{"id":{"repo_id":"toronto-retro","oai_identifier":"oai:utoronto.scholaris.ca:1807/150060"},"canonical_url":"https://search.dev.ndltd.org/etd/toronto-retro/oai:utoronto.scholaris.ca:1807/150060","repository":{"repo_id":"toronto-retro","name":"University of Toronto","base_url":"https://utoronto.scholaris.ca/server/oai/request"},"display":{"title":"Modelling resting-state neural oscillations to uncover circuit mechanisms of aging and rTMS response in late-life depression","abstract":"Understanding the brain's intrinsic activity is essential for advancing treatments for psychiatric and neurological disorders. Resting-state oscillations are rhythmic neural patterns observed when the brain is not engaged in a specific task, measurable via magnetoencephalography (MEG) or electroencephalography (EEG). Alpha rhythms, a prominent feature of these oscillations, are altered in several psychiatric and neurological disorders, namely depression. However, their underlying mechanisms remain poorly understood, especially how they vary across brain regions and respond to therapeutic interventions such as transcranial magnetic stimulation (TMS). Gaining mechanistic insight into these dynamics holds critical potential for optimizing neuromodulation strategies and personalizing clinical care. In the first study, we investigated four neural population models (Jansen-Rit (JR), Moran-David-Friston (MDF), Liley-Wright (LW), and Robinson-Rennie-Wright (RRW)) representing the alpha rhythm within cortical and corticothalamic circuits, evaluating their strengths, limitations, and key parameter effects. This provided a deeper understanding of the modelled mechanistic circuits of alpha generation. In the second study, we fitted the previously explored neurophysiological corticothalamic model to resting-state MEG power spectra, to define the changes in spatial and age-related neural mechanisms. We found that corticothalamic activity was most prominent in occipital regions, where aging was associated with increased corticothalamic delays and a slowing of alpha rhythms. In contrast, frontal regions exhibited greater age-related changes in intrathalamic inhibition, suggesting that the mechanisms underlying alpha generation vary across brain regions, and that TMS targeting strategies should account for this spatial heterogeneity. The final study analysed resting-state EEG data from individuals with late-life depression before and after accelerated bilateral rTMS treatment. Successful TMS response was strongly associated with model-derived increased intrathalamic inhibition in the right frontal hemisphere, highlighting the potential role of inhibitory regulation in therapeutic outcomes for late-life depression. This research provides new insights into resting-state oscillations, namely alpha rhythms, in the context of healthy aging and rTMS response in late-life depression. More broadly, it presents a framework for integrating computational modelling with resting-state oscillations to investigate underlying neural circuit mechanisms across space, age, and pathology, showing its application for understanding neuromodulation outcomes and its potential for application across diverse clinical conditions beyond depression.","abstract_html":"Understanding the brain's intrinsic activity is essential for advancing treatments for psychiatric and neurological disorders. Resting-state oscillations are rhythmic neural patterns observed when the brain is not engaged in a specific task, measurable via magnetoencephalography (MEG) or electroencephalography (EEG). Alpha rhythms, a prominent feature of these oscillations, are altered in several psychiatric and neurological disorders, namely depression. However, their underlying mechanisms remain poorly understood, especially how they vary across brain regions and respond to therapeutic interventions such as transcranial magnetic stimulation (TMS). Gaining mechanistic insight into these dynamics holds critical potential for optimizing neuromodulation strategies and personalizing clinical care. In the first study, we investigated four neural population models (Jansen-Rit (JR), Moran-David-Friston (MDF), Liley-Wright (LW), and Robinson-Rennie-Wright (RRW)) representing the alpha rhythm within cortical and corticothalamic circuits, evaluating their strengths, limitations, and key parameter effects. This provided a deeper understanding of the modelled mechanistic circuits of alpha generation. In the second study, we fitted the previously explored neurophysiological corticothalamic model to resting-state MEG power spectra, to define the changes in spatial and age-related neural mechanisms. We found that corticothalamic activity was most prominent in occipital regions, where aging was associated with increased corticothalamic delays and a slowing of alpha rhythms. In contrast, frontal regions exhibited greater age-related changes in intrathalamic inhibition, suggesting that the mechanisms underlying alpha generation vary across brain regions, and that TMS targeting strategies should account for this spatial heterogeneity. The final study analysed resting-state EEG data from individuals with late-life depression before and after accelerated bilateral rTMS treatment. Successful TMS response was strongly associated with model-derived increased intrathalamic inhibition in the right frontal hemisphere, highlighting the potential role of inhibitory regulation in therapeutic outcomes for late-life depression. This research provides new insights into resting-state oscillations, namely alpha rhythms, in the context of healthy aging and rTMS response in late-life depression. More broadly, it presents a framework for integrating computational modelling with resting-state oscillations to investigate underlying neural circuit mechanisms across space, age, and pathology, showing its application for understanding neuromodulation outcomes and its potential for application across diverse clinical conditions beyond depression.","abstract_has_math":false,"creators":["Bastiaens, Sorenza Pawla M"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Medical Science","school":null,"contributors":[],"advisors":["Griffiths, John D"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-10","date_published":"2025-10","updated_at":"2026-07-27T21:28:02Z","subjects":["Alpha oscillations","Circuit mechanisms","Computational Neuroscience","M/EEG","Neural modelling","Power spectrum"],"languages":[],"rights":["Attribution 4.0 International"],"rights_urls":["http://creativecommons.org/licenses/by/4.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1807/150060","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Griffiths, John D"]},{"key":"dc:contributor.department","label":"Department","values":["Medical Science"]},{"key":"dc:creator","label":"Author","values":["Bastiaens, Sorenza Pawla M"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-10"]},{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-11-28T16:58:01Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-10"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Alpha oscillations","Circuit mechanisms","Computational Neuroscience","M/EEG","Neural modelling","Power spectrum"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Attribution 4.0 International"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://creativecommons.org/licenses/by/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1807/150060"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Understanding the brain's intrinsic activity is essential for advancing treatments for psychiatric and neurological disorders. Resting-state oscillations are rhythmic neural patterns observed when the brain is not engaged in a specific task, measurable via magnetoencephalography (MEG) or electroencephalography (EEG). Alpha rhythms, a prominent feature of these oscillations, are altered in several psychiatric and neurological disorders, namely depression. However, their underlying mechanisms remain poorly understood, especially how they vary across brain regions and respond to therapeutic interventions such as transcranial magnetic stimulation (TMS). Gaining mechanistic insight into these dynamics holds critical potential for optimizing neuromodulation strategies and personalizing clinical care. In the first study, we investigated four neural population models (Jansen-Rit (JR), Moran-David-Friston (MDF), Liley-Wright (LW), and Robinson-Rennie-Wright (RRW)) representing the alpha rhythm within cortical and corticothalamic circuits, evaluating their strengths, limitations, and key parameter effects. This provided a deeper understanding of the modelled mechanistic circuits of alpha generation. In the second study, we fitted the previously explored neurophysiological corticothalamic model to resting-state MEG power spectra, to define the changes in spatial and age-related neural mechanisms. We found that corticothalamic activity was most prominent in occipital regions, where aging was associated with increased corticothalamic delays and a slowing of alpha rhythms. In contrast, frontal regions exhibited greater age-related changes in intrathalamic inhibition, suggesting that the mechanisms underlying alpha generation vary across brain regions, and that TMS targeting strategies should account for this spatial heterogeneity. The final study analysed resting-state EEG data from individuals with late-life depression before and after accelerated bilateral rTMS treatment. Successful TMS response was strongly associated with model-derived increased intrathalamic inhibition in the right frontal hemisphere, highlighting the potential role of inhibitory regulation in therapeutic outcomes for late-life depression. This research provides new insights into resting-state oscillations, namely alpha rhythms, in the context of healthy aging and rTMS response in late-life depression. More broadly, it presents a framework for integrating computational modelling with resting-state oscillations to investigate underlying neural circuit mechanisms across space, age, and pathology, showing its application for understanding neuromodulation outcomes and its potential for application across diverse clinical conditions beyond depression."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Modelling resting-state neural oscillations to uncover circuit mechanisms of aging and rTMS response in late-life depression"]}]}],"canonical_facts":{"dc:contributor.advisor":["Griffiths, John D"],"dc:contributor.department":["Medical Science"],"dc:creator":["Bastiaens, Sorenza Pawla M"],"dc:date":["2025-10"],"dc:date.accessioned":["2025-11-28T16:58:01Z"],"dc:date.issued":["2025-10"],"dc:description.abstract":["Understanding the brain's intrinsic activity is essential for advancing treatments for psychiatric and neurological disorders. Resting-state oscillations are rhythmic neural patterns observed when the brain is not engaged in a specific task, measurable via magnetoencephalography (MEG) or electroencephalography (EEG). Alpha rhythms, a prominent feature of these oscillations, are altered in several psychiatric and neurological disorders, namely depression. However, their underlying mechanisms remain poorly understood, especially how they vary across brain regions and respond to therapeutic interventions such as transcranial magnetic stimulation (TMS). Gaining mechanistic insight into these dynamics holds critical potential for optimizing neuromodulation strategies and personalizing clinical care. In the first study, we investigated four neural population models (Jansen-Rit (JR), Moran-David-Friston (MDF), Liley-Wright (LW), and Robinson-Rennie-Wright (RRW)) representing the alpha rhythm within cortical and corticothalamic circuits, evaluating their strengths, limitations, and key parameter effects. This provided a deeper understanding of the modelled mechanistic circuits of alpha generation. In the second study, we fitted the previously explored neurophysiological corticothalamic model to resting-state MEG power spectra, to define the changes in spatial and age-related neural mechanisms. We found that corticothalamic activity was most prominent in occipital regions, where aging was associated with increased corticothalamic delays and a slowing of alpha rhythms. In contrast, frontal regions exhibited greater age-related changes in intrathalamic inhibition, suggesting that the mechanisms underlying alpha generation vary across brain regions, and that TMS targeting strategies should account for this spatial heterogeneity. The final study analysed resting-state EEG data from individuals with late-life depression before and after accelerated bilateral rTMS treatment. Successful TMS response was strongly associated with model-derived increased intrathalamic inhibition in the right frontal hemisphere, highlighting the potential role of inhibitory regulation in therapeutic outcomes for late-life depression. This research provides new insights into resting-state oscillations, namely alpha rhythms, in the context of healthy aging and rTMS response in late-life depression. More broadly, it presents a framework for integrating computational modelling with resting-state oscillations to investigate underlying neural circuit mechanisms across space, age, and pathology, showing its application for understanding neuromodulation outcomes and its potential for application across diverse clinical conditions beyond depression."],"dc:description.degree":["Ph.D."],"dc:identifier.uri":["https://hdl.handle.net/1807/150060"],"dc:rights":["Attribution 4.0 International"],"dc:rights.uri":["http://creativecommons.org/licenses/by/4.0/"],"dc:subject":["Alpha oscillations","Circuit mechanisms","Computational Neuroscience","M/EEG","Neural modelling","Power spectrum"],"dc:title":["Modelling resting-state neural oscillations to uncover circuit mechanisms of aging and rTMS response in late-life depression"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T21:28:02Z"}