Biometric markers for affective disorders

Abstract

Effective treatment of affective disorders is often challenged by incorrect or delayed diagnosis. Patients with bipolar disorder (BD) typically receive between 1 and 4 prior diagnoses of other mental health disorders preceding correct diagnosis, commonly with a delay from illness onset of 10 years, highlighting the difficulty of accurate diagnosis in a clinical setting. Most commonly, bipolar disorder is misdiagnosed as unipolar major depressive disorder (MDD), with between 25 and 50% of people diagnosed with depression being later re-classified as bipolar. The current problem of misdiagnosis is compounded by the lack of laboratory based quantitative testing for affective disorders available in clinical situations.The overarching objectives of this thesis are to demonstrate the utility of machine learning algorithms to neuroimaging data in order to provide a computational platform for classification, diagnosis and prediction of mood disorders. The thesis hence aims to unify three main themes -namely the clinical psychiatry of affective (mood) disorders, analytic principles of neuroimaging data (such as networkbased platforms) and machine learning.I first combine network theory and machine learning to address the issue of diagnostic classification using resting state functional magnetic resonance imaging (fMRI). I find that key metrics describing functional organization of the brain can be harnessed to yield accurate diagnostic boundaries in the data set I examine. In particular, the organization of ongoing brain activity into communities provides key information which distinguishes MDD from healthy controls.I next investigate a core group of brain regions which modulate network efficiency at a global scale, known as the rich club. Although this has previously been examined in structural brain networks, I apply it to resting state functional images. Furthermore, I investigate the robustness of the functional rich club following the correction of systematic artifacts in fMRI scans. Here I conclude a densely connected rich club is present in resting state fMRI across using all processing styles. However, the composition of members changes depending on key choices in the preprocessing of data. Further validation of the rich club should assess the impact of using different scanners or brain atlases.From chapter 4 onward, I shift the focus from depression to bipolar disorder. Here I start by assessing the functional connectivity of the inferior frontal gyrus, a region which is down regulated during an ii emotional recognition task in first degree relatives of bipolar subjects. Using a technique capable of identifying subtle alterations in functional networks I identified a community of connected brain regions with substantially reduced functional connectivity in bipolar subjects. I used machine learning to distinguish people with bipolar from first degree relatives and a healthy cohort. This was most effective between bipolar subjects and control subjects, while identifying the first degree relatives from eith...