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Continuous behavioral tracing-based online functional brain mapping with intracranial electroencephalography

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收录情况: ◇ SCIE ◇ EI

机构: [a]State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China [b]Beijing Institute of Functional Neurosurgery, Xuanwu Hospital of Capital Medical University, Beijing, China
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关键词: continuous behavioral tracing intracranial electroencephalography functional brain mapping high-gamma activities epilepsy

摘要:
Intracranial electroencephalography (iEEG) has proven to be a reliable tool in clinical functional brain mapping. While iEEG signals are believed to be highly informative, making online or real-time functional mapping possible, the utilization of corresponding behavioral information in traditional trial-based paradigms is typically low. This imbalance has limited the efficiency and effectiveness in current iEEG-based functional mapping approaches. Objective. In the current study, we were to investigate whether using more dedicate behavioral information can improve iEEG-based functional mapping. Approach. We continuously monitored behavioral outputs of patients during iEEG recording, transformed them into regressors, and assessed their correlations with iEEG signals. Main results. Functional cortical sites identified in preliminary test using the proposed method showed considerable consistency with the results from electrical cortical stimulation (ECS) as well as a trial-based method. Continuous behavioral tracing yielded more dependency to neural signals than sole event markers-derived regressor. Based on this, we successfully performed online functional brain mapping during five neurosurgery operations, which yielded satisfactory results. Significance. In the present study, we showed the utilization of continuous behavioral tracing can improve the efficiency of iEEG-based functional brain mapping. We then demonstrated this approach is adequate for multi-session intraoperative functional assessment within short time span, which is of considerable clinical value.

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基金编号: 2016YFC1306203 61761166003 81771395

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出版当年[2017]版:
大类 | 2 区 工程技术
小类 | 2 区 工程:生物医学 3 区 神经科学
最新[2023]版:
大类 | 3 区 医学
小类 | 3 区 工程:生物医学 3 区 神经科学
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出版当年[2016]版:
Q1 ENGINEERING, BIOMEDICAL Q2 NEUROSCIENCES
最新[2023]版:
Q2 ENGINEERING, BIOMEDICAL Q2 NEUROSCIENCES

影响因子: 最新[2023版] 最新五年平均 出版当年[2016版] 出版当年五年平均 出版前一年[2015版] 出版后一年[2017版]

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第一作者机构: [a]State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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通讯机构: [a]State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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