AI 中文总结
研究针对阿尔茨海默病静息态功能连接改变问题，通过拟合储层计算模型重建滞后FC，对比两种模型读出方式，发现理想校正需分布式变化，基于此提出个性化靶向方法，证明有效神经调节需模型指导，刺激部位非偏差最大处而是治疗反应最强处。
AI 中文摘要
静息态功能连接（FC）在阿尔茨海默病（AD）中发生改变，被广泛认为是一个分布式网络过程。其FC特征是否可归结为几个焦点部位尚未得到因果检验，这是靶向神经调节的核心问题。研究拟合了特定个体、跨个体可识别的储层计算模型来重建每个个体的滞后FC。两种模型读出方式对AD与对照组的分类准确率一般，低于结构萎缩指标。基于功能读出进行研究，理想的校正应是模型连接核的分布式变化。单部位驱动即使在超生理幅度下也无法改变分类，而根据对疾病判别指标的影响选择部位可实现完全个体化重新分类，实时闭环控制器在较低剂量下仅使用因果可用信息就能达到类似效果。最佳靶点是皮层且具有异质性，有效神经调节需要基于模型的个性化靶向，刺激部位并非读出偏差最大处，而是网络治疗反应最强处。
英文摘要
Resting-state functional connectivity (FC) is altered in Alzheimer's disease (AD), widely regarded as a distributed network process; whether its signature reduces to a few focal sites has not been tested causally, a question central to targeted neuromodulation. We fit subject-specific, cross-subject-identifiable models whose free-running dynamics reproduce those of each individual patient. The fitted model parameters classify AD from controls at modest accuracy, below that of structural atrophy; we build on the functional model nonetheless, because dynamics, not tissue loss, are what stimulation can act on. Changing a virtual patient's model connectivity toward the control template reverts its AD classification, establishing in silico that the disease signature is correctable, yet the required correction is intrinsically distributed: a coordinated, multi-site change of the model connectivity that no single-node edit reproduces. Where, then, should a physically realisable focal drive act? A single-site drive at the node whose connectivity is most altered fails to revert the classification even at supra-physiological amplitudes, whereas selecting each patient's site by its effect on the disease discriminant achieves complete, individualised reclassification from one site, and a real-time closed-loop controller reaches comparable efficacy at lower dose using only causally available information. Optimal targets are cortical and heterogeneous: the site to stimulate is not where connectivity is most altered but where the network is most therapeutically responsive.
