《虚拟多发性硬化症患者》 The virtual multiple sclerosis patient

danelchen

来源：https://mp.weixin.qq.com/s/-JVA3C4EX3FiqaxiWjShhQ

PDF：https://www.cell.com/iscience/pd ... 69%3Fshowall%3Dtrue

Summary
Multiple sclerosis (MS) diagnosis typically involves assessing clinical symptoms, MRI findings, and ruling
out alternative explanations. While myelin damage broadly affects conduction speeds, traditional tests
focus on specific white-matter tracts, which may not reflect overall impairment accurately.
In this study, we integrate DTI and magnetoencephalography (MEG) data into individualized virtual brain
models to estimate conduction velocities for MS patients and controls. Using Bayesian inference, we
demonstrated a causal link between empirical spectral changes and inferred slower conduction velocities in
patients. Remarkably, these velocities proved superior predictors of clinical disability compared to
structural damage.
Our findings underscore a nuanced relationship between conduction delays and large-scale brain
dynamics, suggesting that individualized velocity alterations at the whole-brain level contribute
causatively to clinical outcomes in MS.

摘要
多发性硬化症 (MS) 的诊断通常涉及评估临床症状、MRI 结果和排除其他解释。虽然髓鞘损伤广泛影响传导速度，但传统测试侧重于特定的白质束，这可能无法准确反映整体损伤。
在这项研究中，我们将 扩散张量影像(DTI Diffusion tensor imaging)和脑磁图 (MEG magnetoencephalography) 数据整合到个性化虚拟大脑模型中，以估计 MS 患者和对照组的传导速度。使用贝叶斯推理，我们证明了经验光谱变化与推断的患者传导速度较慢之间存在因果关系。值得注意的是，与结构损伤相比，这些速度被证明是临床残疾的更佳预测指标。
我们的研究结果强调了传导延迟与大规模大脑动力学之间的微妙关系，表明整个大脑水平的个性化速度改变对 MS 的临床结果有因果关系。