Toward a Gene-Brain-Behaviour Model of Clinical Depression: Integrating Clinical, Neuropsychological, Psychophysiological, Neuroimaging and Genetic Markers to Better Understand Disorder Severity

Proposal details

Title: Toward a Gene-Brain-Behaviour Model of Clinical Depression: Integrating Clinical, Neuropsychological, Psychophysiological, Neuroimaging and Genetic Markers to Better Understand Disorder Severity
Research Area(s): Depression
Genetics
Background: This study will extend initial findings reported in healthy controls relating to the previously approved BrainNet project titled: “Genetics integration: Predicting negative affect from genotype-stress interactions and endophenotype markers” by exploring gene-brain-behaviour pathways to clinical depression. A number of potential markers of clinical depression have been identified in the literature including depression heterogeneity, anxiety comorbidity, stressful life events, sleep disturbance, emotion perception deficits, cognitive impairment, psychomotor slowing, reduced HRV, reduced arousal, increased right prefrontal activity, impaired emotion and oddball ERPs, specific genotypes in monoaminergic and BDNF candidate genes and interaction between these systems. However, many of these markers have been identified in different studies (and research groups) and based on separate samples. Previous work conducted by Gatt, Kemp and Williams highlights the utility of combining clinical, neuropsychological, psychophysiological, neuroimaging and genetic markers to predict depressed mood in non-clinical samples (e.g. Gatt et al., 2007, Journal of Integrative Neuroscience, 6, 75-104; Gordon et al., 2007, Journal of Integrative Neuroscience, 6, 1-34; Kemp et al., 2006, Journal of Integrative Neuroscience, 5, 89–110; Williams et al., 2006, The Journal of Neuroscience, 26, 6422– 6430). Moreover, we have previously reported that specific genotype differences impact on neural pathways to influence cognitive and emotional features in individuals with subclinical levels of depressed mood (Gatt et al., 2007, Journal of Integrative Neuroscience, 6, 75-104). However, studies remain to be conducted which explicitly test such models in patients with clinical depression.
Aims: The first aim of this study is to integrate candidate markers of clinical depression in the same sample of participants to determine whether these markers are able to explain observed depression severity. This is a particularly important goal given that depression severity has been considered the single most reliable variable for predicting treatment outcome (i.e. the less severe the depression, the better the outcome will be) (Tedlow et al., 1998; Trivedi and Baker, 2001) It is hypothesised that patients with severe clinical depression will be characterised by melancholia, more comorbidity, increased number of stressful life events, more sleep disturbance, face perception impairment, executive dysfunction and attentional impairment, psychomotor slowing, reduced HRV, reduced arousal (autonomic and brain), increased right prefrontal activity, impaired emotion (reductions in temporal N170) and oddball (increased P200 but reduced P300 amplitudes) ERPs, specific genotypes in monoaminergic (5-HTT short allele) and BDNF (BDNF 66Met allele) candidate genes. The second aim of this study is to explore the proposal that specific genotypes and their interaction impact on neural pathways to influence cognitive and emotional features in patients with clinical depression rather than individuals with subclinical levels of depressed mood.
Method: Patients from the Johnson & Johnson depression study and control subjects will be selected for analysis. 1. Mild and severe clinically depressed participants (relative to controls) will be compared on these clinical, neuropsychological, psychophysiological, neuroimaging and genetic markers using ANOVA. 2. Significant variables will then be entered into a series of stepwise regressions to examine their ability to predict depression scores. 3. We will then use path modelling to examine these measures in relation to hypothesised gene-brain-behaviour pathways.