From Cell to Circuit: Investigating Functional Topological Changes in iPSC-derived Neuronal Networks in Major Depressive Disorder

The functional organization of brain networks maintains a delicate equilibrium between segregation and integration where it facilitates local neural communication together with effective global integration of information across network’s components. While numerous whole-brain imaging studies have linked alterations in functional topology to major depressive disorder (MDD), our comprehension of how these changes manifest at the cellular level remains limited. Here, we explored whether neuronal networks derived from induced pluripotent stem cells (hiPSCs) of nine depressed patients display a distinct functional topology compared to those of matched controls. Spontaneous activity of the derived neuronal networks was captured using calcium imaging, and graph theory analysis was applied to assess functional topology. We computed the graph metrics clustering coefficient and global efficiency to quantify respective network segregation and integration attributes. We also measured the average node degree to assess group differences in the overall number of connections. We observed a decrease in clustering coefficient and average node degree in MDD-derived neural networks compared to those of controls. Global efficiency also exhibited a decreasing trend in patient-derived networks across varying thresholds and network sizes. Together, our findings reveal diminished segregation properties and a reduced number of nodal connections in MDD-derived neural networks, suggesting a predisposition for a less efficient functional topology in depression already at the microscale. This work marks the first attempt to explore microscale alterations in functional topology of human-derived neural networks in MDD and highlights the power of iPSC technology in providing a human cellular model to better understand disease mechanisms.