Authors :
Presenting Author: Hisako Fujiwara, PhD – Cincinnati Children's Hospital Medical Center
Paul Horn, PhD – Cincinnati Children's Hospital Medical Center; Hansel Greiner, MD – Cincinnati Children's Hospital Medical Center; Jesse Skoch, MD – Cincinnati Children's Hospital Medical Center; Ravindra Arya, MD – Cincinnati Children's Hospital Medical Center; Gewalin Aungaroon, MD – Cincinnati Children's Hospital Medical Center; Susan Fong, MD, PhD – Cincinnati Children's Hospital Medical Center; Katherine Holland, MD, PhD – Cincinnati Children's Hospital Medical Center; Kelly Kremer, MD – Cincinnati Children's Hospital Medical Center; Sarah Ihnen, MD, PhD – Cincinnati Children's Hospital Medical Center; Nan Lin, MD – Cincinnati Children's Hospital Medical Center; Wei Liu, MD – Cincinnati Children's Hospital Medical Center; Heather Wied, MD, PhD – Cincinnati Children's Hospital Medical Center; Francesco Mangano, DO – Cincinnati Children's Hospital Medical Center; Jeffrey Tenney, MD, PhD – Cincinnati Children's Hospital Medical Center
Rationale:
Temporal plus epilepsy (TLE+) represents a complex epilepsy in which the epileptogenic zone (EZ) extends beyond the anatomical boundaries of the temporal lobe and involves close neighboring structures. Incomplete characterization of TLE+ likely contributes to the increased surgical failure rate for these patients. Magnetoencephalography (MEG) defines the potential EZ and identifies targets for intracranial electroencephalography (iEEG) which is essential for accurately determining the TLE+ network. Conventional MEG source modeling, such as equivalent current dipole, often does not explain the whole epileptic network well. The objective of this retrospective study is to use MEG connectivity to explain the complex epileptic network and distinguish the TLE and TLE+ presurgically. We hypothesize that MEG network analyses can distinguish the TLE and TLE+, contribute to the iEEG target planning and predict the surgical outcome.
Methods:
MEG recordings obtained as part of the pre-surgical evaluation were used for functional connectivity analysis. Whole brain estimates of source activity were obtained using a linearly constrained minimum variance beamformer, comparing three econd baseline (no spikes) and three second spike phase. Second, functional connectivity was estimated using weighted phase lag index (wPLI). Finally, highly connected nodes (hubs) were quantified using eigenvector centrality. Distribution of the top 10 hubs were categorized in the temporal lobe and its neighboring structures, including amygdalohippocampi, insula, suprasylvian operculum, orbitofrontal cortex, and TPO junction bilaterally. Each of the 12 locations was classified as positive or negative depending on whether a hub was present. A logistic regression was used to examine the effect of these MEG findings on both patient group (TLE/TLE+) and surgical outcome (seizure free [ILAE 1], not seizure free [ILAE 2-6]).
Results:
Forty-one patients (21 females, 20 males) who underwent MEG, iEEG and have one year post-surgical follow-up were included in this study. Twenty-four patients were classified as TLE and 17 patients were TLE+. The number of nodes that were localized in neighboring structures were significantly higher in TLE+ group compared to TLE (p = 0.049). Using TLE / TLE+ classification as the response in a logistic regression showed that for each additional node in neighboring structures of the temporal lobe, the odds of a non-seizure free outcome increased by a factor of more than two (OR = 2.34, 95% Cl: 1.12 - 4.86, p = 0.024).
Conclusions:
MEG connectivity correlated well with temporal plus epilepsy by identifying the key hubs within the temporal lobe as well as its neighboring structures. Furthermore, MEG network analysis predicted seizure outcome: each additional location found in the neighboring structure increased the odds of a non-seizure free outcome by the factor of 2.34. Further MEG network analyses will include comparison to iEEG, conventional MEG analysis methods, and resection/ablation area.
Funding:
5R21NS123630