Page 158 - MEGIN Book Of Abstracts - 2023

computed network connectivity of each technique. "implantation" of virtual sensors (VSs). We analyzed
Then, we confronted obtained noninvasive networks MEG, HD-EEG, and icEEG data from eight children with
to intracerebral EEG transitory network connectivity MRE who underwent surgery having good outcome
using nodes in common, connection strength, distance and performed source localization (beamformer) on
metrics between concordant nodes of MEG and IEEG, noninvasive data to build VSs at the icEEG electrode
and average propagation delay. Coherent Maximum locations. We analyzed data with and without Interictal
Entropy on the Mean (cMEM) proved a high matching Epileptiform Discharges (IEDs) in different frequency
between MEG network connectivity and IEEG based bands, and computed the following FC matrices: Ampli-
on distance between active sources, followed by Exact tude Envelope Correlation (AEC), Correlation (CORR),
low-resolution brain electromagnetic tomography and Phase Locking Value (PLV). Each matrix was used to
(eLORETA), Dynamical Statistical Parametric Mapping generate a graph using Minimum Spanning Tree (MST),
(dSPM), and Minimum norm estimation (MNE). Clinical and for each node (i.e., each sensor) we computed four
performance was interesting for entire methods provid- centrality measures: betweenness, closeness, degree,
ing in an average of 73.5% of active sources detected in and eigenvector. We tested the reliability of VSs mea-
depth and seen in MEG, and vice versa, about 77.15% sures with respect to icEEG (regarded as benchmark)
of active sources were detected from MEG and seen via linear correlation, and compared FC values inside
in IEEG. Investigated problem techniques succeed at vs. outside resection. We observed higher FC inside
least in finding one part of seizure onset zone. dSPM than outside resection (p<0.05) for AEC [alpha (8-12
and eLORETA depict the highest connection strength Hz), beta (12-30 Hz), and broadband (1-50 Hz)] on data
among all techniques. Propagation delay varies in this with IEDs and AEC theta (4-8 Hz) on data without IEDs
range [18, 25]ms, knowing that eLORETA ensures the for icEEG, AEC broadband (1-50 Hz) on data without
lowest propagation delay (18 ms) and the closet one to IEDs for MEG-VSs, as well as for all centrality measures
IEEG propagation delay. of icEEG and MEG/HD-EEG-VSs. Additionally, icEEG
and VSs metrics presented high correlation (0.6-0.9,
Computational and mathematical methods in medicine p<0.05). Our data support the notion that the proposed
(2021), Vol. 2021 (34992674) (0 citations) method can potentially replicate the icEEG ability to
map the epileptogenic network in children with MRE.
Clinical Relevance - The estimation of FC with noninva-
Mapping Functional Connectivity of Epileptogenic sive techniques, such as MEG and HD-EEG, via VSs is a
Networks through Virtual Implantation (2021) promising tool that would help the presurgical evalua-
tion by delineating the EZ without waiting for a seizure
Corona, Ludovica; Tamilia, Eleonora; Madsen, Joseph to occur, and potentially improve the surgical outcome
R; Stufflebeam, Steven M; Pearl, Phillip L; Papadelis, of patients with MRE undergoing surgery.
Christos
Annual International Conference of the IEEE Engineering
ABSTRACT Children with medically refractory epi- in Medicine and Biology Society. IEEE Engineering in
lepsy (MRE) require resective neurosurgery to achieve Medicine and Biology Society. Annual International
seizure freedom, whose success depends on accurate Conference (2021), Vol. 2021 (34891320) (0 citations)
delineation of the epileptogenic zone (EZ). Functional
connectivity (FC) can assess the extent of epileptic
brain networks since intracranial EEG (icEEG) studies Imaging the extent and location of
have shown its link to the EZ and predictive value for spatiotemporally distributed epileptiform sources
surgical outcome in these patients. Here, we propose a from MEG measurements (2022)
new noninvasive method based on magnetoencepha-
lography (MEG) and high-density (HD-EEG) data that Jiang, Xiyuan; Ye, Shuai; Sohrabpour, Abbas; Bagić,
estimates FC metrics at the source level through an Anto; He, Bin

 ontents  Index 137
C