鉴于广泛的工作创造了多种预测多发性硬化症(MS)疾病病程的技术,本文试图对这些方法进行概述,并提出一种预测疾病进展的替代方法。为此,现有的估计和预测病程的方法已分为临床,放射学,生物,和基于计算或人工智能的标记。权衡这些预后组的弱点和优势是一种深刻的方法,目前还需要这种方法,并且直接在患病的连接水平上起作用。因此,我们建议将计算模型与已建立的连接组结合使用,作为MS疾病轨迹的预测工具。通过检查这些研究,基本的基于传导的Hodgkin-Huxley模型很有希望。Hodgkin-Huxley模型的优点是连接体的某些特性,例如神经元连接权重,空间距离,和信号传输速率的调整,可以考虑。正是这些特性在MS中特别改变,并且对加工具有强烈的影响,传输,和神经元信号模式的相互作用。Hodgkin-Huxley(HH)方程作为点神经元模型用于小型网络内部的信号传播。目的是改变神经元模型的传导参数,复制MS中髓磷脂特性的变化,并观察信号在网络中传播的动力学。该模型最初针对不同的长度进行了验证,传导值,和连接权重通过三个节点连接。稍后,这些单独的因素被整合到一个小的网络中,并模拟模拟MS的状况。在网络中的某些节点处引起传导参数的变化之后观察信号传播模式,并将其与在将变化应用于网络之前获得的控制模型模式进行比较。通过适应输入条件,信号传播模式如预期的那样变化。同样,当模型应用于连接体时,模式的变化可以提供对疾病进展的洞察。这种方法开辟了一条探索MS疾病进展的新途径。工作处于初步状态,但是随着未来将这种方法应用于连接体的愿景,提供更好的临床工具。
In light of extensive work that has created a wide range of techniques for predicting the course of multiple sclerosis (MS) disease, this paper attempts to provide an overview of these approaches and put forth an alternative way to predict the disease progression. For this purpose, the existing methods for estimating and predicting the course of the disease have been categorized into clinical, radiological, biological, and computational or artificial intelligence-based markers. Weighing the weaknesses and strengths of these prognostic groups is a profound method that is yet in need and works directly at the level of diseased connectivity. Therefore, we propose using the computational models in combination with established connectomes as a predictive tool for MS disease trajectories. The fundamental conduction-based Hodgkin-Huxley model emerged as promising from examining these studies. The advantage of the Hodgkin-Huxley model is that certain properties of connectomes, such as neuronal connection weights, spatial distances, and adjustments of signal transmission rates, can be taken into account. It is precisely these properties that are particularly altered in MS and that have strong implications for processing, transmission, and interactions of neuronal signaling patterns. The Hodgkin-Huxley (HH) equations as a point-neuron model are used for signal propagation inside a small network. The objective is to change the conduction parameter of the neuron model, replicate the changes in myelin properties in MS and observe the dynamics of the signal propagation across the network. The model is initially validated for different lengths, conduction values, and connection weights through three nodal connections. Later, these individual factors are incorporated into a small network and simulated to mimic the condition of MS. The signal propagation pattern is observed after inducing changes in conduction parameters at certain nodes in the network and compared against a control model pattern obtained before the changes are applied to the network. The signal propagation pattern varies as expected by adapting to the input conditions. Similarly, when the model is applied to a connectome, the pattern changes could give an insight into disease progression. This approach has opened up a new path to explore the progression of the disease in MS. The work is in its preliminary state, but with a future vision to apply this method in a connectome, providing a better clinical tool.