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Masamizu, Y.

Publications and source records attributed to Masamizu, Y..

3 recordsLinked to original sources

Microfluidic platforms for probing spontaneous functional recovery in hierarchically modular neuronal networks

Inherent capacity to flexibly reorganize after injury is a hallmark of brain networks, and recent studies suggest that functional consequences of focal damage are strongly influenced by the networks non-random connectivity. Although many of these insights have been derived from animal models and computational simulations, experimental platforms that enable bottom-up investigations of the structure-function relationships underlying damage and recovery processes remain limited. In this study, we used polydimethylsiloxane microfluidic devices to construct hierarchically modular neuronal networks that mimic the architectural features of the mammalian cortex. Laser microdissection was employed to selectively sever intermodular connections, enabling controlled damage to either hub or peripheral connections. Damage to hub connections led to delayed recovery, requiring more than three days for correlations to re-emerge. In contrast, peripheral damage resulted in faster recovery. Experiments that induced repeated injury to neuronal networks further demonstrated that recovery primarily occurred through the formation of alternative pathways rather than restoration of the original connections. These findings highlight how topological features of neuronal networks shape their response to injury and subsequent reorganization, providing mechanistic insights into the intrinsic self-repair capacity of biological systems.

neuroscience↗

ARViS: A bleed-free multi-site automated injection robot for accurate, fast, and dense delivery of virus to mouse and marmoset brains

Genetically encoded fluorescent sensors continue to be developed and improved. If they could be expressed across multiple cortical areas in non-human primates, it would be possible to measure a variety of spatiotemporal dynamics of primate-specific cortical activity. Here, we develop an Automated Robotic Virus injection System (ARViS) for broad expression of a biosensor. ARViS consists of two technologies: image recognition of vasculature structures on the cortical surface to determine multiple injection sites without hitting them, and robotic control of micropipette insertion perpendicular to the cortical surface with 50-m precision. In mouse cortex, ARViS sequentially injected virus solution into 100 sites over a duration of 100-minutes with a bleeding probability of only 0.1% per site. Furthermore, ARViS successfully achieved 266-site injections over the frontoparietal cortex of a common marmoset. We demonstrate one-photon and two-photon calcium imaging in the marmoset frontoparietal cortex, illustrating the effective expression of biosensors delivered by ARViS.

neuroscience↗

Dynamics of motor direction representation in the primate premotor and primary motor cortices during sensorimotor learning

Sensorimotor learning requires reorganization of neuronal activity in the premotor cortex (PM) and primary motor cortex (M1). However, how PM- and M1-specific reorganization occurs in primates remains unclear. We conducted calcium imaging of these areas in common marmosets while they learned a two-target reaching (pull/push) task. Throughout learning, the dorsorostral PM (PMdr) showed peak activity earlier than the dorsocaudal PM (PMdc) and M1. PMdr showed decreased representation of newly introduced (push) movement, whereas PMdc and M1 maintained high representation. Many task-related neurons in PMdc and M1 exhibited a strong preference to either movement direction. PMdc neurons dynamically switched their preferred direction, whereas M1 neurons stably retained their preferred direction. Differences in preferred direction between adjacent neurons in PMdc increased during learning. These results suggest that in primate sensorimotor learning, dynamic motor representation in PMdc converts the cognitive sensorimotor signals of PMdr to stable and specific motor representation of M1.

neuroscience↗