Clinical Neurophysiology

ObjectiveLocalization of the epileptogenic zone (EZ) requires further refinement. We identified a unique ictal spectral structure, the harmonic pattern (H pattern), which potentially serves as a novel biomarker for localizing the EZ. This study aimed to analyze the clinical significance of the H pattern and to explore its underlying waveform features. MethodsSeventy patients with drug-resistant focal epilepsy, undergoing stereo-EEG (SEEG) evaluation and surgery, were included. Time-frequency maps (TFM) were generated using Morlet wavelet transform analysis. The H pattern was defined as multiple equidistant, high-density bands with varying frequencies on TFM. The upper quartile was employed to confirm contacts expressing dominant H pattern (dH pattern). Bispectral analysis and transfer function modeling were employed to assess nonlinear properties and propagation, respectively. The performance of the dH pattern in evaluating the EZ was compared with other ictal biomarkers. ResultsRegardless of seizure onset patterns, the H pattern commonly occurred during early or late seizure propagation among 57 patients (81.4%). It harbored within specific EEG segments characterized by fast activity and irregular polyspikes. The H pattern often appeared simultaneously across different brain regions at a consistent fundamental frequency, highlighting a crucial stage in seizure propagation characterized by inter-regional synchronization. The dH pattern demonstrated greater nonlinearity compared to the non-dH pattern, as evidenced by bispectral analysis. The waveforms associated with the dH pattern were more stereotyped and showed increased skewness and/or asymmetry. Notably, the complete removal of areas exhibiting the dH pattern, but not high epileptogenicity index ([≥]0.3) or seizure onset zone, was independently associated with seizure freedom after surgery. SignificanceThe H pattern provides unique insights into ictal neural dynamics. Additionally, it is a novel and alternative approach for measuring the EZ over an extended ictal time window. KEY POINTSO_LIThe harmonic pattern (H pattern) is commonly present in focal epileptic seizures and can help to improve the accuracy of EZ localization over an extended time window. C_LIO_LIThe H pattern is a spectral signature of waveform skewness or asymmetry. The dominant H pattern reflects a stronger nonlinearity of ictal EEG signals. C_LIO_LIThe H pattern can appear simultaneously in different areas with a consistent fundamental frequency, indicating a key stage of inter-regional synchronization. C_LI

IMPORTANCEThis research describes a method to accurately predict the onset of epileptic seizures; this will help treat patients timely, prevent future seizures, and improve outcomes. OBJECTIVEWe aimed to assess whether the phase-amplitude coupling (PAC) between infraslow activities (ISA) and high-frequency activities (HFA) increases before seizure onset. DESIGN AND SETTINGThis retrospective, single-center case series included patients admitted to the neurosurgery department at Osaka University Hospital in Suita, Osaka, from July 2018 to July 2019. PARTICIPANTSWe enrolled seven patients with drug-resistant focal epilepsy who underwent intracranial electrode placement as part of a presurgical invasive electroencephalography study. MAIN OUTCOMES AND MEASURESWe comparatively analyzed the ISA, HFA, and ISA-HFA PAC in the seizure onset zone (SOZ) or non-SOZ (nSOZ) in the interictal, preictal, and ictal states. RESULTSWe recorded 15 seizures in seven patients [1 female (14%); mean (SD) age = 26 (12) years; age range, 15-47 years]. HFA and ISA were larger in the ictal states than in the interictal and preictal states. During seizures, the HFA and ISA of the SOZ were larger and earlier than those of nSOZ. In the preictal states, the ISA-HFA PAC was larger than that of the interictal states, and it began increasing at 93 seconds before the seizure onset (95% confidence interval: -116 - -71 s). There were no differences in the values and time of ISA-HFA PAC between both zones. Our phase-based analysis revealed differences between the SOZ- and nSOZ-PAC. In SOZ, the HFA amplitudes were tuned at the trough of the ISA oscillations, and in nSOZ, the HFA amplitudes were tuned at the peak of these oscillations. The receiver-operating characteristic curve showed that the ISA-HFA PAC of the SOZ showed the highest discrimination performance in the preictal and interictal states, with an area under the curve (AUC) of 0.926. However, ISA-HFA PAC was not suitable to differentiate between SOZ and nSOZ (interictal AUC = 0.555, preictal AUC = 0.691, and ictal AUC = 0.646). CONCLUSION AND RELEVANCEThis study demonstrated the novel insight that ISA-HFA PAC increases before the onset of seizures, regardless of the seizure onset zone. Our findings indicate that ISA-HFA PAC is a potential biomarker for predicting the onset of seizures and may be valuable to physicians who routinely treat epileptic patients. Key PointsO_ST_ABSQuestionC_ST_ABSIs phase-amplitude coupling (PAC) between infraslow activities (ISA) and high-frequency activities (HFA) a useful biomarker for seizure prediction? FindingsIn this case series study on 15 focal-onset seizures in seven epileptic patients who underwent intracranial electrode placement, we found that a PAC of the ISA phase and HFA amplitude achieved significantly higher values in preictal states than in the interictal states, and ISA-HFA PAC of the seizure onset zone (SOZ) began increasing at 93 seconds before seizure onset (SO), while both HFA and ISA increased after SO. The receiver-operating characteristic curve showed that the ISA-HFA PAC of the SOZ showed the highest discrimination performance in the preictal and interictal states, with an area under the curve of 0.926. MeaningThis study demonstrates that ISA-HFA PAC can differentiate between the preictal and interictal states of a seizure, indicating that it is a potential marker for seizure prediction.

ObjectiveThe transition from interictal discharges to ictal high-frequency activity (HFA) remains poorly understood. We investigated whether spike-associated high-frequency oscillations (Sp-HFOs) during interictal and preictal periods contribute to the emergence of ictal HFA. MethodsWe retrospectively analyzed the interictal to ictal transition in seizures from six patients with drug-resistant focal epilepsy who underwent stereo-EEG and subsequent surgical resection. Various interictal periods preceding seizure onset were selected for comparison. Time- frequency analysis (TFA) was used to characterize Sp-HFOs and ictal HFA. Frequency overlap was quantified using the I-Fusion metric, and linear regression assessed changes in I-Fusion values over time, with R{superscript 2} indicating correlation strength. ResultsVisual analysis of the time series revealed a preictal phase in all patients, during which brief high-frequency activity gradually emerged within spikes (Sp-HFOs), ultimately transitioning into sustained ictal HFA at the same frequency. TFA demonstrated increasing frequency similarity with time between Sp-HFOs and ictal HFA. I-Fusion values and R{superscript 2} coefficients rose consistently, indicating a progressive convergence in frequency content. Notably, Sp-HFOs and ictal HFA shared narrow-band frequency features within the same electrode contacts, especially in the epileptogenic zone (EZ). InterpretationOur findings support a dynamic, frequency-specific evolution from interictal Sp-HFOs to ictal HFA, suggesting that seizure onset is preceded by a gradual preparatory phase rather than an abrupt transformation. The progressive nature and spectral continuity of Sp-HFOs may reflect increasing neuronal synchrony, providing potential early biomarkers for seizure prediction and improved localization of the EZ.

ObjectiveThe ictal Harmonic pattern (H pattern), produced by the non-linear characteristics of EEG waveforms, may hold significant potential for localizing the epileptogenic zone (EZ) in focal epilepsy. However, further validation is needed to establish the H patterns effectiveness as a biomarker for measuring the EZ. MethodsWe retrospectively enrolled 131 patients diagnosed with drug-resistant focal epilepsy, all of whom had complete stereo-electroencephalographic (SEEG) data. From this cohort, we selected 85 patients for outcome analysis. We analyzed the morphological and time-frequency (TF) features of the H pattern using TF plots. A third quartile (Q3) threshold was applied to classify channels expressing either dominant (ChanneldH pattern) or non-dominant H patterns (Channelnon-dH pattern). We then examined associations between the morphological features of the H pattern and patients clinical characteristics, as well as the correlations between the extent of channel removal and seizure outcomes. ResultsWe found no significant correlations between the morphological features of the ictal H pattern and clinical factors, including lesional MRI findings, epileptic onset patterns, epilepsy type, pathology, or surgical outcomes. The non-localizable H pattern appeared exclusively in patients with non-focal onset patterns. Notably, the proportion of ChanneldH pattern was higher in the seizure-onset zone (SOZ) compared to the early propagation zone. The seizure-free group demonstrated significantly higher removal proportions of ChanneldH pattern, both within and outside the SOZ (p = 0.014; p = 0.036), with AUCs of 0.606 and 0.660, respectively, in a seizure freedom prediction model. Survival analysis confirmed that complete removal of these regions correlated with long-term seizure freedom (p = 0.008; p = 0.028). Further subgroup analysis showed a significant correlation in neocortical epilepsy (p = 0.0004; p = 0.011), but not in mesial temporal lobe epilepsy. Additionally, multivariate analysis identified the complete removal of ChanneldH pattern as the only independent predictor for seizure freedom (p = 0.022; OR 6.035, 95% CI 1.291-28.211). ConclusionsOur study supports the notion that the dominance of the ictal H pattern, regardless of its morphology, serves as a novel biomarker for the EZ in focal epilepsy. The non-linearity in EEG waveforms provides new insights into understanding ictal spreading propagation and offers potential improvements for surgical planning in neocortical epilepsy.

BackgroundIn a recently published study combining transcranial magnetic stimulation and electroencephalography (TMS-EEG), we provided first evidence of M1-P15, an early component of TMS-evoked potentials, as a measure of transcallosal inhibition between motor cortices. However, considering the technical challenges of TMS-EEG recordings, further evidence is needed before M1-P15 can be considered a reliable index. ObjectiveHere, we aimed at validating M1-P15 as a cortical index of transcallosal inhibition, by replicating previous findings on its relationship with the ipsilateral silent period (iSP) and with performance in bimanual coordination. Moreover, we aimed at inducing a task-dependent modulation of transcallosal inhibition. MethodsA new sample of 32 healthy right-handed participants underwent behavioral motor tasks and TMS-EEG recording, in which left and right M1 were stimulated during bimanual tasks and during an iSP paradigm. Hypotheses and methods were preregistered before data collection. ResultsWe successfully replicated our previous findings on the positive relationship between M1-P15 amplitude and the iSP normalized area. However, we did not confirm the relationship between M1-P15 latency and bimanual coordination. Finally, we show a task-dependent modulation of M1-P15 amplitude, which was affected by the characteristics of the bimanual task the participants were performing, but not by the contralateral hand activity during the iSP paradigm. ConclusionsThe present results corroborate our previous findings in validating the M1-P15 as a reliable cortical marker of transcallosal inhibition, and provide novel evidence of its task-dependent modulation. Importantly, we demonstrate the feasibility of a preregistration approach in the TMS-EEG field to increase methodological rigor and transparency.

Spreading Depolarizations (SDs) are often associated with epileptic discharges. While SDs are traditionally thought contributing to the postictal depression and termination of epileptic discharges, seizures may also occur during SDs or may even follow SDs suggesting that interactions between SD and seizures are more complex. Here, we examined the interactions between SD and epileptic activity by spatially separating the epileptic focus and the site of SD initiation. Epileptic focus was induced by local intracortical injection of the potassium channel blocker 4-AP combined with the GABA(A) receptor antagonist gabazine, whereas extrinsic SDs were evoked by distal high potassium application. We found that extrinsic SDs promoted seizure-like events (SLEs) when the SD wave approached the epileptic focus, followed by suppression of epileptic activity when SD spread through the focus. The timing of SLE relatively SD varied at different recordings sites, with SLEs occurring before, during or after SD arrival depending on electrode position along the trajectory of SD propagation between the SD initiation site and the epileptic focus. During intracortical recordings, the proconvulsive effects of SD were associated with a wave of pre-SD neuronal excitation reaching the epileptic focus. The epileptic focus per se also demonstrated a resistance to the SD invasion. Thus, the interactions between SD and epileptic focus are not limited to postictal depression, and SDs may also promote epileptic activity in the hyperexcitable cortex. Key pointsO_LIEffects of SD on epileptic focus are dual: both pro- and anticonvulsive C_LIO_LISDs promote epileptic discharges upon approaching the epileptic focus C_LIO_LIThe timing of epileptic discharges relatively SD varies along the SD trajectory C_LIO_LIProconvulsive effects of SD align with the pre-SD excitation C_LIO_LIEpileptic focus resists the SD propagation C_LI

Genetic Generalized Epilepsy (GGE) represents around 20% of adult epilepsies and involves widespread network dysfunction across both hemispheres. While abnormalities across functional networks are well-documented, the hierarchical organization of these networks in GGE remains unknown. Our goal was to investigate whether GGE alters this hierarchy estimated using gradient analysis based on EEG-informed connectome. We analyzed a high-density EEG dataset from 20 GGE patients and matched healthy controls (HC). Compared to HC, GGE patients had significantly less negative gradient scores in the frontoparietal network (FPN) in the secondary gradient of the beta frequency band (p=0.0201, FDR corrected, quantified as large effect size by Cliffs delta: 0.52). Additionally, in the theta band, the secondary gradient scores in multiple networks were closely associated with epilepsy duration (Spearmans |R|>0.50, all p<0.05, FDR corrected). The findings were robust for different thresholds and not explained by potential confounders. In GGE, FPN moving closer to the other networks might promote the widespread pattern of pathological activity, and the association between gradient scores and epilepsy duration supports a progressive disruption of the network gradients. Altogether, this study presents the first EEG-based evidence of GGE-related gradient signatures and its clinical relevance. Author SummaryGGE represents around 20% of adult epilepsies and involves abnormalities across multiple networks. However, the hierarchical organization of the networks, which could be captured by gradient analysis, associated with GGE remains elusive. Here, we derived the frequency-dependent gradient patterns from high-density EEG of GGE patients and matched healthy controls. In GGE patients, beta band secondary gradient (G2) showed significantly less negative gradient scores in the frontoparietal network compared to HC, indicating a narrowing network hierarchy which might promote the widespread pattern of pathological activity. Additionally, theta band G2 of multiple networks were closely associated with epilepsy duration, suggesting a progressive disruption of network hierarchy as epilepsy advances. This study presents the first EEG-based evidence of GGE-related gradient signatures and its clinical relevance.

ObjectiveThe study was aimed at developing an automatic system, based on complex network analysis and machine learning, to identify interictal network-based biomarkers in patients with drug-resistant focal epilepsy and no visible anatomical lesions candidate for surgery, able to support the localization of the Epileptogenic Zone (EZ) and to further disclose properties of the interictal epileptogenic network. Methods3 min of interictal SEEG signals, recorded in 18 patients with drug-resistant epilepsy, different EZ localization, negative MRI, were analysed. Patients were divided into seizure-free (SF) and non-seizure free (NSF) groups, according to their post-surgical outcome. After a first step of effective connectivity estimation, hubs were defined through the combination of nine graph theory-based indices of centrality. The values of centrality indices related to these hubs were used as input of an ensemble subspace discriminant classifier. ResultsThe proposed procedure was able to automatically localise the EZ with 98% sensitivity and 59% specificity for SF patients. Moreover, our results showed a clear difference between SF and NSF patients, mainly in terms of false positive rate (i.e., the percentage of NEZ leads classified as EZ), which resulted significantly higher in NSF patients. Lastly, the centrality indexes confirmed a different role of the Propagation Zone in NSF and SF groups. SignificanceResults pointed out that network centrality plays a key role in interictal epileptogenic network, even in case of the absence of anatomical alterations and SEEG epileptic abnormalities, and that the combination of connectivity, graph theory, and machine learning analysis can efficiently support interictal EZ localization. These findings also suggest that poorer post-surgical prognosis can be associated with larger connectivity alteration, with wider "hubs", and with a different involvement of the PZ, thus making this approach a promising biomarker for surgical outcome. Impact statementThe correct localization of the epileptogenic zone is still an unsolved question, mainly based on visual and subjective analysis of electrophysiological recordings, and highly time-consuming due to the needing of ictal recording. This issue is even more critical in patients with negative MRI and extra-temporal EZ localization. The approach proposed in this study represents an innovative and effective tool to reveal interictal epileptogenic network abnormalities, able to support and improve the EZ presurgical identification and to capture differences between poor and good post-surgical outcome

The balance perturbation-evoked N1 potential is a reliable cortical response during reactive balance control that is correlated to a variety of cognitive and motor functions. Although the supplementary motor area (SMA) has been identified as the primary source of the N1, it is less understood whether other brain regions contribute to N1 recorded at the scalp. We used source localization on electroencephalography (EEG) data from 25 younger adults recorded during backward whole-body perturbations during stance. We identified the sources that contribute to channel-based N1 recordings and quantified their impact on N1 amplitude and latency. In younger adults, N1 amplitudes can be explained by one single source in a central midline cortical region covering the SMA. When reconstructing N1 signals using backprojections with one versus all independent components (IC) identified as brain sources there was no difference in peak amplitudes and a small but significant difference in N1 peak latencies. Parallel brain sources thus deflect the time course of the N1, but not its magnitude. Brain areas associated with ICs contributing to the shift in N1 latency varied between participants. Our results emphasize the dominant influence of central cortical areas on the N1 response, informing hypothesizes regarding the nature of the signal and its functional role. Importantly, the extent and location of other cortical structures that influence N1 timing, such as parietal cortex areas and the anterior cingulate cortex, may further elucidate cortical contributions to balance. These markers could be crucial for the early detection of balance problems in clinical populations. NEW & NOTEWORTHYWe demonstrate that channel-level amplitudes of the balance perturbation-evoked N1 in younger adults primarily reflect neural activity originating from cortical central midline regions, particularly the SMA. In contrast, contributions from parallel active brain regions evoked by balance perturbations are indicated by an influence on N1 peak latencies. Our findings imply that the perturbation-evoked N1, unlike other evoked potentials, is not a mixture of multiple neural sources in younger adults.

The angular gyrus (AG) is involved in multiple cognitive processes and its structural alterations are commonly observed in many neuropsychiatric syndromes. Since changes in excitability may precede structural changes and clinical symptoms, there is a need for diagnostic tools assessing the functional state of hub brain regions like the AG. The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) can provide such functional readouts by probing the brain response to direct stimulation. This study aimed to characterize TMS-evoked potentials (TEP) elicited by AG stimulation, determine optimal stimulation parameters, and identify TEP biomarkers of AG function. We recorded AG-TEPs in 19 subjects using four TMS orientations and three intensities and compared TEP spatiotemporal features using topographic dissimilarity and microstate analyses. Additionally, we explored the relationship between AG-TEP topography and TMS-evoked muscular activity. Our results showed topographic variability in AG-TEP components P25, N45, and N75. The P25 topography was sensitive to TMS orientation, while the topography of N45 and N75 was highly dependent on both coil orientation and intensity. Interestingly, we found that TMS-evoked muscular activity was also dependent on both these parameters and strongly related to the occurrence of a specific topographic pattern, which therefore possibly corresponds to the somatosensory brain response to muscle contraction. We conclude that the early AG-TEP component P25 likely reflects neural processes triggered by direct AG activation and could provide an index of local excitability. Later components N45 and N75 must be interpreted with caution as they may primarily reflect peripherally evoked activity.

ObjectiveHippocampal ripples are biomarkers of epileptogenicity in patients with epilepsy, and physiological features characterize memory function in healthy individuals. Discriminating between pathological and physiological ripples is important for identifying the epileptogenic (EP) zone; however, distinguishing them from waveforms is difficult. This study hypothesized that the nocturnal synchronization of hippocampal ripples and cortical delta power classifies EP and physiological hippocampi. MethodsWe enrolled 38 patients with electrodes implanted in the hippocampus or the parahippocampal gyrus between April 2014 and March 2023 at our institution. We classified 11 patients (11 hippocampi) into the EP group, who were pathologically diagnosed with hippocampal sclerosis, and five patients (six hippocampi) into the non-epileptogenic (NE) group, whose hippocampi had no epileptogenicity. Hippocampal ripples were detected using intracranial electroencephalography of the hippocampal or parahippocampal electrodes and presented as ripple rates per second. Cortical delta power (0.5-4 Hz) was assessed using cortical electrodes. The Pearson correlation coefficient between the ripple rates and the cortical delta power (CRD) was calculated for the intracranial electroencephalographic signals obtained every night during the recordings. ResultsHippocampal ripples detected from continuous recording for approximately 10 days demonstrated similar frequency characteristics between the EP and NE groups. However, CRDs in the EP group (mean [standard deviation]: 0.20 [0.049]) were significantly lower than those in the NE group (0.67 [0.070], F (1,124) = 29.6, p < 0.0001 (group), F (9,124) = 1.0, p = 0.43 (day); two-way analysis of variance). Based on the minimum CRDs during the 10-day recordings, the two groups were classified with 94.1% accuracy. ConclusionCRD is a biomarker of hippocampal epileptogenicity. Key PointsThe correlation between hippocampal ripple rate and cortical delta power was evaluated for approximately 10 days in patients with drug-resistant epilepsy. Mean correlation coefficients were significantly lower in the epileptogenic group than in the non-epileptogenic group. The minimum value of the correlation coefficients predicts hippocampal sclerosis.

ObjectiveSignal analysis biomarkers, in an intra-operative setting, may be complementary tools to guide and tailor the resection in drug-resistant epilepsy patients. Unbiased assessment of biomarker performances are needed to evaluate their clinical usefulness and translation. We defined a realistic ground-truth scenario and compared the effectiveness of different biomarkers alone and combined to localize epileptogenic tissue. MethodsWe investigated the performances of univariate, bivariate and multivariate signal biomarkers applied to 1 minute inter-ictal intra-operative electrocorticography to discriminate between electrodes covering normal or pathologic activity in 47 drug-resistant people with epilepsy (temporal and extra-temporal) who had been seizure-free one year after the operation. ResultsThe best result using a single biomarker was obtained using the phase-amplitude coupling measure for which the epileptogenic tissue was localized in 16 out of 47 patients. Combining the whole set of biomarkers provided an improvement of the performances: 20 out of 47 patients. Repeating the analysis only on the temporal-lobe resections we reached a sensitivity of 93% (28 out of 30) combining all the biomarkers. ConclusionWe suggest that the assessment of biomarker performances on a ground-truth scenario is required to have a proper estimate on how biomarkers translate into clinical use. Phase-amplitude coupling seems the best performing single biomarker and combining biomarkers improves localization of epileptogenic tissue. However, sensitivity achieved is not adequate for the usage as a tool in the operation theater, but it can improve the understanding of pathophysiological process.

BackgroundSensory tricks (SeT) are various maneuvers that can alleviate dystonic contractions and are a characteristic feature of cervical dystonia (CD). The neurophysiology underlying SeT, however, remains largely unknown. Reducing the abnormal cortical facilitation and modulating the abnormal cortical and subcortical oscillatory activity are mechanisms that have been proposed. The supplementary motor area (SMA) and primary sensorimotor cortices are thought to be relevant to this phenomenon. ObjectiveIn the current study, using concurrent EEG recording during transcranial magnetic stimulation (TMS) of the SMA and primary motor cortex (M1), we aimed at determining the changes in cortical reactivity and oscillatory changes induced by SeT. MethodsWe recruited 13 patients with CD who exhibited SeT and equal number of age- and gender-matched healthy controls. Single TMS pulses were delivered over the SMA and M1 either at rest or during SeT. 32-channel EEG was recorded, and TMS-evoked potentials (TEP) were obtained. Further, time-frequency analysis was performed on the induced data. Correlation analysis for significant neurophysiological parameters was done with clinical measures. ResultsWe found that SeT induced a significant decrease in the amplitude of TEP elicited from M1 stimulation at [~]210-260ms in patients, which correlated with symptom duration. Post hoc analysis of EMG activity in the neck muscles revealed that this effect on TEP was present only in the subset of patients with effective SeT. ConclusionOur results suggest that SeT reduces cortical reactivity over M1 approximately 200ms after stimulation. This adds support to the idea that reduced cortical facilitation underlies the phenomenon.

Background and ObjectivesTimely diagnosis and effective treatment of Lennox-Gastaut Syndrome (LGS) improve prognosis and lower healthcare costs, but the transition from infantile epileptic spasms syndrome (IESS) to LGS is highly variable and insidious. Objective biomarkers are needed to monitor this progression and guide clinical decision making. MethodsWe retrospectively collected longitudinal EEG data at the Childrens Hospital of Orange County from fifteen children who were diagnosed with IESS and later with LGS between 2012 and 2021. EEGs were from IESS and LGS diagnoses, between the two diagnoses, and following LGS diagnosis. Functional connectivity networks were calculated using a cross-correlation-based method and assessed relative to diagnostic timepoint, treatment response, presence of clinical markers of disease, age, and amplitude of interictal spikes. ResultsConnectivity strength was high at LGS diagnosis and decreased after favorable response to treatment, but it remained stable or increased when response was unfavorable. In all subjects, connectivity strength was higher at the time of LGS diagnosis than at the preceding timepoint. Presence of clinical markers of LGS were associated with high connectivity strength, but no single marker predicted connectivity strength. DiscussionComputational EEG analysis can be used to map the evolution from IESS to LGS. Changes in connectivity may enable prediction of impending LGS and treatment response monitoring, thus facilitating earlier LGS treatment and guiding medical management. Key pointsO_LIEEG functional connectivity analysis can track progression from infantile epileptic spasms syndrome (IESS) to Lennox-Gastaut Syndrome (LGS). C_LIO_LIHigh connectivity strength at LGS diagnosis decreases with favorable treatment response but remains high with poor response. C_LIO_LIClinical LGS markers correlate with high connectivity, but no single marker predicts connectivity strength. C_LIO_LIEEG functional connectivity analysis may help predict LGS onset, enabling early intervention and improving prognosis. C_LI

GABAA receptor (GABAAR) - mediated inhibition participates in the control of cortical excitability, and its impairment likely contributes to the pathologic excitability changes that have been associated with multiple neurological disorders. Therefore, there is a need for its direct evaluation in the human brain, and the combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) might represent the optimal tool. TMS-evoked brain potentials (TEPs) capture the spread of activity across the stimulated brain network, and since this process at least partially depends on the GABAAR-mediated inhibition, TEPs may constitute relevant biomarkers of local GABAAergic function. Here, we aimed to assess the effect of GABAARs activation using TEPs, and to identify TEP components that are sensitive to the state of GABAAergic inhibition. In 20 healthy subjects, we recorded TEPs evoked by sub- and supra-threshold stimulation of the primary motor cortex (M1), motor-evoked potentials (MEPs) and resting-state EEG (RS-EEG). GABAARs were activated (1) pharmacologically by oral administration of alprazolam compared to placebo within each subject, and (2) physiologically using a sub-threshold conditioning stimulus to characterize the effect of short-latency intracortical inhibition (SICI). In supra-threshold TEPs, alprazolam suppressed the amplitude of components N17, N100 and P180, and increased component N45. The pharmacological modulation of N17 correlated with the change observed in MEPs and with the alprazolam-induced increase of lower {beta}-band RS-EEG. Only a reduction of N100 and P180 was found in sub-threshold TEPs. TEP SICI manifested as a reduction of N17, P60 and N100, and its effect on N17 correlated with the alprazolam-induced N17 suppression and {beta} increase. Our results indicate that N17 of supra-threshold TEPs could serve as a non-invasive biomarker of local cortical excitability reflecting the state of GABAAR-mediated inhibition in the sensorimotor network. Furthermore, the alprazolam-induced increase of {beta}-band oscillations possibly corresponds to the increased inhibitory neurotransmission within this network.

BackgroundEpilepsy research increasingly emphasizes the role of brain network dynamics in seizure generation and propagation. This study explores static and dynamic functional networks in subjects with drug-resistant epilepsy, to identify mechanisms that enhance or inhibit seizure initiation. To this aim, we analyzed functional connectivity of brain networks preceding ictal minor electrical discharges and major seizures in epileptogenic and non-epileptogenic zones explored with intracerebral electrodes. Material and methodsStereo-electroencephalographic signals were recorded from 39 patients with focal drug-resistant epilepsy during presurgical monitoring. Static functional connectivity was analyzed using graph theory metrics, whereas dynamic connectivity through the analysis of the complexity and dwell times of brain meta-states activations. ResultsStatic connectivity analysis revealed significant alterations in network centrality, integration, and segregation properties, with distinct patterns observed in resting conditions just ahead minor electrical discharges and major seizures. Specifically, network analysis before minor electrical discharges exhibited increased nodal strength and reduced betweenness centrality in the epileptogenic zone, associated with increased integration and reduced segregation in non-epileptogenic zones. Dynamic connectivity analysis showed lower complexity and longer stability of meta-states before minor electrical discharges, particularly in high-frequency signals of non-epileptogenic zones. ConclusionsOur findings provide novel and valuable insights into the dynamic reconfiguration of brain networks before epileptic seizures, suggesting an inhibitory/protective mechanism mainly involving the non-epileptogenic zones. Understanding these network changes is pivotal for improving epilepsy treatment strategies targeting dynamic network alterations.

Background and ObjectivesThe epilepsy diagnosis still represents a complex process, with misdiagnosis reaching 40%. Here, we aimed at building an automatable workflow, to help the clinicians in the diagnostic process, differentiating between controls and a population of patients with temporal lobe epilepsy (TLE). While primarily interested in correctly classifying the participants, we used data features providing hints on the underlying pathophysiological processes. Specifically, we hypothesized that neuronal avalanches (NA) may represent a feature that encapsulates the rich brain dynamics better than the classically used functional connectivity measures (Imaginary Coherence; ImCoh). MethodsWe recorded 10 minutes of resting state activity with high-density scalp electroencephalography (hdEEG; 128 channels). We analyzed large-scale activation bursts (NA) from source activation, to capture altered dynamics. Then, we used machine-learning algorithms to classify epilepsy patients vs. controls, and we described the goodness of the classification as well as the effect of the durations of the data segments on the performance. ResultsUsing a support vector machine (SVM), we reached a classification accuracy of 0.87 {+/-} 0.10 (SD) and an area under the curve (AUC) of 0.94 {+/-} 0.06. The use of avalanches-derived features, generated a mean increase of 16% in the accuracy of diagnosis prediction, compared to ImCoh. Investigating the main features informing the model, we observed that the dynamics of the entorhinal cortex, superior and inferior temporal gyri, cingulate cortex and prefrontal dorsolateral cortex were informing the model with NA. Finally, we studied the time-dependent accuracy in the classification. While the classification performance grows with the duration of the data length, there are specific lengths, at 30s and 180s at which the classification performance becomes steady, with intermediate lengths showing greater variability. Classification accuracy reached a plateau at 5 minutes of recording. DiscussionWe showed that NA represents a better EEG feature for an automated epilepsy identification, being related with neuronal dynamics of pathology-relevant brain areas. Furthermore, the presence of specific durations and the performance plateau might be interpreted as the manifestation of the specific intrinsic neuronal timescales altered in epilepsy. The study represents a potentially automatable and noninvasive workflow aiding the clinicians in the diagnosis.

ObjectiveTo clarify variations in the relationship between high-frequency activities (HFAs) and low-frequency bands from the tonic to the clonic phase in focal to bilateral tonic-clonic seizures (FBTCS), using phase-amplitude coupling. MethodsThis retrospective study enrolled six patients with drug-resistant focal epilepsy who underwent intracranial electrode placement for presurgical invasive electroencephalography at Osaka University Hospital (July 2018-July 2019). We used intracranial electrodes to record seizures in focal epilepsy (11 FBTCS). The magnitude of synchronization index (SIm) and receiver-operating characteristic (ROC) analysis were used to analyze the coupling between HFA amplitude (80-250 Hz) and lower frequencies phase. ResultsThe {theta} (4-8 Hz)-HFA SIm peaked in the tonic phase, whereas the {delta} (2-4 Hz)-HFA SIm peaked in the clonic phase. ROC analysis indicated that the {delta}-HFA SIm discriminated well the clonic from the tonic phase. ConclusionsThe main low-frequency band modulating the HFA shifted from the {theta} band in the tonic phase to the {delta} band in the clonic phase. SignificanceIn FBTCS, low-frequency band coupling with HFA amplitude varies temporally. Especially, the {delta} band is specific to the clonic phase. These results suggest dynamically neurophysiological changes in the thalamus or basal ganglia throughout FBTCS. HighlightsO_LIThe {theta} band (4-8 Hz) was mainly coupled with high-frequency activity (HFA) in the tonic phase of focal to bilateral tonic-clonic seizures (FBTCS). C_LIO_LIThe {delta} band (2-4 Hz) was mainly coupled with HFA in the clonic phase of FBTCS. C_LIO_LIThe magnitude of the synchronization index related to {delta}-HFA phase-amplitude coupling discriminated well the clonic from the tonic phase. C_LI

The diagnosis of amyotrophic lateral sclerosis (ALS) relies on involvement of both upper (UMN) lower motor neurons (LMN). Yet, there remains no objective marker of UMN involvement, limiting early diagnosis of ALS. This study establishes whether TMS combined with EEG can be used to measure short-interval intracortical inhibition (SICI) via TMS evoked potentials (TEP) in healthy volunteers - an essential first step in developing an independent marker of UMN involvement in ALS.\n\nWe hypothesised that a SICI paradigm would result in characteristic changes in the TMS-evoked EEG potentials that directly mirror the changes in MEP.\n\nTMS was delivered to the left motor cortex using single-pulse and three inhibitory stimulation paradigms. SICI was present in all three conditions. TEP peaks were reduced predominantly under the SICI 70 protocol but less so for SICI 80 and not at all for SICI 90. There was a significant negative correlation between MEPs and N45 TEP peak for SICI 70 (rho = -0.54, p = 0.04). In other words, as MEPs becomes inhibited the N45 increases. The same trend was maintained across SICI 80 and 90 (SICI 80, rho = -0.5, p = 0.06; SICI 90, rho = -0.48, p = 0.07). Additional experiments suggest these results cannot be explained by artefact.\n\nWe establish that motor cortical inhibition can be measured during a SICI 70 protocol expanding on previous work. We have carefully considered the role of artefact in TEPs and have taken a number of steps to show that artefact cannot explain these results and we suggesting the differences are cortical in origin. TMS-EEG has potential to aid early diagnosis and to further understand central and peripheral pathophysiology in MND.

BackgroundElectrical stimulation therapy for epilepsy patients is applied either to the epileptogenic region or to a larger network (e.g. with deep brain stimulation). Objective/hypothesisResponses to single pulse electrical stimuli (SPES) reveal potential stimulation sites that target the epileptogenic region for cortical network stimulation therapy. MethodsWe applied SPES to ten epilepsy patients who underwent intracranial electrocorticography recordings for pre-surgical evaluation. We detected cortico-cortical evoked potentials (CCEPs) in response electrodes after stimulating other pairs of electrodes, revealing effective connections. We calculated event-related spectral perturbation (ERSP) plots in all response electrodes after stimulating other electrode pairs. We detected interictal epileptic discharges (IEDs) before and after each single pulse and calculated the logarithmic IED ratio. We analyzed whether power suppression in the ERSP occurred in a response electrode when connected with the stimulus pair. We analyzed whether a larger change in IED ratio was accompanied by power suppression in the response electrode or when this electrode was connected with the stimulus pair. ResultsWe found that SPES has a neuromodulatory effect measured as: 1) the relationship of a CCEP and power suppression, 2) a larger change in IED rate when a CCEP was present, 3) a decrease in IED rate when power suppression was observed. Conclusion(s)Results suggest that stimulation in an area connected to the epileptogenic region can modulate IEDs in this region. SPES might provide a template for localizing a stimulation site outside the epileptogenic region for electrical stimulation treatment of epilepsy. HighlightsO_LIStimulation of an electrode pair can suppress power in an electrode on connected tissue. C_LIO_LIStimulation of an electrode pair changes IED rate in an electrode on connected tissue. C_LIO_LIA decrease in IED rate was accompanied by power suppression. C_LIO_LISPES indicates potential stimulation sites for neurostimulation therapy in epilepsy. C_LI