Electrical and Electronics Engineering Department, Graduate School of Natural Science, Ankara Yıldırım Beyazıt University, Ankara, Turkey; Biomedical Engineering Department, College of Engineering, Al-Nahrain University, Baghdad, Iraq.
Electrical and Electronics Engineering Department, Graduate School of Natural Science, Ankara Yıldırım Beyazıt University, Ankara, Turkey.
J Neurosci Methods. 2018 Apr 1;299:45-54. doi: 10.1016/j.jneumeth.2018.02.007. Epub 2018 Feb 20.
Clustering approaches used in functional magnetic resonance imaging (fMRI) research use brain activity to divide the brain into various parcels with some degree of homogeneous characteristics, but choosing the appropriate clustering algorithms remains a problem.
A novel application of the robust unsupervised learning approach is proposed in the current study. Robust growing neural gas (RGNG) algorithm was fed into fMRI data and compared with growing neural gas (GNG) algorithm, which has not been used for this purpose or any other medical application. Learning algorithms proposed in the current study are fed with real and free auditory fMRI datasets.
The fMRI result obtained by running RGNG was within the expected outcome and is similar to those found with the hypothesis method in detecting active areas within the expected auditory cortices.
COMPARISON WITH EXISTING METHOD(S): The fMRI application of the presented RGNG approach is clearly superior to other approaches in terms of its insensitivity to different initializations and the presence of outliers, as well as its ability to determine the actual number of clusters successfully, as indicated by its performance measured by minimum description length (MDL) and receiver operating characteristic (ROC) analysis.
The RGNG can detect the active zones in the brain, analyze brain function, and determine the optimal number of underlying clusters in fMRI datasets. This algorithm can define the positions of the center of an output cluster corresponding to the minimal MDL value.
在功能磁共振成像（fMRI）研究中使用的聚类方法利用大脑活动将大脑划分为具有一定同质性特征的各个区域，但选择适当的聚类算法仍然是一个问题。
本研究提出了一种稳健无监督学习方法的新应用。将稳健生长神经网络（RGNG）算法应用于 fMRI 数据，并与尚未用于此目的或任何其他医学应用的生长神经网络（GNG）算法进行比较。本研究中提出的学习算法采用真实的、免费的听觉 fMRI 数据集进行训练。
运行 RGNG 得到的 fMRI 结果符合预期，与使用假设方法在预期听觉皮质内检测活跃区域的结果相似。
在不敏感于不同初始化和异常值的存在以及成功确定实际聚类数量方面，所提出的 RGNG 方法在 fMRI 应用方面明显优于其他方法，这一点可以通过最小描述长度（MDL）和接收者操作特性（ROC）分析来衡量其性能。
RGNG 可以检测大脑中的活跃区域，分析大脑功能，并确定 fMRI 数据集中潜在聚类的最佳数量。该算法可以定义与最小 MDL 值对应的输出聚类中心的位置。
