Supervised dimensionality reduction applications in identifying cognitive correlates of psychiatric symptoms and extracting low-dimensional EEG signals to study the effects of tACS
department or school: Science, Faculty of
data provider: DSpace
degree campus: UBCV
creator: Chinchani, Abhijit Mahesh
issued: 2024-09-09T16:19:03Z
issued: 2024
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: In the age of big data and large neuroscience datasets, dimensionality reduction is employed to reduce the complexity of recorded data and explain it with fewer representative variables. Unlike unsupervised methods, which rely solely on the data's inherent structure, supervised techniques leverage additional information to achieve better feature extraction and data representation. In this dissertation, I present two applications of supervised techniques: one to identify cognitive correlates of psychiatric symptoms and the other to estimate latent EEG dimensions to study the effects of transcranial alternating current stimulation (tACS). First, I applied constrained principal component analysis to identify cognitive correlates of psychiatric symptoms in two datasets. In the first dataset, I examined the overlap between cognition and hallucinatory experiences and developed a set of reliability tests to minimize the reporting of spurious results due to overfitting. Next, I improved the reliability testing algorithm and used it to find links between cognition and symptoms in schizophrenia spectrum disorder in another dataset. This approach led to a nuanced and multimodal understanding of psychiatric symptoms. Next, I applied joint decorrelation, another supervised technique, to extract latent EEG oscillations corresponding to frequency-specific oscillations and to study whether tACS affects them. I first investigated the effects of tACS on auditory steady-state responses (ASSRs). Narrowband latent oscillations for ASSRs at 40Hz were extracted, but no effects of tACS on these evoked oscillations were observed. Subsequently, I examined the effects of tACS on broadband alpha oscillations. By extracting latent dimensions corresponding to alpha frequencies, I observed that tACS affected alpha oscillations in a task-specific manner. This adds a critical variable to be considered for applications of tACS, whereby increasing the electric field by placing the anode further away …
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/89207
note: SUPERVISED DIMENSIONALITY REDUCTION APPLICATIONS IN IDENTIFYING COGNITIVE CORRELATES OF PSYCHIATRIC SYMPTOMS AND EXTRACTING LOW-DIMENSIONAL EEG SIGNALS TO STUDY THE EFFECTS OF TACS by ABHIJIT MAHESH CHINCHANI B.E., P.E.S. Institute of Technology, 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Bioinformatics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) September 2024 © Abhijit Mahesh Chinchani, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Supervised Dimensionality Reduction Applications in Identifying Cognitive Correlates of Psychiatric Symptoms and Extracting Low-dimensional EEG Signals to study the effects of tACS submitted by Abhijit Mahesh Chinchani in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Bioinformatics Examining Committee: Todd S. Woodward, Professor, Bioinformatics, UBC Supervisor Anthony Herdman, Associate Professor, Audiology and Speech Sciences, UBC Supervisory Committee Member Rebecca Todd, Associate Professor, Psychology, UBC Supervisory Committee Member Daniela J. Palombo, Associate Professor, Psychology, UBC University Examiner William Panenka, Associate Professor, Psychiatry, UBC University Examiner Oren Griffiths, Senior Lecturer, Psychological Sciences, Newcastle University External Examiner Additional Supervisory Committee Members: Lawrence Ward, Professor, Psychology, UBC Supervisory Committee Member Roger Tam, Associate Professor, Bioinformatics, UBC Supervisory Committee Member iii Abstract In the age of big data and large neuroscience datasets, dimensionality reduction is employed to reduce the complexity of recorded data and explain it with fewer representative variables. Unlike unsupervised methods, which rely solely on the data's inherent structure, supervised te…
has type: Thesis/Dissertation
date issued: 2024-11
is shown at: 10.14288/1.0445354
language: eng
degree discipline: Bioinformatics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Woodward, Todd S.
type: Text
identifier URI: http://hdl.handle.net/2429/89207