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Thesis Format

Monograph

Degree

Master of Engineering Science

Program

Electrical and Computer Engineering

Supervisor

Patel, Rajni V.

Abstract

Levodopa is a dopamine replacement medication administered to patients with Parkinson’s disease (PD) to alleviate their motor symptoms. However, its long-term use can cause adverse side effects, including involuntary motor movements. We studied 16 PD patients before and after taking Levodopa based on resting-state electroencephalography (EEG) recordings to determine how Levodopa affects the functional connectivity of their brain networks. We used several metrics from graph theory, in particular the minimum spanning tree (MST) metric, and analyzed how they change after subjects take Levodopa. We observed significant changes in the lower alpha band toward a more path-like and less globally efficient network after Levodopa intake. We also observed that changes in multiple network metrics after taking Levodopa correlate with changes in the unified Parkinson’s disease rating scale (UPDRS) scores of PD patients in the lower alpha and beta bands.

Summary for Lay Audience

Parkinson's disease (PD) is a brain disorder that severely disrupts the human body's ability to move and perform daily tasks. The slowness of movement and tremors are some of the main visible symptoms of this disease, but PD can also cause dementia, depression, and sleep disorders in patients. Even though PD is more prevalent in older people, individuals much younger (in their forties) can also be diagnosed with PD. PD is a long-term disease, and its symptoms worsen with time, significantly diminishing a patient's quality of life. While there is currently no cure for PD, there are medications to alleviate some of their symptoms, the most important of which is Levodopa. Levodopa is a drug that is converted into dopamine in the brain, which in PD patients is significantly reduced. Levodopa is usually the first treatment PD patients receive, as it can alleviate their physical symptoms. As the disease progresses, patients need to take higher doses of the medication. This can cause side effects such as involuntary movements in the body, which some patients can also experience without the drug. In this study, to better understand the conflicting effects of Levodopa, we use Electroencephalography (EEG) to record brain signals of patients before and after taking Levodopa. We then use several mathematical methods from graph analysis, which were originally used to study social networks. We use these methods to observe how interactions between different brain regions change from a network point of view after taking Levodopa. Our methods show that Levodopa makes the brain less efficient as a network, even though taking Levodopa reduces Parkinsonian symptoms. We believe that these results can be used to provide an approach to adjust Levodopa dosage and better understand the underlying changes in PD patients after taking Levodopa.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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