Proposal for Symposium Symptomics: A network approach to understanding psychopathology and psychiatric disorders

This symposium explores the emerging field of symptomics in research related to psychopathology and psychiatric disorders. Experimental psychopathology has been the primary path to gaining knowledge regarding causality and maintenance of psychiatric disorders. For a long time now, psychiatric research has been dominated by the monocausal and essentialist frameworks, where the bacterial model of disease has been a powerful and entrenched metaphor in shaping the theories, methods, interpretations and expectations of the field. Despite research increasing by leaps and bounds in each decade, be it in the area of neurobiology, genetics or psychopharmacology; we have not been able to substantiate the disorders in our diagnostic system in terms of causation, neurobiology and genetics. Symptomics, integrated with the approach of network analysis, provides a new metaphor into thinking about psychopathology and psychiatric disorders. The first presentation explores the advent of the concept of looking at psychiatric disorders as mutually reinforcing symptom networks that are self-sustaining in nature. In contrast to viewing symptoms as passive reflections of underlying, latent categories or dimensions, network analysis conceptualizes symptoms as constitutive of mental disorders. Disorders emerge from the causal interactions among symptoms themselves, and intervening on central symptoms in disorder networks promises to foster rapid recovery. The second presentation discusses the research trends in the network approach of psychiatric disorders in the last decade. Early research has explored symptom networks in various psychiatric disorders using DSM along with non-DSM symptoms. Research has focussed on network structure as well as network state, trying to understand how centrality of symptoms and network organization and connectedness impact severity, treatment response and outcome of disorders. This new framework has been used to examine the commonly occurring psychiatric comorbidities and also to explain equifinality and multifinality of psychiatric categories in our current nosology. The challenges and limitations of this approach along with the future agenda for research will be highlighted in the third presentation. Causal relationships amongst symptoms occur over different time scales, and the symptoms are related in causal loops rather than in a unitary direction, which are not homogenous with Bayesian network analysis. Also, there is a need for aggregating findings by combining networks derived from similar analyses. Computational models will have a critical role in devising formal theories that specify precisely how any specific disorder operates as a causal system and also to identify markers of impending tipping points whereby people suddenly transform from a healthy to a pathological state. (1)Dr. Dhrubajyoti Chetia, Associate Professor, Psychiatry, LGBRIMH, Tezpur, Assam; (2)Dr. Sourabh Bhattacharya, Senior Resident, Psychiatry, LGBRIMH, Tezpur, Assam; (3)Dr. Chayanika Bharadwaj, Senior Resident, Psychiatry, LGBRIMH, Tezpur, Assam.