Brain network analysis using functional magnetic resonance imaging (fMRI) is a widely used technique. The first step of brain network analysis in fMRI is to detect regions of interest (ROIs). The signals from these ROIs are then used to evaluate neural networks and quantify neuronal dynamics. The two main methods to identify ROIs are based on brain atlas registration and clustering. This work proposes a bioinspired method that combines both paradigms. The method, dubbed HAnt, consists of an anatomical clustering of the signal followed by an ant clustering step. The method is evaluated empirically in both in silico and in vivo experiments. The results show a significantly better performance of the proposed approach compared to other brain parcellations obtained using purely clustering-based strategies or atlas-based parcellations.

中文翻译：

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蚁群聚类用于功能磁共振成像中的ROI识别。

使用功能磁共振成像（fMRI）进行脑网络分析是一种广泛使用的技术。功能磁共振成像中脑网络分析的第一步是检测感兴趣区域（ROI）。然后，将这些ROI的信号用于评估神经网络并量化神经元动力学。识别ROI的两种主要方法是基于脑图谱注册和聚类。这项工作提出了一种结合两种范式的受生物启发的方法。该方法称为HAnt，由信号的解剖学聚类和蚂蚁聚类步骤组成。在计算机和体内均凭经验评估该方法实验。结果表明，与使用纯基于聚类的策略或基于图集的分割获得的其他脑分割相比，该方法的性能明显更好。