Functional magnetic resonance imaging (fMRI) provides insights into complex patterns of brain functional changes, making it a valuable tool for exploring addiction-related brain connectivity. However, effectively extracting addiction-related brain connectivity from fMRI data remains challenging due to the intricate and non-linear nature of brain connections. Therefore, this paper proposed the Graph Diffusion Reconstruction Network (GDRN), a novel framework designed to capture addiction-related brain connectivity from fMRI data acquired from addicted rats. The proposed GDRN incorporates a diffusion reconstruction module that effectively maintains the unity of data distribution by reconstructing the training samples, thereby enhancing the model’s ability to reconstruct nicotine addiction-related brain networks. Experimental evaluations conducted on a nicotine addiction rat dataset demonstrate that the proposed GDRN effectively explores nicotine addiction-related brain connectivity. The findings suggest that the GDRN holds promise for uncovering and understanding the complex neural mechanisms underlying addiction using fMRI data.

中文翻译：

---

通过图扩散重建网络识别成瘾相关的大脑网络

功能磁共振成像（fMRI）可以深入了解大脑功能变化的复杂模式，使其成为探索与成瘾相关的大脑连接的宝贵工具。然而，由于大脑连接的复杂性和非线性性质，从功能磁共振成像数据中有效提取与成瘾相关的大脑连接仍然具有挑战性。因此，本文提出了图扩散重建网络（GDRN），这是一种新颖的框架，旨在从成瘾大鼠获得的功能磁共振成像数据中捕获与成瘾相关的大脑连接。所提出的GDRN结合了扩散重建模块，通过重建训练样本有效地保持了数据分布的统一性，从而增强了模型重建尼古丁成瘾相关大脑网络的能力。对尼古丁成瘾大鼠数据集进行的实验评估表明，所提出的 GDRN 有效地探索了与尼古丁成瘾相关的大脑连接。研究结果表明，GDRN 有希望利用功能磁共振成像数据揭示和理解成瘾背后的复杂神经机制。