Whole-brain MEG connectivity-based analyses reveals critical hubs in childhood absence epilepsy

Detection of short-lasting and ictal spike-and-wave discharges in around-the-ears EEG recordings in children with absence epilepsy

PURPOSE

Long-term ambulatory EEG recordings can improve the monitoring of absence epilepsy in children, but signal quality and increased review workload are a concern. We evaluated the feasibility of around-the-ears EEG arrays (cEEGrids) to capture 3-Hz short-lasting and ictal spike-and-wave discharges and assessed the performance of automated detection software in cEEGrids data. We compared patterns of bilateral synchronisation between short-lasting and ictal spike-and-wave discharges.

METHODS

We recruited children with suspected generalised epilepsy undergoing routine video-EEG monitoring and performed simultaneous cEEGrids recordings. We used ASSYST software to detect short-lasting 3-Hz spike-and-wave discharges (1-3 s) and ictal spike-and-wave discharges in the cEEGrids data. We assessed data quality and sensitivity of cEEGrids for spike-and-wave discharges in routine EEG. We determined the sensitivity and false detection rate for automated spike-and-wave discharge detection in cEEGrids data. We compared bihemispheric synchrony across the onset of short-lasting and ictal spike-and-wave discharges using the mean phase coherence in the 2-4 Hz frequency band.

RESULTS

We included nine children with absence epilepsy (median age = 11 y, range 8-15 y, nine females) and recorded 4 h and 27 min of cEEGrids data. The recordings from seven participants were suitable for quantitative analysis, containing 82 spike-and-wave discharges. The cEEGrids captured 58 % of all spike-and-wave discharges (median individual sensitivity: 100 %, range: 47-100 %). ASSYST detected 82 % of all spike-and-wave discharges (median: 100 %, range: 41-100 %) with a false detection rate of 48/h (median: 6/h, range: 0-154/h). The mean phase coherence significantly increased during short-lasting and ictal spike-and-wave discharges in the 500-ms pre-onset to 1-s post-onset interval.

CONCLUSIONS

cEEGrids are of variable quality for monitoring spike-and-wave discharges in children with absence epilepsy. ASSYST could facilitate the detection of short-lasting and ictal spike-and-wave discharges with clear periodic structures but with low specificity. A similar course of bihemispheric synchrony between short-lasting and ictal spike-and-wave discharges indicates that cortico-thalamic driving may be relevant for both types of spike-and-wave discharges.

Magnetoencephalography for the pediatric population, indications, acquisition and interpretation for the clinician

Magnetoencephalography (MEG) is an imaging technique that enables the assessment of cortical activity via direct measures of neurophysiology. It is a non-invasive and passive technique that is completely painless. MEG has gained increasing prominence in the field of pediatric neuroimaging. This dedicated review article for the pediatric population summarizes the fundamental technical and clinical aspects of MEG for the clinician. We discuss methods tailored for children to improve data quality, including child-friendly MEG facility environments and strategies to mitigate motion artifacts. We provide an in-depth overview on accurate localization of neural sources and different analysis methods, as well as data interpretation. The contemporary platforms and approaches of two quaternary pediatric referral centers are illustrated, shedding light on practical implementations in clinical settings. Finally, we describe the expanding clinical applications of MEG, including its pivotal role in presurgical evaluation of epilepsy patients, presurgical mapping of eloquent cortices (somatosensory and motor cortices, visual and auditory cortices, lateralization of language), its emerging relevance in autism spectrum disorder research and potential future clinical applications, and its utility in assessing mild traumatic brain injury. In conclusion, this review serves as a comprehensive resource of clinicians as well as researchers, offering insights into the evolving landscape of pediatric MEG. It discusses the importance of technical advancements, data acquisition strategies, and expanding clinical applications in harnessing the full potential of MEG to study neurological conditions in the pediatric population.

Abnormal and Changing Information Interaction in Adults with Attention-Deficit/Hyperactivity Disorder Based on Network Motifs

Network motif analysis approaches provide insights into the complexity of the brain's functional network. In recent years, attention-deficit/hyperactivity disorder (ADHD) has been reported to result in abnormal information interactions in macro- and micro-scale functional networks. However, most existing studies remain limited due to potentially ignoring meso-scale topology information. To address this gap, we aimed to investigate functional motif patterns in ADHD to unravel the underlying information flow and analyze motif-based node roles to characterize the different information interaction methods for identifying the abnormal and changing lesion sites of ADHD. The results showed that the interaction functions of the right hippocampus and the right amygdala were significantly increased, which could lead patients to develop mood disorders. The information interaction of the bilateral thalamus changed, influencing and modifying behavioral results. Notably, the capability of receiving information in the left inferior temporal and the right lingual gyrus decreased, which may cause difficulties for patients in processing visual information in a timely manner, resulting in inattention. This study revealed abnormal and changing information interactions based on network motifs, providing important evidence for understanding information interactions at the meso-scale level in ADHD patients.

Connectivity alteration in thalamic nuclei and default mode network-related area in memory processes in mesial temporal lobe epilepsy using magnetoencephalography

This work aimed to investigate the involvement of the thalamic nuclei in mesial temporal lobe epilepsy (MTLE) and identify the influence of interictal epileptic discharges on the neural basis of memory processing by evaluating the functional connectivity (FC) between the thalamic nuclei and default mode network-related area (DMNRA) using magnetoencephalography. Preoperative datasets of nine patients with MTLE with seizure-free status after surgery and those of nine healthy controls were analyzed. The FC between the thalamic nuclei (anterior nucleus [ANT], mediodorsal nucleus [MD], intralaminar nuclei [IL]), hippocampus, and DMNRA was examined for each of the resting, pre-spike, spike, and post-spike periods in the delta to ripple bands using magnetoencephalography. The FC between the ANT, MD, hippocampus, and medial prefrontal cortex increased in the gamma to ripple bands, whereas the FC between the ANT, IL, and DMNRA decreased in the delta to beta bands, compared with that of the healthy controls at rest. Compared with the rest period, the pre-spike period had significantly decreased FC between the ANT, MD, and DMNRA in the ripple band. Different FC changes between the thalamic nuclei, hippocampus, and DMNRA of specific connections in a particular band may reflect impairment or compensation in the memory processes.

EEG based brain functional connectivity analysis for post-autoimmune encephalitis (AE) patients with epilepsy

Autoimmune Encephalitis (AE) refers to a group of conditions that occur when the body's immune system mistakenly attacks healthy brain cells, leading to inflammation of the brain. Seizures are a common symptom of AE and more than a third of patients experiencing seizures secondary to AE become epileptic over time. The objective of the present study is to identify biomarkers that can be used to identify those patients in whom AE will evolve into epilepsy. The bursts of abnormal electrical activity that occur during a seizure can be recorded by using Electroencephalography (EEG). In this work, common EEG (cEEG) and ambulatory EEG (aEEG) were recorded to compare the brain functional connectivity (FC) properties in post-AE patients with epilepsy patients and post-AE patients without epilepsy. The brain functional networks of spike waves were first constructed on the basis of Phase Locking Value (PLV). An analysis was then performed of the differences which existed in the FC properties of clustering coefficient, characteristic path length, global efficiency, local efficiency, and node degree between post-AE patients with epilepsy patients and post-AE patients without epilepsy. From the perspective of brain functional network analysis, post-AE patients with epilepsy showed a more complex network structure. Furthermore, the five FC properties have been found signification different, all FC property values of post-AE patients with epilepsy are higher than those of post-AE patients without epilepsy of cEEG and aEEG. Based on the extracted FC properties, five classifiers were used to classify them, and the results showed that all five FC properties could effectively distinguish between post-AE patients with epilepsy patients and post-AE patients without epilepsy in both cEEG and aEEG. These findings are potentially helpful for diagnosing whether a patient with AE will become epileptic.

Central-positive complexes in ECT-induced seizures: Possible evidence for thalamocortical mechanisms

OBJECTIVE

Central-positive complexes (CPCs) are elicited during electroconvulsive therapy (ECT) as generalized high-amplitude waveforms with maximum positive voltage over the vertex. While these complexes have been qualitatively assessed in previous literature, quantitative analyses are lacking. This study aims to characterize CPCs across temporal, spatial, and spectral domains.

METHODS

High-density 64-electrode electroencephalogram (EEG) recordings during 50 seizures acquired from 11 patients undergoing right unilateral ECT allowed for evaluation of spatiotemporal characteristics of CPCs via source localization and spectral analysis.

RESULTS

Peak-amplitude CPC scalp topology was consistent across seizures, showing maximal positive polarity over the midline fronto-central region and maximal negative polarity over the suborbital regions. The sources of these peak potentials were localized to the bilateral medial thalamus and cingulate cortical regions. Delta, beta, and gamma oscillations were correlated with the peak amplitude of CPCs during seizures induced during ketamine, whereas delta and gamma oscillations were associated with CPC peaks during etomidate anesthesia (excluding the dose-charge titration).

CONCLUSIONS

Our findings demonstrate the consistency of CPC presence across participant, stimulus charge, time, and anesthetic agent, with peaks localized to bilateral medial thalamus and cingulate cortical regions and associated with delta, beta, and gamma band oscillations (depending on the anesthetic condition).

SIGNIFICANCE

The consistency and reproducibility of CPCs offers ECT as a new avenue for studying the dynamics of generalized seizure activity and thalamocortical networks.

The impact of early-life environment on absence epilepsy and neuropsychiatric comorbidities

This review discusses the long-term effects of early-life environment on epileptogenesis, epilepsy, and neuropsychiatric comorbidities with an emphasis on the absence epilepsy. The WAG/Rij rat strain is a well-validated genetic model of absence epilepsy with mild depression-like (dysthymia) comorbidity. Although pathologic phenotype in WAG/Rij rats is genetically determined, convincing evidence presented in this review suggests that the absence epilepsy and depression-like comorbidity in WAG/Rij rats may be governed by early-life events, such as prenatal drug exposure, early-life stress, neonatal maternal separation, neonatal handling, maternal care, environmental enrichment, neonatal sensory impairments, neonatal tactile stimulation, and maternal diet. The data, as presented here, indicate that some early environmental events can promote and accelerate the development of absence seizures and their neuropsychiatric comorbidities, while others may exert anti-epileptogenic and disease-modifying effects. The early environment can lead to phenotypic alterations in offspring due to epigenetic modifications of gene expression, which may have maladaptive consequences or represent a therapeutic value. Targeting DNA methylation with a maternal methyl-enriched diet during the perinatal period appears to be a new preventive epigenetic anti-absence therapy. A number of caveats related to the maternal methyl-enriched diet and prospects for future research are discussed.

EEG Oscillatory Networks in Peri-Ictal Period of Absence Epilepsy

Objective

To investigate the dynamical brain network changes before and after an absence seizure episode in absence epilepsy (AE).

Methods

21 AE patients with a current high frequency of seizures and 21 sex- and age-matched health control (HC) who reported no history of neurological or psychiatric disorders and visited the hospital for routine physical examinations were included. Each included subject underwent a 2-h and 19-channel video EEG examination. For AE patients, five epochs of 10-s EEG data in inter-ictal, pre-ictal, and post-ictal states were collected. For the HC group, five 10-s resting-state EEG epochs were extracted. Functional independent components analysis (ICA) was carried out using the LORETA KEY tool.

Results

Compared with the resting-state EEG data of the HC group, the EEG data from AE patients during inter-ictal periods showed decreased alpha oscillations in regions involving the superior frontal gyrus (SFG) (BA11). From inter-ictal to pre-ictal, SFG (BA10) showed maximum decreased delta oscillations. Additionally, from pre-ictal to post-ictal, superior temporal gyrus (STG) (BA 22) presented maximum increased neural activity in the alpha band. Moreover, compared with inter-ictal EEG, post-ictal EEG showed significantly decreased theta activity in SFG (BA8).

Conclusion

The changes in SFG alpha oscillations are the key brain network differences between inter-ictal EEG of AE patients and resting-state EEG of HCs. The brain networks of EEG oscillatory during peri-ictal episodes are mainly involving SFG and STG. Our study suggests that altered EEG brain networks dynamics exist between inter-ictal EEG of AE patients and resting-state EEG of HCs and between pre- and post-ictal EEG in AE patients.

Contributions of Magnetoencephalography to Understanding Mechanisms of Generalized Epilepsies: Blurring the Boundary Between Focal and Generalized Epilepsies?

According to the latest operational 2017 ILAE classification of epileptic seizures, the generalized epileptic seizure is still conceptualized as "originating at some point within and rapidly engaging, bilaterally distributed networks." In contrast, the focal epileptic seizure is defined as "originating within networks limited to one hemisphere." Hence, one of the main concepts of "generalized" and "focal" epilepsy comes from EEG descriptions before the era of source localization, and a presumed simultaneous bilateral onset and bi-synchrony of epileptiform discharges remains a hallmark for generalized seizures. Current literature on the pathophysiology of generalized epilepsy supports the concept of a cortical epileptogenic focus triggering rapidly generalized epileptic discharges involving intact corticothalamic and corticocortical networks, known as the cortical focus theory. Likewise, focal epilepsy with rich connectivity can give rise to generalized spike and wave discharges resulting from widespread bilateral synchronization. Therefore, making this key distinction between generalized and focal epilepsy may be challenging in some cases, and for the first time, a combined generalized and focal epilepsy is categorized in the 2017 ILAE classification. Nevertheless, treatment options, such as the choice of antiseizure medications or surgical treatment, are the reason behind the importance of accurate epilepsy classification. Over the past several decades, plentiful scientific research on the pathophysiology of generalized epilepsy has been conducted using non-invasive neuroimaging and postprocessing of the electromagnetic neural signal by measuring the spatiotemporal and interhemispheric latency of bi-synchronous or generalized epileptiform discharges as well as network analysis to identify diagnostic and prognostic biomarkers for accurate diagnosis of the two major types of epilepsy. Among all the advanced techniques, magnetoencephalography (MEG) and multiple other methods provide excellent temporal and spatial resolution, inherently suited to analyzing and visualizing the propagation of generalized EEG activities. This article aims to provide a comprehensive literature review of recent innovations in MEG methodology using source localization and network analysis techniques that contributed to the literature of idiopathic generalized epilepsy in terms of pathophysiology and clinical prognosis, thus further blurring the boundary between focal and generalized epilepsy.

Transcranial Direct Current Stimulation Reduces Anxiety, Depression and Plasmatic Corticosterone in a Rat Model of Atypical Generalized Epilepsy

Affective disorders (i.e. anxiety and depression) are commonly observed in patients with epilepsy and induce seizure aggravation. Animal models of epilepsy that exhibit affective disorder features are essential in developing new neuromodulatory treatments. GEAS-W rats (Generalized Epilepsy with Absence Seizures, Wistar background) are an inbred model of generalized epilepsy showing spontaneous spike-wave discharges concomitant with immobility. Transcranial Direct Current Stimulation (tDCS) is a safe non-invasive neuromodulatory therapy used to modulate dysfunctional circuitries frequently and successfully applied in affective disorders for symptom alleviation. Here we investigated anxiolytic and antidepressant effects of tDCS in GEAS-W rats and the role of corticosterone as a possible mechanism of action. GEAS-W and Wistar rats were randomly divided into control, sham-tDCS and active-tDCS groups. Both tDCS groups received 15 sessions of sham or active-tDCS (1 mA, cathode). Behavioural tests included the Open Field and Forced Swimming tests followed by corticosterone analysis. We observed a main effect of treatment and a significant treatment by strain interaction on anxiety-like and depressive-like behaviours, with active-tDCS GEAS-W rats entering the center of the open field more often and showing less immobility in the forced swimming test. Furthermore, there was a main effect of treatment on corticosterone with active-tDCS animals showing marked reduction in plasmatic levels. This study described preclinical evidence to support tDCS treatment of affective disorders in epilepsy and highlights corticosterone as a possible mechanism of action.

Cross-Frequency Coupling in Childhood Absence Epilepsy

Objective: Absence seizures are the prototypic primarily generalized seizures, but there is incomplete understanding regarding their generation and maintenance. A core network for absence seizures has been defined, including focal cortical and thalamic regions that have frequency-dependent interactions. The purpose of this study was to investigate within-frequency coupling and cross-frequency coupling (CFC) during human absence seizures, to identify key regions (hubs) within the absence network that contribute to propagation and maintenance. Methods: Thirteen children with new-onset and untreated childhood absence epilepsy had over 60 typical absence seizures during both electroencephalography-functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) recordings. The spatial map of the ictal network was defined using fMRI and used as prior information for MEG connectivity. A multilayer network approach was used to investigate within-frequency coupling and CFC for canonical frequency bands. A rigorous null-modeling approach was used to determine connections outside the noise floor. Results: Strong coupling between beta and gamma frequencies, within the left frontal cortex, and between the left frontal and right parietal regions was observed. There was also strong connectivity between left frontal and right parietal nodes within the gamma band. Multilayer versatility analysis identified a cluster of network hubs in the left frontal region. Interpretation: Cortical regions commonly identified as being critical for absence seizure generation (frontal cortex, precuneus) have strong CFC and within-frequency coupling between beta and gamma bands. As nonpharmacologic treatments, such as neuromodulation, become available for generalized epilepsies, detailed mechanistic understanding of how "diffuse" seizures are generated and maintained will be necessary to provide optimal outcomes.

Functional reorganization of brain regions into a network in childhood absence epilepsy: A magnetoencephalography study

OBJECTIVE

Epilepsy is considered as a network disorder. However, it is unknown how normal brain activity develops into the highly synchronized discharging activity seen in disordered networks. This study aimed to explore the epilepsy brain network and the significant re-combined brain areas in childhood absence epilepsy (CAE).

METHODS

Twenty-two children with CAE were recruited to study the neural source activity during ictal-onset and interictal periods at frequency bands of 1-30 Hz and 30-80 Hz with magnetoencephalography (MEG) scanning. Accumulated source imaging (ASI) was used to analyze the locations of neural source activity and peak source strength.

RESULTS

Most of the participants had more active source activity locations in the ictal-onset period rather than in the interictal period, both at 1-30 Hz and 30-80 Hz. The frontal lobe (FL), the temporo-parietal junction (T-P), and the parietal lobe (PL) became the main active areas of source activity during the ictal period, while the precuneus (PC), cuneus, and thalamus were relatively inactive.

CONCLUSIONS

Some brain areas become more excited and have increased source activity during seizures. These significant brain regions might be re-combined to form an epilepsy network that regulates the process of absence seizures.

SIGNIFICANCE

The study confirmed that important brain regions are reorganized in an epilepsy network, which provides a basis for exploring the network mechanism of CAE development. Imaging findings may provide a reference for clinical characteristics.

Functional connectivity of the thalamocortical circuit in patients with seizure relapse after antiseizure medication withdrawal

OBJECTIVE

To characterize the features of thalamocortical functional connectivity during seizure recurrence at the time of antiseizure medication (ASM) withdrawal.

METHODS

Patients with chronic epilepsy who attempted to discontinue medications were prospectively registered and followed up; 19 patients remained seizure-free (SF-group), 18 patients had seizure relapses (SR-group) after ASM withdrawal, and 28 healthy controls were recruited. Resting-state functional magnetic resonance imaging was performed before ASM withdrawal. Thalamus subdivisions were set as seeds to calculate voxelwise functional connectivity. Partial correlation analysis between functional connectivity and clinical variables was performed. A support vector machine was used to assess the predictive ability of the specific functional connectivity for seizure relapse.

RESULTS

The within-group comparison indicated that the SR-group had more extensive functional connectivity than the SF-group; the left inferior pulvinar, left medial pulvinar, and right anterior pulvinar showed a significantly stronger functional connection with the precuneus in the SR-group than in the SF-group (Gaussian random field correction, voxel-level p < .001 and cluster-level p < .05). In the SR-group, a positive correlation was found between the left inferior pulvinar-precuneus connectivity and the active period (r = .46, p = .05), seizure-free period (r = .67, p = .002), and disease duration (r = .53, p = .02), and between the left medial pulvinar-precuneus connectivity and the seizure-free period (r = .58, p = .01). The combination of these thalamocortical connections showed a high predictive ability, with an area under the curve of .92 and accuracy of .90 (p = .01).

SIGNIFICANCE

This study determined distinct features of thalamocortical functional connectivity at the time of ASM withdrawal in patients with and without seizure relapse, showing a potential for predicting seizure outcomes following ASM withdrawal.

Topological Organization Alterations of Whole-Brain Functional Networks in Patients with Childhood Absence Epilepsy: Associations with Treatment Effects

PURPOSE

The purpose of the current study is to detect changes of topological organization of whole-brain functional networks and their relationship with the clinical treatment effects of antiepileptic drugs (AEDs) for patients with childhood absence epilepsy (CAE) using resting-state functional MRI (RS-fMRI). Patients and Methods. RS-fMRI data from 30 CAE patients were collected and compared with findings from 30 age- and gender-matched healthy controls (HCs). The patients were treated with first-line AEDs for 46.03 months before undergoing a second RS-fMRI scan.

RESULTS

CAE children at baseline showed a reduced clustering coefficient (Cp) and local efficiency (El) than the HC group, implying the reduction of functional segregation. CAE children at baseline also showed smaller characteristic path length (Lp) and higher global efficiency (Eg) compared with the HC group, implying the impairment of functional segregation. However, those metrics showed no significant differences between CAE children at follow-up and the HC group which indicated a clear renormalization of topological organization after AED treatments. CAE at follow-up also showed significantly decreased connectivity between several network regions, with which the thalamus is mainly involved. Furthermore, the reduced connectivity change between the left superior parietal gyrus and the left thalamus is positively correlated with the symptom improvements after AED treatment.

CONCLUSION

We highlighted the convergence and divergence of brain functional network dysfunctions in CAE patients and provided crucial insights into pathophysiological mechanisms and the AED effects.

Data-driven beamforming technique to attenuate ballistocardiogram artefacts in electroencephalography-functional magnetic resonance imaging without detecting cardiac pulses in electrocardiography recordings

Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a very promising non‐invasive neuroimaging technique. However, EEG data obtained from the simultaneous EEG–fMRI are strongly influenced by MRI‐related artefacts, namely gradient artefacts (GA) and ballistocardiogram (BCG) artefacts. When compared to the GA correction, the BCG correction is more challenging to remove due to its inherent variabilities and dynamic changes over time. The standard BCG correction (i.e., average artefact subtraction [AAS]), require detecting cardiac pulses from simultaneous electrocardiography (ECG) recording. However, ECG signals are also distorted and will become problematic for detecting reliable cardiac peaks. In this study, we focused on a beamforming spatial filtering technique to attenuate all unwanted source activities outside of the brain. Specifically, we applied the beamforming technique to attenuate the BCG artefact in EEG–fMRI, and also to recover meaningful task‐based neural signals during an attentional network task (ANT) which required participants to identify visual cues and respond accurately. We analysed EEG–fMRI data in 20 healthy participants during the ANT, and compared four different BCG corrections (non‐BCG corrected, AAS BCG corrected, beamforming + AAS BCG corrected, beamforming BCG corrected). We demonstrated that the beamforming approach did not only significantly reduce the BCG artefacts, but also significantly recovered the expected task‐based brain activity when compared to the standard AAS correction. This data‐driven beamforming technique appears promising especially for longer data acquisition of sleep and resting EEG–fMRI. Our findings extend previous work regarding the recovery of meaningful EEG signals by an optimized suppression of MRI‐related artefacts.

Recent Advances in Neuroimaging of Epilepsy

Human neuroimaging has had a major impact on the biological understanding of epilepsy and the relationship between pathophysiology, seizure management, and outcomes. This review highlights notable recent advancements in hardware, sequences, methods, analyses, and applications of human neuroimaging techniques utilized to assess epilepsy. These structural, functional, and metabolic assessments include magnetic resonance imaging (MRI), positron emission tomography (PET), and magnetoencephalography (MEG). Advancements that highlight non-invasive neuroimaging techniques used to study the whole brain are emphasized due to the advantages these provide in clinical and research applications. Thus, topics range across presurgical evaluations, understanding of epilepsy as a network disorder, and the interactions between epilepsy and comorbidities. New techniques and approaches are discussed which are expected to emerge into the mainstream within the next decade and impact our understanding of epilepsies. Further, an increasing breadth of investigations includes the interplay between epilepsy, mental health comorbidities, and aberrant brain networks. In the final section of this review, we focus on neuroimaging studies that assess bidirectional relationships between mental health comorbidities and epilepsy as a model for better understanding of the commonalities between both conditions.

Patient, interrupted: MEG oscillation dynamics reveal temporal dysconnectivity in schizophrenia

Current theories of schizophrenia emphasize the role of altered information integration as the core dysfunction of this illness. While ample neuroimaging evidence for such accounts comes from investigations of spatial connectivity, understanding temporal disruptions is important to fully capture the essence of dysconnectivity in schizophrenia. Recent electrophysiology studies suggest that long-range temporal correlation (LRTC) in the amplitude dynamics of neural oscillations captures the integrity of transferred information in the healthy brain. Thus, in this study, 25 schizophrenia patients and 25 controls (8 females/group) were recorded during two five-minutes of resting-state magnetoencephalography (once with eyes-open and once with eyes-closed). We used source-level analyses to investigate temporal dysconnectivity in patients by characterizing LRTCs across cortical and sub-cortical brain regions. In addition to standard statistical assessments, we applied a machine learning framework using support vector machine to evaluate the discriminative power of LRTCs in identifying patients from healthy controls. We found that neural oscillations in schizophrenia patients were characterized by reduced signal memory and higher variability across time, as evidenced by cortical and subcortical attenuations of LRTCs in the alpha and beta frequency bands. Support vector machine significantly classified participants using LRTCs in key limbic and paralimbic brain areas, with decoding accuracy reaching 82%. Importantly, these brain regions belong to networks that are highly relevant to the symptomology of schizophrenia. These findings thus posit temporal dysconnectivity as a hallmark of altered information processing in schizophrenia, and help advance our understanding of this pathology.

Comparison of beamformer implementations for MEG source localization

Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (MNE-Python, FieldTrip, DAiSS (SPM12), and Brainstorm) using datasets both with and without SSS interference suppression.

We analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with the signal-to-noise ratio (SNR) in all four toolboxes.

When applied carefully to MEG data with a typical SNR (3–15 ​dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs for the first three toolboxes while Brainstorm showed greater robustness but with lower spatial resolution. We also found that the SNR improvement offered by SSS led to more accurate localization.

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Different beamformer implementations are reported to sometimes yield differing source estimates for the same MEG data.

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We compared beamformers in four major open-source MEG analysis toolboxes.

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All toolboxes provide consistent and accurate results with 3–15-dB input SNR.

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However, localization errors are high at very high input SNR for the tested scalar beamformers.

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We discuss the critical differences between the implementations.

Differences Between Interictal and Ictal Generalized Spike-Wave Discharges in Childhood Absence Epilepsy: A MEG Study

Purpose: To investigate the differences between interictal and ictal generalized spike-wave discharges (GSWDs) for insights on how epileptic activity propagates and the physiopathological mechanisms underlying childhood absence epilepsy (CAE).

Methods: Twenty-five patients with CAE were studied using magnetoencephalography (MEG). MEG data were digitized at 6,000 Hz during the interictal and ictal GSWDs. GSWDs were analyzed at both neural magnetic source levels and functional connectivity (FC) in multifrequency bands: delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), gamma (30–80 Hz), ripple (80–250 Hz), and fast ripple (250–500 Hz). Brain FC was studied with the posterior cingulate cortex/precuneus (PCC/pC) as the seed region.

Results: The magnetic source of interictal GSWDs mainly locates in the PCC/pC region at 4–8 and 8–12 Hz, while that of ictal GSWDs mainly locates in the medial frontal cortex (MFC) at 80–250 Hz. There were statistically significant differences between interictal and ictal GSWDs (p < 0.05). The FC network involving the PCC/pC showed strong connections in the anterior-posterior pathways (mainly with the frontal cortex) at 80–250 Hz during ictal GSWDs, while the interictal GSWDs FC were mostly limited to the posterior cortex region. There was no significant difference in the magnetic source strength among interictal and ictal GSWDs at all bandwidths.

Conclusions: There are significant disparities in the source localization and FC between interictal and ictal GSWDs. Low-frequency activation in the PCC/pC during inhibition of seizures possibly relates to the maintenance of consciousness during interictal GSWDs. High-frequency oscillations (HFOs) of the MFC during CAE may associate with the inducing or occurrence of GSWDs. Weakened network connections may be in favor of preventing overexcitability and relates to the termination of GSWDs.

Altered Structural Network in Newly Onset Childhood Absence Epilepsy

BACKGROUND AND PURPOSE

Recent quantitative neuroimaging studies of childhood absence epilepsy (CAE) have identified various structural abnormalities that might be involved in the onset of absence seizure and associated cognitive and behavioral functions. However, the neuroanatomical alterations specific to CAE remain unclear, and so this study investigated the regional alterations of brain structures associated with newly diagnosed CAE.

METHODS

Surface and volumetric magnetic resonance imaging data of patients with newly diagnosed CAE (n=18) and age-matched healthy controls (n=18) were analyzed using Free-Surfer software. A group comparison using analysis of covariance was performed with significance criteria of p<0.05 and p<0.01 in global and regional analyses, respectively.

RESULTS

Compared with control subjects, the patients with CAE had smaller total and regional volumes of cortical gray-matter (GM) in the right rostral middle frontal, right lateral orbitofrontal, and left rostral middle frontal regions, as well as in the right precentral, right superior, middle, left middle, and inferior temporal gyri. The cortex in the right posterior cingulate gyrus and left medial occipital region was significantly thicker in patients with CAE than in controls.

CONCLUSIONS

Patients with CAE showed a reduced bilateral frontotemporal cortical GM volume and an increased posterior medial cortical thickness, which are associated with the default mode network. These structural changes can be suggested as the neural basis of the absence seizures and neuropsychiatric comorbidities in CAE.

The epileptic network and cognition: What functional connectivity is teaching us about the childhood epilepsies

Our objective was to summarize and evaluate the rapidly expanding body of literature studying functional connectivity in childhood epilepsy. In the self-limited childhood epilepsies, awareness of cognitive comorbidities has been steadily increasing, and recent advances in our understanding of the network effects of these disorders promise insights into the underlying neurobiology. We reviewed publications addressing functional connectivity in children with epilepsy with an emphasis on studies of children with self-limited childhood epilepsies. The majority of studies have been published in the past 10 years and predominantly examine childhood epilepsy with centrotemporal spikes and childhood absence epilepsy. Cognitive network alterations are commonly observed across the childhood epilepsies. Some of these effects appear to be nonspecific to epilepsy syndrome or even to category of neurological disorder. Other patterns, such as changes in the connectivity of cortical language areas in childhood epilepsy with centrotemporal spikes, provide clues to the underlying cognitive deficits seen in affected children. The literature to date is dominated by general observations of connectivity patterns without a priori hypotheses. These data-driven studies build an important foundation for hypothesis generation and are already providing useful insights into the neuropathology of the childhood epilepsies. Future work should emphasize hypothesis-driven approaches and rigorous clinical correlations to better understand how the knowledge of network alterations can be applied to guidance and treatment for the children in our clinics.

Mapping critical hubs of receptive and expressive language using MEG: A comparison against fMRI

The complexity of the widespread language network makes it challenging for accurate localization and lateralization. Using large-scale connectivity and graph-theoretical analyses of task-based magnetoencephalography (MEG), we aimed to provide robust representations of receptive and expressive language processes, comparable with spatial profiles of corresponding functional magnetic resonance imaging (fMRI). We examined MEG and fMRI data from 12 healthy young adults (age 20-37 years) completing covert auditory word-recognition task (WRT) and covert auditory verb-generation task (VGT). For MEG language mapping, broadband (3-30 Hz) beamformer sources were estimated, voxel-level connectivity was quantified using phase locking value, and highly connected hubs were characterized using eigenvector centrality graph measure. fMRI data were analyzed using a classic general linear model approach. A laterality index (LI) was computed for 20 language-specific frontotemporal regions for both MEG and fMRI. MEG network analysis showed bilateral and symmetrically distributed hubs within the left and right superior temporal gyrus (STG) during WRT and predominant hubs in left inferior prefrontal gyrus (IFG) during VGT. MEG and fMRI localization maps showed high correlation values within frontotemporal regions during WRT and VGT (r = 0.63, 0.74, q < 0.05, respectively). Despite good concordance in localization, notable discordances were observed in lateralization between MEG and fMRI. During WRT, MEG favored a left-hemispheric dominance of left STG (LI = 0.25 ± 0.22) whereas fMRI supported a bilateral representation of STG (LI = 0.08 ± 0.2). Laterality of MEG and fMRI during VGT consistently showed a strong asymmetry in left IFG regions (MEG-LI = 0.45 ± 0.35 and fMRI-LI = 0.46 ± 0.13). Our results demonstrate the utility of a large-scale connectivity and graph theoretical analyses for robust identification of language-specific regions. MEG hubs are in great agreement with the literature in revealing with canonical and extra-canonical language sites, thus providing additional support for the underlying topological organization of receptive and expressive language cortices. Discordances in lateralization may emphasize the need for multimodal integration of MEG and fMRI to obtain an excellent predictive value in a heterogeneous healthy population and patients with neurosurgical conditions.