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1.
Nat Commun ; 9(1): 338, 2018 01 23.
Article in English | MEDLINE | ID: mdl-29362373

ABSTRACT

Cortical computation is distributed across multiple areas of the cortex by networks of reciprocal connectivity. However, how such connectivity contributes to the communication between the connected areas is not clear. In this study, we examine the communication between sensory and motor cortices. We develop an eye movement task in mice and combine it with optogenetic suppression and two-photon calcium imaging techniques. We identify a small region in the secondary motor cortex (MOs) that controls eye movements and reciprocally connects with a rostrolateral part of the higher visual areas (VRL/A/AL). These two regions encode both motor signals and visual information; however, the information flow between the regions depends on the direction of the connectivity: motor information is conveyed preferentially from the MOs to the VRL/A/AL, and sensory information is transferred primarily in the opposite direction. We propose that reciprocal connectivity streamlines information flow, enhancing the computational capacity of a distributed network.


Subject(s)
Cerebral Cortex/physiology , Eye Movements/physiology , Motor Cortex/physiology , Nerve Net/physiology , Animals , Brain Mapping , Humans , Mice, Inbred C57BL , Mice, Transgenic , Motor Neurons/physiology , Photic Stimulation/methods , Psychomotor Performance/physiology , Sensory Receptor Cells/physiology , Somatosensory Cortex/physiology
2.
Cell Rep ; 18(11): 2676-2686, 2017 03 14.
Article in English | MEDLINE | ID: mdl-28297671

ABSTRACT

Prepared movements are more efficient than those that are not prepared for. Although changes in cortical activity have been observed prior to a forthcoming action, the circuits involved in motor preparation remain unclear. Here, we use in vivo two-photon calcium imaging to uncover changes in the motor cortex during variable waiting periods prior to a forepaw reaching task in mice. Consistent with previous reports, we observed a subset of neurons with increased activity during the waiting period; however, these neurons did not account for the degree of preparation as defined by reaction time (RT). Instead, the suppression of activity of distinct neurons in the same cortical area better accounts for RT. This suppression of neural activity resulted in a distinct and reproducible pattern when mice were well prepared. Thus, the selective suppression of network activity in the motor cortex may be a key feature of prepared movements.


Subject(s)
Motor Cortex/physiology , Movement/physiology , Nerve Net/physiology , Animals , Male , Mice , Motor Activity/physiology , Neurons/physiology , Psychomotor Performance/physiology , Pupil/physiology , Reaction Time/physiology
4.
Nano Lett ; 15(10): 7043-50, 2015 Oct 14.
Article in English | MEDLINE | ID: mdl-26437378

ABSTRACT

The development of synthetic nanomotors for technological applications in particular for life science and nanomedicine is a key focus of current basic research. However, it has been challenging to make active nanosystems based on biocompatible materials consuming nontoxic fuels for providing self-propulsion. Here, we fabricate self-propelled Janus nanomotors based on hollow mesoporous silica nanoparticles (HMSNPs), which are powered by biocatalytic reactions of three different enzymes: catalase, urease, and glucose oxidase (GOx). The active motion is characterized by a mean-square displacement (MSD) analysis of optical video recordings and confirmed by dynamic light scattering (DLS) measurements. We found that the apparent diffusion coefficient was enhanced by up to 83%. In addition, using optical tweezers, we directly measured a holding force of 64 ± 16 fN, which was necessary to counteract the effective self-propulsion force generated by a single nanomotor. The successful demonstration of biocompatible enzyme-powered active nanomotors using biologically benign fuels has a great potential for future biomedical applications.


Subject(s)
Enzymes/metabolism , Nanostructures , Microscopy, Electron, Transmission
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