通过马氏白化与布雷斯几何，消除fMRI信号中的个体差异。
De-Individualizing fMRI Signals via Mahalanobis Whitening and Bures Geometry
- 用马氏白化预处理，分离出受试者与刺激因素的影响。
- 基于布雷斯距离的两阶段去个体化，提升信号可比性。
- 适合脑疾病机制研究与阿尔茨海默病早期诊断。
功能连接在临床研究和基于影像的神经科学中被广泛用于理解脑部疾病，分析功能连接的变化对揭示疾病或实验刺激对脑功能影响具有重要意义。通过在降维算法前使用马氏数据白化，我们能够从fMRI信号中提取出关于受试者和诱发刺激的有意义信息。此外，我们将马氏白化解释为一种基于布雷斯距离（Bures distance）相似性驱动的两阶段去个体化过程，该距离与量子力学相关。这些方法有助于揭示脑功能与认知、行为之间的联系机制，并可能提升阿尔茨海默病，尤其是其临床前期阶段诊断的准确性与一致性。
原文摘要 · Abstract (English)
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.
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