Source imaging of deep-brain activity using the regional spatiotemporal Kalman filter


Background and objective: 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. Methods: 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. Results: 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. Conclusions: 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.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Mathematics
College of Engineering & Physical Sciences > Aston Institute of Urban Technology and the Environment (ASTUTE)
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
College of Engineering & Physical Sciences
Additional Information: Funding Information: Laith Hamid, Natia Japaridze and Andreas Galka were funded by the D3 subproject of the Collaborative Research Center CRC 855 “Magnetoelectric Composites - Future Biomagnetic Interfaces” of the German Science Society (DFG). Laith Hamid, Nawar Habboush and Andreas Galka were funded by the B3 subproject of the Collaborative Research Center CRC 1261 “Magnetoelectric Sensors: From Composite Materials to Biomagnetic Diagnostics” of the DFG, . Laith Hamid and Natia Japaridze were also funded by the European Union’s Seventh Framework Programme for research, technological development and demonstration through the project DESIRE “Development & Epilepsy” (Grant Agreement no: 602531), WP2 & WP4, . Ümit Aydin and Carsten H. Wolters were financed by DFG projects WO1425/2-1,7-1. © 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Uncontrolled Keywords: Deep sources,Dynamical inverse solution,EEG,EEG inverse problem,EEG source imaging,Electroencephalography,Epilepsy,Epileptiform activity,Kalman filter,LORETA,RSTKF,Source reconstruction,Spatiotemporal Kalman filter,State space,STKF,Subcortical sources,Software,Computer Science Applications,Health Informatics
Publication ISSN: 1872-7565
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.sci ... 6631?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2021-03-01
Published Online Date: 2020-11-09
Accepted Date: 2020-10-31
Authors: Hamid, Laith
Habboush, Nawar
Stern, Philipp
Japaridze, Natia
Aydin, Ümit
Wolters, Carsten H.
Claussen, Jens Christian (ORCID Profile 0000-0002-9870-4924)
Heute, Ulrich
Stephani, Ulrich
Galka, Andreas
Siniatchkin, Michael

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