Credibility, Replicability, and Reproducibility in Simulation for Biomedicine and Clinical Applications in...

Area of Science:

• Computational neuroscience
• Biomedical simulation
• Neuroscience modeling

Background:

• Current computational neuroscience models aim to understand neural systems but lack clinical applicability.
• Bridging the gap requires significant advancements and adherence to best practices for regulatory and clinical acceptance.
• Lessons can be drawn from established simulation practices in engineering and other biomedical fields.

Purpose of the Study:

• To introduce fundamental concepts for developing credible clinical neuroscience models.
• To outline procedures for mechanistic multiscale modeling.
• To explore the potential of simulation in Simulation-Based Medical Education.

Main Methods:

• Discussing core principles: reproducibility, replicability, verification, and validation.
• Detailing model configuration and credible mechanistic multiscale modeling processes.
• Highlighting the importance of community involvement for model credibility.

Main Results:

• Identified key concepts crucial for establishing credibility in clinical neuroscience models.
• Proposed a framework for developing reliable and valid multiscale models.
• Emphasized the role of community engagement in fostering trust and adoption.

Conclusions:

• Establishing credibility is paramount for translating computational neuroscience models into clinical practice.
• Adopting rigorous software engineering and validation practices is essential.
• Simulation holds significant potential for both direct patient care and Simulation-Based Medical Education.