Hamid Laith, Habboush Nawar, Stern Philipp, Japaridze Natia, Aydin Ümit, Wolters Carsten H, Claussen Jens Christian, Heute Ulrich, Stephani Ulrich, Galka Andreas, Siniatchkin Michael
Department of Medical Psychology and Medical Sociology, University of Kiel, D-24113 Kiel, Germany.
Department of Medical Psychology and Medical Sociology, University of Kiel, D-24113 Kiel, Germany.
Comput Methods Programs Biomed. 2021 Mar;200:105830. doi: 10.1016/j.cmpb.2020.105830. Epub 2020 Nov 9.
The human brain displays rich and complex patterns of interaction within and among brain networks that involve both cortical and subcortical brain regions. Due to the limited spatial resolution of surface electroencephalography (EEG), EEG source imaging is used to reconstruct brain sources and investigate their spatial and temporal dynamics. The majority of EEG source imaging methods fail to detect activity from subcortical brain structures. The reconstruction of subcortical sources is a challenging task because the signal from these sources is weakened and mixed with artifacts and other signals from cortical sources. In this proof-of-principle study we present a novel EEG source imaging method, the regional spatiotemporal Kalman filter (RSTKF), that can detect deep brain activity.
The regional spatiotemporal Kalman filter (RSTKF) is a generalization of the spatiotemporal Kalman filter (STKF), which allows for the characterization of different regional dynamics in the brain. It is based on state-space modeling with spatially heterogeneous dynamical noise variances, since models with spatial and temporal homogeneity fail to describe the dynamical complexity of brain activity. First, RSTKF is tested using simulated EEG data from sources in the frontal lobe, putamen, and thalamus. After that, it is applied to non-averaged interictal epileptic spikes from a presurgical epilepsy patient with focal epileptic activity in the amygdalo-hippocampal complex. The results of RSTKF are compared to those of low-resolution brain electromagnetic tomography (LORETA) and of standard STKF.
Only RSTKF is successful in consistently and accurately localizing the sources in deep brain regions. Additionally, RSTKF shows improved spatial resolution compared to LORETA and STKF.
RSTKF is a generalization of STKF that allows for accurate, focal, and consistent localization of sources, especially in the deeper brain areas. In contrast to standard source imaging methods, RSTKF may find application in the localization of the epileptogenic zone in deeper brain structures, such as mesial frontal and temporal lobe epilepsies, especially in EEG recordings for which no reliable averaged spike shape can be obtained due to lack of the necessary number of spikes required to reach a certain signal-to-noise ratio level after averaging.
人类大脑在涉及皮层和皮层下脑区的脑网络内部及之间呈现出丰富而复杂的相互作用模式。由于表面脑电图(EEG)的空间分辨率有限,EEG源成像被用于重建脑源并研究其空间和时间动态。大多数EEG源成像方法无法检测到皮层下脑结构的活动。皮层下源的重建是一项具有挑战性的任务,因为这些源发出的信号会被削弱,并与来自皮层源的伪迹和其他信号混合在一起。在这项原理验证研究中,我们提出了一种新型的EEG源成像方法——区域时空卡尔曼滤波器(RSTKF),它能够检测深部脑活动。
区域时空卡尔曼滤波器(RSTKF)是时空卡尔曼滤波器(STKF)的推广,它能够描述大脑中不同区域的动态。它基于具有空间异质动态噪声方差的状态空间建模,因为具有空间和时间同质性的模型无法描述脑活动的动态复杂性。首先,使用来自额叶、壳核和丘脑源的模拟EEG数据对RSTKF进行测试。之后,将其应用于一名术前癫痫患者的非平均发作间期癫痫棘波,该患者在杏仁核 - 海马复合体有局灶性癫痫活动。将RSTKF的结果与低分辨率脑电磁断层扫描(LORETA)和标准STKF的结果进行比较。
只有RSTKF成功地持续且准确地定位了深部脑区的源。此外,与LORETA和STKF相比,RSTKF显示出更高的空间分辨率。
RSTKF是STKF的推广,它能够对源进行准确、局部且一致的定位,尤其是在更深的脑区。与标准源成像方法不同,RSTKF可能在深部脑结构如内侧额叶和颞叶癫痫的致痫区定位中得到应用,特别是在由于缺乏平均后达到一定信噪比水平所需的必要棘波数量而无法获得可靠平均棘波形状的EEG记录中。