Deep phenotyping of adult ADHD traits and their associations with grey matter density and genetics

dc.contributor.advisorTiego, Jeggan
dc.contributor.advisorArnatkeviciute, Aurina
dc.contributor.advisorFornito, Alex
dc.contributor.advisorFranke, Barbara
dc.contributor.advisorBellgrove, Mark
dc.contributor.authorRovný, Maroš
dc.description.abstractAttention deficit hyperactivity disorder (ADHD) is recognized by its expressions across three core traits: hyperactivity, impulsivity, and inattention. By influencing behavior across contexts, the three traits further propel variations across a fourth trait, problems with self-concept. As psychiatry moves towards a dimensional understanding of psychopathology, it becomes exceedingly important to understand how these traits relate to the genetics and brain structure not only within patients diagnosed with ADHD but also across the entire continuum of variation present in the healthy population. A total of 850 participants (ages 18-50) were recruited from the general community in Melbourne, Australia. The behavioral data measured by Conners’ Adult ADHD Rating Scale Self-Report: Long Version (CAARS-S:L) underwent extensive psychometric analysis using structural equation modeling (SEM) and item response theory (IRT), providing improved estimates of the underlying traits. These new psychometric measures were then combined with polygenic risk scores and voxel-based morphometric images using both univariate (general linear model) and multivariate (canonical correlation analysis) approaches. Through SEM analysis, two severity classes with four factors were found to provide the best fit for our sample. This is in contrast to subtype classes and higher-order factor solutions usually fitted to clinical samples and provides an insight into important psychometric differences between general and patient populations. Following the IRT modeling, the original eight dimensions and 66 items of the CAARS-S:L were reduced to four dimensions (Inattention, Impulsivity, Hyperactivity, Problems with Self-Concept) captured by 21 items, accentuating the importance of addressing measurement variability of psychometric instruments per sample basis. In the subsequent analyses, no significant associations were found with genetic or brain measures. These results should be interpreted in light of the study limitations, including the relatively small sample size and small expected effect sizes. In absence of cross-modal associations, the primary insights of this thesis relate to psychometric discoveries. Keywords: adult ADHD, community sample, psychometric modelling, voxel based morphometry, polygenic risk scores
dc.thesis.facultyFaculteit der Sociale Wetenschappen
dc.thesis.specialisationspecialisations::Faculteit der Sociale Wetenschappen::Researchmaster Cognitive Neuroscience::Researchmaster Cognitive Neuroscience
dc.thesis.studyprogrammestudyprogrammes::Faculteit der Sociale Wetenschappen::Researchmaster Cognitive Neuroscience
dc.titleDeep phenotyping of adult ADHD traits and their associations with grey matter density and genetics
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