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Computational Modelling of Depression

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Joshi, A, McGinnity, TM, Prasad, G and Sinha, RK (2010) Computational Modelling of Depression. In: NCF UK Node Congress on Analysing and modelling neural systems in health and disease . NCF UK Node. 1 pp. [Conference contribution]

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Abstract

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.

Item Type:Conference contribution (Poster)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:18521
Deposited By:Professor Girijesh Prasad
Deposited On:19 May 2011 15:24
Last Modified:19 May 2011 15:24

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