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.

[1]  Panayiotopoulos Cp,et al.  The Epilepsies: Seizures, Syndromes and Management , 2004 .

[2]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[3]  Thomas R. Knösche,et al.  Prior knowledge on cortex organization in the reconstruction of source current densities from EEG , 2013, NeuroImage.

[4]  Aamir Saeed Malik,et al.  A survey of methods used for source localization using EEG signals , 2014, Biomed. Signal Process. Control..

[5]  Thomas R. Knösche,et al.  Spatio-temporal Regularization in Linear Distributed Source Reconstruction from EEG/MEG: A Critical Evaluation , 2013, Brain Topography.

[6]  Karl J. Friston,et al.  Dynamic causal modeling of evoked responses in EEG and MEG , 2006, NeuroImage.

[7]  Jan-Mathijs Schoffelen,et al.  A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls , 2016, Front. Syst. Neurosci..

[8]  Christoph M. Michel,et al.  Towards the utilization of EEG as a brain imaging tool , 2012, NeuroImage.

[9]  K. G. Mideksa,et al.  Neuronal networks in epileptic encephalopathies with CSWS , 2016, Epilepsia.

[10]  Tohru Ozaki,et al.  A solution to the dynamical inverse problem of EEG generation using spatiotemporal Kalman filtering , 2004, NeuroImage.

[11]  Y. Attal,et al.  Assessment of Subcortical Source Localization Using Deep Brain Activity Imaging Model with Minimum Norm Operators: A MEG Study , 2013, PloS one.

[12]  Dimitrios I. Fotiadis,et al.  A Kalman filter based methodology for EEG spike enhancement , 2007, Comput. Methods Programs Biomed..

[13]  T. Ozaki,et al.  Whitening as a Tool for Estimating Mutual Information in Spatiotemporal Data Sets , 2006 .

[14]  Y. Okada,et al.  Modeling and Detecting Deep Brain Activity with MEG & EEG , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  Andreas Galka,et al.  The performance of the spatiotemporal Kalman filter and LORETA in seizure onset localization , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[16]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[17]  Tohru Ozaki,et al.  GARCH modelling of covariance in dynamical estimation of inverse solutions , 2004 .

[18]  Richard M. Leahy,et al.  Electromagnetic brain mapping , 2001, IEEE Signal Process. Mag..

[19]  Andrew Harvey,et al.  Forecasting, Structural Time Series Models and the Kalman Filter , 1990 .

[20]  Cristiano Micheli,et al.  Enhancing the Signal of Corticomuscular Coherence , 2012, Comput. Math. Methods Medicine.

[21]  A. R. Anwar,et al.  Dynamic Imaging of Coherent Sources Reveals Different Network Connectivity Underlying the Generation and Perpetuation of Epileptic Seizures , 2013, PloS one.

[22]  A. Cukiert,et al.  Deep brain stimulation targeting in refractory epilepsy , 2017, Epilepsia.

[23]  M Muthuraman,et al.  Essential constraints for detecting deep sources in EEG - application to orthostatic tremor , 2011, 2011 4th International Congress on Image and Signal Processing.

[24]  Tohru Ozaki,et al.  A data-driven model of the generation of human EEG based on a spatially distributed stochastic wave equation , 2008, Cognitive Neurodynamics.

[25]  A. R. Anwar,et al.  Neuronal Networks in West Syndrome as Revealed by Source Analysis and Renormalized Partial Directed Coherence , 2012, Brain Topography.

[26]  Tohru Ozaki,et al.  Time Series Modeling of Neuroscience Data , 2012 .

[27]  Daniel Brandeis,et al.  Developmental changes of functional and directed resting-state connectivities associated with neuronal oscillations in EEG , 2013, NeuroImage.

[28]  Emery N. Brown,et al.  An Analysis of How Spatiotemporal Dynamic Models of Brain Activity Could Improve MEG/EEG Inverse Solutions , 2015, 1511.03726.

[29]  Thomas R. Knösche,et al.  A guideline for head volume conductor modeling in EEG and MEG , 2014, NeuroImage.

[30]  Arnold W. Heemink,et al.  Modeling and prediction of environmental data in space and time using Kalman filtering , 2002 .

[31]  D. Lehmann,et al.  Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. , 1994, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[32]  G. Deuschl,et al.  Representation and propagation of epileptic activity in absences and generalized photoparoxysmal responses , 2013, Human brain mapping.

[33]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[34]  Tohru Ozaki,et al.  Recursive penalized least squares solution for dynamical inverse problems of EEG generation , 2004, Human brain mapping.

[35]  Hugh F. Durrant-Whyte,et al.  Evaluating the Performance of Kalman-Filter-Based EEG Source Localization , 2009, IEEE Transactions on Biomedical Engineering.

[36]  Pierre Brasseur,et al.  Ocean Data Assimilation Using Sequential Methods Based on the Kalman Filter , 2006 .

[37]  Steven J. Schiff,et al.  Neural Control Engineering: The Emerging Intersection Between Control Theory and Neuroscience , 2011 .

[38]  Dattatraya Muzumdar,et al.  Mesial temporal lobe epilepsy - An overview of surgical techniques. , 2016, International journal of surgery.

[39]  Xavier Tricoche,et al.  Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: A simulation and visualization study using high-resolution finite element modeling , 2006, NeuroImage.

[40]  R. Pascual-Marqui.  Review of methods for solving the EEG inverse problem , 1999 .

[41]  Tohru Ozaki,et al.  Decomposition of Neurological Multivariate Time Series by State Space Modelling , 2011, Bulletin of mathematical biology.

[42]  R D Pascual-Marqui,et al.  Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. , 2002, Methods and findings in experimental and clinical pharmacology.

[43]  Sergiu Groppa,et al.  Locating the STN-DBS electrodes and resolving their subsequent networks using coherent source analysis on EEG , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[44]  Fabrice Wendling,et al.  Source reconstruction via the spatiotemporal Kalman filter and LORETA from EEG time series with 32 or fewer electrodes , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[45]  Xu Lei,et al.  Incorporating priors for EEG source imaging and connectivity analysis , 2015, Front. Neurosci..

[46]  J. Schoffelen,et al.  Source connectivity analysis with MEG and EEG , 2009, Human brain mapping.

[47]  Fabrice Wendling,et al.  Spatial projection as a preprocessing step for EEG source reconstruction using spatiotemporal Kalman filtering , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[48]  Andreas Galka,et al.  The choice of the source space and the Laplacian matrix in LORETA and the spatio-temporal Kalman filter EEG inverse methods , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[49]  A. Friederici,et al.  Dynamic Causal Modeling of Subcortical Connectivity of Language , 2011, The Journal of Neuroscience.

[50]  Martin Burger,et al.  Hierarchical Bayesian inference for the EEG inverse problem using realistic FE head models: Depth localization and source separation for focal primary currents , 2012, NeuroImage.

[51]  Jerome Engel,et al.  Outcome with respect to epileptic seizures. , 1993 .

[52]  Andreas Galka,et al.  Combining EEG and MEG for the Reconstruction of Epileptic Activity Using a Calibrated Realistic Volume Conductor Model , 2014, PloS one.

[53]  Bart Vanrumste,et al.  Journal of Neuroengineering and Rehabilitation Open Access Review on Solving the Inverse Problem in Eeg Source Analysis , 2022 .

[54]  Torgeir Moberget,et al.  Annals of the New York Academy of Sciences Cerebellar Contributions to Motor Control and Language Comprehension: Searching for Common Computational Principles , 2022 .

[55]  Carsten H. Wolters,et al.  Efficient algorithms for the regularization of dynamic inverse problems: II. Applications , 2002 .

[56]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[57]  F. L. D. Silva,et al.  The Impact of EEG/MEG Signal Processing and Modeling in the Diagnostic and Management of Epilepsy , 2008 .

[58]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[59]  Jerome Engel,et al.  A Proposed Diagnostic Scheme for People with Epileptic Seizures and with Epilepsy: Report of the ILAE Task Force on Classification and Terminology , 2001, Epilepsia.

[60]  F. H. Lopes da Silva.  EEG and MEG: relevance to neuroscience. , 2013, Neuron.

[61]  A. Friederici,et al.  Head models and dynamic causal modeling of subcortical activity using magnetoencephalographic/electroencephalographic data , 2012, Reviews in the neurosciences.

[62]  Carsten H. Wolters,et al.  MEG-EEG fusion by Kalman filtering within a source analysis framework , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[63]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[64]  S. Lakshmivarahan,et al.  Ensemble Kalman filter , 2009, IEEE Control Systems.

[65]  Pavitra Krishnaswamy,et al.  Sparsity enables estimation of both subcortical and cortical activity from MEG and EEG , 2017, Proceedings of the National Academy of Sciences.