Gaining Insight into Brain Damage and Rehabilitation through Digital Twins

5 pages•Published: April 19, 2026

Abstract

While digital twins have become an essential tool in many areas of engineering, they are still relatively rare in brain science. While gradient-descent-based machine learning approaches can model behavior at an aggregate level, they do not replicate neural mechanisms of individual subjects or patients, and therefore cannot serve as twins for them. Other approaches can, such as associatively connected neural maps. For example the BiLex model can be fit to individual patient's language history and impairment in stroke or dementia, and then used to evaluate different rehabilitation and mitigation strategies. This approach was found promising in an actual clinical trial, paving way for more digital twin studies in the future.

Keyphrases: clinical trial, digital twins, rehabilitation, self organizing maps

In: Jernej Masnec, Hamid Reza Karimian, Parisa Kordjamshidi and Yan Li (editors). Proceedings of AI for Accelerated Research Symposium, vol 3, pages 108-112.

Links:	https://easychair.org/publications/paper/f8LG
	https://doi.org/10.29007/25m8