Uncovering Brain Network Insights for Prognosis in Disorders of Consciousness: EEG Source Space Analysis and Brain Dynamics
揭示脑网络对意识障碍预后的见解：脑电图源空间分析和脑动力学

This work was supported in part by the National Key Research and Development Program of China (2022YFC3601100 and 2022YFC3601105) and the National Natural Science Foundation of China (61171002). (Corresponding authors: Xiaoyu Xia and Weibei Dou.)
这项工作部分得到了中国国家重点研发计划（2022YFC3601100 和 2022YFC3601105）和中国国家自然科学基金 （61171002） 的支持。（通讯作者：Xiaoyu Xia 和 Weibei Dou。

This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the PLA Army General Hospital (No.2017-36).
这项工作涉及人类受试者或动物的研究。解放军陆军总医院 （No.2017-36） 批准了所有伦理和实验程序和协议。

Zexuan Hao and Weibei Dou are with the Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China (e-mail: hzx18@mails.tsinghua.edu.cn; douwb@tsinghua.edu.cn).
Zexuan Hao 和 Weibei Dou 就职于清华100084大学北京信息科学与技术国家研究中心 （BNRist） 电子工程系（电子邮件：hzx18@mails.tsinghua.edu.cn;douwb@tsinghua.edu.cn）。
Xiaoyu Xia is with the Senior Department of Neurosurgery, The First Medical Center of PLA General Hospital, Beijing 100853, China, and also with the Department of Neurosurgery, The Seventh Medical Center of Chinese PLA General Hospital, Beijing 100700, China (e-mail: jiaxy02@163.com).
夏晓宇就职于中国100853人民解放军总医院第一医学中心神经外科高级科，中国人民解放军总医院第七医学中心神经外科，北京 100700（电子邮件：jiaxy02@163.com）。
Yu Pan and Bo Peng is with the Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China (e-mail: py10335@163.com; gxhpb123@163.com).
Yu Pan 和 Bo Peng 就职于清华大学北京清华大学临床医学院康复医学科，中国北京102218 py10335@163.com，gxhpb123@163.com）。

Yang Bai is with the Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanchang University, Nanchang 330006, China, and also with the Rehabilitation Medicine Clinical Research Center of Jiangxi Province, Nanchang 330006, China (e-mail: baiyang1126@gmail.com).
Yang Bai 就职于南昌大学第一附属医院康复医学科（中国南昌330006）和江西省康复医学临床研究中心330006（电子邮件：baiyang1126@gmail.com）。
Yong Wang is with the Zhuhai UM Science & Technology Research Institute, Zhuhai 519031, China (e-mail: wy599580@163.com).
王勇就职于珠海澳大科学技术研究院，珠海519031（电子邮件：wy599580@163.com）。
lying consciousness improvement or recovery.
说谎意识的改善或恢复。
Index Terms-Disorders of consciousness (DOC), electroencephalography (EEG), microstates, prognosis, source-space functional connectivity.
索引术语 - 意识障碍 （DOC）、脑电图 （EEG）、微观状态、预后、源空间功能连接。

SEVERE brain injuries, often resulting from traumatic brain injury, cardiac arrest, and stroke, frequently lead to prolonged disorders of consciousness (DOC), characterized by unconsciousness lasting longer than 28 days [1], [2]. Patients with DOC are diagnosed with either a vegetative state/unresponsive wakefulness syndrome (VS/UWS) or a minimally conscious state (MCS; subclassification: MCSand MCS + ), based on repeated assessments of the level of consciousness [3]. Accurate and individualized prognostication of outcomes for patients with DOC is a critical clinical priority. It has direct implications for treatment decisions, care strategies, and family education [4], [5]. However, the neurobiological heterogeneity in DOC and the limited understanding of the mechanisms of consciousness recovery hinder the identification of reliable prognostic markers [4]. Various outcome prediction tools still face substantial challenges in their translation into clinical practice [6], [7].
严重的脑损伤通常由创伤性脑损伤、心脏骤停和中风引起，经常导致长时间意识障碍 （DOC），其特征是意识丧失持续时间超过 28 天 [1]，[2]。根据对意识水平的反复评估，DOC 患者被诊断为植物人状态/无反应觉醒综合征 （VS/UWS） 或最低意识状态 （MCS;亚分类：MCS和 MCS +） [3]。对 DOC 患者结局进行准确和个体化的预测是关键的临床优先事项。它对治疗决策、护理策略和家庭教育有直接影响 [4]，[5]。然而，DOC 的神经生物学异质性和对意识恢复机制的有限理解阻碍了可靠预后标志物的识别 [4]。各种结果预测工具在转化为临床实践的过程中仍然面临重大挑战 [6]， [7]。

Demographic and clinical characteristics have been extensively investigated, and certain factors were considered to impact the prognosis of DOC: Younger age [5], [8], [9], traumatic (vs. nontraumatic) etiology [2], [8], [9], shorter time postinjury [5], [8], [9], a MCS (vs. VS/UWS) diagnosis [2], [8] and a higher Coma Recovery Scale—Revised (CRS-R) total score [5], [8] were associated with a better prognosis/outcome. Recently, a variable named the consciousness domain index, defined by cluster labels obtained through $k$$k$kk-means clustering (two clusters) of the six CRS-R subscores, may improve the prediction of recovery of consciousness compared to clinical diagnosis and the CRS-R total score [10]. However, demographic and clinical candidate prognostic markers have not been consistently identified across studies nor have they provided insight into brain function and mechanisms of recovery in consciousness.
人口统计学和临床特征已被广泛研究，并且某些因素被认为会影响 DOC 的预后：年龄较小 [5]、[8]、[9]、创伤性（与非创伤性相比）病因 [2]、[8]、[9]、受伤后时间较短 [5]、[8]、[9]、MCS（与 VS/UWS）诊断 [2]、[8] 和较高的昏迷恢复量表 - 修订版 （CRS-R） 总分 [5]， [8] 与更好的预后/结局相关。最近，一个名为意识域指数的变量由通过 $k$$k$kk 6 个 CRS-R 子评分的均值聚类（两个集群）获得的聚类标签定义，与临床诊断和 CRS-R 总分相比，可能会改善对意识恢复的预测 [10]。然而，人口统计学和临床候选预后标志物在研究中尚未得到一致鉴定，也未提供对大脑功能和意识恢复机制的见解。
Thanks to the applications and advances in neuroimaging, there is a growing consensus that brain functions emerge from complex interactions between brain regions [11], [12]. Previous research has suggested that consciousness rests on the brain’s ability to sustain dynamic signal coordination and a balance between segregation and integration [11], [13], [14]. Severe brain injury often leads to large-scale structural disconnections and abnormal functional connectivity (FC). This
由于神经影像学的应用和进步，人们越来越一致地认为，大脑功能源于大脑区域之间的复杂相互作用 [11]，[12]。先前的研究表明，意识取决于大脑维持动态信号协调的能力以及分离和整合之间的平衡 [11]、[13]、[14]。严重的脑损伤通常会导致大规模的结构断开和功能连接 （FC） 异常。这
impairs the neural infrastructures that support consciousness and reduces the capacity of brain-wide information spreading and dynamic emergence [4], [8], [13], [14]. Therefore, low consciousness is thought to be associated with increased network segregation [13], decreased network integration [13], [14], and reduced dynamic richness [11] and functional diversity [14]. Moreover, the spatiotemporal evolution of brain signals (referred to as brain dynamics) can be characterized by several states (e.g., identified by clustering analysis [11], [15] or hidden Markov model [16]; referred to as brain states) that transition and reoccur in time. There is a common finding [11], [17], [18] that different brain states correspond to distinct FC patterns (i.e., state-specific FC [18]). However, brain injuries cause damage/alterations in brain dynamics that are not uniform across different states and can even be statespecific [11], [16], [17]. In the context of prognosis, numerous neural indicators derived from neuroimaging data, such as EEG and functional magnetic resonance imaging (fMRI), have demonstrated prognostic predictive potential for patients with DOC, including EEG qualitative [5] and quantitative features [8], fMRI-based FC [9], among others [6], [7]. Several prognostic prediction models for patients with DOC using clinical and/or neural features have shown promising performance, such as the use of fMRI FC [19], clinical features + fMRI FC [9], fMRI FC + EEG features [20], clinical + EEG qualitative features [21], the TMS-EEG perturbational complexity index [22]. However, the relatively small sample sizes and large differences among patients in studies (e.g., baseline diagnoses, time postinjury, and definition of the outcomes) limit the objective evaluation of the performance of different models.
损害支持意识的神经基础设施，降低全脑信息传播和动态涌现的能力 [4]， [8]， [13]， [14]。因此，低意识被认为与网络分离增加 [13]、网络整合减少 [13]、[14] 以及动态丰富度 [11] 和功能多样性降低 [14] 有关。此外，大脑信号的时空演变（称为大脑动力学）可以用几种状态（例如，通过聚类分析[11]、[15]或隐马尔可夫模型[16]识别;称为大脑状态）来表征，这些状态会随着时间的推移而转换和重复出现。有一个共同的发现 [11]、[17]、[18] 不同的大脑状态对应于不同的 FC 模式（即状态特异性 FC [18]）。然而，脑损伤会导致大脑动力学的损伤/改变，这些损伤/改变在不同状态之间并不均匀，甚至可能是特定于状态的[11]、[16]、[17]。在预后方面，来自神经影像学数据的许多神经指标，如脑电图和功能磁共振成像 （fMRI），已经证明了 DOC 患者的预后预测潜力，包括 EEG 定性 [5] 和定量特征 [8]、基于 fMRI 的 FC [9] 等 [6]、[7]。几种使用临床和/或神经特征的 DOC 患者预后预测模型显示出有希望的性能，例如使用 fMRI FC [19]、临床特征 + fMRI FC [9]、fMRI FC + EEG 特征 [20]、临床 + EEG 定性特征 [21]、TMS-EEG 扰动复杂性指数 [22]。然而，研究中相对较小的样本量和患者之间的较大差异（例如、基线诊断、受伤后时间和结果定义）限制了对不同模型性能的客观评估。

Overall, noninvasive multimodal neural features are of particular interest for developing personalized prognostic prediction models for patients with DOC. Compared to other modalities or research paradigms, resting-state EEG represents an attractive choice owing to its safety, cost-effectiveness, direct detection of high temporal-resolution neural activity, and ease of deployment at the bedside. Previous studies have demonstrated the fundamental predictive power of restingstate EEG features [6], [7]. However, there are two critical issues that are largely unexplored, and they are important for insights into the discovery of new prognostic markers and mechanisms underlying the recovery of consciousness. First, the relationship between the EEG source space brain network and prognosis remains unclear. Numerous efforts have been made to study scalp EEG brain networks in relation to the prognosis of patients with DOC [6]-[8]. However, the spatial mixture of signals caused by volume conduction [23] and the limited spatial resolution substantially constrain the biological interpretation of the findings and the integration of information with other modalities. Second, as a dynamic system, the brain presents a research gap regarding whether brain networks underlying different brain states have the same or similar prognostic utility. Previous studies have shown that brain injuries can lead to significant alterations in brain dynamic parameters (e.g., mean state duration and mean state interval [18], [24]), but they do not necessarily result in marked alterations to the EEG brain state templates (e.g., EEG topographic maps corresponding to cluster centroids [18], [24]). Research on
总体而言，无创多模态神经特征对于为 DOC 患者开发个性化预后预测模型特别感兴趣。与其他模式或研究范式相比，静息态脑电图是一个有吸引力的选择，因为它具有安全性、成本效益、直接检测高时间分辨率神经活动以及易于在床旁部署。以前的研究已经证明了静息态脑电图特征的基本预测能力 [6]， [7]。然而，有两个关键问题在很大程度上尚未探索，它们对于发现新的预后标志物和意识恢复机制非常重要。首先，脑电图源空间脑网络与预后之间的关系尚不清楚。已经做出了大量努力来研究与 DOC 患者预后相关的头皮脑电图脑网络 [6]-[8]。然而，由体积传导引起的信号的空间混合 [23] 和有限的空间分辨率在很大程度上限制了对研究结果的生物学解释以及信息与其他模式的整合。其次，作为一个动态系统，大脑在支撑不同大脑状态的大脑网络是否具有相同或相似的预后效用方面存在研究空白。以前的研究表明，脑损伤会导致大脑动力学参数（例如，平均状态持续时间和平均状态间隔 [18]、[24]）的显着变化，但它们不一定会导致脑电图大脑状态模板的显着改变（例如，对应于集群质心的脑电地形图 [18]、[24]）。研究
the prognostic abilities of brain networks underlying different brain states will provide opportunities for the identification of novel biomarkers and a deeper understanding of brain dynamics.
不同大脑状态背后的大脑网络的预后能力将为识别新的生物标志物和更深入地了解大脑动力学提供机会。

In this study, we retrospectively recruited 254 patients with DOC to investigate the key factors predicting prognosis and explore the neural underpinnings of different prognoses. In addition, the analysis in this study focused on the delta band ( $1-4\mathrm{Hz}$$1-4\mathrm{Hz}$1-4Hz1-4 \mathrm{~Hz} ) for the following three main reasons: (1) A firm link between delta cortex activity and consciousness [25], [26]. (2) Reducing the number of statistical tests to improve the statistical power. (3) Strong independence of temporal sequences of brain states between the spectral bands [27]. In general, the novel contributions of our study are as follows: (1) We show that source-space FC during only specific states in brain dynamics can predict outcomes, and large-scale hypersynchrony in the delta band has a negative effect on the improvement of consciousness. (2) We employ graph theory and higher-order topological analysis of brain networks to offer insights into the mechanism of consciousness improvement. (3) We present the key clinical and neural features for predicting prognosis based on a large cohort of patients.
在这项研究中，我们回顾性招募了 254 例 DOC 患者，以调查预测预后的关键因素并探索不同预后的神经基础。此外，本研究的分析集中在 delta 带 （ $1-4\mathrm{Hz}$$1-4\mathrm{Hz}$1-4Hz1-4 \mathrm{~Hz} ） 上，主要有三个原因：（1） delta 皮层活动与意识之间存在牢固的联系 [25]，[26]。（2） 减少统计检验的数量以提高统计功效。（3） 谱带之间大脑状态的时间序列具有很强的独立性 [27]。总的来说，我们研究的新贡献如下：（1） 我们表明，仅在大脑动力学的特定状态下的源空间 FC 可以预测结果，并且 delta 波段的大规模超同步对意识的改善有负面影响。（2） 我们采用图论和大脑网络的高阶拓扑分析来提供对意识改进机制的见解。（3） 我们提出了基于大量患者预测预后的关键临床和神经特征。

This study was approved by the Ethics Committee of the PLA Army General Hospital (now the Seventh Medical Center of the PLA General Hospital) (No.2017-36) and the Institutional Review Board of Tsinghua University (No.20220214). Written informed consent was obtained from each participant or their legal representatives. No power analysis was conducted to predetermine sample sizes, but our sample size was larger than those reported in previous publications [8], [13], [20].
本研究获得中国人民解放军陆军总医院（现解放军总医院第七医学中心）伦理委员会（编号：2017-36）和清华大学机构审查委员会（编号：20220214）批准。从每个参与者或其法定代表人那里获得书面知情同意书。没有进行功效分析来确定样本量，但我们的样本量大于以前出版物中报道的样本量 [8]、[13]、[20]。
2) Clinical Assessment and Outcome Definition: The level of consciousness in each patient with DOC was diagnosed by specialized physicians based on the highest score obtained from three CRS-R assessments [3] conducted at both admission and the six-month follow-up. Demographic and clinical characteristics for each patient are available at https://osf.io/fjwst/. In this study, the term “prognosis” denotes
2） 临床评估和结果定义：每位 DOC 患者的意识水平由专业医生根据入院和六个月随访时进行的三项 CRS-R 评估 [3] 获得的最高分进行诊断。每位患者的人口统计学和临床特征可在 https://osf.io/fjwst/ 获得。在这项研究中，术语“预后”表示

TABLE I  表 I
Demographic and clinical characteristics of patients with disorders of consciousness at the admission
入院时意识障碍患者的人口统计学和临床特征

Univariate statistics are based on the chi-square test, two-sample $t$$t$tt-test, Mann-Whitney $U$$U$UU test, one-way independent-measures ANOVA, or Kruskal-Wallis test, as appropriate. FDR-corrected $p<0.05$$p<0.05$p < 0.05p<0.05 is marked in bold. Abbreviations: VS/UWS $=$$=$== vegetative state/unresponsive wakefulness syndrome; MCS $=$$=$== minimally conscious state; TBI = traumatic brain injury; TPI = time postinjury; IQR = interquartile range; CRS-R = Coma Recovery Scale-Revised;
单变量统计量基于卡方检验、双样本 $t$$t$tt 检验、Mann-Whitney $U$$U$UU 检验、单因子独立测度方差分析或 Kruskal-Wallis 检验（视情况而定）。FDR 校正后 $p<0.05$$p<0.05$p < 0.05p<0.05 以粗体标记。缩写：VS/UWS $=$$=$== 植物人状态/无反应觉醒综合征;MCS $=$$=$== 最低意识状态;TBI = 创伤性脑损伤;TPI = 受伤后时间;IQR = 四分位距;CRS-R = 昏迷恢复量表修订版;
ANOVA = analysis of variance; FDR = false discovery rate.
方差分析 = 方差分析;FDR = 错误发现率。
a prognosis for improvement in awareness. A positive outcome (prognosis) was defined as any improvement in the level of consciousness at the six-month follow-up compared to admission, whereas a negative outcome was defined as a lack of improvement or retrogress in consciousness.
意识提高的预后。积极结局 （预后） 定义为与入院相比，6 个月随访时意识水平的任何改善，而消极结局定义为意识缺乏改善或倒退。

Resting-state EEG data were recorded using 62-channel acquisition equipment (BrainAmp 64 MR plus, Brain Products, Germany) with electrodes ( $\mathrm{Ag}/\mathrm{AgCl}$$\mathrm{Ag}/\mathrm{AgCl}$Ag//AgCl\mathrm{Ag} / \mathrm{AgCl} ) placed according to the standard 10-10 system at a sampling rate of 2500 Hz . We used data from 59 EEG channels for subsequent analyses. The reference channel was placed at FCz. The electrode impedance was maintained below $5\mathrm{k}\mathrm{\Omega }$$5\mathrm{k}\mathrm{\Omega }$5kOmega5 \mathrm{k} \Omega. The arousal procedures were applied (with recording suspended) if needed (e.g., in the presence of signs of drowsiness and sleep onset) during data collection. There is one EEG recording per patient. For most patients with DOC, EEG acquisition occurred between 8-10 am ( $29.2\mathrm{\%}$$29.2\mathrm{\%}$29.2%29.2 \% ) or $2-4\mathrm{pm}(57.9\mathrm{\%})$$2-4\mathrm{pm}(57.9\mathrm{\%})$2-4pm(57.9%)2-4 \mathrm{pm}(57.9 \%). During the data recording, we ensured that patients were awake and had their eyes open. The resting-state EEG ( 59 EEG channels) acquisition protocol for the HC dataset has been described in detail elsewhere [18]. The duration of the recordings was over 5 minutes ( $760\pm 211$$760\pm 211$760+-211760 \pm 211 s) for the patients and approximately 4 minutes ( $243\pm 62\mathrm{s}$$243\pm 62\mathrm{s}$243+-62s243 \pm 62 \mathrm{~s} ) for the HC group.
使用 62 通道采集设备（BrainAmp 64 MR plus，Brain Products，德国）记录静息态脑电图数据，电极 （ $\mathrm{Ag}/\mathrm{AgCl}$$\mathrm{Ag}/\mathrm{AgCl}$Ag//AgCl\mathrm{Ag} / \mathrm{AgCl} ） 根据标准 10-10 系统以 2500 Hz 的采样率放置。我们使用来自 59 个 EEG 通道的数据进行后续分析。参考通道位于 FCz。电极阻抗保持在 以下 $5\mathrm{k}\mathrm{\Omega }$$5\mathrm{k}\mathrm{\Omega }$5kOmega5 \mathrm{k} \Omega 。在数据收集期间，如果需要（例如，在存在嗜睡和入睡迹象的情况下），应用唤醒程序 （暂停记录）。每位患者有一次脑电图记录。对于大多数 DOC 患者，脑电图采集发生在上午 8-10 点 （ $29.2\mathrm{\%}$$29.2\mathrm{\%}$29.2%29.2 \% ） 或 $2-4\mathrm{pm}(57.9\mathrm{\%})$$2-4\mathrm{pm}(57.9\mathrm{\%})$2-4pm(57.9%)2-4 \mathrm{pm}(57.9 \%) 之间。在数据记录过程中，我们确保患者是清醒的，并且他们的眼睛是睁开的。HC 数据集的静息态 EEG（59 个 EEG 通道）采集协议已在其他地方详细描述 [18]。患者的记录持续时间超过 5 分钟 （ $760\pm 211$$760\pm 211$760+-211760 \pm 211 s），HC 组约为 4 分钟 （ $243\pm 62\mathrm{s}$$243\pm 62\mathrm{s}$243+-62s243 \pm 62 \mathrm{~s} ）。

All EEG recordings in this study were preprocessed using the same semi-automated procedures based on the EEGLAB toolbox (v2019.0) [18]. First, the EEG data were lowpass filtered (cutoff frequency: 45 Hz ; passband ending at 42.5 Hz ) using a finite impulse response (FIR) filter, resampled to 250 Hz , and highpass filtered (FIR; cutoff frequency: 0.5 Hz ; passband starting at 1 Hz ). The bad channels in each recording were then identified and removed semi-automatically. Badchannel signals were interpolated using spherical interpolation but marked as “bad”. The EEG signals were then rereferenced to the common average across all channels. Finally, independent component analysis was performed for each recording,
本研究中的所有脑电图记录均使用基于 EEGLAB 工具箱 （v2019.0） [18] 的相同半自动程序进行预处理。首先，使用有限脉冲响应 （FIR） 滤波器对脑电图数据进行低通滤波（截止频率：45 Hz;通带结束于 42.5 Hz），重采样至 250 Hz，并进行高通滤波（FIR;截止频率：0.5 Hz;通带从 1 Hz 开始）。然后，每个记录中的不良通道被识别并半自动删除。Badchannel 信号使用 Spherical 插值进行插值，但标记为 “bad”。然后将 EEG 信号重新引用所有通道的公共平均值。最后，对每个记录进行独立的成分分析，
and bad components (e.g., eye blinks, movements, muscle activities, channel noise, and heart artifacts) were removed with the assistance of EEGLAB plugin extensions (ICLabel v1.3 and DIPFIT v3.3). We selected the middle 5 minutes of the preprocessed EEG data for subsequent analyses to maintain consistent data length across all patients.
在 EEGLAB 插件扩展 （ICLabel v1.3 和 DIPFIT v3.3） 的帮助下，去除了不良成分 （例如，眨眼、运动、肌肉活动、通道噪声和心脏伪影）。我们选择了预处理后的 EEG 数据的中间 5 分钟进行后续分析，以保持所有患者的数据长度一致。

Different source localization algorithms and FC measures may induce variability in the results. In our study, we chose to use the weighted minimum norm estimate (wMNE) [28] for source localization and the weighted phase-lag index (wPLI) [29] for FC, based on three primary considerations. (1) The wMNE is less sensitive to inaccuracies in the forward model, when using template anatomy and digitized electrode positions are not available [30], [31]; (2) The wPLI is insensitive to zerolag spurious correlations induced by field spread or volume conduction. (3) Recent research suggests that the combination of wMNE with wPLI yields an optimal performance in some contexts [32].
不同的源定位算法和 FC 测量可能会导致结果的可变性。在我们的研究中，我们选择使用加权最小标准估计 （wMNE） [28] 进行源定位，使用加权相位滞后指数 （wPLI） [29] 进行 FC，基于三个主要考虑因素。（1） 当使用模板解剖学和数字化电极位置不可用时，wMNE 对正向模型中的不准确性不太敏感 [30]，[31];（2） wPLI 对场扩散或体积传导诱导的零滞后杂散相关性不敏感。（3） 最近的研究表明，wMNE 与 wPLI 的组合在某些情况下会产生最佳性能 [32]。
For each EEG recording, source reconstruction was carried out using the wMNE method [28] in Brainstorm [33]. This process converted the EEG signals from sensor space to source space, encompassing 15002 dipoles distributed across the cerebral cortex. We established a realistic three-layer head model (scalp, skull, and brain) based on the FreeSurfer average anatomy [34], employing the boundary element method with the OpenMEEG plugin (v2.4) [35]. The conductivity values for the scalp, skull, and brain were set at $0.33,0.0066$$0.33,0.0066$0.33,0.00660.33,0.0066, and 0.33 $\mathrm{S}/\mathrm{m}$$\mathrm{S}/\mathrm{m}$S//m\mathrm{S} / \mathrm{m}, respectively [36]. We constructed the noise covariance matrix as an identity matrix and constrained dipole orientations to be perpendicular to the cortical surface. Subsequently, the lead field matrix (i.e., the transfer gain from each source to each channel) was computed by solving the forward problem. Finally, the wMNE method (using default parameters) was applied to estimate the inverse solution (i.e., the imaging kernel). The current source densities were calculated by converting the sensor-space EEG signals using the obtained imaging kernel.
对于每个脑电图记录，使用 Brainstorm [33] 中的 wMNE 方法 [28] 进行源重建。这个过程将脑电图信号从传感器空间转换为源空间，包括分布在大脑皮层的 15002 个偶极子。我们基于 FreeSurfer 平均解剖结构 [34] 建立了一个逼真的三层头部模型（头皮、头骨和大脑），采用边界元方法和 OpenMEEG 插件 （v2.4） [35]。头皮、颅骨和大脑的电导率值分别设置为 $0.33,0.0066$$0.33,0.0066$0.33,0.00660.33,0.0066 和 0.33 $\mathrm{S}/\mathrm{m}$$\mathrm{S}/\mathrm{m}$S//m\mathrm{S} / \mathrm{m} [36]。我们将噪声协方差矩阵构建为单位矩阵，并将偶极子方向约束为垂直于皮层表面。随后，通过求解正向问题计算导程场矩阵（即从每个源到每个通道的传输增益）。最后，应用 wMNE 方法 （使用默认参数） 来估计逆解 （即成像内核）。通过使用获得的成像内核转换传感器空间 EEG 信号来计算电流源密度。