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从脑磁图测量中对时空分布的癫痫源的范围和位置进行成像。

Imaging the extent and location of spatiotemporally distributed epileptiform sources from MEG measurements.

机构信息

Department of Biomedical Engineering, Carnegie Mellon University, USA.

University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), University of Pittsburgh Medical School, USA.

出版信息

Neuroimage Clin. 2022;33:102903. doi: 10.1016/j.nicl.2021.102903. Epub 2021 Nov 29.

Abstract

Non-invasive MEG/EEG source imaging provides valuable information about the epileptogenic brain areas which can be used to aid presurgical planning in focal epilepsy patients suffering from drug-resistant seizures. However, the source extent estimation for electrophysiological source imaging remains to be a challenge and is usually largely dependent on subjective choice. Our recently developed algorithm, fast spatiotemporal iteratively reweighted edge sparsity minimization (FAST-IRES) strategy, has been shown to objectively estimate extended sources from EEG recording, while it has not been applied to MEG recordings. In this work, through extensive numerical experiments and real data analysis in a group of focal drug-resistant epilepsy patients' interictal spikes, we demonstrated the ability of FAST-IRES algorithm to image the location and extent of underlying epilepsy sources from MEG measurements. Our results indicate the merits of FAST-IRES in imaging the location and extent of epilepsy sources for pre-surgical evaluation from MEG measurements.

摘要

无创性 MEG/EEG 源成像提供了有关致痫性脑区的有价值信息,可用于帮助耐药性癫痫患者的术前规划。然而,电生理源成像的源范围估计仍然是一个挑战,通常在很大程度上取决于主观选择。我们最近开发的算法,快速时空迭代重新加权边缘稀疏最小化(FAST-IRES)策略,已被证明可从 EEG 记录中客观估计扩展源,但尚未应用于 MEG 记录。在这项工作中,通过在一组局灶性耐药性癫痫患者的发作间期棘波的广泛数值实验和真实数据分析,我们证明了 FAST-IRES 算法从 MEG 测量中成像潜在癫痫源的位置和范围的能力。我们的结果表明 FAST-IRES 在从 MEG 测量中成像术前评估的癫痫源的位置和范围方面的优势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8cfe/8648830/3b05f20dae0f/gr1.jpg

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