In this study we used a multi-disciplinary approach to objectively characterise the ictal brain networks from non-invasive MEG data without the risks and constraints of iEEG. We have demonstrated that non-invasive ictal ViEEG signals preserve the most important characteristics for spatial distribution and morphology. Dynamical network models and a virtual resection technique were applied to ictal ViEEG signals to identify a sub-network VIZ that helps elucidate the EZ. Alternative surgical strategies can potentially be devised from VIZ results for non-seizure-free patients. More crucially, the VIZ from MEG data alone can predict the earliest solution out of MSL and ESL, the earliest solution best informing the likely EZ in our recent study using simultaneous HDEEG and MEG data31.
To our knowledge, this is the first study that reconstructs ictal source signals using a high number of spontaneous seizures captured by MEG31. Ictal ViEEG signals from at least one seizure per patient present distinct characteristics of ictal events, such as hyper-synchronised rhythms, clear transitions from background activity to a seizure state, and spatial patterns of seizure propagation. Such characteristics of ictal ViEEG are also confirmed by ictal iEEG data from the same patient (Fig. 2). These findings also support previous studies that show epileptic activity from deep structures can be detected and reconstructed using MEG45, 46. From the 36 seizures recorded by MEG, 11 seizures do not present identifiable morphological features of ictal activity and hence were not included in the analysis. We suspect that compared to source localisation, source reconstruction may require higher SNRs to resolve identifiable ictal features in source space. As well, certain geometries of anatomical structures, such as gyral areas, may impair MEG source reconstruction accuracy. These 11 seizures might be amenable to source reconstruction with corresponding ictal HDEEG signals (the subject of our future work in a larger cohort). Ictal source signals have been reconstructed using ictal scalp EEG in prior studies47, 48, 49. However, as opposed to MEG, scalp EEG signals are more distorted when the electrical field propagates through inhomogeneous head tissue. More sophisticated techniques are needed to process and analyse the EEG signals in source space49.
This study demonstrates proof-of-concept that dynamical network models using ViEEG signals identify a sub-network VIZ that provide a valid characterisation of the EZ and prediction of the clinical localisation. This finding is significant for the following reasons. First, it demonstrates the feasibility of translating dynamical network models developed from iEEG to non-invasive ViEEG. Second, our proposed approach also identifies non-ictogenic brain areas that are less likely to overlap with the EZ, which may help clinicians fine-tune clinical hypotheses to be tested using invasive approaches. Third, our approach is data-driven and requires less human input compared to routine clinical investigations, making it a more objective assessment. As opposed to the original work of Goodfellow et al17 that only analysed the first seizure from each patient, we analysed all seizures that were successfully reconstructed from ictal MEG data. This lends confidence to our approach, as consistency of VIZ hotspots was found between seizures for a given patient – refer to Patient 2 (Supplementary Fig. 6), Patient 8 (Supplementary Fig. 12), and Patient 11 (Supplementary Fig. 15).
Ding et al47 is the first study to reconstruct ictal EEG source signals to localise the EZ in source space and investigated the causal interaction patterns to identify the primary epileptic sources. Recent work from Lopes et al41 and Sohrabpour et al49 applied network models and connectivity analysis to EEG source-space networks and demonstrated the feasibility of non-invasive lateralisation and characterisation of the EZ. Motivated by recent studies using invasive iEEG data17, 18, 50, 51, our work extends the dynamical network models and virtual resection from Goodfellow et al17 and Lopes et al41 to ictal MEG source signals, which has achieved similar performance in characterising the EZ as Sohrabpour et al49 but with a different imaging modality. It is also notable that we analysed the MEG data recorded from complex cases only (MRI-normal or complex lesions) using a ViEEG approach that is intuitive to clinicians given its resemblance to iEEG arrays. Future work is needed to compare different techniques using EEG and MEG source signals.
The iEEG SOZ is often regarded as a subset of the EZ52. This is in part because resection is often performed beyond the extent of the iEEG SOZ and includes non-SOZ electrodes to ensure the removal of the entire putative EZ. However, the resection margin between SOZ and non-SOZ electrodes is often determined by the experience of the treating team which is less objective and less amenable to hypothesis-testing. Recent network studies17, 50 indicate there may exist a regulatory mechanism surrounding the SOZ in the form of pathological dynamics that synchronise and de-synchronise the network and hence regulate seizure generation and propagation. Such regulatory mechanisms merit further consideration in the surgical work-up50. Our VIZ, particularly the hotspots, can non-invasively offer such an objective boundary for surgical planning. Further, ViEEG combined with dynamical network models enables hypothesis-testing to assess surgical strategies prior to resection by virtually resecting one or more nodes from the network17, 42, 51, 53. For example, all non-seizure-free post-operative patients revealed pre-operative VIZ hotspots that sat outside the resection margins – Patient 1 (Supplementary Fig. 5), Patient 3 (Supplementary Fig. 7), Patient 4 (Supplementary Fig. 8), Patient 9 (Supplementary Fig. 13), Patient 10 (Supplementary Fig. 14), and Patient 11 (Fig. 3, Supplementary Fig. 15).
There has been a long-standing discussion on network models versus source localisation for epilepsy and epilepsy surgery54, 55. An important question that has been raised is whether network models using EEG and MEG reconstructed source data better characterise the EZ than the solutions offered by source localisation. As demonstrated by Sohrabpour et al49, connectivity imaging provides similar accuracy to source localisation in determining the extent of the EZ using sophisticated source imaging algorithms on ictal HDEEG data. Our simultaneously acquired HDEEG-MEG dataset and prospective study validating the clinical utility of electromagnetic source localisation offers the unique opportunity to address such a question31. To the best of our knowledge, this is the first study that comprehensively compares findings from dynamical network modelling of ictal MEG data with source localisation solutions using simultaneous HDEEG and MEG. In our previous work, the earliest sLORETA solution of early-ESL and early-MSL best characterises the EZ than either ESL, MSL, or combined electromagnetic source localisation (EMSL) alone31. On combining ictal ViEEG signals from MEG alone with dynamical network models, MI-VIZ predicts the earliest solution that can be only offered by source localisation with two modalities (HDEEG and MEG data). Specifically, MI-VIZ can predict the earliest solution better than predicting the early-MSL solution (Fig. 6A). This finding suggests dynamical network models can provide valuable information beyond source localisation using a single modality.
We also observe that our proposed approach identifies the wider extent of the VIZ to the EZ as well as inter-seizure variability. The extent of the VIZ is likely to reflect propagation of seizure activity56. Our previous work argues that source localisation of ictal discharges at the mid to late phase of the averaged discharge complex usually reflects areas involved in seizure propagation. Statistical analysis suggests AEC-VIZ and MI-VIZ hotspot and boundary are less likely to predict mid-MSL and late-MSL (Fig. 6A, Supplementary Table 1 and Supplementary Table 2). Hence, the VIZ is perhaps not a simple representation of seizure propagation networks but its extent could still be affected by seizure propagation. More detailed analysis of seizure propagation networks using iEEG seizures is needed to test this hypothesis.
Two connectivity methods – linear and nonlinear coupling between signals – were employed to better understand the underlying network structures responsible for seizure generation. AEC is believed to solely characterise linear correlations between amplitude envelopes57, 58, while MI measures both linear and nonlinear relationships by quantifying shared and unique information between two time-series59. Our results demonstrate that MI-VIZ outperforms AEC-VIZ in characterising the EZ and predicting the clinical localisation, which suggests a connectivity approach that captures both linear and non-linear interactions might offer more information about ictal network structures than an approach that only captures linear interactions. This finding also lends support to a previous theoretical model using nonlinear dynamics from the seizure onset zone to identify the EZ from seizure propagation and predict seizure propagation and termination16, 60. More broadly, our evidence supports the view that ictogenesis involves complex network neural systems that are highly nonlinear across multiple temporo-spatial scales60.
We find six non-seizure-free patients have at least one VIZ hotspot node outside the resection margin – Patient 1 (Supplementary Fig. 5), Patient 3 (Supplementary Fig. 7), Patient 4 (Supplementary Fig. 8), Patient 9 (Supplementary Fig. 13), Patient 10 (Supplementary Fig. 14), and Patient 11 (Fig. 3, Supplementary Fig. 15) – which has been previously reported using ictal iEEG data17. An example is given in Patient 11 (Fig. 3, Supplementary Fig. 15), where both VIZ hotspot and iEEG SOZ are superior to the resection margin and this patient is non-seizure-free with a 60% seizure reduction rate (Engel III). Our results suggest alternative surgical strategies can be devised for non-seizure-free patients with, perhaps, reduced need for invasive monitoring for those patients who are being considered for a second resection. Some VIZs also overlap eloquent cortex as occurs in Patient 10 (Supplementary Fig. 14) and Patient 11 (Supplementary Fig. 15), which is not accounted for in our proposed approach. The balance between seizure outcome and compromise of eloquent cortical function may be integrated into future models to better facilitate decision-making for patients and clinicians.
There are some limitations in this study. Only ictal data were analysed. This is because our dynamical network models investigate the change of cortical excitability that are more likely to be revealed during a seizure state. Our model is phenomenological and does not include details of physiology that underlies the functioning of individual epilepsy patients. Findings from our proposed approach can only serve as a statistical biomarker that offers complementary clinical information to the pre-surgical evaluation. We did not employ connectivity methods that reduce spurious connections (often introduced by volume conduction) in source space to construct functional networks because in our study we looked at dynamical network models to characterise the EZ for epilepsy surgery, instead of investigating neural mechanisms. More details on volume conduction in source-space networks are discussed in the Supplementary material (Volume Conduction and functional networks). In addition, the computational workload does not allow us to model all the sources (5000-25000 sources) at once from the whole brain at a spatial resolution (10mm) comparable to iEEG electrodes. At present, the feasible scale of networks is between 100 and 300 nodes for dynamical modelling and results to be interpreted. In a prospective fashion, ViEEG with on feasible network scale can be moved around freely and modelled iteratively across the whole-brain, although admittedly the ideal scenario is to model a ViEEG covering the whole-brain at once (refer to Considerations for ViEEG locations in Supplementary material). Our study is retrospective with a modest number of patients and seizures analysed compared to studies using iEEG seizures, although it contains one of the highest seizure counts obtained across ictal MEG studies. Our findings motivate further investigation in methodology and clinical utility using multi-centre datasets or prospective studies to devise and optimise surgical strategies objectively and safely.