The significant features identified in a representative subset of the dataset during the learning process of an artificial intelligence model are referred to as a 'global' explanation. Three-dimensional (3D) global explanations are crucial in neuroimaging where a complex representational space demands more than basic two-dimensional interpretations. Curently, studies in the literature lack accurate, low-complexity, and 3D global explanations in neuroimaging and beyond. To fill this gap, we develop a novel explainable artificial intelligence (XAI) 3D-Framework that provides robust, faithful, and low-complexity global explanations. We evaluated our framework on various 3D deep learning networks trained, validated, and tested on a well-annotated cohort of 596 MRI images. The focus of detection was on the presence or absence of the paracingulate sulcus, a highly variable feature of brain topology associated with symptoms of psychosis. Our proposed 3D-Framework outperformed traditional XAI methods in terms of faithfulness for global explanations. As a result, these explanations uncovered new patterns that not only enhance the credibility and reliability of the training process but also reveal the broader developmental landscape of the human cortex. Our XAI 3D-Framework proposes for the first time, a way to utilize global explanations to discover the context in which detection of specific features are embedded, opening our understanding of normative brain development and atypical trajectories that can lead to the emergence of mental illness.

翻译：在人工智能模型学习过程中，从数据集的代表性子集中识别出的重要特征被称为"全局"解释。在神经影像学领域，复杂的表征空间需要超越基本二维解释的三维全局解释，这类解释至关重要。目前，文献研究在神经影像学及其他领域均缺乏精确、低复杂度且真正三维的全局解释方法。为填补这一空白，我们开发了一种新颖的可解释人工智能三维框架，该框架能够提供鲁棒、忠实且低复杂度的全局解释。我们在596张经过精细标注的MRI图像队列上训练、验证并测试了多种三维深度学习网络，并在此过程中评估了我们提出的框架。检测重点集中于旁扣带沟的存在与否——这是与精神病症状相关的脑拓扑结构中高度可变的特征。我们提出的三维框架在全局解释的忠实性方面优于传统可解释人工智能方法。因此，这些解释揭示了新的模式，不仅增强了训练过程的可信度与可靠性，同时展现了人类大脑皮层更广泛的发育图景。我们的可解释人工智能三维框架首次提出了一种利用全局解释来发现特定特征检测所处情境的方法，从而拓展了我们对规范性大脑发育以及可能导致精神疾病出现的非典型发展轨迹的理解。