Personalized dynamic network models of the human brain as a future tool for planning and optimizing epilepsy therapy

Abstract

Epilepsy is a common neurological disorder, with one third of patients not responding to currently available antiepileptic drugs. The proportion of pharmacoresistant epilepsies has remained unchanged for many decades. To cure epilepsy and control seizures requires a paradigm shift in the development of new approaches to epilepsy diagnosis and treatment. Contemporary medicine has benefited from the exponential growth of computational modeling, and the application of network dynamics theory to understanding and treating human brain disorders. In epilepsy, the introduction of these approaches has led to personalized epileptic network modeling that can explore the patient's seizure genesis and predict the functional impact of resection on its individual network's propensity to seize. The application of the dynamic systems approach to neurostimulation therapy of epilepsy allows designing stimulation strategies that consider the patient's seizure dynamics and long‐term fluctuations in the stability of their epileptic networks. In this article, we review, in a nontechnical fashion suitable for a broad neuroscientific audience, recent progress in personalized dynamic brain network modeling that is shaping the future approach to the diagnosis and treatment of epilepsy.

Chronicles of Change: The Shrinking Brain in Epilepsy

“…Overall, interest in improved modeling methods to predict disease trajectory and personalize patient care in epilepsy is rapidly growing. 10 Both hypothesis-driven and machine-learning approaches have shown value. There are challenging practical barriers to integrating new analytical methods and technologies into the treatment pipeline in epilepsy, but the chronicles of change may be upon us in this field.…”

Amplitude entropy to capture chimera-like behavior in the altered brain dynamics during seizures

“…This implies a need to understand the mechanisms at play further while the brain is undergoing a seizure. The modern approach to understanding epilepsy stresses the importance of considering it (as well as other brain disorders [16]) as a network disease that is fundamentally driven by dynamical principles [17]- [21].…”

Predictive factors for successful vagus nerve stimulation in patients with refractory epilepsy: real-life insights from a multicenter study

“…Furthermore, the utilization of computational models holds promise for enhancing the outcomes of neurostimulation in epilepsy. These models, which integrate biophysical principles, anatomical data, and patient-specific factors, can guide the optimization of stimulation parameters and personalization of treatment strategies ( Dallmer-Zerbe et al, 2023 ). Nevertheless, the clinical application of these models remains an ongoing prospect rather than an established reality.…”