TAPAS: an open-source software package for Translational Neuromodeling and Computational Psychiatry

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.

Interoception of breathing and its relationship with anxiety

“…While the RW model assumes a fixed learning rate, the HGF allows for online adaption of learning rate as a function of volatility. All learning models were paired with a unit-square sigmoid response model (Equation 2) and were implemented using the Hierarchical Gaussian Filter Toolbox ( Mathys et al., 2011 , 2014 ) (version 5.3) from the open-source TAPAS software ( Frässle et al., 2021 ) ( https://www.translationalneuromodeling.org/tapas/ ).…”

Interoception of breathing and its relationship with anxiety

“…While the RW model assumes a fixed learning rate, the HF allows for online adaption of learning rate as a function of volatility. All learning models were paired with a unit-square sigmoid response model (Equation 2) and were implemented using the Hierarchical Gaussian Filter Toolbox 72,73 (version 5.3) from the open-source TAPAS software 74 (http://www.translationalneuromodeling.org/tapas/). The alternative models were formally compared using random effects Bayesian model selection (BMS) as implemented in SPM12 75,76 .…”

Advances in Spiral fMRI: A High-resolution Study with Single-shot Acquisition

“…All computations were performed in Matlab R2019b, using the Unified NeuroImaging Quality Control Toolbox (UniQC, (Bollmann et al, 2018; Frässle et al, 2021)), and SPM12 (Wellcome Centre for Human Neuroimaging, London, UK, http://www.fil.ion.ucl.ac.uk/spm/).…”