5th Edition of International Neurology Conference 2026

Abstract

INTRODUCTION: This study aims to provide an accessible and interpretable framework for examining how neurosurgical interventions affect large-scale brain networks. The objectives are to develop a domain-specific language that simplifies the representation of brain regions and their interactions, implement a computational engine to simulate network dynamics, and demonstrate the framework’s utility through representative lesion and stimulation scenarios.
AIMS AND OBJECTIVE: The aim of this work is to develop an interpretable and accessible computational framework for exploring the network-level effects of neurosurgical interventions. The objectives are to (i) design a domain-specific language (DSL) that abstracts brain network dynamics into human-readable constructs, (ii) implement a parser and interpreter to execute simulations of lesions and stimulation, and (iii) demonstrate the framework’s utility through representative neurosurgical scenarios.
METHODS: We introduce NeuroSurgLang, a Python-based domain-specific language that allows users to describe brain networks using intuitive, human-readable constructs. Brain regions are modeled as nodes, connections as directed pathways, and interventions such as lesions and stimulation as explicit modifiers of network behavior. A custom parser translates these descriptions into structured representations that are executed by an interpreter operating over discrete time steps. Simulation outputs include time-series activity traces, steady-state summaries, and network visualizations to support intuitive interpretation. 
RESULTS AND DISCUSSSION: Three illustrative cases were simulated: (i) focal lesioning of the primary somatosensory cortex, (ii) compensatory upstream stimulation in the presence of a motor cortex lesion, and (iii) a network incorporating feedback via the supplementary motor area. The results demonstrate that lesions produce sustained local suppression with downstream effects, stimulation enables partial functional recovery through preserved pathways, and feedback mechanisms promote rapid stabilization of network dynamics. These findings highlight the importance of network topology and intervention context in shaping system-level behavior and support the value of network-aware reasoning in neurosurgical planning and education.
LIMITATION: The proposed framework employs simplified abstractions and does not aim to replicate detailed neurophysiological processes or patient-specific anatomy. The simulations are conceptual rather than predictive and are not intended for direct clinical decision-making.
CONCLUSION AND FUTURE DIRECTION: NeuroSurgLang offers a transparent and approachable platform for exploring the networklevel consequences of neurosurgical interventions. By prioritizing interpretability over biological detail, the framework supports education, hypothesis generation, and conceptual preoperative reasoning. Future work will focus on incorporating richer dynamics, integrating empirical connectivity data, and expanding interactive visualization capabilities to further enhance clinical and educational usability.