使用 fMRI 的分形功能连接和逻辑回归对 ASD 进行诊断分类。

Diagnostic Classification of ASD Using Fractal Functional Connectivity of fMRI and Logistic Regression.

机构信息

School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.

National Institute of Speech & Hearing, Kerala, India.

出版信息

Stud Health Technol Inform. 2023 Jun 29;305:60-63. doi: 10.3233/SHTI230424.

DOI:10.3233/SHTI230424

PMID:37386958

Abstract

Our study used functional magnetic resonance imaging and fractal functional connectivity (FC) methods to analyze the brain networks of Autism Spectrum Disorder (ASD) and typically developing participants using data available on ABIDE databases. Blood-Oxygen-Level-Dependent time series were extracted from 236 regions of interest of cortical, subcortical, and cerebellar regions using Gordon's, Harvard Oxford, and Diedrichsen atlases respectively. We computed the fractal FC matrices which resulted in 27,730 features, ranked using XGBoost feature ranking. Logistic regression classifiers were used to analyze the performance of the top 0.1%, 0.3%, 0.5%, 0.7%, 1%, 2%, and 3% of FC metrics. Results showed that 0.5% percentile features performed better, with average 5-fold accuracy of 94%. The study identified significant contributions from dorsal attention (14.75%), cingulo-opercular task control (14.39%), and visual networks (12.59%). This study could be used as an essential brain FC method to diagnose ASD.

摘要

我们的研究使用功能磁共振成像和分形功能连接（FC）方法，利用 ABIDE 数据库中的数据，分析自闭症谱系障碍（ASD）和典型发育参与者的大脑网络。使用 Gordon、Harvard Oxford 和 Diedrichsen 图谱分别从皮质、皮质下和小脑区域的 236 个感兴趣区域中提取血氧水平依赖时间序列。我们计算了分形 FC 矩阵，得到了 27730 个特征，使用 XGBoost 特征排序进行了排名。逻辑回归分类器用于分析 FC 指标的前 0.1%、0.3%、0.5%、0.7%、1%、2%和 3%的性能。结果表明，0.5%分位数特征的性能更好，平均 5 倍精度为 94%。该研究确定了背侧注意（14.75%）、扣带回运动控制（14.39%）和视觉网络（12.59%）的显著贡献。这项研究可以作为一种重要的脑 FC 方法，用于诊断 ASD。