发表机构
The University of Queensland; Mawlana Bhashani Science and Technology University（昆士兰大学; 毛拉纳·巴沙尼科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于稀疏投影残差网络的可解释深度学习框架，利用静息态功能连接预测青少年智力，在5,285名参与者中取得优于现有方法的性能，并揭示智力源于局部枢纽与分布式通路交互的网络架构。
AI 中文摘要
将静息态大脑组织映射到认知能力的个体差异仍然是群体神经信息学中的一项重大挑战。尽管深度学习能够灵活地对大脑连接进行建模，但其有限的可解释性限制了其在科学和临床上的实用性。为解决这一目标，我们开发了一个基于稀疏投影残差网络的可解释深度学习框架，用于预测来自青少年大脑认知发展研究中5,285名参与者的静息态功能磁共振成像的流体智力、晶体智力和总智力。我们整合了三种互补的可解释性方法（积分梯度、梯度Shapley加性解释和遮挡）来解释模型行为。该框架优于现有方法，在流体智力、晶体智力和总智力方面分别达到了0.44、0.58和0.56的皮尔逊相关系数，对应预测性能提升了6%至9%。所有三种可解释性方法产生了几乎相同的特征排名（两两排名相关性大于0.99）。共识图揭示了一种双层功能架构，其中主要预测枢纽位于经典系统内，而最强的全局预测通路常常通过这些枢纽，经由分布式的长程中继连接绕过这些枢纽。这些发现表明，智力产生于局部计算枢纽与分布式通信通路之间的相互作用。最终，这些规范性网络架构提供了临床参考图谱，以检测个体偏差，支持非典型神经发育中的早期诊断、认知亚型分层和治疗监测。
英文摘要
Mapping resting-state brain organization to individual differences in cognitive ability remains a major challenge in population neuroinformatics. Although deep learning enables flexible modeling of brain connectivity, limited interpretability restricts its scientific and clinical utility. To address this objective, we developed an explainable deep learning framework based on sparse projected residual networks to predict fluid, crystallized, and total intelligence from resting-state functional magnetic resonance imaging in 5,285 participants from the Adolescent Brain Cognitive Development study. We incorporated three complementary explainability methods (Integrated Gradients, Gradient Shapley Additive Explanations, and Occlusion) to interpret model behavior. The framework outperformed existing approaches, achieving Pearson correlations of 0.44, 0.58, and 0.56 for fluid, crystallized, and total intelligence, respectively, corresponding to predictive improvements of 6 to 9 percent. All three explainability methods produced near-identical feature rankings (pairwise rank correlations greater than 0.99). Consensus maps revealed a dual-layered functional architecture where primary predictive hubs localized within canonical systems, while the strongest global predictive pathways frequently bypassed these hubs through distributed, long-range relay connections. These findings suggest that intelligence emerges from the interaction between localized computational hubs and distributed communication pathways. Ultimately, these normative network architectures provide clinical reference maps to detect individual deviations, supporting earlier diagnosis, cognitive subtype stratification, and treatment monitoring in atypical neurodevelopment.
Comments12 pages, 2 figures. This work has been submitted to the IEEE Journal of Biomedical and Health Informatics for possible publication
