Learning Representations of Individual Di erences Using Low-Rank Approximations to Normative Models

No Thumbnail Available
Issue Date
Journal Title
Journal ISSN
Volume Title
Early detection of psychiatric disorders can help the prevention of symptoms and the detection of relevant biomarkers. Therefore, it is important to study neurodevelopment and its relation to symptoms of psychiatric disorders. One major problem in the study of psychiatric disorders is the heterogeneity of the clinical groups, meaning that the symptoms and underlying mechanism of the disorder can di er per individual. Normative modeling has been introduced as a method to deal with heterogeneity. It is a Bayesian method developed to model individual di erences with respect to a normal range. From the model, normative probability maps (NPM), which contain the individual deviance scores per voxel from the normative modeling, can be calculated. The NPMs can be used to study abnormalities in the brain, however, interpretation of these maps is a challenging task. To study the developing brain, normative modeling is applied to the n-back task fMRI from The Philadelphia Neurodevelopmental Cohort, while taking gender and age into account as covariates. To assist the interpretation of the NPMs, independent component analysis (ICA) and dictionary learning (DL), two decomposition techniques with di erent objective functions, are applied to the NPMs. These techniques provide a way to summarize interesting deviations in brain activation into a small number of components. Lastly, to study the accuracy of these techniques, the ICA and DL components are correlated to the behavioral data of the participants. Relevant components are assumed to have stronger correlations to the behavioral data. The results of the normative modeling show clear neurodevelopmental e ects in the brain areas associated with working memory. No clear di erences were found between the performance of ICA and DL, indicating no in uence of the di erent objective functions for this problem. Lastly, no strong correlation between the components and the clinical data were found.
Faculteit der Sociale Wetenschappen