Escuela de Ingeniería Civil Biomédica, Universidad de Valparaíso, Valparaíso 2340000, Chile.
Centro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso 2340000, Chile.
Comput Intell Neurosci. 2019 Dec 26;2019:5259643. doi: 10.1155/2019/5259643. eCollection 2019.
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 and 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.
利用功能磁共振成像（fMRI）进行脑网络分析是一种广泛应用的技术。fMRI 脑网络分析的第一步是检测感兴趣区域（ROIs）。然后，使用这些 ROI 的信号来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配准和聚类。这项工作提出了一种结合这两种范例的仿生方法。该方法称为 HAnt，由信号的解剖聚类和蚁群聚类步骤组成。该方法在 和 实验中进行了实证评估。结果表明，与使用纯聚类策略或基于图谱的分割获得的其他脑分割方法相比，所提出的方法具有显著更好的性能。
