A Robust Causal Brain Network Measure and Its Application on Ictal Electrocorticogram Analysis of Drug-Resistant Epilepsy

• Main Authors: Yalin Wang (Author), Wentao Lin (Author), Hong Peng (Author), Ligang Zhou (Author), Wei Chen (Author), Bin Hu (Author)
• Format: Book
• Published: IEEE, 2024-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

Measuring causal brain network is a significant topic for exploring complex brain functions. While various data-driven algorithms have been proposed, they still have some drawbacks such as ignoring time non-separability, cumbersome parameter settings, and poor robustness. To solve these deficiencies, we developed a novel framework: “time-shift permutation cross-mapping, TPCM,” integrating steps of: <xref rid="deqn1" ref-type="disp-formula">(1)</xref> delayed improved phase-space reconstruction (DIPSR), <xref rid="deqn2" ref-type="disp-formula">(2)</xref> rank transformation of embedding vectors’ distances, <xref rid="deqn3" ref-type="disp-formula">(3)</xref> cross-mapping with a fitting estimation, and <xref rid="deqn4" ref-type="disp-formula">(4)</xref> causality quantification using multi-delays. Based on the synthetic models and comparison with baseline methods, numerical validation results demonstrate that TPCM significantly improves the robustness for data length with or without noise interference, and achieves the best quantification accuracy in detecting time delay and coupling strength, with the highest determination coefficient (<inline-formula> <tex-math notation="LaTeX">${R}^{{2}}={0}.<U+0096>\text {)}$ </tex-math></inline-formula> of fitting verse coupling parameters. The developed TPCM was finally applied to ictal electrocorticogram (ECoG) analysis of patients with drug-resistant epilepsy (DRE). A total of 17 patients with DRE were included into the retrospective study. For 8 patients undergoing successful surgeries, the causal coupling strength (<inline-formula> <tex-math notation="LaTeX">$0.58~\pm ~0.20$ </tex-math></inline-formula>) within epileptogenic zone network is significantly higher than those suffering failed surgeries (<inline-formula> <tex-math notation="LaTeX">$0.38~\pm ~0.16$ </tex-math></inline-formula>) with <inline-formula> <tex-math notation="LaTeX">${P}\lt {0}. {001}$ </tex-math></inline-formula> through Mann-Whitney-U-test. Therefore, the epileptic brain network measured by TPCM is a credible biomarker for predicting surgical outcomes. These findings additionally confirm TPCM’s superior performance and promising potential to advance precision medicine for neurological disorders.

Item Description

1534-4320
1558-0210
10.1109/TNSRE.2024.3378426