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Localization of the Epileptogenic Zone by Multimodal Neuroimaging and High-Frequency Oscillation.

ABSTRACT: Accurate localization of the epileptogenic zone (EZ) is a key factor to obtain good surgical outcome for refractory epilepsy patients. However, no technique, so far, can precisely locate the EZ, and there are barely any reports on the combined application of multiple technologies to improve the localization accuracy of the EZ. In this study, we aimed to explore the use of a multimodal method combining PET-MRI, fluid and white matter suppression (FLAWS)-a novel MRI sequence, and high-frequency oscillation (HFO) automated analysis to delineate EZ. We retrospectively collected 15 patients with refractory epilepsy who underwent surgery and used the above three methods to detect abnormal brain areas of all patients. We compared the PET-MRI, FLAWS, and HFO results with traditional methods to evaluate their diagnostic value. The sensitivities, specificities of locating the EZ, and marking extent removed versus not removed [RatioChann(ev)] of each method were compared with surgical outcome. We also tested the possibility of using different combinations to locate the EZ. The marked areas in every patient established using each method were also compared to determine the correlations among the three methods. The results showed that PET-MRI, FLAWS, and HFOs can provide more information about potential epileptic areas than traditional methods. When detecting the EZs, the sensitivities of PET-MRI, FLAWS, and HFOs were 68.75, 53.85, and 87.50%, and the specificities were 80.00, 33.33, and 100.00%. The RatioChann(ev) of HFO-marked contacts was significantly higher in patients with good outcome than those with poor outcome (p< 0.05). When intracranial electrodes covered all the abnormal areas indicated by neuroimaging with the overlapping EZs being completely removed referred to HFO analysis, patients could reach seizure-free (p < 0.01). The periphery of the lesion marked by neuroimaging may be epileptic, but not every lesion contributes to seizures. Therefore, approaches in multimodality can detect EZ more accurately, and HFO analysis may help in defining real epileptic areas that may be missed in the neuroimaging results. The implantation of intracranial electrodes guided by non-invasive PET-MRI and FLAWS findings as well as HFO analysis would be an optimized multimodal approach for locating EZ.

SUBMITTER: Li X

PROVIDER: S-EPMC8217465 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background and purposePatients with MR imaging-negative epilepsy could have subtle FCD. Our aim was to determine if structural changes could be identified by using DTI in children with intractable epilepsy, from MR imaging-visible FCD and MR imaging-negative localization-related epilepsy, that were concordant with the epileptogenic zone as defined by using the MEG dipole cluster.Materials and methodsEight children with MR imaging-visible FCD and 16 with MR imaging-negative epilepsy underwent DTI and MEG. Twenty-six age-matched healthy children underwent DTI. Analysis was performed on controls across individual patients. Agreement between the location of DTI abnormalities and FCD and MEG dipole clusters was assessed.ResultsIn patients with MR imaging-visible FCD, abnormal FA, MD, λ(1), λ(2), and λ(3) were lobar concordant with the MEG dipole cluster in 4/8 (50.0%), 5/8 (62.5%), 3/8 (37.5%), 6/8 (75.0%), and 5/8 (62.5%), respectively. In patients with MR imaging-visible FCD, abnormal FA, MD, λ(1), λ(2), and λ(3) overlapped the x-, y-, and z-axes of the MEG dipole cluster in 1/8 (12.5%), 4/8 (50%), 4/8 (50%), 6/8 (75%), and 4/8 (50%), respectively, and with FCD in 1/8 (12.5%), 3/8 (37.5%), 0/8 (0%), 3/8 (37.5%), and 1/8 (12.5%), respectively. In patients with MR imaging-negative epilepsy, abnormal FA, MD, λ(1), λ(2), and λ(3) were lobar-concordant with the MEG dipole cluster in 11/16 (68.8%), 11/16 (68.8%), 8/16 (50.0%), 10/16 (62.5%), and 10/16 (62.5%), respectively, and overlapped the x-, y-, and z-axes of the MEG dipole cluster in 9/16 (56.3%), 10/16 (62.5%), 8/16 (50%), 8/16 (50%), and 8/16 (50%), respectively. There was no significant difference between abnormal DTI lobar concordance with the MEG dipole cluster in patients with MR imaging-visible FCD and MR imaging-negative epilepsy.ConclusionsWhite matter changes can be detected with DTI in children with MR imaging-visible FCD and MR imaging-negative epilepsy, which were concordant with the epileptogenic zone in more than half of the patients.

Project description:ObjectivesWe aimed to investigate the antiepileptic effects of cathodal transcranial direct current stimulation (c-tDCS) and mechanisms of action based on its effects on the neurotransmitters responsible for the abnormal synchrony patterns seen in pharmacoresistant epilepsy. This is the first study to test the impact of neurostimulation on epileptiform interictal discharges (IEDs) and to measure brain metabolites in the epileptogenic zone (EZ) and control regions simultaneously in patients with pharmacoresistant epilepsy.MethodsThis is a hypothesis-driven pilot prospective single-blinded repeated measure design study in patients diagnosed with pharmacoresistant epilepsy of temporal lobe onset. We included seven patients who underwent two sessions of c-tDCS (sham followed by real). The real tDCS session was 20 min in duration and had a current intensity of 1.5 mA delivered via two surface electrodes that had dimensions of 3 × 4 cm. The cathode electrode was placed at FT7 in the center whereas the anode at Oz in the center. After each session, we performed electroencephalographic recording to count epileptiform IEDs over 30 min. We also performed magnetic resonance spectroscopy (MRS) to measure brain metabolite concentrations in the two areas of interest (EZ and occipital region), namely, gamma-aminobutyric acid (GABA), glutamate (Glx), and glutathione. We focused on a homogenous sample where the EZ and antiepileptic medications are shared among patients.ResultsReal tDCS decreased the number of epileptiform IEDs per min (from 9.46 ± 2.68 after sham tDCS to 5.37 ± 3.38 after real tDCS), p = 0.018, as compared to sham tDCS. GABA was decreased in the EZ after real c-tDCS stimulation as compared to sham tDCS (from 0.129 ± 0.019 to 0.096 ± 0.018, p = 0.02). The reduction in EZ GABA correlated with the reduction in the frequency of epileptiform IED per min (rho: 0.9, p = 0.003).ConclusionThese results provide a window into the antiepileptic mechanisms of action of tDCS, based on local and remote changes in GABA and neural oscillatory patterning responsible for the generation of interictal epileptiform discharges.

Project description:Electromagnetic source imaging (ESI) offers unique capability of imaging brain dynamics for studying brain functions and aiding the clinical management of brain disorders. Challenges exist in ESI due to the ill-posedness of the inverse problem and thus the need of modeling the underlying brain dynamics for regularizations. Advances in generative models provide opportunities for more accurate and realistic source modeling that could offer an alternative approach to ESI for modeling the underlying brain dynamics beyond equivalent physical source models. However, it is not straightforward to explicitly formulate the knowledge arising from these generative models within the conventional ESI framework. Here we investigate a novel source imaging framework based on mesoscale neuronal modeling and deep learning (DL) that can learn the sensor-source mapping relationship directly from MEG data for ESI. Two DL-based ESI models were trained based on data generated by neural mass models and either generic or personalized head models. The robustness of the two DL models was evaluated by systematic computer simulations and clinical validation in a cohort of 29 drug-resistant focal epilepsy patients who underwent intracranial EEG (iEEG) evaluation or surgical resection. Results estimated from pre-operative MEG interictal spikes were quantified using the overlap with resection regions and the distance to the seizure-onset zone (SOZ) defined by iEEG recordings. The DL-based ESI provided robust results when no personalized head geometry is considered, reaching a spatial dispersion of 21.90 ± 19.03 mm, sublobar concordance of 83 %, and sublobar sensitivity and specificity of 66 and 97 % respectively. When using personalized head geometry derived from individual patients' MRI in the training data, personalized DL-based ESI model can further improve the performance and reached a spatial dispersion of 8.19 ± 8.14 mm, sublobar concordance of 93 %, and sublobar sensitivity and specificity of 77 and 99 % respectively. When compared to the SOZ, the localization error of the personalized approach is 15.78 ± 5.54 mm, outperforming the conventional benchmarks. This work demonstrates that combining generative models and deep learning enables an accurate and robust imaging of epileptogenic zone from MEG recordings with strong sublobar precision, suggesting its added value to enhancing MEG source localization and imaging, and to epilepsy source localization and other clinical applications.

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Localization of the Epileptogenic Zone by Multimodal Neuroimaging and High-Frequency Oscillation.

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