在认知衰退中研究冷漠的多维结构:对冷漠评估尺度的探索性网络分析
Claudia Savia Guerrera1, Giuseppe Alessio Platania1, Simone Varrasi1
1Department of Educational Sciences (CSG, GAP, SM, FMB, VT, GF, CP, SDN, SC), University of Catania, Catania, Italy.
概括
探索性图形分析揭示了认知衰退中的四因素冷漠结构,提供了比传统方法更清晰的理解. 这种基于网络的方法有助于开发针对阿尔茨海默病等疾病的有针对性的干预措施.
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.
- 老年学是指老年学的学科.
背景情况:
- 漠不关心是一种神经退行性疾病的复杂综合征,如阿尔茨海默病 (AD) 和轻度认知障碍 (MCI).
- 现有的评估工具可能无法充分捕捉冷漠的多维性质.
- 区分冷漠和抑郁症对于有效治疗至关重要.
研究的目的:
- 通过探索性图形分析 (EGA) 探索认知衰退的个体中冷漠的潜在结构.
- 将EGA应用于冷漠度评估量表 - 临床版本 (AES-C) 以将冷漠度作为一个复杂系统进行建模.
- 以传统的因数模型来比较由EGA衍生的基于网络的结构.
主要方法:
- 共有214名参与者 (MCI,AD,健康对照) 完成了AES-C和神经心理测试.
- 探索图分析 (EGA) 用于确定冷漠症状的网络结构.
- 确认因素分析验证了EGA衍生模型与现有结构相对应.
主要成果:
- EGA确定了四个因素的冷漠结构:兴趣/动机,自主性,新奇性和社会冷漠.
- 与传统模型相比,EGA模型显示出更好的适应性,节性和概念清晰度.
- 网络结构显示出良好的内部一致性,有效性和MCI和AD子组之间的一致性.
结论:
- EGA提供了对认知衰退中冷漠的维度的更精确的理解.
- 这种基于网络的方法有助于开发针对冷漠的有针对性的干预措施.
- 这些发现支持通过网络方法验证的新评估工具的创建.
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