In the field of neuroimaging reverse inferences can lead us to suppose the involvement of cognitive processes from certain patterns of brain activity. However, the same reasoning holds if we substitute “brain activity” with “brain alteration” and “cognitive process” with “brain disorder.” The fact that different brain disorders exhibit a high degree of overlap in their patterns of structural alterations makes forward inference‐based analyses less suitable for identifying brain areas whose alteration is specific to a certain pathology. In the forward inference‐based analyses, in fact, it is impossible to distinguish between areas that are altered by the majority of brain disorders and areas that are specifically affected by certain diseases. To address this issue and allow the identification of highly pathology‐specific altered areas we used the Bayes' factor technique, which was employed, as a proof of concept, on voxel‐based morphometry data of schizophrenia and Alzheimer's disease. This technique allows to calculate the ratio between the likelihoods of two alternative hypotheses (in our case, that the alteration of the voxel is specific for the brain disorder under scrutiny or that the alteration is not specific). We then performed temporal simulations of the alterations' spread associated with different pathologies. The Bayes' factor values calculated on these simulated data were able to reveal that the areas, which are more specific to a certain disease, are also the ones to be early altered. This study puts forward a new analytical instrument capable of innovating the methodological approach to the investigation of brain pathology.

中文翻译：

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通过贝叶斯反向推理发现大脑结构改变的特异性。

在神经影像学领域，反向推论可以让我们假设认知过程涉及某些大脑活动模式。然而，如果我们用“大脑改变”代替“大脑活动”，用“大脑障碍”代替“认知过程”，同样的推理也成立。不同的大脑疾病在其结构改变模式中表现出高度重叠的事实使得基于前向推理的分析不太适合识别其改变特定于某种病理的大脑区域。事实上，在基于前向推理的分析中，无法区分被大多数脑部疾病改变的区域和受某些疾病特别影响的区域。为了解决这个问题并允许识别高度病理特异性的改变区域，我们使用了贝叶斯因子技术，作为概念验证，该技术被用于精神分裂症和阿尔茨海默病的基于体素的形态测量数据。这种技术允许计算两个替代假设的可能性之间的比率（在我们的例子中，体素的改变是特定于被审查的大脑疾病，或者改变不是特定的）。然后，我们对与不同病理相关的改变传播进行了时间模拟。根据这些模拟数据计算的贝叶斯因子值能够揭示对某种疾病更具体的区域也是早期改变的区域。