基于卷积神经网络的重度抑郁症患者大脑年龄评估

doi:10.11938/cjmr20233081

摘要/Abstract

摘要：

大脑年龄已成为神经退行性疾病诊断和机理研究的重要分析对象. 重度抑郁症是否会增加患者的大脑年龄，尚未得到一致的结论，且该方向的研究在中国人口内开展较少. 本文使用从中国25家医院收集的REST-meta-MDD数据集，构建基于高分辨率T1-加权3D大脑结构磁共振图像的卷积神经网络模型，用于预测患者的大脑年龄，计算与实际年龄的差异. 最终结果的平均绝对误差和相关系数为3.16和0.93，与健康组对比，重度抑郁症患者的平均大脑年龄增加了3.94年，进一步确认了重度抑郁症会加速大脑衰老，且患病程度与患者的性别、年龄和受教育程度相关. 对比传统机器学习算法，该模型取得结果的平均绝对误差更小，相关系数更高.

关键词: 大脑年龄, 重度抑郁症, 卷积神经网络, 大脑衰老

Abstract:

Brain age has become an important analysis object in the diagnosis and mechanism research of neurodegenerative diseases. There is no consistent conclusion on whether major depression increases the brain age of patients, and few studies in this direction have been conducted in the Chinese population. In this paper, a REST-meta-MDD (resting-state functional magnetic resonance imaging dataset of major depressive disorder) dataset collected from 25 hospitals in China was used to construct a convolutional neural network model based on high-resolution T1-weighted three-dimensional magnetic resonance images of brain structures to predict the brain age of patients and calculate the difference from the actual age. The mean absolute error and correlation coefficients of the final results were 3.16 and 0.93, and the mean brain age of the patients with major depression increased by 3.94 years compared with the healthy group, further confirming that major depression accelerates brain aging, and the severity of the disease is related to the gender, age, and education of the patients. Compared with the traditional machine learning algorithms, the average absolute error of the results obtained by this model is smaller and the correlation coefficient is higher.

Key words: brain age, major depression, convolutional neural networks, brain aging

中图分类号:

O482.53

章浩伟, 汪郧城, 刘颖. 基于卷积神经网络的重度抑郁症患者大脑年龄评估[J]. 波谱学杂志, 2024, 41(2): 139-150.

ZHANG Haowei, WANG Yuncheng, LIU Ying. Brain Age Assessment of Patients with Major Depressive Disorder Based on Convolutional Neural Network[J]. Chinese Journal of Magnetic Resonance, 2024, 41(2): 139-150.

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对象	年龄/岁	健康组		MDD组
对象	年龄/岁	总数	占比	总数	占比
青少年组	12~35	650	59.04%	696	54.55%
中年组	36~59	332	30.15%	468	36.68%
老年组	60~82	119	10.81%	112	8.78%

受教育程度	受教育年限/年	健康组		MDD组
受教育程度	受教育年限/年	总数	占比	总数	占比
小学及以下	0~6	142	12.89%	151	11.83%
初中	7~9	136	12.35%	272	21.32%
高中	10~12	197	17.90%	327	25.63%
大专及以上	13及以上	626	56.86%	526	41.22%

方法	MAE	r	R²
SVR	5.65	0.92	0.85
RR	4.84	0.94	0.88
Lasso	4.97	0.93	0.86
CNN	2.85	0.98	0.96

作者	数据	方法	数据量N	PAD/年	MAE	r	R²
Koutsouleris^[30]	sMRI	SVR	104	+4.00	4.61	0.91	0.83
Han^[31]	sMRI	RR	2 675	+1.08	6.61	0.85	0.72
Dunlop^[32]	fMRI	SVR	112	+2.11	7.48	0.75	0.56
本研究	sMRI	CNN	2 377	+4.32	3.16	0.93	0.86