Accelerated Brain Ageing in Mood and Psychotic Disorders - Ballester, Pedro Lemos (My University, 2022)
Introduction: Through large neuroimaging consortia, researchers have identified a series of neuroanatomical alterations in mood and psychotics disorders, such as major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ). However, the mechanism behind these alterations is not well understood. One of the existing hypotheses suggests that the observed brain changes are related to a process of accelerated brain ageing. Studies investigating this hypothesis use a measure called the brain age gap (i.e., the difference between machine learning model predictions of brain age and chronological age). Thus far, there is limited understanding on how mood and psychotic disorders affect model predictions, how can predictions be clinically useful, and what is the biological meaning behind the brain age gap. In this thesis, we investigated accelerated brain ageing in mood and psychotic disorders. We sought to estimate the effect of the brain age gap and propose new ways of modeling brain age. We also explored the clinical utility and meaning of the brain age gap. Results: We confirmed the presence of a brain age gap in MDD, BD, and SCZ through a systematic review and meta-analysis. SCZ presented the highest levels of brain age gap, followed by BD and MDD. We analyzed the clinical utility of brain age for antidepressant treatment response and concluded that the brain age gap is not a predictor of antidepressant treatment response in weeks 8 and 16. We proposed a new method for brain age prediction that is more interpretable than previous approaches while preserving good predictive performance. We have also used model explanation strategies and identified that the brain age gap is largely associated with total gray matter volume reduction and ventricle enlargement in SCZ. Conclusion: The results of this thesis suggest that the brain age gap is present across mood and psychotic disorders. The results have also helped to clarify the meaning behind the brain age gap, a largely used but still poorly understood measure in neuroimaging research. So far, there is no indication that the brain age gap can be a useful tool for treatment response prediction in MDD.
Keywords: mood disorders; psychotic disorders; machine learning; neuroimaging; accelerated ageing