Choosing a regression model to predict cognition from a connectivity analysis of the brain

dc.contributor.advisorHinne, M.
dc.contributor.advisorGerven, M.A.J. van
dc.contributor.authorWijnands, I.M.
dc.description.abstractThe human brain has been of great interest to researchers for a very long time, as the source of our cognitive abilities and our behavior. The advances made in neuroimaging in the second part of the previous century, enabling us to visualize the brain in vivo [Ogawa et al. 1990] instead of during surgery or post mortum, resulted in an increase in knowledge of brain functioning. Different brain regions have different functions, which is especially apparent if there is a lesion in the brain, e.g. ischemia caused by a cerebrovascular accident (stroke), hemorrhaging or tumors, presenting with different symptoms depending on the region (e.g. slurred speech, lethargy, personality changes) [Kase and Caplan 1994; Keschner, Bender, and Strauss 1938; McCormick and Rosenfield 1973; Little et al. 1979; Uribe 1986]. More recently, technological advances in neuroscience and neuroimaging made it possible to look at the whole brain as a network, e.g. functional MRI. The network perspective has become increasingly important in neurology and neuroscience. Although the notion that the brain is a network is not new, the discipline of studying the relationship between broken connections and diseases (neurodegenerative disorders) has sped up in the last decades due to those technological advances.en_US
dc.embargo.typePermanent embargoen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationMaster Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.titleChoosing a regression model to predict cognition from a connectivity analysis of the brainen_US
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