Transcranial direct current stimulation in Alzheimer's disease

The focus of this work was to integrate results from the clinical trials related to transcranial direct current stimulation (tDCS) treatment of Alzheimer’s disease (AD) into a machine learning model of the disease to understand the long term impact of the treatment on disease progression over a decade. The three main contributions of this work are: Firstly, this work proposes the extension of otherwise sparse clinical data for tDCS stimulation AD patients to predict patient outcomes over a decade long period. Secondly, it introduces scientific evidence of long-term improvement in AD with tDCS using brain region volume and MMSE score as key metrics. Thirdly, it calculates an optimal tDCS regimen for improvement in cognitive function for patients with AD. This paper synthesizes data from an emerging field of research (tDCS treatment) into general guidelines for best stimulation practices and potential outcomes which can be replicated to other diseases and treatments.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Sen, Pranay

Contributors dc:contributor

• Aggarwal, Anu

• Tdcs
• Alzheimer's Disease
• Machine Learning
• Regression

Statement dc:rights

• Copyright 2024 Pranay Sen

Language dc:language

eng, en

Handle dc:identifier

https://hdl.handle.net/2142/124454

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/124454