ENVIRONMENTAL INFLUENCES AND EPIGENETIC MECHANISMS IN RISK FOR DEPRESSION

Genetic and environmental factors interact to influence vulnerability for internalizing psychopathology, including Major Depressive Disorder (MDD). The mechanisms that account for how environmental stress can alter biological systems are not yet well understood yet are critical to develop more accurate models of vulnerability and targeted interventions. Epigenetic influences, and more specifically, DNA methylation, may provide a mechanism by which stress could program gene expression, thereby altering key systems implicated in depression, such as frontal-limbic circuitry and its critical role in emotion regulation. This thesis investigated the role of environmental factors from infancy and throughout the lifespan affecting the serotonergic (5-HT) system in the vulnerability to and treatment of depression and anxiety and potential underlying DNA methylation processes. First, we investigated the contributions of additive genetic vs. environmental factors on an early trait phenotype for depression (negative emotionality) in infants and their stability over time in the first 2 years of life. We provided evidence of the substantial contributions of both genetic and shared environmental factors to this trait, as well as genetically- and environmentally- mediated stability and innovation. Second, we studied how childhood environmental stress is associated with peripheral DNA methylation of the serotonin transporter gene, SLC6A4, as well as long-term trajectories of internalizing behaviours. There was a relationship between childhood psychosocial adversity and SLC6A4 methylation in males, as well as between SLC6A4 methylation and internalizing trajectory in both sexes. Third, we investigated changes in emotion processing and epigenetic modification of the SLC6A4 gene in depressed adolescents before and after Mindfulness-Based Cognitive Therapy (MBCT). The alterations from pre- to post-treatment in connectivity between the ACC and other network regions and SLC6A4 methylation suggested that MBCT may work to optimize the connectivity of brain networks involved in cognitive control of emotion as well as also normalize the relationship between SLC6A4 methylation and activation patterns in frontal-limbic circuitry. Our results from these three studies strengthen the theory that environmental influences are critical in establishing early vulnerability factors for MDD, driving epigenetic processes, and altering brain processes as an individual undergoes treatment, or experiences relapse.

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.