Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition.
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In this paper, we present the Translational Algorithms for Psychiatry- Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops “computational assays” for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients.
1Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland.Petzschner 1, Sudhir Raman 1, Dario Schöbi 1, Birte Toussaint 1, Lilian A. Lomakina 1,6, Christoph Mathys 1,10, Matthias Müller-Schrader 1, Inês Pereira 1, Frederike H. Harrison 1, Jakob Heinzle 1, Sandra Iglesias 1, Lars Kasper 1,9, Ekaterina I. Aponte 1, Saskia Bollmann 1,2,3,4,5, Kay H.
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