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Relating the circadian dynamics of cortical glutamate to human motor plasticity: a trimodal MRS-EEG-fMRI imaging study

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Relating the circadian dynamics of cortical glutamate to human motor plasticity: a trimodal MRS-EEG-fMRI imaging study

Ezzatdoost, Kiana (2023) Relating the circadian dynamics of cortical glutamate to human motor plasticity: a trimodal MRS-EEG-fMRI imaging study. Masters thesis, Concordia University.

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Abstract

Performing voluntary motor actions, ranging from basic movements such as walking to more complex movements like playing piano, is an integral part of our daily life. Understanding the underlying mechanism of motor learning can benefit education, sports training, and clinical rehabilitation. This study aims to investigate motor learning using diurnal variation in glutamate concentration, the main excitatory neurotransmitter in the central nervous system. To do so, glutamate concentration was measured by magnetic resonance spectroscopy (MRS) at several time points during a control visit and an MSL visit, which employed a finger-tapping sequence task. The study focused on two regions of interest: the supplementary motor area (SMA) as a motor-implicated region and the posterior cingulate cortex (PCC) as a control region.
Electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) techniques were used alongside MRS to comprehensively track the underlying neuronal mechanism that facilitates learning.
The finding of this study suggests that neuronal plasticity and the creation of memory traces rely on the modulation of glutamate concentrations, specifically in the motor-implicated area. Additionally, the results reveal a tight coupling between metabolism, cerebral blood flow, and neuronal activity, indicating the necessity of employing multi-modal imaging studies to explore learning-induced processes.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Ezzatdoost, Kiana
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Date:26 December 2023
Thesis Supervisor(s):Benali, Habib
ID Code:993317
Deposited By: Kiana Ezzatdoost
Deposited On:05 Jun 2024 15:18
Last Modified:05 Jun 2024 15:18
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