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Mitochondrial transport is essential for neuronal function, but the evidence of connections between mitochondrial transport and axon regeneration in the central nervous system (CNS) of living vertebrates remains limited. Here, we developed a novel model to explore mitochondrial transport in a single Mauthner axon (M axon) of zebrafish with non-invasive in vivo imaging. To confirm the feasibility of using this model, we treated labeled zebrafish with nocodazole and demonstrated that it could disrupt mitochondrial transport. We also used two-photon laser axotomy to precisely axotomize M axons and simultaneously recorded their regeneration and the process of mitochondrial transport in living zebrafish larvae. The findings showed that the injured axons with stronger regenerative capability maintain greater mitochondrial motility. Furthermore, to stimulate axon regeneration, treatment with dibutyryl cyclic adenosine monophosphate (db-cAMP) could also augment mitochondrial motility. Taken together, our results provide new evidence that mitochondrial motility is positively correlated with axon regeneration in the living vertebrate CNS. This promising model will be useful for further studies on the interaction between axon regeneration and mitochondrial dynamics, using various genetic and pharmacological techniques.

Myelin plasticity occurs when newly-formed and pre-existing oligodendrocytes remodel existing myelination. Recent studies show these processes occur in response to changes in neuronal activity and are required for learning and memory. However, the link between behaviorally-relevant neuronal activity and circuit-specific changes in myelination remains unknown. Using longitudinal, in vivo two-photon imaging and targeted labeling of behaviorally-activated neurons, we explore how the pattern of intermittent myelination is altered on individual cortical axons during learning of a dexterous reach task. We show that learning-induced plasticity is targeted to behaviorally-activated axons and occurs in a staged response across cortical layers. During learning, myelin sheaths retract, lengthening nodes of Ranvier. Following learning, addition of new sheaths increases the number of continuous stretches of myelination. Computational modeling suggests these changes initially slow and subsequently increase conduction speed. Thus, behaviorallyactivated, circuit-specific changes to myelination may fundamentally alter how information is transferred in neural circuits during learning.

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