Sorrentino P, Pathak A, Ziaeemehr A, Troisi Lopez E, Cipriano L, Romano A, Sparaco M, Quarantelli M, Banerjee A, Sorrentino G, Jirsa V, Hashemi M
Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France.
Institute of Applied Sciences and Intelligent Systems, National Research Council, Pozzuoli, Italy.
iScience. 2024 May 24;27(7):110101. doi: 10.1016/j.isci.2024.110101. eCollection 2024 Jul 19.
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 diffusion tensor immaging (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.
多发性硬化症（MS）的诊断通常涉及评估临床症状、MRI 检查结果，并排除其他可能的病因。虽然髓鞘损伤广泛影响传导速度，但传统测试集中于特定的白质束，这可能无法准确反映整体损伤情况。在本研究中，我们将扩散张量成像（DTI）和脑磁图（MEG）数据整合到个体化虚拟脑模型中，以估计 MS 患者和对照组的传导速度。通过贝叶斯推理，我们证明了患者经验性频谱变化与推断出的较慢传导速度之间存在因果关系。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好预测指标。我们的研究结果强调了传导延迟与大规模脑动力学之间的细微关系，表明全脑水平的个体化速度改变对 MS 的临床结果有因果贡献。
