{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/369993"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/369993","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Exploring Neuroimaging-Specific Deep Learning Biases: Uncertainty, Dynamic Graphs, and Communities","abstract":"The human brain is a complex dynamical system composed of numerous interacting regions. It has long been a subject of immense interest for neuroscientists seeking to understand brain function. Recent technological advancements have introduced non-invasive functional neuroimaging techniques, offering unparalleled insights into neural activity. Diverse imaging modalities, from electroencephalography's measurement of electrical signals to functional magnetic resonance imaging's visualisation of blood flow, have generated vast datasets that serve as a bedrock for quantitative neuroscience. Leveraging this data, new methods are emerging for investigating both normal brain function and pathological conditions. The use of machine learning (ML) in quantitative neuroscience has played a pivotal role in advancing understanding of brain function. Specifically, ML has been instrumental in discovering neuroimaging biomarkers that are fundamental to assessments of brain function. More recently, deep learning (DL), a subfield of ML, has become the forefront of this pursuit. To date, DL's accomplishments in the field of functional neuroimaging have predominantly centred on brain disorder classification, achieving performance levels on par with domain experts, albeit within mainly research settings. DL's success can be attributed, in part, to its unparalleled ability for representation learning, where highly complex non-linear patterns are extracted directly from raw neuroimaging data. This proficiency is further refined by the inductive biases incorporated in DL model designs, specifically crafted to fit the unique characteristics of the input data. This thesis embarks on a deeper exploration of DL methods applied to functional neuroimaging data, moving beyond conventional classification tasks. We introduce novel neural network architectures, influenced by neuroscientific-specific inductive biases, to enhance the representation learning process. These biases focus on efficient uncertainty quantification, dynamic brain graph structure learning, and dynamic brain graph community detection, all grounded in established neuroscientific findings. Through meticulous experiments, we prove these biases boost performance, enriching our understanding of model trustworthiness, robustness, and interpretability. The neuroimaging-focused DL architectures presented in this thesis offer the prospect of pioneering innovative data representations, driving transformative insights that stand to redefine the realm of functional neuroimaging research.","abstract_html":"The human brain is a complex dynamical system composed of numerous interacting regions. It has long been a subject of immense interest for neuroscientists seeking to understand brain function. Recent technological advancements have introduced non-invasive functional neuroimaging techniques, offering unparalleled insights into neural activity. Diverse imaging modalities, from electroencephalography's measurement of electrical signals to functional magnetic resonance imaging's visualisation of blood flow, have generated vast datasets that serve as a bedrock for quantitative neuroscience. Leveraging this data, new methods are emerging for investigating both normal brain function and pathological conditions. The use of machine learning (ML) in quantitative neuroscience has played a pivotal role in advancing understanding of brain function. Specifically, ML has been instrumental in discovering neuroimaging biomarkers that are fundamental to assessments of brain function. More recently, deep learning (DL), a subfield of ML, has become the forefront of this pursuit. To date, DL's accomplishments in the field of functional neuroimaging have predominantly centred on brain disorder classification, achieving performance levels on par with domain experts, albeit within mainly research settings. DL's success can be attributed, in part, to its unparalleled ability for representation learning, where highly complex non-linear patterns are extracted directly from raw neuroimaging data. This proficiency is further refined by the inductive biases incorporated in DL model designs, specifically crafted to fit the unique characteristics of the input data. This thesis embarks on a deeper exploration of DL methods applied to functional neuroimaging data, moving beyond conventional classification tasks. We introduce novel neural network architectures, influenced by neuroscientific-specific inductive biases, to enhance the representation learning process. These biases focus on efficient uncertainty quantification, dynamic brain graph structure learning, and dynamic brain graph community detection, all grounded in established neuroscientific findings. Through meticulous experiments, we prove these biases boost performance, enriching our understanding of model trustworthiness, robustness, and interpretability. The neuroimaging-focused DL architectures presented in this thesis offer the prospect of pioneering innovative data representations, driving transformative insights that stand to redefine the realm of functional neuroimaging research.","abstract_has_math":false,"creators":["Campbell, Alexander"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Lio, Pietro"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-10-31","date_published":"2023-10-31","updated_at":"2026-07-22T22:24:13Z","subjects":["Deep learning","Generative model","Graph neural network","Health AI","Neural network","Neuroimaging","Uncertainty"],"languages":["eng"],"rights":[],"rights_urls":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/68b76ded-cf89-4296-8a78-32c1d81a2942/download","https://www.rioxx.net/licenses/all-rights-reserved/"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.109575","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Lio, Pietro"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["The Alan Turing Institute"]},{"key":"dc:creator","label":"Author","values":["Campbell, Alexander"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2023-10-31"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/369993"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Deep learning","Generative model","Graph neural network","Health AI","Neural network","Neuroimaging","Uncertainty"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/68b76ded-cf89-4296-8a78-32c1d81a2942/download","https://www.rioxx.net/licenses/all-rights-reserved/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.109575"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/7cf7bb7d-82ba-46d6-ac0b-ac3fdfbeef59/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The human brain is a complex dynamical system composed of numerous interacting regions. 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These biases focus on efficient uncertainty quantification, dynamic brain graph structure learning, and dynamic brain graph community detection, all grounded in established neuroscientific findings. Through meticulous experiments, we prove these biases boost performance, enriching our understanding of model trustworthiness, robustness, and interpretability. 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