大脑年龄模型的年龄预测准确度较低,对疾病检测的敏感性更高
Marc-Andre Schulz1,2,3,4, Nys Tjade Siegel1,2,3,4, Kerstin Ritter1,2,3,4
1Department of Psychiatry and Neurosciences, Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Berlin, Germany.
PLoS biology
|October 28, 2025
概括
简单的脑年龄模型比复杂的更好地检测神经系统疾病. 优先考虑疾病敏感性而不是年龄预测准确性,揭示了更有意义的大脑健康生物标志物.
科学领域:
- 神经成像是一种神经成像.
- 机器学习 机器学习
- 精神病学是一个精神病学.
背景情况:
- 大脑年龄模型通过MRI扫描来估计大脑年龄.
- 目前的模型优先考虑时间表年龄预测的准确性.
- 这种精确度的焦点可能与检测大脑疾病不一致.
研究的目的:
- 重新评估神经和精神疾病检测的大脑年龄模型.
- 挑战对时间年代预测准确性的强调.
- 调查疾病相关变化的简单模型.
主要方法:
- 分析了来自46,381名英国生物库参与者的T1MRI数据.
- 比较简单和复杂的机器学习模型.
- 评估模型对疾病相关变化的敏感度与年龄预测准确度.
主要成果:
- 更简单的模型,特别是正规化的模型,对疾病的敏感性更高.
- 这些模型在时间学年龄预测中不那么准确.
- 精度优化的模型可能会错过疾病信息变异.
结论:
- 优化年龄准确性的模型不是理想的规范性大脑衰老模型.
- 更简单的模型通过捕捉疾病变异,提供更好的大脑健康生物标志物.
- 专注于具有较大的患者控制效应大小的模型有助于早期发现疾病.
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