脑功能连接模型在机器学习中分类鲁棒性研究——以静息态功能磁共振定位癫痫发作侧为例

doi:10.3969/j.issn.0258-8021.2021.05.02

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Abstract Currently, machine learning has promoted the application of resting-state functional magnetic resonance imaging (rfMRI) in epilepsy, where the functional connectivity model of Pearson correlation (FC) has been widely applied as a traditional imaging algorithm. However, the classification stability of functional connectivity model is rarely studied in the machine learning. To address this issue, a FC-based index model specific to the healthy people was proposed in this work, the classification stability was studied by a random cross validation in the supervised machine learning models, and compared to the results of FC, aiming to provide a new algorithm in extracting FC features input into machine learning. The rfMRI data of a total of twenty patients of medial temporal lobe epilepsy with a positive indicator of hippocampus on structure MRI (equally involved in a group of left side and a group of right side), and a total of 142 healthy people from a connectome including Southwest Adult Lifespan Dataset (SALD) in the same age group were collected. A rfMRI FC-based index model was built up, specific to the healthy people, referred as FC-based specificity index model. Thus, every FC of each brain area in an individual patient could be scored, and the brain areas sensitive to paroxysmal side could be extracted by the ROC curve. The sensitivity analysis curve was taken as the functional bio-markers, whose indexes were assigned as the feature vectors to input into the supervised machine learning models such as probabilistic neural network (PNN) and support vector machine (SVM) to classify paroxysmal side. Additionally, the classification stability was validated by a random cross validation (10 times), and the linear correlation of feature vectors between sensitive brain areas and between patients were estimated to evaluate their interdependence, aiming to find out the underlying cause to affect the classification stability. Finally, the same procedures as above were fulfilled by the FC model instead of FC-based specificity index model, and the classification stability was compared. The AUC of the feature vector of FC was 0.76, and the feature vector of specificity index was 0.84. The classification sensitivity of the FC-based specificity index model was higher than that of FC. In addition, the classification accuracy of FC fluctuated strongly between 25%~100%, the variance was as high as 25.99%, and the average correlation coefficient of the feature vector was 0.67, which had a strong correlation; while the accuracy of the exponential model was stable at 75%~100%. The variance was as low as 7.10%, and the average correlation coefficient of the feature vector was 0.28, which was relatively small. When paroxysmal side of medial temporal lobe epilepsy was subjected to the resting-state functional magnetic resonance imaging in the machine learning, the proposed FC-based specificity index model performed robustly, much better than the traditional FC model such as Pearson correlation, and the larger correlation between the feature vectors formed by the traditional FC model might be the main cause that led to the low classification stability.

Key words： functional connectivity specificity Pearson correlation resting-state fMRI supervised machine learning localization of paroxysmal side in epilepsy

Received: 20 November 2020

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R318

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	Articles by authors
	Yang Zekun
	Ge Manling
	Fu Xiaoxuan
	Chen Shenghua
	Zhang Fuyi
	Guo Zhitong
	Zhang Zhiqiang

Cite this article:

Yang Zekun,Ge Manling,Fu Xiaoxuan, et al. A Study on the Robustness of Brain Functional Connectivity Model in Machine Learning Classification ——Taking the Resting-State Functional Magnetic Resonance Imaging to Localize Paroxysmal Side in TLE as Example[J]. Chinese Journal of Biomedical Engineering, 2021, 40(5): 521-530.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2021.05.02 OR http://cjbme.csbme.org/EN/Y2021/V40/I5/521

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