자료유형  

 학위논문

Control Number  

0017161196

International Standard Book Number  

9798382757803

Dewey Decimal Classification Number  

153

Main Entry-Personal Name  

Porter, Alexis Grace.

Publication, Distribution, etc. (Imprint  

[S.l.] : Northwestern University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

187 p.

General Note  

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

General Note  

Advisor: Gratton, Caterina.

Dissertation Note  

Thesis (Ph.D.)--Northwestern University, 2024.

Summary, Etc.  

요약The study of psychiatric disorders often involves tracking changes in brain function as it relates to symptoms. While research has found significant differences in functional network organization as it relates to various clinical outcomes, the exact neural biomarkers that contribute to symptom severity is often inconsistent. Prior research has shown that methodological considerations in sample size, motion artifacts, and increasing data quantity at the individual level can lead to substantial improvements in reliability. In Chapter 1, I provide a brief overview on existing work surrounding different methodological approaches that improve prediction of behavioral variables from brain network data. In the next three chapters, I describe research projects that aimed to use different cutting-edge approaches to improve machine learning prediction of behavior. In Chapter 2, I tested whether machine learning classification can improve our understanding of how brain networks are altered during tasks by using an individual specific approach. I found that individual focused approaches can uncover robust features of brain states, including features obscured in cross-subject analyses. In Chapter 3 I asked whether prediction of Schizophrenia Spectrum Disorders could be improved by joining together different neuroimaging modalities. I conducted a meta-analysis and systematic review of the existing literature and found no significant evidence for an advantage of multimodal relative to unimodal imaging approaches. However, this result could have been driven by biased effect sizes, particularly highlighting the need for improvements in data quality and quantity. In Chapter 4 I sought to test whether the prediction of clinical (especially psychosis) and cognitive measures from brain network data could be improved by either using person-specific parcellations or extended amounts of data from each participant. I found that increasing the quantity of data at the individual level exhibited significant improvements at predicting clinical and cognitive measures compared to resting state models, with only smaller scale effects associated with individual parcellations. Finally, in Chapter 5 I provide a brief general discussion to highlight the contributions of this work and future directions.

Subject Added Entry-Topical Term  

Cognitive psychology.

Subject Added Entry-Topical Term  

Neurosciences.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Medical imaging.

Index Term-Uncontrolled  

Cognitive measures

Index Term-Uncontrolled  

Psychosis

Index Term-Uncontrolled  

Clinical outcomes

Index Term-Uncontrolled  

Individual differences

Index Term-Uncontrolled  

Machine learning

Added Entry-Corporate Name  

Northwestern University Psychology

Host Item Entry  

Dissertations Abstracts International. 85-11B.

Electronic Location and Access  

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Control Number  

joongbu:654257