Predicting Conversion from SCD to MCI: A Machine Learning Study
· 2026-09-08 · 原文
DOI：10.64898/2026.09.04.26362240v1?rss=1
BACKGROUND: Subjective cognitive decline (SCD) may precede mild cognitive impairment (MCI), but not all individuals with SCD progress to MCI. Identifying which individuals are most likely to convert and over what time frame remains an important goal in Alzheimer's disease research. MRI measures of white matter hyperintensity (WMH) burden and gray matter (GM) atrophy may improve prediction beyond demographic and cognitive predictors, but their incremental value across different time intervals has not been established. METHODS: Data were obtained from four longitudinal cohorts (ADNI, NACC, CIMA-Q, and PREVENT-AD). A total of 1,352 participants with SCD at baseline were included. Machine learning models (logistic regression, random forest, XGBoost) were used to predict conversion from SCD to
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1. 人话版
BACKGROUND: Subjective cognitive decline (SCD) may precede mild cognitive impairment (MCI), but not all individuals with SCD progress to MCI.
Identifying which individuals are most likely to convert and over what time frame remains an important goal in Alzheimer's disease research.
2. 领域脉络
来源板块：板块一 · 研究前沿。
3. 机制拆解
METHODS: Data were obtained from four longitudinal cohorts (ADNI, NACC, CIMA-Q, and PREVENT-AD).
4. 证据与数字
A total of 1,352 participants with SCD at baseline were included.
5. 反例与边界
MRI measures of white matter hyperintensity (WMH) burden and gray matter (GM) atrophy may improve prediction beyond demographic and cognitive predictors, but their incremental value across different time intervals has not been established.
6. 跨领域连接与意外收获
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