Precisely identifying sulcal features in brain MRI is made challenging by the
variability of brain folding. This research introduces an innovative 3D
explainability frame-work that validates outputs from deep learning networks in
their ability to detect the paracingulate sulcus, an anatomical feature that
may or may not be present on the frontal medial surface of the human brain.
This study trained and tested two networks, amalgamating local explainability
techniques GradCam and SHAP with a dimensionality reduction method. The
explainability framework provided both localized and global explanations, along
with accuracy of classification results, revealing pertinent sub-regions
contributing to the decision process through a post-fusion transformation of
explanatory and statistical features. Leveraging the TOP-OSLO dataset of MRI
acquired from patients with schizophrenia, greater accuracies of paracingulate
sulcus detection (presence or absence) were found in the left compared to right
hemispheres with distinct, but extensive sub-regions contributing to each
classification outcome. The study also inadvertently highlighted the critical
role of an unbiased annotation protocol in maintaining network performance
fairness. Our proposed method not only offers automated, impartial annotations
of a variable sulcus but also provides insights into the broader anatomical
variations associated with its presence throughout the brain. The adoption of
this methodology holds promise for instigating further explorations and
inquiries in the field of neuroscience.

本研究介绍了一种创新的 3D 可解释性框架，通过将本地可解释性技术 GradCam 和 SHAP 与降维方法相结合，验证了深度学习网络在检测前额媒体表面可能存在的解剖特征 —— 副扣带沟，该框架通过解释和统计特征的后融合转换，提供了局部和全局解释以及分类结果的准确性，揭示了对决策过程有贡献的相关子区域。利用来自精神分裂症患者的 TOP-OSLO MRI 数据集，发现了左侧大脑半球中副扣带沟存在与否的检测正确率较右侧更高，且各分类结果有各自明显的子区域贡献。同时，本研究无意中强调了公正的注释协议在维护网络性能公平性方面的关键作用。我们提出的方法不仅能自动、公正地注释可变的沟，还能深入了解其在整个脑部中存在的广泛解剖变化，该方法的采用有望在神经科学领域引发进一步的探索和研究。