up different possible strategies of data reduction, and considering those might improve post-stroke outcome prediction. Secondly, the role of non-topographical causal predictors of post stroke behaviour is discussed. The present article argues that, given these predictors, different strategies are required in the evaluation of model quality in lesion behaviour mapping and post stroke outcome prediction.

中文翻译：

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重新思考脑病变行为推断中的因果关系和数据复杂性及其对病变行为建模的影响。

基于结构性病变成像的行为缺陷建模是一种在人脑中绘制功能图的流行方法，并且在翻译上应用病变行为建模以预测中风后结局的工作也在不断增加。但是，病变数据的高维复杂性在病变行为映射和中风后结果预测方面都提出了挑战。本文旨在通过从数据中因果关系和非因果关系的角度重新定义这种复杂性，并讨论这种复杂性对不同数据建模方法的含义，从而加深对这种复杂性的理解。通过理论讨论和实证检验，挑战了几种常见的策略和观点，并概述了未来的研究前景。一个主要结论是，病变行为推断会受到病变解剖学偏见的影响，而使用多变量模型或任何其他对数据关系背后的因果关系视而不见的算法都无法克服。这会影响病灶行为映射的有效性，甚至可能错误地识别病灶诱发的功能促进的悖论效应-但是，正如本文所论证的那样，这只是很小的程度。因此，多元病变脑推断似乎是加深我们对人脑理解的一种有价值的工具，但这仅仅是因为它考虑了脑区域之间的功能关系。因果关系和变量间相关性的观点被进一步用来指出改善病变行为模型的挑战。首先，数据中的依存关系开辟了不同的数据缩减可能策略，并考虑到这些可能会改善卒中后结果的预测。其次，讨论了卒中后行为的非地形因果预测因素的作用。本文认为，鉴于这些预测因素，在病变行为图谱和卒中后结果预测中评估模型质量需要不同的策略。