Functional connectivity has been widely investigated to understand brain disease in clinical studies and imaging-based neuroscience, and analyzing changes in functional connectivity has proven to be valuable for understanding and computationally evaluating the effects on brain function caused by diseases or experimental stimuli. By using Mahalanobis data whitening prior to the use of dimensionality reduction algorithms, we are able to distill meaningful information from fMRI signals about subjects and the experimental stimuli used to prompt them. Furthermore, we offer an interpretation of Mahalanobis whitening as a two-stage de-individualization of data which is motivated by similarity as captured by the Bures distance, which is connected to quantum mechanics. These methods have potential to aid discoveries about the mechanisms that link brain function with cognition and behavior and may improve the accuracy and consistency of Alzheimer's diagnosis, especially in the preclinical stage of disease progression.

翻译：功能连接性在临床研究与影像神经科学中已被广泛用于探究脑部疾病，分析功能连接性的变化对于理解和计算评估疾病或实验刺激对脑功能的影响具有重要价值。通过在应用降维算法前进行马氏数据白化处理，我们能够从fMRI信号中提取关于受试者及所用实验刺激的有效信息。进一步地，我们将马氏白化解释为基于Bures距离（该距离与量子力学相关）所度量的相似性驱动的两阶段数据去个体化过程。这些方法有望助力揭示脑功能与认知行为之间的关联机制，并可能提升阿尔茨海默病诊断（尤其在疾病进展的临床前阶段）的准确性与一致性。