Abstract
Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. 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. 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). 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 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. 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. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.
Highlights
Contemporary psychiatry uses disease classifications that are almost entirely based on syndromes as defined by the Diagnostic and Statistical Manual of Mental Disorders [DSM; [1]] or the International Classification of Diseases [ICD; [2]]
We focus on the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package which represents a collection of toolboxes that, collectively, aim to advance computational modeling of neuroimaging and behavioral data
We have described the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of toolboxes that aim to facilitate the acquisition and analysis of neuroimaging and behavioral data
Summary
Contemporary psychiatry uses disease classifications that are almost entirely based on syndromes (i.e., patterns of symptoms and signs) as defined by the Diagnostic and Statistical Manual of Mental Disorders [DSM; [1]] or the International Classification of Diseases [ICD; [2]]. While applicable to study human behavior and brain function in health, TAPAS differs from the aforementioned software packages in that its designated purpose is to provide clinically useful tools at every stage of the aforementioned endto-end pipeline in order to advance translational success of computational approaches to psychiatry. TAPAS contains: (i) tailored experimental paradigms (tasks) that probe psychopathologically and/or pathophysiologically relevant processes, (ii) tools for optimization and monitoring of data quality in the specific context of fMRI, (iii) model-based physiological noise correction techniques for fMRI data, and (iv) generative models and associated statistical techniques that enable inference on latent (hidden) neurophysiological or cognitive quantities from neuroimaging or behavioral data. For readers interested in a more in-depth treatment of a particular toolbox, references will be provided in the respective sections
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