Wikiversity:Fellow-Programm Freies Wissen/Einreichungen/Towards a bigger picture - Crowdsourcing the mosaic of the mind

Towards a bigger picture - Crowdsourcing the mosaic of the mind A fragmented picture of mental disorders Mental disorders are one of the most pervasive problems for public health. In the developed countries, they are one of the leading causes of disability comprising detrimental consequences on psychosocial well-being and an increased risk of mortality . In the past decades, many studies have sought to unravel the neurobiological causes of mental disorders to delineate mechanisms for targeted treatment. However, progress towards this end has been limited so far by marked differences across small studies culminating in low success rates in the replication of key results . Several reasons are contributing to this heterogeneity such as small samples of selected non-representative patients or lack of routine publication of validated paradigms, analysis scripts, and verified datasets. In contrast to questionnaires, many paradigms employed to study cognitive or motivational deficits are not standardized across labs or formally validated. Critically, many reasons could be effectively addressed by facilitating the widespread use of open and well-maintained software running on different yet commonly available platforms. How to merge the pieces Here, within the Open Science Fellowship, I plan to implement tasks that tap into key facets of human cognition, which have been demonstrated to be altered using tools from computational psychiatry . The great promise of such initiatives in improving public health has been recently demonstrated by the Great Brain Experiment, which was supported by the Wellcome Trust . For example, Rutledge et al. (2017) have found that a key hypothesis about depression as a disorder of aberrant updating of reward expectations, derived from several small-scale clinical trials, could not be substantiated using data sampled from more than 1800 participants. Other large-scale studies (e.g., Gillan et al, 2016 ) have demonstrated how alterations in goal-directed control of reward seeking are indicative of compulsive behavior, which is a key characteristic of several mental disorders. Collectively, these studies have demonstrated the feasibility and big potential of such approaches in guiding the development of better treatment options including the identification of strategies that are unlikely to contribute to the disorder. Facilitating large-scale usage To facilitate the widespread use in future research, the paradigms will be implemented using Haxe, a programming language intended for building cross-platform tools that can be directly exported to native apps for every major platform including Android and web-based HTML5. Ease of access has been a major limiting factor in collecting sufficient data required to describe individual behavior in detail among many critical dimensions of cognition such as reward sensitivity or emotional stability at the same time. Furthermore, large-scale online testing enables tests for the dimensionality of mental disorders, which could be employed to identify individuals at risk or at an early stage of the disorder in the future. Accompanying my efforts on the side of data collection, a web interface will be implemented which provides an easily accessible summary of the individual results of the user relative to other individuals in the database or, alternatively, a comprehensive summary for researchers, who are using the task. This web interface will be implemented using R Shiny, an open web application framework for R. Lastly, all applications built including the source code and documentations on the paradigm will be made publicly available by release under an open-source license. Benefits for public health The finished project will therefore help research scientists in building more appropriate models of individual behavior in key dimensions of cognition which in turn supports healthcare professionals in identifying individuals at risk of developing a mental disorder. References Data management plan See /Datenmanagementplan Zwischenbericht Der Zwischenbericht ist hier zu finden. Contact Information Name: Vanessa Teckentrup Institution: University of Tübingen Kontakt: vanessa.teckentrup@uni-tuebingen.de