Addiction in the brain is associated with adaptive changes that reshape addiction-related brain regions and lead to functional abnormalities that cause a range of behavioral changes, and functional magnetic resonance imaging (fMRI) studies can reveal complex dynamic patterns of brain functional change. However, it is still a challenge to identify functional brain networks and discover region-level biomarkers between nicotine addiction (NA) and healthy control (HC) groups. To tackle it, we transform the fMRI of the rat brain into a network with biological attributes and propose a novel feature-selected framework to extract and select the features of addictive brain regions and identify these graph-level networks. In this framework, spatial attention recurrent network (SARN) is designed to capture the features with spatial and time-sequential information. And the Bayesian feature selection(BFS) strategy is adopted to optimize the model and improve classification tasks by restricting features. Our experiments on the addiction brain imaging dataset obtain superior identification performance and interpretable biomarkers associated with addiction-relevant brain regions.

中文翻译：

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通过具有特征选择的空间注意循环网络识别成瘾性脑网络

大脑中的成瘾与重塑成瘾相关大脑区域的适应性变化有关，并导致导致一系列行为变化的功能异常，功能磁共振成像 (fMRI) 研究可以揭示大脑功能变化的复杂动态模式。然而，识别功能性大脑网络并发现尼古丁成瘾 (NA) 组和健康对照组 (HC) 组之间的区域级生物标志物仍然是一个挑战。为了解决这个问题，我们将大鼠大脑的 fMRI 转化为具有生物属性的网络，并提出了一种新的特征选择框架来提取和选择成瘾大脑区域的特征，并识别这些图级网络。在这个框架下，空间注意力循环网络 (SARN) 旨在捕获具有空间和时间序列信息的特征。并采用贝叶斯特征选择（BFS）策略通过限制特征来优化模型和改进分类任务。我们在成瘾大脑成像数据集上的实验获得了与成瘾相关大脑区域相关的卓越识别性能和可解释的生物标志物。