Functional brain networks in epilepsy: recent advances in noninvasive mapping

Local and network changes after multichannel transcranial direct current stimulation using magnetoencephalography in patients with refractory epilepsy

OBJECTIVE

Non-invasive neuromodulation techniques, particularly transcranial direct current stimulation (tDCS), are promising for drug-resistant epilepsy (DRE), though the mechanisms of their efficacy remain unclear. This study aims to (i) investigate tDCS neurophysiological mechanisms using a personalized multichannel protocol with magnetoencephalography (MEG) and (ii) assess post-tDCS changes in brain connectivity, correlating them with clinical outcomes.

METHODS

Seventeen patients with focal DRE underwent three cycles of tDCS over five days, each consisting of 40-minute stimulations targeting the epileptogenic zone (EZ) identified via stereo-EEG. MEG was performed before and after sessions to assess functional connectivity (FC) and power spectral density (PSD),estimated at source level (beamforming).

RESULTS

Five of fourteen patients experienced a seizure frequency reduction > 50 %. Distinct PSD changes were seen across frequency bands, with reduced FC in responders and increased connectivity in non-responders (p < 0.05). No significant differences were observed between EZ network and non-involved networks. Responders also had higher baseline FC, suggesting it could predict clinical response to tDCS in DRE.

CONCLUSIONS

Personalized multichannel tDCS induces neurophysiological changes associated with seizure reduction in DRE.

SIGNIFICANCE

These results provide valuable insights into tDCS effects on epileptic brain networks, informing future clinical applications in epilepsy treatment.

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.

Neurostimulation for Generalized Epilepsy: Should Therapy be Syndrome-specific?

Current applications of neurostimulation for generalized epilepsy use a one-target-fits-all approach that is agnostic to the specific epilepsy syndrome and seizure type being treated. The authors describe similarities and differences between the 2 "archetypes" of generalized epilepsy-Lennox-Gastaut syndrome and Idiopathic Generalized Epilepsy-and review recent neuroimaging evidence for syndrome-specific brain networks underlying seizures. Implications for stimulation targeting and programming are discussed using 5 clinical questions: What epilepsy syndrome does the patient have? What brain networks are involved? What is the optimal stimulation target? What is the optimal stimulation paradigm? What is the plan for adjusting stimulation over time?

Arousal and salience network connectivity alterations in surgical temporal lobe epilepsy

OBJECTIVE

It is poorly understood why patients with mesial temporal lobe epilepsy (TLE) have cognitive deficits and brain network changes that extend beyond the temporal lobe, including altered extratemporal intrinsic connectivity networks (ICNs). However, subcortical arousal structures project broadly to the neocortex, are affected by TLE, and thus may contribute to these widespread network effects. The authors' objective was to examine functional connectivity (FC) patterns between subcortical arousal structures and neocortical ICNs, possible neurocognitive relationships, and FC changes after epilepsy surgery.

METHODS

The authors obtained resting-state functional magnetic resonance imaging (fMRI) in 50 adults with TLE and 50 controls. They compared nondirected FC (correlation) and directed FC (Granger causality laterality index) within the salience network, default mode network, and central executive network, as well as between subcortical arousal structures; these 3 ICNs were also compared between patients and controls. They also used an fMRI-based vigilance index to relate alertness to arousal center FC. Finally, fMRI was repeated in 29 patients > 12 months after temporal lobe resection.

RESULTS

Nondirected FC within the salience (p = 0.042) and default mode (p = 0.0008) networks, but not the central executive network (p = 0.79), was decreased in patients in comparison with controls (t-tests, corrected). Nondirected FC between the salience network and subcortical arousal structures (nucleus basalis of Meynert, thalamic centromedian nucleus, and brainstem pedunculopontine nucleus) was reduced in patients in comparison with controls (p = 0.0028-0.015, t-tests, corrected), and some of these connectivity abnormalities were associated with lower processing speed index, verbal comprehension, and full-scale IQ. Interestingly, directed connectivity measures suggested a loss of top-down influence from the salience network to the arousal nuclei in patients. After resection, certain FC patterns between the arousal nuclei and salience network moved toward control values in the patients, suggesting that some postoperative recovery may be possible. Although an fMRI-based vigilance measure suggested that patients exhibited reduced alertness over time, FC abnormalities between the salience network and arousal structures were not influenced by the alertness levels during the scans.

CONCLUSIONS

FC abnormalities between subcortical arousal structures and ICNs, such as the salience network, may be related to certain neurocognitive deficits in TLE patients. Although TLE patients demonstrated vigilance abnormalities, baseline FC perturbations between the arousal and salience networks are unlikely to be driven solely by alertness level, and some may improve after surgery. Examination of the arousal network and ICN disturbances may improve our understanding of the downstream clinical effects of TLE.

Transcranial current stimulation in epilepsy: A systematic review of the fundamental and clinical aspects

Purpose

Transcranial electrical current stimulation (tES or tCS, as it is sometimes referred to) has been proposed as non-invasive therapy for pharmacoresistant epilepsy. This technique, which includes direct current (tDCS) and alternating current (tACS) stimulation involves the application of weak currents across the cortex to change cortical excitability. Although clinical trials have demonstrated the therapeutic efficacy of tES, its specific effects on epileptic brain activity are poorly understood. We sought to summarize the clinical and fundamental effects underlying the application of tES in epilepsy.

Methods

A systematic review was performed in accordance with the PRISMA guidelines. A database search was performed in PUBMED, MEDLINE, Web of Science and Cochrane CENTRAL for articles corresponding to the keywords “epilepsy AND (transcranial current stimulation OR transcranial electrical stimulation)”.

Results

A total of 56 studies were included in this review. Through these records, we show that tDCS and tACS epileptic patients are safe and clinically relevant techniques for epilepsy. Recent articles reported changes of functional connectivity in epileptic patients after tDCS. We argue that tDCS may act by affecting brain networks, rather than simply modifying local activity in the targeted area. To explain the mechanisms of tES, various cellular effects have been identified. Among them, reduced cell loss, mossy fiber sprouting, and hippocampal BDNF protein levels. Brain modeling and human studies highlight the influence of individual brain anatomy and physiology on the electric field distribution. Computational models may optimize the stimulation parameters and bring new therapeutic perspectives.

Conclusion

Both tDCS and tACS are promising techniques for epilepsy patients. Although the clinical effects of tDCS have been repeatedly assessed, only one clinical trial has involved a consistent number of epileptic patients and little knowledge is present about the clinical outcome of tACS. To fill this gap, multicenter studies on tES in epileptic patients are needed involving novel methods such as personalized stimulation protocols based on computational modeling. Furthermore, there is a need for more in vivo studies replicating the tES parameters applied in patients. Finally, there is a lack of clinical studies investigating changes in intracranial epileptiform discharges during tES application, which could clarify the nature of tES-related local and network dynamics in epilepsy.

Common functional connectivity alterations in focal epilepsies identified by machine learning

OBJECTIVE

This study was undertaken to identify shared functional network characteristics among focal epilepsies of different etiologies, to distinguish epilepsy patients from controls, and to lateralize seizure focus using functional connectivity (FC) measures derived from resting state functional magnetic resonance imaging (MRI).

METHODS

Data were taken from 103 adult and 65 pediatric focal epilepsy patients (with or without lesion on MRI) and 109 controls across four epilepsy centers. We used three whole-brain FC measures: parcelwise connectivity matrix, mean FC, and degree of FC. We trained support vector machine models with fivefold cross-validation (1) to distinguish patients from controls and (2) to lateralize the hemisphere of seizure onset in patients. We reported the regions and connections with the highest importance from each model as the common FC differences between the compared groups.

RESULTS

FC measures related to the default mode and limbic networks had higher importance relative to other networks for distinguishing epilepsy patients from controls. In lateralization models, regions related to somatosensory, visual, default mode, and basal ganglia showed higher importance. The epilepsy versus control classification model trained using a 400-parcel connectivity matrix achieved a median testing accuracy of 75.6% (median area under the curve [AUC] = .83) in repeated independent testing. Lateralization accuracy using the 400-parcel connectivity matrix reached a median accuracy of 64.0% (median AUC = .69).

SIGNIFICANCE

Machine learning models revealed common FC alterations in a heterogeneous group of patients with focal epilepsies. The distribution of the most altered regions supports the hypothesis that shared functional alteration exists beyond the seizure onset zone and its epileptic network. We showed that FC measures can distinguish patients from controls, and further lateralize focal epilepsies. Future studies are needed to confirm these findings by using larger numbers of epilepsy patients.

Efficacy of cathodal transcranial direct current stimulation on electroencephalographic functional networks in patients with focal epilepsy: Preliminary findings

BACKGROUND

Neuromodulation is a promising therapeutic alternative for epilepsy. We aimed to explore the efficacy and safety of cathodal transcranial current direct stimulation (ctDCS) on electroencephalographic functional networks in focal epilepsy.

METHODS

A sham-controlled, double-blinded, randomized study was conducted on 25 participants with focal epilepsy who underwent a 5-day, -1.0 mA, 20 min ctDCS, which targeted at the most active interictal epileptiform discharge (IED) region. We examined the electroencephalograms (EEGs) at baseline, immediately and at 4 weeks following ctDCS. The graph theory-based brain networks were established through time-variant partial directed coherence (TVPDC), and were calculated between each pair of EEG signals. The functional networks were characterized using average clustering coefficient, characteristic path length, and small-worldness index. The seizure frequencies, IEDs, graph-theory metrics and cognitive tests were compared.

RESULTS

Preliminary findings indicated an IED reduction of 30.2% at the end of 5-day active ctDCS compared to baseline (p < 0.10) and a significant IED reduction of 33.4% 4 weeks later (p < 0.05). In terms of the EEG functional network, the small-worldness index significantly reduced by 3.5% (p < 0.05) and the characteristic path length increased by 1.8% (p < 0.10) at the end of the session compared to the baseline. No obvious change was found in the seizure frequency during follow-up (p > 0.05). The Mini-Mental State Examination (MMSE) showed no difference between the active and sham groups (p > 0.05). No severe adverse reactions were observed.

CONCLUSIONS

In focal epilepsy, the 5-day consecutive ctDCS may potentially decrease the IEDs and ameliorate the EEG functional network, proposing a novel personalized therapeutic scenario for epilepsy.

Validation of semi-automated anatomically labeled SEEG contacts in a brain atlas for mapping connectivity in focal epilepsy

OBJECTIVE

Stereotactic electroencephalography (SEEG) has been widely used to explore the epileptic network and localize the epileptic zone in patients with medically intractable epilepsy. Accurate anatomical labeling of SEEG electrode contacts is critically important for correctly interpreting epileptic activity. We present a method for automatically assigning anatomical labels to SEEG electrode contacts using a 3D-segmented cortex and coregistered postoperative CT images.

METHOD

Stereotactic electroencephalography electrode contacts were spatially localized relative to the brain volume using a standard clinical procedure. Each contact was then assigned an anatomical label by clinical epilepsy fellows. Separately, each contact was automatically labeled by coregistering the subject's MRI to the USCBrain atlas using the BrainSuite software and assigning labels from the atlas based on contact locations. The results of both labeling methods were then compared, and a subsequent vetting of the anatomical labels was performed by expert review.

RESULTS

Anatomical labeling agreement between the two methods for over 17 000 SEEG contacts was 82%. This agreement was consistent in patients with and without previous surgery (P = .852). Expert review of contacts in disagreement between the two methods resulted in agreement with the atlas based over manual labels in 48% of cases, agreement with manual over atlas-based labels in 36% of cases, and disagreement with both methods in 16% of cases. Labels deemed incorrect by the expert review were then categorized as either in a region directly adjacent to the correct label or as a gross error, revealing a lower likelihood of gross error from the automated method.

SIGNIFICANCE

The method for semi-automated atlas-based anatomical labeling we describe here demonstrates potential to assist clinical workflow by reducing both analysis time and the likelihood of gross anatomical error. Additionally, it provides a convenient means of intersubject analysis by standardizing the anatomical labels applied to SEEG contact locations across subjects.

Identification of neural networks preferentially engaged by epileptogenic mass lesions through lesion network mapping analysis

Lesion network mapping (LNM) has been applied to true lesions (e.g., cerebrovascular lesions in stroke) to identify functionally connected brain networks. No previous studies have utilized LNM for analysis of intra-axial mass lesions. Here, we implemented LNM for identification of potentially vulnerable epileptogenic networks in mass lesions causing medically-refractory epilepsy (MRE). Intra-axial brain lesions were manually segmented in patients with MRE seen at our institution (EL_INST). These lesions were then normalized to standard space and used as seeds in a high-resolution normative resting state functional magnetic resonance imaging template. The resulting connectivity maps were first thresholded (p_{Bonferroni_cor} < 0.05) and binarized; the thresholded binarized connectivity maps were subsequently summed to produce overall group connectivity maps, which were compared with established resting-state networks to identify potential networks prone to epileptogenicity. To validate our data, this approach was also applied to an external dataset of epileptogenic lesions identified from the literature (EL_LIT). As an additional exploratory analysis, we also segmented and computed the connectivity of institutional non-epileptogenic lesions (NEL_INST), calculating voxel-wise odds ratios (VORs) to identify voxels more likely to be functionally-connected with EL_INST versus NEL_INST. To ensure connectivity results were not driven by anatomical overlap, the extent of lesion overlap between EL_INST, and EL_LIT and NEL_INST was assessed using the Dice Similarity Coefficient (DSC, lower index ~ less overlap). Twenty-eight patients from our institution were included (EL_INST: 17 patients, 17 lesions, 10 low-grade glioma, 3 cavernoma, 4 focal cortical dysplasia; NEL_INST: 11 patients, 33 lesions, all brain metastases). An additional 23 cases (25 lesions) with similar characteristics to the EL_INST data were identified from the literature (EL_LIT). Despite minimal anatomical overlap of lesions, both EL_INST and EL_LIT showed greatest functional connectivity overlap with structures in the Default Mode Network, Frontoparietal Network, Ventral Attention Network, and the Limbic Network-with percentage volume overlap of 19.5%, 19.1%, 19.1%, and 12.5%, respectively-suggesting them as networks consistently engaged by epileptogenic mass lesions. Our exploratory analysis moreover showed that the mesial frontal lobes, parahippocampal gyrus, and lateral temporal neocortex were at least twice as likely to be functionally connected with the EL_INST compared to the NEL_INST group (i.e. Peak VOR > 2.0); canonical resting-state networks preferentially engaged by EL_INSTs were the Limbic and the Frontoparietal Networks (Mean VOR > 1.5). In this proof of concept study, we demonstrate the feasibility of LNM for intra-axial mass lesions by showing that ELs have discrete functional connections and may preferentially engage in discrete resting-state networks. Thus, the underlying normative neural circuitry may, in part, explain the propensity of particular lesions toward the development of MRE. If prospectively validated, this has ramifications for patient counseling along with both approach and timing of surgery for lesions in locations prone to development of MRE.

History of the Network Approach in Epilepsy Surgery

We provide a history and overview of the network approach to epilepsy surgery. Models of the epileptogenic zone (EZ) have evolved considerably over the years with more recent models accounting for the connectivity and network properties of epileptic foci. Next, we describe several examples of network phenotypes of focal epilepsy and how these have the potential to influence surgical decision-making and patient outcome. Future research will provide new insight into how network models of the EZ can determine optimal surgical interventions that improve seizure outcomes and optimize cognitive outcomes.

Mechanisms of Psychiatric Comorbidities in Epilepsy

Psychiatric illnesses, including depression and anxiety, are highly comorbid with epilepsy (for review see Josephson and Jetté (Int Rev Psychiatry 29:409-424, 2017), Salpekar and Mula (Epilepsy Behav 98:293-297, 2019)). Psychiatric comorbidities negatively impact the quality of life of patients (Johnson et al., Epilepsia 45:544-550, 2004; Cramer et al., Epilepsy Behav 4:515-521, 2003) and present a significant challenge to treating patients with epilepsy (Hitiris et al., Epilepsy Res 75:192-196, 2007; Petrovski et al., Neurology 75:1015-1021, 2010; Fazel et al., Lancet 382:1646-1654, 2013) (for review see Kanner (Seizure 49:79-82, 2017)). It has long been acknowledged that there is an association between psychiatric illnesses and epilepsy. Hippocrates, in the fourth-fifth century B.C., considered epilepsy and melancholia to be closely related in which he writes that "melancholics ordinarily become epileptics, and epileptics, melancholics" (Lewis, J Ment Sci 80:1-42, 1934). The Babylonians also recognized the frequency of psychosis in patients with epilepsy (Reynolds and Kinnier Wilson, Epilepsia 49:1488-1490, 2008). Despite the fact that the relationship between psychiatric comorbidities and epilepsy has been recognized for thousands of years, psychiatric illnesses in people with epilepsy still commonly go undiagnosed and untreated (Hermann et al., Epilepsia 41(Suppl 2):S31-S41, 2000) and systematic research in this area is still lacking (Devinsky, Epilepsy Behav 4(Suppl 4):S2-S10, 2003). Thus, although it is clear that these are not new issues, there is a need for improvements in the screening and management of patients with psychiatric comorbidities in epilepsy (Lopez et al., Epilepsy Behav 98:302-305, 2019) and progress is needed to understand the underlying neurobiology contributing to these comorbid conditions. To that end, this chapter will raise awareness regarding the scope of the problem as it relates to comorbid psychiatric illnesses and epilepsy and review our current understanding of the potential mechanisms contributing to these comorbidities, focusing on both basic science and clinical research findings.

Lesion Network Localization of Seizure Freedom following MR-guided Laser Interstitial Thermal Ablation

Treatment-resistant epilepsy is a common and debilitating neurological condition, for which neurosurgical cure is possible. Despite undergoing nearly identical ablation procedures however, individuals with treatment-resistant epilepsy frequently exhibit heterogeneous outcomes. We hypothesized that treatment response may be related to the brain regions to which MR-guided laser ablation volumes are functionally connected. To test this, we mapped the resting-state functional connectivity of surgical ablations that either resulted in seizure freedom (N = 11) or did not result in seizure freedom (N = 16) in over 1,000 normative connectomes. There was no difference seizure outcome with respect to the anatomical location of the ablations, and very little overlap between ablation areas was identified using the Dice Index. Ablations that did not result in seizure-freedom were preferentially connected to a number of cortical and subcortical regions, as well as multiple canonical resting-state networks. In contrast, ablations that led to seizure-freedom were more functionally connected to prefrontal cortices. Here, we demonstrate that underlying normative neural circuitry may in part explain heterogenous outcomes following ablation procedures in different brain regions. These findings may ultimately inform target selection for ablative epilepsy surgery based on normative intrinsic connectivity of the targeted volume.

The impact of MEG source reconstruction method on source-space connectivity estimation: A comparison between minimum-norm solution and beamforming

Despite numerous important contributions, the investigation of brain connectivity with magnetoencephalography (MEG) still faces multiple challenges. One critical aspect of source-level connectivity, largely overlooked in the literature, is the putative effect of the choice of the inverse method on the subsequent cortico-cortical coupling analysis. We set out to investigate the impact of three inverse methods on source coherence detection using simulated MEG data. To this end, thousands of randomly located pairs of sources were created. Several parameters were manipulated, including inter- and intra-source correlation strength, source size and spatial configuration. The simulated pairs of sources were then used to generate sensor-level MEG measurements at varying signal-to-noise ratios (SNR). Next, the source level power and coherence maps were calculated using three methods (a) L2-Minimum-Norm Estimate (MNE), (b) Linearly Constrained Minimum Variance (LCMV) beamforming, and (c) Dynamic Imaging of Coherent Sources (DICS) beamforming. The performances of the methods were evaluated using Receiver Operating Characteristic (ROC) curves. The results indicate that beamformers perform better than MNE for coherence reconstructions if the interacting cortical sources consist of point-like sources. On the other hand, MNE provides better connectivity estimation than beamformers, if the interacting sources are simulated as extended cortical patches, where each patch consists of dipoles with identical time series (high intra-patch coherence). However, the performance of the beamformers for interacting patches improves substantially if each patch of active cortex is simulated with only partly coherent time series (partial intra-patch coherence). These results demonstrate that the choice of the inverse method impacts the results of MEG source-space coherence analysis, and that the optimal choice of the inverse solution depends on the spatial and synchronization profile of the interacting cortical sources. The insights revealed here can guide method selection and help improve data interpretation regarding MEG connectivity estimation.

Stereo-EEG Implantation Strategy

Stereoelectroencephalography denotes the strategic placement of multiple depth electrodes for invasive localization of focal epilepsy in surgical patients. It differs significantly from the alternative subdural grid approach, in both conceptualization of partial epilepsy-as a 3-D distributed network, rather than as focal pathology with contiguous spread-and by the method of sampling used-which is sparse and directed rather than continuous over adjacent brain areas. The electrode implantation strategy in stereoelectroencephalography involves appreciation of these features, which are illustrated by four cases drawn from distinct electroclinical epilepsy syndromes.

Electrophysiological resting-state biomarker for diagnosing mesial temporal lobe epilepsy with hippocampal sclerosis

The main aim of the present study was to evaluate whether resting-state functional connectivity of magnetoencephalography (MEG) signals can differentiate patients with mesial temporal lobe epilepsy (MTLE) from healthy controls (HC) and can differentiate between right and left MTLE as a diagnostic biomarker. To this end, a support vector machine (SVM) method among various machine learning algorithms was employed. We compared resting-state functional networks between 46 MTLE (right MTLE=23; left MTLE=23) patients with histologically proven HS who were free of seizure after surgery, and 46 HC. The optimal SVM group classifier distinguished MTLE patients with a mean accuracy of 95.1% (sensitivity=95.8%; specificity=94.3%). Increased connectivity including the right posterior cingulate gyrus and decreased connectivity including at least one sensory-related resting-state network were key features reflecting the differences between MTLE patients and HC. The optimal SVM model distinguished between right and left MTLE patients with a mean accuracy of 76.2% (sensitivity=76.0%; specificity=76.5%). We showed the potential of electrophysiological resting-state functional connectivity, which reflects brain network reorganization in MTLE patients, as a possible diagnostic biomarker to differentiate MTLE patients from HC and differentiate between right and left MTLE patients.

An acetylcholine alpha7 positive allosteric modulator rescues a schizophrenia-associated brain endophenotype in the 15q13.3 microdeletion, encompassing CHRNA7

The 15q13.3 microdeletion copy number variation is strongly associated with schizophrenia and epilepsy. The CHRNA7 gene, encoding nicotinic acetylcholine alpha 7 receptors (nAChA7Rs), is hypothesized to be one of the main genes in this deletion causing the neuropsychiatric phenotype. Here we used a recently developed 15q13.3 microdeletion mouse model to explore whether an established schizophrenia-associated connectivity phenotype is replicated in a murine model, and whether positive modulation of nAChA7 receptor might pharmacologically normalize the connectivity patterns. Resting-state fMRI data were acquired from male mice carrying a hemizygous 15q13.3 microdeletion (N=9) and from wild-type mice (N=9). To study the connectivity profile of 15q13.3 mice and test the effect of nAChA7 positive allosteric modulation, the 15q13.3 mice underwent two imaging sessions, one week apart, receiving a single intraperitoneal injection of either 15mg/kg Lu AF58801 or saline. The control group comprised wild-type mice treated with saline. We performed seed-based functional connectivity analysis to delineate aberrant connectivity patterns associated with the deletion (15q13.3 mice (saline treatment) versus wild-type mice (saline treatment)) and their modulation by Lu AF58801 (15q13.3 mice (Lu AF58801 treatment) versus 15q13.3 mice (saline treatment)). Compared to wild-type mice, 15q13.3 mice evidenced a predominant hyperconnectivity pattern. The main effect of Lu AF58801 was a normalization of elevated functional connectivity between prefrontal and frontal, hippocampal, striatal, thalamic and auditory regions. The strongest effects were observed in brain regions expressing nAChA7Rs, namely hippocampus, cerebral cortex and thalamus. These effects may underlie the antiepileptic, pro-cognitive and auditory gating deficit-reversal effects of nAChA7R stimulation.

Non-invasive brain mapping in epilepsy: Applications from magnetoencephalography

BACKGROUND

Non-invasive in vivo neurophysiological recordings with EEG/MEG are key to the diagnosis, classification, and further understanding of epilepsy. Historically the emphasis of these recordings has been the localisation of the putative sources of epileptic discharges. More recent developments see new techniques studying oscillatory dynamics, connectivity and network properties.

NEW METHOD

New analysis strategies for whole head MEG include the development of spatial filters or beamformers for source localisation, time-frequency analysis for cortical dynamics and graph theory applications for connectivity.

RESULTS

The idea of epilepsy as a network disorder is not new, and new applications of structural and functional brain imaging show differences in cortical and subcortical networks in patients with epilepsy compared to controls. Concepts of 'focal' and 'generalised' are challenged by evidence of focal onsets in generalised epileptic discharges, and widespread network changes in focal epilepsy. Spectral analyses can show differences in induced cortical response profiles, particularly in photosensitive epilepsy.

COMPARISON WITH EXISTING METHOD

This review focuses on the application of MEG in the study of epilepsy, starting with a brief historical perspective, followed by novel applications of source localisation, time-frequency and connectivity analyses.

CONCLUSION

Novel MEG analyses approaches show altered cortical dynamics and widespread network alterations in focal and generalised epilepsies, and identification of regional network abnormalities may have a role in epilepsy surgery evaluation.