深度学习用于基于风险的认知障碍个体的分层
Michael F Romano1,2, Xiao Zhou1,3, Akshara R Balachandra1,4
1Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
iScience
|August 30, 2023
概括
在轻度认知障碍 (MCI) 中预测阿尔茨海默病 (AD) 进展对于早期干预至关重要. 这项研究开发了一个新的框架,使用脑MRI和液体生物标志物来准确地按AD风险分层MCI患者.
科学领域:
- 神经成像是一种神经成像.
- 生物标志物 生物标志物
- 计算神经科学是一种神经科学.
背景情况:
- 量化阿尔茨海默病 (AD) 进展的风险对于轻度认知障碍 (MCI) 患者的早期干预至关重要.
- 目前的风险分层方法可能无法完全捕捉神经成像和流体生物标志物的复杂相互作用.
- 识别高风险个体可以促进及时的治疗策略.
研究的目的:
- 在轻度认知障碍 (MCI) 患者中开发和验证阿尔茨海默病 (AD) 进展的预测模型.
- 根据大脑脊髓液 (CSF) 的粉样蛋白-β水平和大脑灰质模式,将MCI患者分为不同的风险组.
- 用先进的计算技术识别与疾病预后相关的关键大脑区域.
主要方法:
- 利用了来自阿尔茨海默病神经成像计划 (ADNI) 和国家阿尔茨海默病协调中心 (NACC) 队列的数据.
- 通过将神经网络与生存分析融合而开发了预测模型,并通过T1加权脑MRI进行训练.
- 采用解释性技术来识别AD进展预测的关键大脑区域.
主要成果:
- 开发的模型在发现和验证队伍中准确地预测了MCI到AD转换轨迹 (综合布赖尔得分分别为0.192和0.108).
- 确定了与AD进展的不同风险水平相关的特定灰质模式.
- 验证了模型的预测区域与已知的AD受影响的大脑区域保持一致,由死后数据证实.
结论:
- 拟议的框架为患有MCI的个体基于风险的分层提供了一个强有力的战略.
- 该研究成功地确定了对预测阿尔茨海默病预后至关重要的关键大脑区域.
- 这种方法有望改善早期检测和个性化治疗策略.
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