  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.
