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从MDD青少年的fNIRS信号的分类自杀高和低风险组使用机器学习

Seonghyeon Kim¹, Haram Yoon¹, Jaeyoung Shin²

¹Department of Psychiatry, School of Medicine, Wonkwang University, Iksan 54538, Republic of Korea.

Journal of affective disorders

|August 3, 2023

概括

患有严重抑郁症的自杀高风险青少年表现出明显的前额叶皮质血液动力反应. 特定的功能近红外光谱通道可以识别神经生物标志物,以区分这些患者的风险水平.

科学领域:

神经科学是一个神经科学.
精神病学是一个精神病学.
生物医学工程生物医学工程

背景情况:

大型抑郁症 (MDD) 与认知任务期间前额叶皮层 (PFC) 激活的改变有关.
患有MDD的青少年,特别是那些处于高自杀风险 (SHR) 的青少年,表现出鲜为人知的血液动力学反应特征.

研究的目的:

研究患有多重抑郁症的青少年血液动力学反应与自杀风险之间的关系.
确定潜在的神经成像生物标志物,以区分SHR青少年与自杀低风险 (SLR) 青少年.

主要方法:

招募了30名被诊断患有MDD的青少年 (13-19岁).
在Stroop测试中利用功能近红外光谱 (fNIRS) 来收集血液动力学数据.
雇员休假一次交叉验证与基于随机搜索的特征选择,以进行可靠的分类.

主要成果:

在区分风险组时,获得了70.3±5.0%的通用分类准确率.
确定了特定的fNIRS通道 (Ch08oxy和Ch09deoxy) 作为关键预测因素.
与SLR组相比,SHR组在大多数氧-血红蛋白 (oxy-Hb) 通道中观察到过度激活,但例外.

结论:

特定的fNIRS通道可以作为潜在的神经生物标志物,用于识别MDD的SHR青少年.
血液动力学PFC激活模式在MDD的SHR和SLR青少年之间有显著差异,为自杀风险的神经生物学基础提供了洞察力.

关键词:

青少年青少年青少年机器学习是机器学习.在Stroop测试中,自杀自杀的自杀是自杀的自杀.在FNIRS中使用.

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