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Thalamic Contributions to Motor Lear...

Sibener, Leslie Joan.

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Thalamic Contributions to Motor Learning and Performance.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Thalamic Contributions to Motor Learning and Performance./
Author:	Sibener, Leslie Joan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	167 p.
Notes:	Source: Dissertations Abstracts International, Volume: 84-10, Section: B.
Contained By:	Dissertations Abstracts International84-10B.
Subject:	Neurosciences. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30316545
ISBN:	9798379413798

Thalamic Contributions to Motor Learning and Performance.
Sibener, Leslie Joan.

Thalamic Contributions to Motor Learning and Performance. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 167 p.

Source: Dissertations Abstracts International, Volume: 84-10, Section: B.

Thesis (Ph.D.)--Columbia University, 2023.

.

Movement is the key to animal behavior. From fighting off predators to reaching for food, our survival relies on movement. Losing the ability to move the body through the world in a purposeful way would be dire. We learn to perform a wide variety of actions, which require exact motor control. How are such skilled actions refined over time? The neural mechanism of motor learning has been posited to arise from integrating neuronal signals about motor commands, environmental context, and outcome through the cortico-basal ganglia-thalamic loop. Here, I investigate the role of two thalamic nuclei - the parafascicular (Pf) and ventroanterior/ventrolateral (VAL) -in the process of motor learning. In an introductory Chapter 1, I introduce some key behavioral signatures of motor learning and the distributed neural circuity for movement through the cortico-basal ganglia-thalamic network. Pf and VAL are at the center of this network. Both receive basal ganglia output but differ in primary projection patterns. Pf sends large excitatory projections directly to the striatum (the main input area of the basal ganglia), while VAL projects back to the cortex. Despite their critical place in the movement system, little is known about their changing roles in motor learning. In Chapter 2, I highlight a novel skilled forelimb joystick target task for mice; the JTT. In the JTT, head-fixed mice learn reaches to spatial targets in 2D space by moving an unrestricted joystick without visual feedback. This task allows for multiple windows of learning and refinement of various reaches in space. Over the learning of targeted reaching movements, mice increase their accuracy and individual trajectories become less variable, showing that they have learned the location of the target in space, and also refine the reaching movements. In Chapter 3, I use 2-photon calcium imaging of the forelimb-related areas of Pf and VAL to investigate how their activity changes over learning of forelimb reaching actions. Both Pf and VAL are highly engaged during movements. Neural population engagement of Pf decreases over time, suggesting a specific role early in learning. Additionally, the underlying neural dynamics of Pf and VAL shift and occupy different state spaces over learning, as shown through principal component analysis. To investigate if neural activity in Pf or VAL encodes behavioral information, we used a ridge regression model to predict the initial direction of movements from neural data. We were able to predict the initial direction from Pf activity on early training days, but not from VAL. In Chapter 4, I performed pre and post-learning lesions to Pf or VAL to investigate if they are needed for learning and/or performance of targeted reaches. Results show that Pf is needed for learning, but not the performance of accurate spatial reaches. VAL, on the other hand, does not affect the learning or performance of target reaches, but does affect the speed of movements. In a discussion-based Chapter 5, I summarize these above experiments, which suggest different roles for PF and VAL over learning of multiple targeted reaches, and reflect on future directions of my findings in the broader context of motor learning research in neuroscience. In particular, my findings highlight a novel and critical role for Pf in learning and processing directional information during early skill learning. This work demonstrates that the thalamus is an essential node of the brain networks involved in motor learning.

ISBN: 9798379413798Subjects--Topical Terms:

588700
Neurosciences.
Subjects--Index Terms:

Motor learning

Thalamic Contributions to Motor Learning and Performance.
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500 $a Source: Dissertations Abstracts International, Volume: 84-10, Section: B.
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502 $a Thesis (Ph.D.)--Columbia University, 2023.
506 $a .
520 $a Movement is the key to animal behavior. From fighting off predators to reaching for food, our survival relies on movement. Losing the ability to move the body through the world in a purposeful way would be dire. We learn to perform a wide variety of actions, which require exact motor control. How are such skilled actions refined over time? The neural mechanism of motor learning has been posited to arise from integrating neuronal signals about motor commands, environmental context, and outcome through the cortico-basal ganglia-thalamic loop. Here, I investigate the role of two thalamic nuclei - the parafascicular (Pf) and ventroanterior/ventrolateral (VAL) -in the process of motor learning. In an introductory Chapter 1, I introduce some key behavioral signatures of motor learning and the distributed neural circuity for movement through the cortico-basal ganglia-thalamic network. Pf and VAL are at the center of this network. Both receive basal ganglia output but differ in primary projection patterns. Pf sends large excitatory projections directly to the striatum (the main input area of the basal ganglia), while VAL projects back to the cortex. Despite their critical place in the movement system, little is known about their changing roles in motor learning. In Chapter 2, I highlight a novel skilled forelimb joystick target task for mice; the JTT. In the JTT, head-fixed mice learn reaches to spatial targets in 2D space by moving an unrestricted joystick without visual feedback. This task allows for multiple windows of learning and refinement of various reaches in space. Over the learning of targeted reaching movements, mice increase their accuracy and individual trajectories become less variable, showing that they have learned the location of the target in space, and also refine the reaching movements. In Chapter 3, I use 2-photon calcium imaging of the forelimb-related areas of Pf and VAL to investigate how their activity changes over learning of forelimb reaching actions. Both Pf and VAL are highly engaged during movements. Neural population engagement of Pf decreases over time, suggesting a specific role early in learning. Additionally, the underlying neural dynamics of Pf and VAL shift and occupy different state spaces over learning, as shown through principal component analysis. To investigate if neural activity in Pf or VAL encodes behavioral information, we used a ridge regression model to predict the initial direction of movements from neural data. We were able to predict the initial direction from Pf activity on early training days, but not from VAL. In Chapter 4, I performed pre and post-learning lesions to Pf or VAL to investigate if they are needed for learning and/or performance of targeted reaches. Results show that Pf is needed for learning, but not the performance of accurate spatial reaches. VAL, on the other hand, does not affect the learning or performance of target reaches, but does affect the speed of movements. In a discussion-based Chapter 5, I summarize these above experiments, which suggest different roles for PF and VAL over learning of multiple targeted reaches, and reflect on future directions of my findings in the broader context of motor learning research in neuroscience. In particular, my findings highlight a novel and critical role for Pf in learning and processing directional information during early skill learning. This work demonstrates that the thalamus is an essential node of the brain networks involved in motor learning.
590 $a School code: 0054.
650 4 $a Neurosciences. $3 588700
650 4 $a Developmental biology. $3 592588
653 $a Motor learning
653 $a Thalamus
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653 $a VAL nucleus
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690 $a 0758
710 2 $a Columbia University. $b Neurobiology and Behavior. $3 3173198
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793 $a English
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