Dissertation Analysis
Creators: name: Pisner, Derek
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Physical Sciences and Mathematics
subject: Medicine and Health Sciences
subject: Social and Behavioral Sciences

description: The clinical utility of a personalized medicine algorithm hinges on the reproducible precision of its predictions. If consistently accurate prognostic models were available to predict persistent depression, for instance, they would revolutionize care for one of the most common and disabling psychological disorders. Due to depression's heterogeneity, however, existing models have so far failed to demonstrate a level of predictive performance that would warrant algorithm deployment as a standard-of-care. To improve the utility of these algorithms further, new feature-engineering methods might succeed where previous model-focused efforts have proven limited. Towards this end, I propose using emerging techniques in statistical learning to optimize rumination brain network (i.e. connectome) features for predicting depression course. To realize this objective, I will make use of multiple independent, longitudinal neuroimaging datasets made publicly available by the Consortium for Reliability and Reproducibility. For instance, N~=100 dysphoric subjects will be used as a tuning-set and N~=250 clinically depressed subjects will be used for benchmarking predictive models of depression outcome. Depression diagnosis will be established from diagnostic interview data when available, and the Beck Depression Inventory (BDI&gt;=10) or Hamilton-Depression Rating Scale (HDRS&gt;=8) when unavailable. All subjects have at least one usable diffusion MRI, T1-weighted structural MRI, and resting-state fMRI dataset. All data was acquired on a Siemens Magnetom TrioTim Syngo MR B17 scanner. I first hypothesize that if connectome features are optimized to capture autoregressive variance in rumination (i.e. its persistence-- a key mechanism of depression maintenance), they will yield more out-of-sample sensitivity when predicting depression outcome than will comparative models based on `features-as-usual'. I secondly hypothesize that if structural and functional connectomes are jointly embedded as features, they will yield more sensitivity than when they are embedded separately. I thirdly hypothesize that when minimal behavioral features (age, gender, initial depression severity, initial rumination severity) accompany connectome features in a hierarchical mixture of models, predictions will be more sensitive and specific compared to models trained on brain or behavioral features only. To optimize connectomes, at least five key 'meta-parameters' of interest will be manipulated per modality. In the case of functional connectomes, these will minimally include: Time-Series Extraction Method, High-Pass Filter Frequency, Connectivity Estimator, Node Resolution, and Smoothing Tolerance. In the case of structural connectomes, these will minimally include: Time-Series Extraction Method, High-Pass Filter Frequency, Connectivity Estimator, Node Resolution, and Smoothing Tolerance. I therefore hypothesize that there will be multiple connectome recipes that optimally predict rumination persistence, albeit some will be more optimal than others, and each recipe confers its own unique level of reproducible discriminability. Moreover, I anticipate that optimal recipes will vary across brain network communities of interest (e.g. triple-network as a whole vs. Default-Mode RSN in isolation), and that this variation reflects an ensemble multidimensionality inherent to the human connectome. In sum, I propose evaluating a set of predictive models of depression persistence using rumination connectome features that have been optimized through statistical learning. Regardless of the outcome, the comparative nature of my approach guarantees contribution to the larger corpus of knowledge in psychology by critically investigating the predictive and translational value of connectome statistical learning for depression prognosis.