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Abstract <p>Preserving critical neural pathways during neurosurgery is paramount to minimizing postoperative functional deficits. Connectomics offers a graph-theoretic framework to model these pathways, where the "minimum cut" between two brain regions represents the maximum information flow capacity or structural bottleneck. However, traditional algorithms for calculating minimum cuts are computationally prohibitive for real-time scenarios, such as assessing the impact of removing a specific node (brain region) on the connectivity between functional areas. To address this, we propose the application of the rapid sensitivity analysis algorithm for neural pathways in large-scale brain networks. By exploiting the injection between traversal trees and graph cuts, it generates a tight upper bound estimation of min-cut values through the aggregation of cuts from diverse traversal trees. This unique mechanism allows for the microsecond-level retrieval of approximate min-cut values between arbitrary node pairs after a brief preprocessing phase. Specifically, we utilize it to perform rapid sensitivity analysis: by virtually removing a target node and instantly querying the pre-computed tree structures, the system can detect precipitous drops in the min-cut upper bound. This serves as a high-speed proxy for identifying critical "bridge" nodes whose removal would sever or severely impair the connectivity between regions of interest. Experimental results on high-resolution connectome data demonstrate that this approach achieves 3 orders-of-magnitude speedup compared to standard max-flow algorithms, enabling interactive, real-time surgical planning and functional risk assessment on standard computing hardware.</p> Graphical Abstract <p>The proposed algorithm rapidly estimates min-cut values in brain networks via traversal trees, enabling microsecond-level queries after preprocessing. It achieves a 1000 times speedup over max-flow methods, allowing real-time assessment of critical neural pathways during surgical planning.</p>

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Real-Time Functional Lesion Simulation in Connectomics: Rapid Sensitivity Analysis of Neural Pathways via Traversal Tree-Correlated Cuts

  • 期刊论文

  • 发表日期：

    2026年4月1日

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  • Zeming Li

摘要

Abstract

Preserving critical neural pathways during neurosurgery is paramount to minimizing postoperative functional deficits. Connectomics offers a graph-theoretic framework to model these pathways, where the "minimum cut" between two brain regions represents the maximum information flow capacity or structural bottleneck. However, traditional algorithms for calculating minimum cuts are computationally prohibitive for real-time scenarios, such as assessing the impact of removing a specific node (brain region) on the connectivity between functional areas. To address this, we propose the application of the rapid sensitivity analysis algorithm for neural pathways in large-scale brain networks. By exploiting the injection between traversal trees and graph cuts, it generates a tight upper bound estimation of min-cut values through the aggregation of cuts from diverse traversal trees. This unique mechanism allows for the microsecond-level retrieval of approximate min-cut values between arbitrary node pairs after a brief preprocessing phase. Specifically, we utilize it to perform rapid sensitivity analysis: by virtually removing a target node and instantly querying the pre-computed tree structures, the system can detect precipitous drops in the min-cut upper bound. This serves as a high-speed proxy for identifying critical "bridge" nodes whose removal would sever or severely impair the connectivity between regions of interest. Experimental results on high-resolution connectome data demonstrate that this approach achieves 3 orders-of-magnitude speedup compared to standard max-flow algorithms, enabling interactive, real-time surgical planning and functional risk assessment on standard computing hardware.

Graphical Abstract

The proposed algorithm rapidly estimates min-cut values in brain networks via traversal trees, enabling microsecond-level queries after preprocessing. It achieves a 1000 times speedup over max-flow methods, allowing real-time assessment of critical neural pathways during surgical planning.