東華大學圖書館 |

Identification of Brain Networks Involved in Sensorimotor Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Identification of Brain Networks Involved in Sensorimotor Learning./
作者:	Babadi, Saeed.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	178 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-10, Section: B.
Contained By:	Dissertations Abstracts International82-10B.
標題:	Adaptation. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28384017
ISBN:	9798708712110

Identification of Brain Networks Involved in Sensorimotor Learning.
Babadi, Saeed.

Identification of Brain Networks Involved in Sensorimotor Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 178 p.

Source: Dissertations Abstracts International, Volume: 82-10, Section: B.

Thesis (Ph.D.)--McGill University (Canada), 2020.

This item must not be sold to any third party vendors.

It is of great interest to study neural correlates of sensorimotor learning and the roles of different brain areas involved in the acquisition of motor skills. There are several forms of sensorimotor learning, each of which likely recruits distinct neural mechanisms and processes. However, there is still conjecture about how these processes are realized in the brain. This dissertation aimed to investigate brain networks involved in learning novel sensorimotor tasks. Our experimental paradigms required subjects to learn movements in novel haptic environments rendered by robotic interfaces. Training was followed by resting-state functional magnetic resonance imaging (fMRI). We performed functional connectivity analysis of resting-state fMRI obtained before and after training to determine the neural substrates of sensorimotor learning. By correlating change in the functional connectivity with metrics extracted from electromyography and behavioral data, we identified brain networks involved in specific aspects of sensorimotor learning.The objective of the first study was to identify brain networks involved in the control of muscle co-contraction, which is a fundamental element of motor learning and a necessity for stable interaction between humans and their physical environments. We employed a dynamic motor adaptation paradigm and acquired resting-state fMRI scans at distinct phases of adaptation. By analyzing the evolution of functional connectivity across the three phases of the adaptation process, we found that connections between the cerebellum and frontal and parietal regions are involved in modulating muscle co-contraction. Our results suggest that these brain networks play a significant role in regulating the mechanical impedance of a limb.The second study aimed at identifying the brain networks involved in learning to use small-scale tactile features to control the position of the hand. Although brain areas involved in tactile perception and discrimination of texture have been previously studied, the networks which map small-scale tactile features, such as differences in texture, to dexterous actions of the hand have received relatively little attention. Participants learned to locate a target strip on a flat surface where the intensity of the texture changed abruptly compared to the surround. As learning progressed, task difficulty was increased by reducing the difference in texture intensity between the target and surround. Functional connectivity analysis of resting-state fMRI before and after training identified a distributed parietal-cerebellar-frontal network which correlated with the ability to locate small-scale tactile features and dynamically map them to hand position. This network features regions that are particularly involved in focusing attention and high level processing of somatosensory information for control of movement. We also found that functional connectivity between primary and secondary somatosensory cortices increased, an indication of the computation required to process small-scale tactile features.In the third study, we aimed to determine whether there were common connections in the brain networks involved in the sensorimotor learning tasks of the first and second studies, namely motor adaptation and tactile learning. By correlating changes in functional connectivity with the learning rate for each task, we were able to identify a common connection which we believe plays a similar role in learning of both tasks. The networks also featured different functional connectivity to memory structures. In motor adaptation, functional connectivity between hippocampus and cerebellum increased with a negative correlation. In tactile learning, functional connectivity between medial frontal gyrus and cerebellum increased with a positive correlation. Superior frontal gyrus also featured prominently in both networks, albeit with seemingly different roles.In summary, this research investigated the neural substrates of two very different types of sensorimotor learning, which involved differences in the type of sensory information (visual and proprioceptive versus tactile), differences in the constancy of the required movement (consistent versus random) and differences in the sensorimotor control mechanism (feedforward versus feedback). The insights gained about common and specific brain networks in normal sensorimotor learning from these studies can be used to aid research related to rehabilitation of sensorimotor function and developing therapeutic and assistive technology for patients with damage to sensorimotor networks in the brain.

ISBN: 9798708712110Subjects--Topical Terms:

3562958
Adaptation.
Subjects--Index Terms:

Brain networks

Identification of Brain Networks Involved in Sensorimotor Learning.
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100 1 $a Babadi, Saeed. $3 3564635
245 1 0 $a Identification of Brain Networks Involved in Sensorimotor Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 178 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-10, Section: B.
500 $a Advisor: Milner, Theodore.
502 $a Thesis (Ph.D.)--McGill University (Canada), 2020.
506 $a This item must not be sold to any third party vendors.
520 $a It is of great interest to study neural correlates of sensorimotor learning and the roles of different brain areas involved in the acquisition of motor skills. There are several forms of sensorimotor learning, each of which likely recruits distinct neural mechanisms and processes. However, there is still conjecture about how these processes are realized in the brain. This dissertation aimed to investigate brain networks involved in learning novel sensorimotor tasks. Our experimental paradigms required subjects to learn movements in novel haptic environments rendered by robotic interfaces. Training was followed by resting-state functional magnetic resonance imaging (fMRI). We performed functional connectivity analysis of resting-state fMRI obtained before and after training to determine the neural substrates of sensorimotor learning. By correlating change in the functional connectivity with metrics extracted from electromyography and behavioral data, we identified brain networks involved in specific aspects of sensorimotor learning.The objective of the first study was to identify brain networks involved in the control of muscle co-contraction, which is a fundamental element of motor learning and a necessity for stable interaction between humans and their physical environments. We employed a dynamic motor adaptation paradigm and acquired resting-state fMRI scans at distinct phases of adaptation. By analyzing the evolution of functional connectivity across the three phases of the adaptation process, we found that connections between the cerebellum and frontal and parietal regions are involved in modulating muscle co-contraction. Our results suggest that these brain networks play a significant role in regulating the mechanical impedance of a limb.The second study aimed at identifying the brain networks involved in learning to use small-scale tactile features to control the position of the hand. Although brain areas involved in tactile perception and discrimination of texture have been previously studied, the networks which map small-scale tactile features, such as differences in texture, to dexterous actions of the hand have received relatively little attention. Participants learned to locate a target strip on a flat surface where the intensity of the texture changed abruptly compared to the surround. As learning progressed, task difficulty was increased by reducing the difference in texture intensity between the target and surround. Functional connectivity analysis of resting-state fMRI before and after training identified a distributed parietal-cerebellar-frontal network which correlated with the ability to locate small-scale tactile features and dynamically map them to hand position. This network features regions that are particularly involved in focusing attention and high level processing of somatosensory information for control of movement. We also found that functional connectivity between primary and secondary somatosensory cortices increased, an indication of the computation required to process small-scale tactile features.In the third study, we aimed to determine whether there were common connections in the brain networks involved in the sensorimotor learning tasks of the first and second studies, namely motor adaptation and tactile learning. By correlating changes in functional connectivity with the learning rate for each task, we were able to identify a common connection which we believe plays a similar role in learning of both tasks. The networks also featured different functional connectivity to memory structures. In motor adaptation, functional connectivity between hippocampus and cerebellum increased with a negative correlation. In tactile learning, functional connectivity between medial frontal gyrus and cerebellum increased with a positive correlation. Superior frontal gyrus also featured prominently in both networks, albeit with seemingly different roles.In summary, this research investigated the neural substrates of two very different types of sensorimotor learning, which involved differences in the type of sensory information (visual and proprioceptive versus tactile), differences in the constancy of the required movement (consistent versus random) and differences in the sensorimotor control mechanism (feedforward versus feedback). The insights gained about common and specific brain networks in normal sensorimotor learning from these studies can be used to aid research related to rehabilitation of sensorimotor function and developing therapeutic and assistive technology for patients with damage to sensorimotor networks in the brain.
520 $a Il est d'un grand interet d'etudier les correlats neuronaux de l'apprentissage sensorimoteur et le role des differentes zones du cerveau impliquees dans l'acquisition des aptitudes motrices. Il existe plusieurs formes d'apprentissage sensorimoteur, chacune d'entre elles recrutant vraisemblablement des mecanismes et des processus neuronaux distincts. Cependant, il existe encore des conjectures sur la facon dont ces processus sont realises dans le cerveau. Cette these a consiste a etudier les reseaux cerebraux impliques dans l'apprentissage de nouvelles taches sensorimotrices. Nos paradigmes experimentaux exigeaient que les sujets apprennent des mouvements dans de nouveaux environnements haptiques obtenus par des interfaces robotiques. Une imagerie par resonance magnetique fonctionnelle (IRMf) au repos etait obtenue apres les apprentissages. Nous avons effectue une analyse de la connectivite fonctionnelle de l'IRMf au repos avant et apres la formation afin de determiner les substrats neuronaux de l'apprentissage sensorimoteur. En correlant les changements de la connectivite fonctionnelle avec les mesures obtenues par electromyographie et des donnees comportementales, nous avons identifie les reseaux cerebraux impliques dans des aspects specifiques de l'apprentissage sensorimoteur.L'objectif de la premiere etude etait d'identifier les reseaux cerebraux impliques dans le controle de la co-contraction musculaire, qui est un element fondamental de l'apprentissage moteur et une necessite pour une interaction stable entre les humains et leur environnement physique. Nous avons utilise un paradigme d'adaptation motrice dynamique et acquis des images IRMf a l'etat de repos a des phases distinctes d'adaptation. En analysant l'evolution de la connectivite fonctionnelle a travers les trois phases du processus d'adaptation, nous avons decouvert que les connexions entre le cervelet et les regions frontale et parietale sont impliquees dans la modulation de la cocontraction musculaire. Nos resultats suggerent que ces reseaux cerebraux jouent un role important dans la regulation de l'impedance mecanique d'un membre.La seconde etude visait a identifier les reseaux cerebraux impliques dans l'apprentissage de l'utilisation de petites fonctions tactiles pour controler la position de la main. Bien que les zones cerebrales impliquees dans la perception tactile et la discrimination de la texture aient ete etudiees precedemment, les reseaux qui permettent de faire correspondre des caracteristiques tactiles a petite echelle, telles que les differences de texture, aux actions d'habilete de la main ont recu relativement peu d'attention. Les participants ont appris a localiser une bande cible sur une surface plane ou l'intensite de la texture changeait brusquement par rapport a l'environnement. Au fur et a mesure de l'apprentissage, la difficulte de la tache a ete accrue en reduisant la difference d'intensite de la texture entre la cible et l'environnement. L'analyse de la connectivite fonctionnelle de l'IRMf au repos, avant et apres la formation, a permis d'identifier un reseau entre les regions parietale, cerebelleuse et frontale qui etait en correlation avec la capacite de localiser des caracteristiques tactiles a petite echelle et de les mettre en correspondance de maniere dynamique avec la position de la main. Ce reseau comprend des regions qui sont particulierement impliquees dans la focalisation de l'attention et le traitement de haut niveau des informations somato-sensorielles pour le controle des mouvements. Nous avons egalement constate une augmentation de la connectivite fonctionnelle entre les cortex somato-sensoriels primaires et secondaires, indicatrice du calcul necessaire pour traiter les composantes tactiles a petite echelle. Dans la troisieme etude, nous avons cherche a determiner s'il existait des connexions communes dans les reseaux cerebraux impliques dans les taches d'apprentissage sensorimoteur de la premiere et de la deuxieme etude, a savoir l'adaptation motrice et l'apprentissage tactile. En correlant les changements de connectivite fonctionnelle avec le taux d'apprentissage pour chaque tache, nous avons pu identifier une connexion commune entre le cervelet et l'amygdale qui, selon nous, joue un role similaire dans l'apprentissage des deux taches. Dans l'adaptation motrice, le cervelet etait lie au cortex prefrontal tandis que dans l'apprentissage tactile, il etait lie au cortex parietal posterieur. Cette difference peut etre liee a l'importance relative de la memoire de travail et a la difference des modalites sensorielles fournissant un retour d'information dans les deux taches. Les reseaux presentaient egalement une connectivite fonctionnelle differente aux structures de la memoire. Dans l'adaptation motrice, la connectivite fonctionnelle entre l'hippocampe et le cervelet a augmente avec une correlation negative. Dans l'apprentissage tactile, la connectivite fonctionnelle entre le gyrus frontal medial et le cervelet a augmente avec une correlation positive. Le gyrus frontal superieur est egalement tres present dans les deux reseaux, bien qu'il semble jouer des roles differents.En resume, cette recherche a examine les substrats neuronaux de deux types tres differents d'apprentissage sensorimoteur, qui impliquent des differences dans le type d'informations sensorielles (visuelles et proprioceptives versus tactile), des differences dans la constance du mouvement requis (coherent versus aleatoire) et des differences dans le mecanisme de controle sensorimoteur (« feedforward » versus « feeback »). Les connaissances acquises sur les reseaux cerebraux communs et specifiques dans l'apprentissage sensorimoteur normal a partir de ces etudes peuvent etre utilisees pour aider la recherche en rehabilitation des fonctions sensorimotrices et au developpement de technologies therapeutiques et d'assistance pour les patients ayant des dommages de leurs reseaux sensorimoteurs cerebraux.
590 $a School code: 0781.
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650 4 $a Neurosciences. $3 588700
650 4 $a Biology. $3 522710
650 4 $a Memory. $3 522110
650 4 $a Neurophysiology. $3 580468
650 4 $a Brain research. $3 3561789
653 $a Brain networks
653 $a Sensorimotor learning
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710 2 $a McGill University (Canada). $3 1018122
773 0 $t Dissertations Abstracts International $g 82-10B.
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856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28384017