Studies showing that repetitive visual stimulation protocols alter perception and induce cortical reorganization, as well-reported for the tactile domain, have been sparse. In this study, we investigated how “long-term potentiation [LTP]-like” and “long-term depression [LTD]-like” repetitive visual stimulation affects orientation discrimination ability in human observers. LTP-like stimulation with features most closely resembling the stimuli used during behavioral assessment evoked the largest improvement, while the effects were smaller in protocols that differed in shape or orientation features. This gradient suggests lower learning specificity than classical perceptual learning experiments, possibly because of an interplay of task- and feature-based factors. All modulatory effects of repetitive stimulation were superimposed on top of spontaneous task learning. Moreover, blockwise analysis revealed that LTP-like stimulation, in contrast to LTD-like or sham stimulation, prevented a loss of practice-related gain of orientation discrimination thresholds. This observation highlights a critical role of LTP-like stimulation for consolidation, typically observed during sleep.

Introduction: Due to an increasing demand for the initiation and control of non-invasive ventilation (NIV), digital algorithms are suggested to support therapeutic decisions and workflows in an ambulatory setting. The DIGIVENT project established and implemented such algorithms for patients with chronic hypercapnic respiratory failure due to chronic obstructive pulmonary disease (COPD) by a predefined process. Methods: Based on long-term clinical experience and guideline recommendations as provided by the German Respiratory Society, detailed graphical descriptions of how to perform NIV in stable COPD patients were created. Subsequently, these clinical workflows were implemented in the Business Process Model and Notation (BPMN) as one tool to formalize these workflows serving as input for an executable digital implementation. Results: We succeeded in creating an executable digital implementation that reflects clinical decision-making and workflows in digital algorithms. Furthermore, we built a user-friendly graphical interface that allows easy interaction with the DIGIVENT support algorithms. Conclusion: The DIGIVENT project established digital treatment algorithms and implemented a decision- and workflow-support system for NIV whose validation in a clinical cohort is planned.