Computational Modelling of Depression

Depression is currently one of the major psychological disorders present in society, and is one of the major causes of disability, with substantial impact upon patients, their families and society. The etiology of depression is still a challenge for neuroscientists and in particular it is extremely difficult to relate neuronal level activity with the observed behaviour of the subject. This poster considers the challenges in development of a first order computational model of the disorder, considering the neurobiological theory of depression, contributions from experimental techniques in neuroscience in support of a computational model and issues in modelling across scales in this context.The proposed computational model will operate at three levels, neuronal, functional and behavioural. The model will seek to provide an approach to relate neuronal level details with the functional and behavioural aspects of the subject. The concept is to correlate the firing patterns of neuronal level data of specific brain regions with biomarkers, biomaps, psychophysical experiments and imaging techniques and to relate the relevant component parts of the brain with the symptoms as specified in the DSM-IV criterion of depression. The desired outcome will be a model that can predict, to at least a limited extent, specific observed external behaviours, based upon the firing patterns of particular clusters of neurons and functioning details of the region or group of regions of the brain, in the context of variations in neurotransmitter behaviour. The ultimate objective is to create a model that assists in developing a greater understanding (as opposed to diagnosis) of the condition, that permits experimental evaluations, and that may ultimately contribute to improved therapeutic approaches for treatment of depression.