使用在横截面数据上预先训练的规范模型来评估神经成像数据中的个人内纵向变化
Barbora Rehak Buckova1,2,3, Charlotte Fraza4, Rastislav Rehák5,6
1Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic.
eLife
|March 12, 2025
概括
这项研究引入了一种分析纵向神经成像数据的新方法,揭示了随着时间的推移大脑的变化. 新的z-diff得分有效地跟踪个人的大脑发育和疾病进展,为精神分裂症等疾病提供了新的见解.
科学领域:
- 神经成像是一种神经成像.
- 大脑发育 大脑发育
- 疾病的进展 疾病的进展
背景情况:
- 纵向神经成像对于理解随时间推移的大脑变化至关重要.
- 当前的方法往往侧重于人口变化,限制了对个体动态的分析.
- 需要方法,将人口标准与个人纵向变化整合起来.
研究的目的:
- 扩大用于分析纵向神经成像数据的规范建模框架.
- 引入一个定量指标 (z-diff分数) 来评估个人与人口标准之间的时间变化.
- 将这一框架应用于精神分裂症患者,以确定与疾病相关的大脑变化.
主要方法:
- 扩展了规范建模框架,以评估相对于人口动态的纵向变化.
- 开发了一个"z-diff"得分来量化个体时间变化.
- 将框架应用于98名早期精神分裂症患者的纵向MRI数据集.
主要成果:
- 在精神分裂症患者中,z-diff得分显示,额叶灰质厚度在一年内显著正常化.
- 这种正常化没有通过传统的横截面或纵向分析检测到.
- 横截面分析显示,全球灰色物质在初次访问时变薄.
结论:
- 拟议的框架提供了一种灵活有效的方法来分析纵向神经成像数据.
- 它为疾病进展提供了新的见解,特别是在精神分裂症等疾病中.
- 这种方法增强了对健康和疾病中的个体大脑动态的理解.
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