{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124454"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124454","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Transcranial direct current stimulation in Alzheimer's disease","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Sen, Pranay"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Aggarwal, Anu"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05-02","date_published":"2024-05-02","updated_at":"2026-07-22T22:25:00Z","subjects":["Tdcs","Alzheimer's Disease","Machine Learning","Regression"],"languages":["eng","en"],"rights":["Copyright 2024 Pranay Sen"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124454","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Aggarwal, Anu"]},{"key":"dc:creator","label":"Author","values":["Sen, Pranay"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05-02","2024-05"]},{"key":"dc:type","label":"Dc Type","values":["Text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Tdcs","Alzheimer's Disease","Machine Learning","Regression"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng","en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Pranay Sen"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124454"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Pranay Sen, accepted the attached license on 2024-05-01 at 14:37.","The student, Pranay Sen, submitted this Thesis for approval on 2024-05-01 at 14:41.","This Thesis was approved for publication on 2024-05-02 at 11:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20740 on 2024-09-16 at 00:37:46"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Transcranial direct current stimulation in Alzheimer's disease"]}]}],"canonical_facts":{"dc:contributor":["Aggarwal, Anu"],"dc:creator":["Sen, Pranay"],"dc:date":["2024-05-02","2024-05"],"dc:description":["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.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Pranay Sen, accepted the attached license on 2024-05-01 at 14:37.","The student, Pranay Sen, submitted this Thesis for approval on 2024-05-01 at 14:41.","This Thesis was approved for publication on 2024-05-02 at 11:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20740 on 2024-09-16 at 00:37:46"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124454"],"dc:language":["eng","en"],"dc:rights":["Copyright 2024 Pranay Sen"],"dc:subject":["Tdcs","Alzheimer's Disease","Machine Learning","Regression"],"dc:title":["Transcranial direct current stimulation in Alzheimer's disease"],"dc:type":["Text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}