Amoroso Nicola, Quarto Silvano, La Rocca Marianna, Tangaro Sabina, Monaco Alfonso, Bellotti Roberto
Dipartimento di Farmacia-Scienze del Farmaco, Universitá degli Studi di Bari Aldo Moro, Bari, Italy.
Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy.
Front Aging Neurosci. 2023 Aug 31;15:1238065. doi: 10.3389/fnagi.2023.1238065. eCollection 2023.
The advent of eXplainable Artificial Intelligence (XAI) has revolutionized the way human experts, especially from non-computational domains, approach artificial intelligence; this is particularly true for clinical applications where the transparency of the results is often compromised by the algorithmic complexity. Here, we investigate how Alzheimer's disease (AD) affects brain connectivity within a cohort of 432 subjects whose T1 brain Magnetic Resonance Imaging data (MRI) were acquired within the Alzheimer's Disease Neuroimaging Initiative (ADNI). In particular, the cohort included 92 patients with AD, 126 normal controls (NC) and 214 subjects with mild cognitive impairment (MCI). We show how graph theory-based models can accurately distinguish these clinical conditions and how Shapley values, borrowed from game theory, can be adopted to make these models intelligible and easy to interpret. Explainability analyses outline the role played by regions like putamen, middle and superior temporal gyrus; from a class-related perspective, it is possible to outline specific regions, such as hippocampus and amygdala for AD and posterior cingulate and precuneus for MCI. The approach is general and could be adopted to outline how brain connectivity affects specific brain regions.
可解释人工智能（XAI）的出现彻底改变了人类专家，尤其是来自非计算领域的专家接触人工智能的方式；在临床应用中更是如此，因为算法复杂性常常会影响结果的透明度。在此，我们研究了阿尔茨海默病（AD）如何影响一组432名受试者的大脑连通性，这些受试者的T1脑磁共振成像数据（MRI）是在阿尔茨海默病神经影像倡议（ADNI）中获取的。具体而言，该队列包括92名AD患者、126名正常对照（NC）和214名轻度认知障碍（MCI）受试者。我们展示了基于图论的模型如何能够准确区分这些临床状况，以及如何采用从博弈论借鉴而来的夏普利值，使这些模型变得易懂且易于解释。可解释性分析概述了壳核、颞中回和颞上回等区域所起的作用；从与类别相关的角度来看，可以勾勒出特定区域，如AD患者的海马体和杏仁核，以及MCI患者的后扣带回和楔前叶。该方法具有通用性，可用于概述大脑连通性如何影响特定脑区。
