时间标签：数据日期 2026-10-28 · 生成时间 2026-10-31 05:30（Asia/Shanghai）｜ 档位：观察哨29 ｜ 窗口 T-3 ｜ 上游评级 C
这项研究想回答什么
MEG 的组水平差异能否转译为个体水平的疾病分型？
方法与数据
临床表型 + 功率谱密度 + 动态因果模型推断的潜网络机制，以分类作为探索性探针评估组水平到个体水平的转译。
核心结论
以下所有数字均可在官方 abstract 原文（见第 7 节）中逐字核对；上游未给出的定量一律写「未给出」，不估算、不换算。
- 一句话贡献：在 74 名健康对照、45 例 bvFTD 与 55 例 PSP 的静息态 MEG 上，临床评分的个体分类 accuracy 达 0.94（对照 vs PSP）与 0.83（bvFTD vs PSP），而 MEG 频谱特征仅约 0.6，DCM 推断的生物物理参数居中（PSP vs 对照 0.70、bvFTD vs PSP 0.74）。
局限与未核验项
样本量中等（对照 74、bvFTD 45、PSP 55），分类仅为探索性探针且摘要未给置信区间，两种疾病临床与生理特征部分重叠。
补全文时优先核：分类的置信区间与交叉验证方案、临床评分与 MEG 特征的重叠程度、DCM 参数的稳定性。
⚠️ 本页为摘要级解读：未取得 PDF 全文，上述所有结论均未核至 Results/Methods；效应量、样本量、统计方法与多重比较校正在多数条目中仍未知。
对本领域的价值
对神经退行精准医学方法学有明确提醒价值（组间差异不等于个体可用），但与衰老核心机制关联薄弱，属长尾命中。
编辑研判
方法学提醒很有价值，且给出了干净的落差数字：临床评分个体分类 accuracy 0.94（对照 vs PSP）/0.83（bvFTD vs PSP），MEG 频谱特征仅约 0.6，DCM 推断的生物物理参数居中（0.70/0.74）。组间显著差异 ≠ 个体可用，这条对我们评估任何「影像/电生理标志物」是否够格做个体预测，是一把可直接套用的尺子。与衰老核心机制关联薄弱；样本量中等且未给 CI。
官方摘要原文（逐字，供回溯）
展开 api.biorxiv.org / api.medrxiv.org 返回的 abstract 原文
Precision medicine approaches require physiological effects to be informative when translated to the individual. Reliable group differences may have limited individual value when distributions overlap, mechanisms are heterogeneous, or patients differ in phenotype. A feature can differ between group means and still overlap heavily across individuals Moreover, combinations of physiological abnormalities may reveal subject-level structure that is not captured by any single marker at the group level. We evaluate the translation between group- and individual-level features derived from resting-state magnetoencephalography (MEG) using behavioural-variant frontotemporal dementia (bvFTD) and progressive supranuclear palsy (PSP) as demonstrator conditions. These illnesses have distinct molecular pathology but share the pressing need for therapeutic development and have partially overlapping clinical and physiological features. We combine clinical phenotype, power spectral density (PSD), and latent network mechanisms inferred by dynamic causal modelling (DCM) to compare groups and to access their individuals mapping translations. We used classification as an exploratory probe of how group-defined physiological features from 74 healthy controls, 45 participants with bvFTD and 55 participants with PSP support individual assignment. Clinical scores showed strong individual-level translation, consistent with clinical diagnostic definitions (e.g, controls-vs-PSP accuracy 0.94; bvFTD-vs-PSP accuracy 0.83). MEG spectra feature classified individuals weakly (accuracy ~0.6), despite group level re-organization (e.g., reduced beta-band and increased fronto-insular alpha-phase/beta-amplitude coupling in bvFTD). Inferred biophysical parameters gave intermediate performance (e.g. PSP-vs-control accuracy 0.70; bvFTD-vs-PSP accuracy 0.74) while retaining mechanistic interpretability. Using this approach, experimental medicine studies of neurological disease can formally trade between mechanistic insight and classification performance, in using biomarkers that can account for individual heterogeneity within and between diagnostic groups.
本文为预印本，未经同行评审，结论可能在正式发表时变更。生成任务 12819187（预印本全文级解读）· 上游 12784209（预印本速递）。
C 级证据分级：C 级（预印本，未经同行评审；本页为摘要级，未核至全文）
本文为预印本，未经同行评审；且本页为「摘要级」——未取得 PDF 全文（源站封锁），全部结论只依据官方 API 摘要原文。效应量、样本量、统计方法与多重比较校正在多数条目中仍未知，摘要未给出之处一律写「未给出」/「待补全文」，未做任何数值推算补全。
关于本文的文献来源：本站依据元层《预印本全文级解读_2026-10-31》（上游《预印本速递_2026-10-31》）撰写，附官方 abstract 原文供逐字回溯。