Structure-Function Coupling to Predict Surgical Outcomes in Epilepsy
Poster No:
324
Submission Type:
Abstract Submission
Authors:
Louise de Wouters1, Nicolas Roehri1, Stanislas Lagarde1, Emeline Mullier2, Margitta Seeck1, Dimitri Van De Ville3,4, Patric Hagmann2, Isotta Rigoni1, Serge Vulliémoz1,4
Institutions:
1EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland, 2Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland, 3Neuro-X Institute, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland, 4Center for Biomedical Imaging (CIBM), Switzerland
First Author:
Louise de Wouters
EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva
Geneva, Switzerland
EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva
Geneva, Switzerland
Co-Author(s):
Nicolas Roehri
EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva
Geneva, Switzerland
EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva
Geneva, Switzerland
Stanislas Lagarde
EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva
Geneva, Switzerland
EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva
Geneva, Switzerland
Emeline Mullier
Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL)
Lausanne, Switzerland
Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL)
Lausanne, Switzerland
Margitta Seeck
EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva
Geneva, Switzerland
EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva
Geneva, Switzerland
Dimitri Van De Ville
Neuro-X Institute, École polytechnique fédérale de Lausanne (EPFL)|Center for Biomedical Imaging (CIBM)
Geneva, Switzerland|Switzerland
Neuro-X Institute, École polytechnique fédérale de Lausanne (EPFL)|Center for Biomedical Imaging (CIBM)
Geneva, Switzerland|Switzerland
Patric Hagmann
Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL)
Lausanne, Switzerland
Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL)
Lausanne, Switzerland
Isotta Rigoni
EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva
Geneva, Switzerland
EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva
Geneva, Switzerland
Serge Vulliémoz
EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva|Center for Biomedical Imaging (CIBM)
Geneva, Switzerland|Switzerland
EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva|Center for Biomedical Imaging (CIBM)
Geneva, Switzerland|Switzerland
Introduction:
Epilepsy is a neurological disorder characterised by altered brain network organisation. For patients with drug-resistant epilepsy, surgical resection of the epileptogenic zone is the best therapeutic option. This work leverages the graph signal processing (GSP) approach to study brain structure-function (SF) coupling during interictal epileptic discharges (IEDs), aiming to distinguish between good and poor surgical outcomes in temporal lobe epilepsy (TLE). Based on a previous GSP study that demonstrated an increase in SF coupling during IEDs (Rigoni et al., 2023), we hypothesised that the SF coupling increase during IEDs differentiates patients with good surgical outcomes from those with poor ones. Such biomarker could ultimately aid to select best candidates for surgery.
Methods:
IEDs during high-density EEG recordings (129 or 257 channels, Electrical Geodesic Inc. system, sampling frequency = 1kHz) of 30 TLE patients (15 right TLE, mean age = 37.4y (11.8y)) were extracted and source-reconstructed using an individual head model based on 3T T1-weighted magnetic resonance imaging (MRI) scans, and a distributed inverse solution. For each region of interest (ROI) of the Lausanne 2018 scale-2 parcellation, the source activities were summarised into ROI time-series using the first component of the singular value decomposition (SVD) (Rubega et al., 2019) with an in-house correction for the sign ambiguity that was validated on simulated and real data. Employing a template structural connectome (SC) built from diffusion MRI scans of 70 healthy subjects (Griffa et al., 2019), the eigenvectors of the SC's graph Laplacian, termed 'network harmonics', were extracted. The ROI time-series were decomposed as a weighted sum of the network harmonics through the graph Fourier Transform. For each subject, the energy spectrum of the transformed signal was divided into low-frequency harmonics (LF) and high-frequency ones (HF) (Preti & Van De Ville, 2019). The first were used to reconstruct the part of the signal mostly coupled to the underlying structure, while the latter the decoupled one. The norms of the coupled and decoupled signals were calculated over all brain regions and the dynamics of their energy distribution along the IED were compared with a cluster-based permutation test across patients. The ratio between the norms of the coupled and decoupled signal, interpreted as the level of SF coupling, at the peak of the IED were compared between patients with good surgical outcomes (ILAE score of 1 or 2, n=17) and patients with poor surgical outcomes (ILAE score between 3 and 5, n=13) with a Wilcoxon rank-sum test.
Results:
The correction for the SVD sign ambiguity confirmed that, in about half of the cases the wrong sign was attributed to the ROI time-series, which led to an artefactual increase of energy on HF harmonics. After correcting for such bias, we replicated the results of (Rigoni et al., 2023) that confirmed the presence of increased coupling during the IEDs' peak (Figure 1a, p=0.0460) and increased decoupling right before the IED start (p=0.0004). Additionally, an increase of decoupling was also observed after the IED's peak (p=0.0464). Patients with poor surgical outcomes presented a significantly lower SF coupling at the peak of IEDs than those with good outcomes (Figure 1b, p=0.0213).
Conclusions:
SF coupling during IEDs differs between patients with good and poor surgical outcome. IEDs of patients with good outcome are better explained by the LF network harmonics, meaning that the spatial pattern of the IED is smoother on the SC. Contrarily, IEDs of patients with poor outcome require more HF network harmonics to be characterised, which suggests that their spatial distribution is more complex and coarser on the SC.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Keywords:
Electroencephaolography (EEG)
Epilepsy
Other - Structure-Function Coupling;Graph Signal Processing (GSP)
1|2Indicates the priority used for review
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Preti, M. G. (2019). Decoupling of brain function from structure reveals regional behavioral specialization in humans. Nature Communications, 10(1), 1–7.
Rigoni, I. (2023). Structure-function coupling increases during interictal spikes in temporal lobe epilepsy: A graph signal processing study. Clinical Neurophysiology, 153, 1–10.
Rubega, M. (2019). Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis. Brain Topography, 32(4), 704–719.