Advancing Neuronal Research: Exploring Motor Neurons Cellular Changes during ALS via Bifurcation Analysis

Neurons, the fundamental units of the nervous system, facilitate communication through electrical and chemical signals. Motor neurons, a specific type of neuron, play a vital role in controlling muscle contraction and movement. Unfortunately, motor neurons are susceptible to various diseases, including the devastating and fatal amyotrophic lateral sclerosis (ALS), which leads to muscle weakness, spasticity, and respiratory failure. To better comprehend motor neuron function in health and disease, scientists employ numerous tools and techniques. One such tool is bifurcation analysis. Bifurcation analysis is a technique used in dynamical systems theory (and in neuroscience) to study the qualitative changes that occur in a system’s behavior (such as the neuronal circuits that shape the response of the nervous system) as a parameter is varied. It helps understand how a system transitions from one stable state to another, or how new states emerge, which is important in understanding how the cellular properties of neurons change during the progression of a disease. The importance of bifurcation analysis lies in its ability to reveal critical points and transitions in complex systems, which provides insights into the stability, periodicity, chaos, and other behaviors of the system. Therefore, by characterizing the bifurcation points, scientists can predict and control the system’s response to changes in parameters, which is crucial for making informed decisions and designing effective interventions or treatments to manage a disease. Importantly, bifurcation analysis must be applied on computer models of neurons and, to date, it has been commonly applied on simplified, not realistic, neuronal models due to the complexity of developing high-fidelity neuronal models using available software. This limitation hindered the use of bifurcation analysis in understanding the pathological neuronal behaviors that could emerge during diseases due to the need for detailed representation of cell properties.

In a recent study published in Frontiers in Cellular Neuroscience from Professor Sherif Elbasiouny’s research group at Wright State University, Mustafa et al. (2023) developed a groundbreaking high-fidelity neuronal model to study the excitability of spinal motoneurons (MNs) under both normal and ALS conditions using bifurcation analysis. Specifically, the researchers successfully developed a multi-compartment computer model of a spinal MN using the XPPAUT software, which is widely used for bifurcation analysis in neuroscience. To ensure accuracy, they verified their model against experimental data and anatomically detailed cell models that incorporated known MN non-linear firing mechanisms. Using this novel computer model, the researchers were able to characterize the impact of many ion channels in the soma and dendrites, on the bifurcation diagram of spinal MNs under normal and ALS conditions for the first time.

Elbasiouny’s research team discovered that small-conductance Ca²⁺-activated K (SK) channels in the soma and L-type Ca²⁺ channels in the dendrites play important roles, over other ion channels, in shaping the neuronal firing behavior during normal and ALS conditions. Particularly, somatic SK channels extend limit cycles and generate a subcritical Hopf bifurcation node in the V-I bifurcation diagram of MNs under normal conditions. This finding suggested that somatic SK channels enhance the stability and robustness of MN firing, which is critical for the generation of stable and seamless movements. Furthermore, the team observed that dendritic L-type Ca²⁺ channels had a strong effect on the bifurcation diagram, shifting limit cycles towards negative currents, indicating increased excitability. This implied that dendritic L-type Ca²⁺ channels reduce the threshold for MN firing, heightening their responsiveness to synaptic inputs, which is important for generating strong muscle contractions.

Under ALS conditions, the study also explored the excitability of MNs when the dendrites pathologically overbranch and the cell enlarges its soma and dendrites. The researchers discovered that dendritic enlargement had opposing effects, increasing membrane area and capacitance while also increasing leak conductance. Interestingly, they found that dendritic enlargement had a greater overall impact than somatic enlargement on the bifurcation diagram of MNs under ALS conditions. Additionally, the team observed that dendritic overbranching counteracted the hyperexcitability effects of dendritic enlargement. It reduced the effective electrotonic length and increased the input resistance of the dendrites, thus altering the balance between somatic and dendritic excitability. Combined, these changes reduce the excitability of MNs in ALS, which explains the muscle weakness and paralysis in the disease.

The study by Professor Sherif Elbasiouny and associates represents a significant step forward in understanding the mechanisms underlying MN excitability in both normal and ALS conditions. By successfully employing bifurcation analysis in high-fidelity neuronal models, the researchers provided new insights into the intricate dynamics of MNs, which would not be possible from experiments alone. Their findings shed light on the role of somatic and dendritic ion channels, emphasizing how they contribute to MN firing patterns and responsiveness to synaptic inputs. Moreover, the study demonstrated the feasibility and usefulness of utilizing bifurcation analysis in studying complex neuronal models relevant to health and disease. In a statement to Medicine Innovates Series, Professor Elbasiouny said “ our study is an important contribution to the field of computational neuroscience with an expected significant impact. We expect this study to foster the use of bifurcation analysis in studying neurodegenerative diseases and other disorders of the nervous system.”

In conclusion, bifurcation analysis is an important tool for investigating the behavior of dynamic systems. Its ability to identify critical points and understand system transitions is valuable for predicting, controlling, and comprehending complex phenomena in diverse fields. By expanding the application of bifurcation analysis, scientists can continue to unravel the mysteries of MN function and identify potential therapeutic targets for devastating conditions like ALS. This research paves the way for future investigations into the intricate interplay between ion channels, neuronal structure, and disease, ultimately offering hope for improved treatment strategies.