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癫痫网络的中尺度见解:一个多模态颅内数据集。

Mesoscale insights in Epileptic Networks: A Multimodal Intracranial Dataset.

作者信息

Bougou Vasiliki, Vanhoyland Michaël, Cleeren Evy, Janssen Peter, Van Paesschen Wim, Theys Tom

机构信息

Research Group of Experimental Neurosurgery and Neuroanatomy, Department of Neurosciences, KU Leuven and the Leuven Brain Institute, Leuven, Belgium.

Laboratory for Neuro - and Psychophysiology, Research Group Neurophysiology, Department of Neurosciences, KU Leuven and the Leuven Brain Institute, Leuven, Belgium.

出版信息

Sci Data. 2025 May 10;12(1):774. doi: 10.1038/s41597-025-05026-4.

DOI:10.1038/s41597-025-05026-4
PMID:40348768
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12065801/
Abstract

Understanding the intricate dynamics of epileptic networks at the mesoscale is crucial for advancing our knowledge of epilepsy pathophysiology and developing targeted interventions. In this data descriptor, we present a comprehensive dataset encompassing intracranial electroencephalography (iEEG) recordings, Local Field Potentials (LFP), and Multiunit Activity (MUA) data obtained from Microelectrode arrays (MEA; Utah array; Blackrock). 12 seizures were recorded in 5 epilepsy patients with the MEA. Our dataset offers a unique opportunity to investigate the complex interactions between diverse neural signals across brain areas and to study the mesoscale networks in focal epilepsy. This dataset can be used to explore the modulations of LFP and MUA in conjunction with iEEG, offering potential insights into the spatiotemporal dynamics of epileptic networks. Additionally, the high temporal resolution of the data allows for the computation of High-Frequency Oscillations (HFOs) in both LFP and iEEG signals, facilitating the investigation of their potential relationship with MUA activity.

摘要

了解中尺度癫痫网络的复杂动态对于推进我们对癫痫病理生理学的认识以及开发针对性干预措施至关重要。在本数据描述中,我们展示了一个综合数据集,其中包括从微电极阵列(MEA;犹他阵列;黑岩)获得的颅内脑电图(iEEG)记录、局部场电位(LFP)和多单元活动(MUA)数据。5名癫痫患者使用MEA记录了12次癫痫发作。我们的数据集提供了一个独特的机会来研究不同脑区之间各种神经信号的复杂相互作用,并研究局灶性癫痫中的中尺度网络。该数据集可用于结合iEEG探索LFP和MUA的调制,为癫痫网络的时空动态提供潜在见解。此外,数据的高时间分辨率允许计算LFP和iEEG信号中的高频振荡(HFO),便于研究它们与MUA活动的潜在关系。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c61b/12065801/2f2405a2e721/41597_2025_5026_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c61b/12065801/1b9d27eac5f8/41597_2025_5026_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c61b/12065801/14ae8003d283/41597_2025_5026_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c61b/12065801/03cf6d188fbe/41597_2025_5026_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c61b/12065801/ddffead6d0e6/41597_2025_5026_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c61b/12065801/4ed5f449a2c5/41597_2025_5026_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c61b/12065801/2f2405a2e721/41597_2025_5026_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c61b/12065801/1b9d27eac5f8/41597_2025_5026_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c61b/12065801/148c8049ef5d/41597_2025_5026_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c61b/12065801/34d51eca5be0/41597_2025_5026_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c61b/12065801/14ae8003d283/41597_2025_5026_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c61b/12065801/03cf6d188fbe/41597_2025_5026_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c61b/12065801/ddffead6d0e6/41597_2025_5026_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c61b/12065801/4ed5f449a2c5/41597_2025_5026_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c61b/12065801/2f2405a2e721/41597_2025_5026_Fig8_HTML.jpg

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