Phenotype and outcome prediction of depression patients using digital twins
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URL
Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu |
Master's thesis
Authors
Date
2024-01-22
Department
Major/Subject
Biomedical Engineering
Mcode
SCI3059
Degree programme
Master’s Programme in Life Science Technologies
Language
en
Pages
5+38
Series
Abstract
Depression is a heterogeneous disorder comprising different subtypes that need different treatment approaches. Understanding the relationships between subgroups and associated biomarkers is essential for personalized treatment. Studying brain oscillations to identify biomarkers is promising for facilitating the precision of diagnosis and treatment. However, mapping structural data to functional data is challenging. Computational models that reproduce activity similar to brain signals can show possible relationships through model fitting. Parameters of personalized models can provide additional information on brain oscillations. This work utilizes the parameters of the Hierarchical Kuramoto model to phenotyping and predicting outcomes of patients with depression. The fitted parameters in specific functional subsystems, such as Control, Default, Salience Ventral Attention, and Limbic, correlate with depression severity. In addition, the model parameters can predict the treatment outcomes with an accuracy of 60%.Description
Supervisor
Palva, MatiasThesis advisor
Palva, SatuMyrov, Vladislav
Keywords
depression, phenotyping, prediction, personalization