Digital twins of the brain: High-fidelity computational models for simulating neural disorders and therapeutic outcomes

The "Digital Twin" concept, a virtual, high-fidelity representation of a physical system, is transforming engineering and is now gaining significant momentum in medicine. In neuroscience, creating a Digital Twin involves integrating a patient’s structural connectivity, functional dynamics, and physiological parameters into a personalized computational model. While traditional computational neuroscience has focused on generalized "average brain" models to understand fundamental principles, these often fail to account for the vast inter-individual variability seen in clinical populations. With the advent of high-resolution neuroimaging, long-term wearable sensing, and increased supercomputing power, we can now simulate the brain's activity at multiple scales. This paradigm shift allows for the in silico testing of interventions, moving us closer to a future where neurological and psychiatric treatments are tailored to the unique physiological landscape of each patient.The primary goal of this Research Topic is to showcase how high-fidelity Digital Twins can be used to simulate neural disorders and predict therapeutic outcomes. Currently, a major challenge in treating conditions like epilepsy, Parkinson’s disease, and clinical depression is the "trial-and-error" nature of therapeutic selection, whether it involves pharmacological agents, deep brain stimulation (DBS), or non-invasive neuromodulation. To address this, we aim to gather research that demonstrates the predictive validity of personalized models. We seek to explore how integrating multi-modal data (such as fMRI, EEG, DTI, and genetic markers) into dynamic circuit models can identify optimal targets for intervention. Furthermore, this topic aims to address the technical hurdles of Digital Twin development, including model parameterization, real-time data integration, and the scalability of biophysically detailed simulations. By bridging the gap between computational theory and clinical practice, this collection intends to establish a framework for "virtual clinical trials" that can refine treatment protocols and reduce the risks associated with invasive procedures.This Research Topic seeks contributions that advance the state-of-the-art in personalized brain modeling. We invite original research, methodological papers, and reviews that utilize computational frameworks to model disease states and simulate therapeutic responses.Specific themes of interest include:- Patient-specific modeling of seizure propagation and surgical planning in epilepsy.- Optimization of Deep Brain Stimulation (DBS) and Transcranial Magnetic Stimulation (TMS) parameters using personalized head models.- Multi-scale Digital Twins integrating molecular pathways with network-level dynamics.- The use of machine learning and data assimilation techniques to calibrate individual brain models.- Simulations of neurodegenerative disease progression (e.g., Alzheimer’s, ALS) and potential drug interventions.- Virtual cohorts and in silico clinical trials for evaluating new neurotechnologies.- Challenges in model validation, uncertainty quantification, and ethical considerations of Digital Twin technology.- Software platforms and computational pipelines designed for clinical "Digital Twin" deployment.- Personalized brain Digital Twins for predicting social functioning and treatment response in disorders such as autism spectrum.- Personalized neuromusculoskeletal frameworks linking motor system dynamics to whole-body biomechanics for clinical trials in conditions such as Parkinson’s disease, post-stroke spasticity, Cerebral Palsy, Multiple Sclerosis, and Spinal Cord Injury. Topic editors: Saied Sabaghypour, Farhad Farkhondeh Tale Navi Source: https://www.frontiersin.org/research-topics/82094/digital-twins-of-the-brain-high-fidelity-computational-models-for-simulating-neural-disorders-and-therapeutic-outcomes