TAPAS

TAPAS implements algorithms to infer patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data to support Translational Neuromodeling (TN) and Computational Psychiatry (CP).

Key Features:

• Computational assays: Development of computational assays that infer patient-specific disease processes and enable mechanistically guided differential diagnosis, prediction of clinical trajectories, and estimation of treatment responses.
• Multimodal data support: Integration and analysis of neuroimaging, electrophysiological, and behavioral data for unified mechanistic modeling.
• Pipeline components: Provision of tailored experimental designs, optimization strategies prior to data acquisition, quality control during data collection, artifact correction, statistical inference, and clinical application post-acquisition.
• Clinical application support: Generation of objective analytical outputs aimed at supporting mechanistic interpretation and clinical decision-making in psychiatric, neurological, and psychosomatic contexts.

Scientific Applications:

• Mechanistic investigation of psychiatric disorders: Modeling neural and cognitive mechanisms underlying psychiatric and related disorders using computational assays applied to multimodal data.
• Individualized prognosis and treatment prediction: Prediction of individual patient clinical trajectories and treatment responses from model-derived parameters.
• Translation to clinical practice: Integration of computational model outputs into clinical workflows to improve diagnostic accuracy and therapeutic efficacy.

Methodology:

Designing experiments and optimizing measurement strategies before acquisition; quality control during data collection; artifact correction and statistical inference post-acquisition; and translation of model outputs into clinically relevant insights.