Unravelling the brain at resting state
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BORIS DOI
Subtitle
what differentiates physical activity levels in people’s default mode network?
Abstract
This dissertation investigates the intersection of physical activity (PA) and brain function, focusing on the Default Mode Network (DMN) and its neural correlates. The overarching aim is to explore how PA levels influence DMN activity and its potential implications for cognitive health and behavior change interventions.
The thesis comprises three studies, each addressing critical aspects of this relationship. The first study conducted a meta-scoping review connecting PA and the DMN to clinical and non-clinical paradigms such as attention, executive function, self-perception, stress, and mental health disorders like depression and anxiety. This extensive review identified 541 studies linking the DMN and PA indirectly, laying a foundation for understanding their interaction and supporting future research.
The second study focused on developing tools for PA measurement and classification. An open-source accelerometer-based activity recognition system was created, incorporating a trainable deep learning classifier. This approach addressed limitations in existing PA measurement tools, such as cost, transparency, and accessibility. The resulting system demonstrated high accuracy and adaptability, making it suitable for diverse applications, including integration with neurofeedback and brain-computer interface technologies.
The third study examined the direct connection between PA levels and DMN activity using electroencephalography (EEG) microstate analysis. The study revealed significant differences in microstate patterns between individuals with high and low PA levels. Specifically, Microstates B and C, associated with visual processing, self-referential thought, and emotional regulation, showed distinct variations. Active individuals exhibited higher durations and frequencies of Microstate C, linked to mind-wandering and introspection, suggesting enhanced cognitive and emotional processing capabilities.
This research highlights the bidirectional influence between PA and DMN activity, providing insights into how PA can affect cognitive and emotional states. The findings also underscore the potential of integrating PA and DMN-focused interventions, such as brain stimulation and neurofeedback, into health behavior change strategies.
The dissertation emphasizes open science principles, with all methods and tools developed to ensure transparency, reproducibility, and accessibility. By advancing understanding in this interdisciplinary domain, the work contributes to the fields of neuroscience, public health, and behavioral psychology, offering a pathway for innovative interventions to improve mental and physical health outcomes globally.
The thesis comprises three studies, each addressing critical aspects of this relationship. The first study conducted a meta-scoping review connecting PA and the DMN to clinical and non-clinical paradigms such as attention, executive function, self-perception, stress, and mental health disorders like depression and anxiety. This extensive review identified 541 studies linking the DMN and PA indirectly, laying a foundation for understanding their interaction and supporting future research.
The second study focused on developing tools for PA measurement and classification. An open-source accelerometer-based activity recognition system was created, incorporating a trainable deep learning classifier. This approach addressed limitations in existing PA measurement tools, such as cost, transparency, and accessibility. The resulting system demonstrated high accuracy and adaptability, making it suitable for diverse applications, including integration with neurofeedback and brain-computer interface technologies.
The third study examined the direct connection between PA levels and DMN activity using electroencephalography (EEG) microstate analysis. The study revealed significant differences in microstate patterns between individuals with high and low PA levels. Specifically, Microstates B and C, associated with visual processing, self-referential thought, and emotional regulation, showed distinct variations. Active individuals exhibited higher durations and frequencies of Microstate C, linked to mind-wandering and introspection, suggesting enhanced cognitive and emotional processing capabilities.
This research highlights the bidirectional influence between PA and DMN activity, providing insights into how PA can affect cognitive and emotional states. The findings also underscore the potential of integrating PA and DMN-focused interventions, such as brain stimulation and neurofeedback, into health behavior change strategies.
The dissertation emphasizes open science principles, with all methods and tools developed to ensure transparency, reproducibility, and accessibility. By advancing understanding in this interdisciplinary domain, the work contributes to the fields of neuroscience, public health, and behavioral psychology, offering a pathway for innovative interventions to improve mental and physical health outcomes globally.
Date of Publication
2023
Year of graduation
2023
Theses Type
dissertation
Language(s)
en
Author(s)
Faculty/Graduate School
Institute
Access(Rights)
open.access
Primary OA Publication
true