Saliency mapping of RS-fMRI data in GCNs for sex and brain age prediction

dc.contributor.authorAndersson, Kevin
dc.contributor.authorLindgren, Eric
dc.contributor.departmentChalmers tekniska högskola / Institutionen för fysiksv
dc.contributor.examinerVolpe, Giovanni
dc.contributor.supervisorDeimante Neimantaite, Alice
dc.contributor.supervisorSjöblom, Lisa
dc.date.accessioned2021-06-09T13:51:41Z
dc.date.available2021-06-09T13:51:41Z
dc.date.issued2021sv
dc.date.submitted2020
dc.description.abstractInsight into how biological sex and healthy ageing affects the human brain are important for an increased understanding of the brain. Healthy ageing insights are also useful for clinical applications, for instance in identifying unhealthy ageing due to neurodegenerative disease. To this end, several studies in the last few years have used machine learning methods on neuroscientific data to predict subject sex and brain age. One particularly interesting approach has been to represent functionally connected networks in the brain as graphs, and apply Graph Convolutional Networks (GCNs). To investigate which functional brain networks are connected with sex and age, we develop and analyse GCN-based models that predict sex and age from resting-state fMRI data. The analysis of the models is done using saliency mapping techniques that give insight into which functional brain networks in the data are relevant for the predictions. With this approach, we obtain a sex prediction accuracy of up to 79% and an age prediction MAE of 5.9 years. Furthermore, we find indications that the Somatomotor Medial Network and the cerebellum are among the more important functional brain networks for predicting sex and brain age.sv
dc.identifier.coursecodeTIFX05sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/302435
dc.language.isoengsv
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectmachine learningsv
dc.subjectsupervised learningsv
dc.subjectGNNsv
dc.subjectGCNsv
dc.subjectexplainability in AIsv
dc.subjectgraph theorysv
dc.subjectpopulation graphssv
dc.subjectbrain agesv
dc.subjectsexsv
dc.subjectfunctional connectivitysv
dc.subjectrestingstate fMRIsv
dc.subjectsaliency mappingsv
dc.titleSaliency mapping of RS-fMRI data in GCNs for sex and brain age predictionsv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
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