BOLD fMRI detectable alterations of brain activity in children and adolescents on the autism spectrum
Thesis event information
Date and time of the thesis defence
Place of the thesis defence
Auditorium 7, Oulu university hospital
Topic of the dissertation
BOLD fMRI detectable alterations of brain activity in children and adolescents on the autism spectrum
Doctoral candidate
Licenciate of Medicine Jyri-Johan Paakki
Faculty and unit
University of Oulu Graduate School, Faculty of Medicine, The University of Oulu Graduate School; Faculty of Medicine; Medical Research Center Oulu, Medical Imaging, Physics, and Technology; Oulu University Hospital
Subject of study
Medicine, radiology
Opponent
Professor Juhana Hakumäki, University of Eastern Finland
Custos
Professor Vesa Kiviniemi, University of Oulu
Functional magnetic resonance imaging detects brain activity alterations in children and adolescents on the autism spectrum
Licentiate of Medicine Jyri-Johan Paakki's doctoral dissertation investigated possible differences in spontaneous and stimulated brain activity in autistic children and adolescents compared to neurotypical controls. Functional magnetic resonance imaging (fMRI) of the brain was used to examine the participants at rest and while they were looking at facial expressions.
Brain networks were identified using independent component analysis (ICA). The resting state was analyzed over the entire measurement period using the regional homogeneity (ReHo) method, which measures the local connectivity of the brain. In addition, resting state brain activity was analyzed based on the states of different brain networks grouped into shorter periods using the co-activation patterns (CAP) method. Statistically significant differences between groups were found, more clearly with the CAP method. Also, significant differences in brain activity were found between the groups regarding the observation of facial expressions. The observed differences manifested in several brain networks: default mode, vision, salience, and attention networks, as well as other executive function networks, but also in auditory and somatomotor networks.
The dissertation increases the understanding of changes in brain networks related to the autism spectrum, strengthening and supplementing previous research results. Based on our results, analyses of brain networks grouped into similar activation phases of shorter duration are worth further development. The new information can help develop earlier and more accurate imaging diagnostics, tentatively recognizing possible intervention target brain networks and evaluating therapeutic effects.
Brain networks were identified using independent component analysis (ICA). The resting state was analyzed over the entire measurement period using the regional homogeneity (ReHo) method, which measures the local connectivity of the brain. In addition, resting state brain activity was analyzed based on the states of different brain networks grouped into shorter periods using the co-activation patterns (CAP) method. Statistically significant differences between groups were found, more clearly with the CAP method. Also, significant differences in brain activity were found between the groups regarding the observation of facial expressions. The observed differences manifested in several brain networks: default mode, vision, salience, and attention networks, as well as other executive function networks, but also in auditory and somatomotor networks.
The dissertation increases the understanding of changes in brain networks related to the autism spectrum, strengthening and supplementing previous research results. Based on our results, analyses of brain networks grouped into similar activation phases of shorter duration are worth further development. The new information can help develop earlier and more accurate imaging diagnostics, tentatively recognizing possible intervention target brain networks and evaluating therapeutic effects.
Last updated: 23.1.2024