Early diagnosis of attention-deficit/hyperactivity disorder (ADHD) in children plays a crucial role in improving outcomes in education and mental health. Diagnosing ADHD using neuroimaging data, however, remains challenging due to heterogeneous presentations and overlapping symptoms with other conditions. To address this, we propose a novel parameter-efficient transfer learning approach that adapts a large-scale 3D convolutional foundation model, pre-trained on CT images, to an MRI-based ADHD classification task. Our method introduces Low-Rank Adaptation (LoRA) in 3D by factorizing 3D convolutional kernels into 2D low-rank updates, dramatically reducing trainable parameters while achieving superior performance. In a five-fold cross-validated evaluation on a public diffusion MRI database, our 3D LoRA fine-tuning strategy achieved state-of-the-art results, with one model variant reaching 71.9% accuracy and another attaining an AUC of 0.716. Both variants use only 1.64 million trainable parameters (over 113x fewer than a fully fine-tuned foundation model). Our results represent one of the first successful cross-modal (CT-to-MRI) adaptations of a foundation model in neuroimaging, establishing a new benchmark for ADHD classification while greatly improving efficiency.

翻译：儿童注意缺陷多动障碍（ADHD）的早期诊断对于改善其教育和心理健康预后至关重要。然而，利用神经影像数据诊断ADHD仍面临挑战，主要源于其异质性表现以及与其他病症症状的重叠。为此，我们提出了一种新颖的参数高效迁移学习方法，该方法将基于CT图像预训练的大规模三维卷积基础模型，适配至基于MRI的ADHD分类任务。我们的方法通过将三维卷积核分解为二维低秩更新，首次在三维卷积中引入了低秩适应（LoRA），从而在实现卓越性能的同时，大幅减少了可训练参数量。在一个公开扩散MRI数据库的五折交叉验证评估中，我们的三维LoRA微调策略取得了最先进的成果：其中一个模型变体达到了71.9%的准确率，另一个变体则获得了0.716的AUC值。两个变体均仅使用了164万个可训练参数（比完全微调的基础模型减少了超过113倍）。我们的研究结果代表了神经影像领域中基础模型首次成功的跨模态（CT到MRI）适应案例，在为ADHD分类建立新基准的同时，极大地提升了效率。