A mathematical model suggests collectivity and inconstancy enhance the efficiency of neuronal migration in the adult brain.

Neuronal regeneration in the adult brain, which is restricted compared to that in the embryonic brain, is a long-standing topic in neuroscience and medical research. Based on studies in mammals, a small number of newly generated immature neurons (neuroblasts) migrate toward damaged sites and contribute to functional recovery. During migration, neuroblasts form chain-like collectives and modify the morphology of glial cells (astrocytes), which are the main components of the surrounding environment. However, it remains unclear how neuroblasts form collectives and how efficient migration is achieved through collective formation in a pool of astrocytes. The main difficulty lies in tracking individual neuroblasts within the collective, both in vitro and in vivo, over a period. To address this impasse, we built a mathematical model of the neuroblast-astrocyte system to assess its long-term performance in silico. Our simulations showed that individual neuroblasts gathered into chain-like collectives through occasional contact, astrocyte confinement, and moderate adhesion between the neuroblasts. The forward movement of neuroblasts in an astrocyte-dense environment was accelerated if we assumed a simple interaction: the higher the number of neuroblasts near an astrocyte, the stronger the shrinkage of astrocytic protrusions. Furthermore, temporal changes in neuroblast behavior, as indicated by our observation of living neuroblasts in culture, reinforce the advantages of simulated collectives. A collective of neuroblasts with constant behavior sometimes repeated non-migratory movements, whereas those with inconstant behavior were easily untangled, resulting in a rapid migration. These results highlight the potential for neuroblast collectivity and inconstancy in enhancing neuronal regeneration in the adult brain.