使用静止状态功能连接磁共振成像和极端学习机器来对健康老年人的认知能力进行分类
Shiying Zhang1,2,3, Manling Ge4,5,6, Hao Cheng7,8,9
1State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China. 202121401030@stu.hebut.edu.cn.
BMC medical imaging
|March 27, 2024
概括
一个新的休息状态功能连接得分模型 (rs-FCSM) 准确地对老年人的认知状态进行了分类. 这种方法可以识别大脑生物标志物,以便早期发现与年龄相关的认知衰退.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 医疗成像医学成像
背景情况:
- 评估老年大脑的认知能力需要了解功能生物标志物.
- 休息状态功能连接 (FC) 是识别这些生物标志物的关键领域.
- 将FC分析与机器学习相结合,为认知评估提供了一个有前途的方法.
研究的目的:
- 开发和验证休息状态功能连接 (rs-FCSM) 的新型指数模型,以与老年人的认知表现相关联.
- 识别特定的大脑区域及其与认知状态相关的功能连接模式.
- 利用机器学习根据已识别的功能性大脑生物标志物对认知表现进行分类.
主要方法:
- 从健康的老年人和一个年轻的队列中收集了休息状态的功能性MRI数据和认知测试得分.
- 开发了一个静止状态FC分数模型 (rs-FCSM) 来比较老年人和年轻人之间的FC差异.
- 采用极端学习机器分类器,使用rs-FCSM的大脑区域得分,通过N倍交叉验证验证.
主要成果:
- rs-FCSM成功地对健康老年人的认知状态进行了分类,准确率为86.67%.
- 与传统的相关性分析方法相比,分类准确性更高.
- 关键的大脑区域,包括额叶,叶和叶,被确定为对认知测试分数敏感.
结论:
- 开发的rs-FCSM提供了一种有效的方法来对老年人认知状态进行分类.
- 这种方法可能有助于早期发现与年龄相关的认知衰退.
- 这项研究提供了关于认知衰老潜在的病理机制的见解.
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