Identifying and Modulating Pathological Brain Connectivity: A Computational Approach

Abstract

Neuromodulation is a promising treatment methodology for disorders of the brain. However, current clinical applications are primarily focused on a rudimentary form of neuromodulation, exclusively targeting individual regions in the brain. This has led to limitations in effective treatment options for patients suffering from neurological and neuropsychiatric diseases, with brain stimulation yielding a success rate of only 50% in 50% of patients after five years. In recent years, it has been strongly theorized that most brain disorders are emergent properties of pathological network connectivity rather than dysfunction of isolated brain regions. Targeting brain connectivity via neuromodulation is, therefore, a promising approach for improving treatment efficacy. This study aims at bridging this therapeutic gap by advancing treatment strategies towards network-based neuromodulation. To achieve this goal, a computational framework was designed and implemented that analyzes brain activity to identify biomarkers of abnormal connectivity networks. Furthermore, software pipelines for the construction of patient-specific,
virtual brain models were designed and implemented which enable analysis of simulated neural activity and connectivity networks, and enable in silico neuromodulation. The framework is tested and validated using an EEG dataset consisting of neural-activity recordings belonging to patients diagnosed with neuropathic pain and a separate healthy control group. Our computational framework shows high ability in identifying disease-specific abnormalities in network connectivity and in constructing pathological network hubs, with accuracies up to 98.6% in explaining neuropathic pain. Patient-specific,
brain-model construction is also able to accurately represent empirical patterns of connectivity in simulation. Furthermore, the simulated neuromodulation with personalized virtual brains demonstrates the ability to accurately model dynamic modulation of brain connectivity networks. These findings illustrate the effectiveness of our overall framework and highlight the potential for integration into clinical decision-making and closed-loop implantable neuromodulation systems, enabling more effective treatment of brain disorders.