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Azim, E.

Publications and source records attributed to Azim, E..

2 recordsLinked to original sources

Modulation of tactile feedback for the execution of dexterous movement

While dexterity relies on the constant transmission of sensory information, unchecked feedback can be disruptive to behavior. Yet how somatosensory feedback from the hands is regulated as it first enters the brain, and whether this modulation exerts any influence on movement, remain unclear. Leveraging molecular-genetic access in mice, we find that tactile afferents from the hand recruit neurons in the brainstem cuneate nucleus whose activity is modulated by distinct classes of local inhibitory neurons. Selective manipulation of these inhibitory circuits can suppress or enhance the transmission of tactile information, affecting behaviors that rely on movement of the hands. Investigating whether these local circuits are subject to top-down control, we identify distinct descending cortical pathways that innervate cuneate in a complementary pattern. Somatosensory cortical neurons target the core tactile region of cuneate, while a large rostral cortical population drives feed-forward inhibition of tactile transmission through an inhibitory shell. These findings identify a circuit basis for tactile feedback modulation, enabling the effective execution of dexterous movement.

neuroscience

Anipose: a toolkit for robust markerless 3D pose estimation

AO_SCPLOWBSTRACTC_SCPLOWQuantifying movement is critical for understanding animal behavior. Advances in computer vision now enable markerless tracking from 2D video, but most animals live and move in 3D. Here, we introduce Anipose, a Python toolkit for robust markerless 3D pose estimation. Anipose is built on the popular 2D tracking method DeepLabCut, so users can easily expand their existing experimental setups to obtain accurate 3D tracking. It consists of four components: (1) a 3D calibration module, (2) filters to resolve 2D tracking errors, (3) a triangulation module that integrates temporal and spatial regularization, and (4) a pipeline to structure processing of large numbers of videos. We evaluate Anipose on four datasets: a moving calibration board, fruit flies walking on a treadmill, mice reaching for a pellet, and humans performing various actions. By analyzing 3D leg kinematics tracked with Anipose, we identify a key role for joint rotation in motor control of fly walking. We believe this open-source software and accompanying tutorials (anipose.org) will facilitate the analysis of 3D animal behavior and the biology that underlies it.

neuroscience