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神经生理学生物标志物用于抑郁症分类:利用微状态k-mers和一个词包模型

Dong-Dong Zhou¹, Xin-Yu Peng², Lin Zhao²

¹Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China.

Journal of psychiatric research

|July 30, 2023

概括

微态k-mers显示出作为神经生理学生物标志物用于抑郁症检测的希望. 这项研究使用了自然语言处理技术来分析大脑活动模式,在将抑郁症患者与对照人群区分开来时取得了高准确度.

科学领域:

神经科学是一个神经科学.
计算精神病学是一种计算精神病学.
生物标志物发现发现

背景情况:

可以使用微状态序列分析大脑活动,类似于语言.
确定可靠的抑郁症神经生理生物标志物对于诊断和治疗至关重要.

研究的目的:

调查微状态k-mers作为生物标志物的潜力,用于分类抑郁症.
评估自然语言处理 (NLP) 模型在分析微状态序列以检测抑郁症时的有效性.

主要方法:

微态序列是使用一个字袋模型处理的,用k-mers (1-10) 作为特征.
术语频率 (TF) 和TF-反向文档频率 (TF-IDF) 被用作特征提取方法.
嵌套交叉验证和交叉数据集培训/测试是在两个独立的数据集上进行的.

主要成果:

最好的分类性能在数据集2中使用9mers的TF实现了曲线下的面积 (AUC) 为88.9%,数据集2中的TF为9mers.
使用L1规范化与TF的4-mers (数据集1) 和TF的9-mers (数据集2) 的模型显示高准确度.
交叉数据集验证的AUC为74.1%和70.2%,证明了可概括性.

结论:

微态k-mers代表了一个新且有前途的神经生理学生物标志物,用于个人级别的抑郁症分类.
对微状态序列的NLP技术的应用为理解精神疾病中的大脑动态开辟了新的途径.
这些发现可能有助于开发抑郁症的客观诊断工具.

关键词:

一个词袋 - - 一个词袋.生物标志物生物标志物抑郁症抑郁症抑郁症电脑脑电图微观状态卡梅尔斯 (K-mers) 是一个字体.机器学习机器学习

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