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.

Reactive astrocytes are a hallmark of several neurological diseases in multiple sclerosis and experimental demyelination models. Their morphological alterations are commonly assessed by qualitative histopathology, yet quantitative tools are required to better capture astrocytic heterogeneity and to allow correlations with imaging-derived biomarkers. Here, we present a workflow for the quantitative analysis of Glial Fibrillary Acidic Protein (GFAP) network remodeling in astrocytes in the cuprizone model of demyelination. C57BL/6 mice were intoxicated with cuprizone for 3 or 5 weeks to induce progressive demyelination, microglial activation, and reactive astrogliosis. Brain sections were processed for anti-GFAP immunohistochemistry, and individual astrocytes from the stratum oriens of the hippocampus were digitally reconstructed. Diverse parameters of GFAP topology, including soma size, process length, branching order, convex hull area, and ramification index, were extracted using either the commercial Neurolucida^® 360 software or the open-source Simple Neurite Tracer (SNT) plugin in ImageJ. Principal component analysis revealed clear differences between control astrocytes and astrocytes in cuprizone-intoxicated animals, with reactive astrocytes displaying increased numbers of primary processes, enhanced bifurcation, and process complexity. Comparative evaluation of Neurolucida^® 360 and SNT demonstrated that both tools are suitable for astrocyte reconstruction, although Neurolucida^® 360 enabled faster and more detailed tracing. This protocol provides a reproducible pipeline for the quantitative assessment of astrocyte morphology under control and pathological conditions, thereby supporting future efforts to link cellular remodeling to functional outcomes in neuroinflammatory disease models.