Deep brain stimulation for neurodegenerative disease: a computational blueprint using dynamic causal modeling

Area of Science:

• Neuroscience
• Computational psychiatry
• Systems neuroscience

Background:

• Deep brain stimulation (DBS) offers a new therapeutic avenue for neurological and psychiatric disorders, potentially complementing pharmacological treatments.
• Neurostimulation development currently lacks a structured pipeline, unlike pharmacological development which relies on extensive preclinical and clinical trials.
• Biophysical models of brain networks, optimized with neuroimaging data, can provide a computational framework for developing neurostimulation interventions.

Purpose of the Study:

• To propose a computational pipeline for testing and developing neurostimulation interventions using biophysical models and empirical neuroimaging data.
• To outline the dynamic characterization of brain circuits using Dynamic Causal Modeling (DCM).
• To present a blueprint for in silico testing of stimulation effects in neurodegenerative disorders affecting cognition.

Main Methods:

• Utilizing biophysical models of connected brain networks, optimized with empirical neuroimaging data from patients and healthy controls.
• Employing Dynamic Causal Modeling (DCM) to test effective connectivity within and between brain regions.
• Linking brain dynamics with behavior by assessing experimental task effects under different cognitive sets.

Main Results:

• DCM allows for the mathematical definition of healthy brain dynamics in specific circuits.
• Simulations can predict the effects of neurostimulation interventions in pathological conditions, aiming to restore normal functionality.
• The framework enables in silico prediction of distributed DBS effects on neural circuitry and network connectivity.

Conclusions:

• Biophysical models optimized with neuroimaging data and DCM offer a principled computational pipeline for developing and testing neurostimulation interventions.
• This approach facilitates the prediction of in silico effects of DBS on brain networks, particularly for cognitive deficits in neurodegenerative diseases.
• DCM has been successfully applied to characterize DBS effects in Parkinson's disease, demonstrating the framework's utility.