De-Individualizing fMRI Signals via Mahalanobis Whitening and Bures Geometry
Aaron Jacobson, Tingting Dan, Martin Styner, Guorong Wu, Shahar Kovalsky, Caroline Moosmueller
功能连接已被广泛研究,以在临床研究和基于成像的神经科学中了解大脑疾病,并且分析功能连接的变化已被证明对于理解和计算评估疾病或实验刺激对大脑功能的影响是有价值的。 通过使用减少尺寸算法之前的Mahanobis数据美白,我们能够从fMRI信号中提炼出有关受试者和用于提示他们的实验刺激的有意义的信息。 此外,我们还将Mahalanibis美白解释为数据的两阶段去个性化,其动机与与量子力学相连的Bures距离相似。 这些方法有可能帮助发现将大脑功能与认知和行为联系起来的机制,并可能提高阿尔茨海默氏症诊断的准确性和一致性,特别是在疾病进展的临床前阶段。
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 subjec...
