Zhao Feng, Chen Zhiyuan, Rekik Islem, Liu Peiqiang, Mao Ning, Lee Seong-Whan, Shen Dinggang
School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.
BASIRA Lab, Faculty of Computer and Informatics Engineering, Istanbul Technical University, Istanbul, Turkey.
Front Neurosci. 2021 Mar 22;15:651574. doi: 10.3389/fnins.2021.651574. eCollection 2021.
The sliding-window-based dynamic functional connectivity networks (SW-D-FCN) derive from resting-state functional Magnetic Resonance Imaging has become an increasingly useful tool in the diagnosis of various neurodegenerative diseases. However, it is still challenging to learn how to extract and select the most discriminative features from SW-D-FCN. Conventionally, existing methods opt to select a single discriminative feature set or concatenate a few more from the SW-D-FCN. However, such reductionist strategies may fail to fully capture the personalized discriminative characteristics contained in each functional connectivity (FC) sequence of the SW-D-FCN. To address this issue, we propose a unit-based personalized fingerprint feature selection (UPFFS) strategy to better capture the most discriminative feature associated with a target disease for each unit. Specifically, we regard the FC sequence between any pair of brain regions of interest (ROIs) is regarded as a unit. For each unit, the most discriminative feature is identified by a specific feature evaluation method and all the most discriminative features are then concatenated together as a feature set for the subsequent classification task. In such a way, the personalized fingerprint feature derived from each FC sequence can be fully mined and utilized in classification decision. To illustrate the effectiveness of the proposed strategy, we conduct experiments to distinguish subjects diagnosed with autism spectrum disorder from normal controls. Experimental results show that the proposed strategy can select relevant discriminative features and achieve superior performance to benchmark methods.
基于滑动窗口的动态功能连接网络（SW-D-FCN）源自静息态功能磁共振成像，已成为诊断各种神经退行性疾病中越来越有用的工具。然而，学习如何从SW-D-FCN中提取和选择最具判别力的特征仍然具有挑战性。传统上，现有方法选择单个判别特征集或从SW-D-FCN中再拼接几个。然而，这种简化策略可能无法完全捕捉SW-D-FCN的每个功能连接（FC）序列中包含的个性化判别特征。为了解决这个问题，我们提出了一种基于单元的个性化指纹特征选择（UPFFS）策略，以更好地捕捉与每个单元的目标疾病相关的最具判别力的特征。具体来说，我们将任意一对感兴趣脑区（ROI）之间的FC序列视为一个单元。对于每个单元，通过特定的特征评估方法识别最具判别力的特征，然后将所有最具判别力的特征拼接在一起作为后续分类任务的特征集。通过这种方式，从每个FC序列派生的个性化指纹特征可以在分类决策中得到充分挖掘和利用。为了说明所提出策略的有效性，我们进行了实验，以区分被诊断患有自闭症谱系障碍的受试者和正常对照。实验结果表明，所提出的策略可以选择相关的判别特征，并取得优于基准方法的性能。
