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Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but one in three patients continue to have seizures one year after surgery. In order to improve the chances of good outcomes, computational models of seizure dynamics are being integrated into surgical planning to simulate the effects of the planned surgeries. These modelling frameworks require several conceptual and methodological choices, as well as large amounts of patient-specific data, which hinders their clinical applicability. To address this problem, we considered the patient-specific brain network, derived from magnetoencephalography (MEG) recordings, and a simple epidemic spreading model as the dynamical basis for seizure propagation. This simple model was enough to reproduce the seizure propagation patterns derived from stereo-tactical electroencephalography recordings (SEEG) of all considered patients (N= 15), when the patients’ resected areas (RA) were used as the origin of epidemic spreading. The model yielded a more accurate fit for the seizure-free (SF,N= 11) than the non-SF (NSF) group and, even though the difference between the groups was not significant, the goodness-of-fit distinguished NSF from SF patients with an area under the curve AUC = 84.1%. We also explored the definition of a population model that combined data from different patients to fit the model parameters but was still individualized by considering the patient-specific MEG network. Even though the goodness-of-fit decreased compared to the individualized models, the difference between the SF and NSF groups held, and in fact became stronger and significant (p= 0.023), and the group classification also improved slightly (AUC= 88.6%). Therefore, combining data from different patients may pave the way not only to generalize this framework to patients without SEEG recordings, but also to reduce the risk of over-fitting and improve the stability of the models. Finally, we considered the individualized models to derive alternative hypothesis of the seizure onset zones and to test the surgical strategyin silicofor each patient. We found that RA regions were on average more likely to originate the seizures, but that alternative explanations were possible. Virtual resections of the RA when considering these alternative seeds significantly reduced seizure propagation, and to a greater extend for SF than NSF patients (although the difference was not significant). Overall, our findings indicate that spreading models based on the patient-specific MEG network can be used to predict surgical outcomes, with better fit results and greater reduction on seizure spreading linked to higher likelihood of seizure freedom after surgery.

BackgroundGlioma is associated with pathologically high peritumoral brain activity, which relates to faster progression. Functional connectivity is disturbed locally and throughout the entire brain, associating with symptomatology. We, therefore, investigated how local activity and network measures relate to better understand how the intricate relationship between the tumor and the rest of the brain may impact disease and symptom progression.MethodsWe obtained magnetoencephalography in 84de novoglioma patients and 61 matched healthy controls. The offset of the power spectrum, a proxy of neuronal activity, was calculated for 210 cortical regions. We calculated patients’ regional deviations in delta, theta and lower alpha network connectivity as compared to controls, using two network measures: clustering coefficient, a measure of local connectivity, and eigenvector centrality (integrative connectivity). We then tested group differences in activity and connectivity between peritumoral, contralateral homologue regions, and the rest of the brain. We also correlated regional offset to connectivity.ResultsAs expected, patients’ peritumoral activity was pathologically high, and patients showed higher clustering and lower centrality than controls. At the group-level, regionally high activity related to high clustering in controls and patients alike. However, within-patient analyses revealed negative associations between regional deviations in brain activity and clustering, such that pathologically high activity coincided with low network clustering, while regions with ‘normal’ activity levels showed high network clustering.ConclusionsOur results indicate that pathological activity and connectivity co-localize in a complex manner in glioma. This insight is relevant to our understanding of disease progression and cognitive symptomatology.KeypointsRegional activity and network clustering are pathologically high in gliomaHowever, high-activity regions show low clustering and vice versaThis finding could be relevant to understand functioning and prognosis in gliomaImportance of the studyGlioma patients show high peritumoral brain activity, which relates to faster tumor progression. Moreover, patients have local and global functional network disturbances, which associate with cognitive dysfunction and other symptoms. However, how such activity and network deviations correlate across and within patients is unclear. We, therefore, studied a large cohort of newly diagnosed glioma patients and matched healthy controls, extracting activity and connectivity from the entire cortex. We find a surprising relationship between deviations in activity and local clustering: while higher activity and clustering go hand in hand in controls, the pathologically high activity we observe in individual glioma patients coincides with exceedingly low clustering, while areas with normal activity levels have pathologically high clustering. These insights indicate an intricate relationship between aberrant activity and connectivity throughout the brain in glioma. It remains to be seen how this complex relationship impacts tumor growth and potentially cognitive deficits.

Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but up to 50% of patients continue to have seizures one year after the resection. In order to aid presurgical planning and predict postsurgical outcome in a patient-by-patient basis, we developed a framework of individualized computational models that combine epidemic spreading with patient-specific connectivity and epileptogeneity maps: the Epidemic Spreading Seizure and Epilepsy Surgery framework (ESSES). The ESSES parameters were fitted in a retrospective study (N= 15) to reproduce invasive electroencephalography (iEEG)-recorded seizures. ESSES could not only reproduce the iEEG-recorded seizures, but significantly better so for patients with good (seizure-free, SF) than bad (non-seizure-free, NSF) outcome (area under the curveAUC= 0.73). Once the model parameters were set in the retrospective study, ESSES can be applied also to patients without iEEG data. We illustrate here the clinical applicability of ESSES with apseudo-prospective study(N= 34) with a blind setting (to the resection strategy and surgical outcome) that emulated the presurgical conditions. ESSES could predict the chances of good outcome afteranyresection by finding patient-specific optimal resection strategies, which we found to be smaller for SF than NSF patients, suggesting an intrinsic difference in the network organization or presurgical evaluation results of NSF patients. The actual surgical plan also overlapped more with the optimal resection, and had a larger effect in decreasing modeled seizure propagation, for SF patients than for NSF patients. Overall, ESSES could correctly predict 75% of NSF and 80.8% of SF cases pseudo-prospectively. Our results show that individualised computational models may inform surgical planning by suggesting optimal resections and providing information on the likelihood of a good outcome after a proposed resection. This is the first time that such a model is validated on a fully independent cohort without the need for iEEG recordings.

Magneto- and Electroencephalography (MEG/EEG) are important techniques for the diagnosis and pre-surgical evaluation of epilepsy. Yet, in current cryogen-based MEG systems the sensors are offset from the scalp, which limits the signal-to-noise ratio (SNR) and thereby the sensitivity to activity from deep structures such as the hippocampus. This effect is amplified in children, for whom adult-sized fixed-helmet systems are typically too big. Moreover, ictal recordings with fixed-helmet systems are problematic because of limited movement tolerance. Optically Pumped Magnetometers (OPMs) can be placed directly on the scalp, thereby improving SNR and consequently the sensitivity to, and localisation accuracy of, epileptiform activity. In addition, recording during seizures becomes feasible with these wearable sensors.We aimed to demonstrate these advantages of OPMs in a clinical population. Three adults with known weak sources of interictal epileptiform discharges (IEDs), along with three children with focal epilepsy and one adult with frequent seizures underwent MEG recordings using a 12-channel OPM-system and a 306-channel cryogen-based whole-head system. Performance of the two systems was compared in terms of IED-rate and SNR.In one patient the OPMs detected IEDs that were not found with the SQUID-system. In one patient the spike yield was higher for the OPM data (9.00 versus 6.76), with negligible difference in SNR compared to the SQUID data (3.85 versus 3.93; U = -2.86, d = -0.14). This was also the case for a patient with a spike yield that was comparable to that for the SQUID data (after accounting for unilateral coverage with the OPMs; SNR 4.47 versus 4.57; U = -3.81, d = -0.14). For one patient the spike yield (11.03 versus 24.50) and SNR (4.39 versus 4.05; U = 9.53, d = -0.36) were both lower for the OPMs. In two patients no IEDs were found with either system. Importantly, the wearability of OPMs enabled the recording of seizure activity in a patient with hyperkinetic movements during the seizure. The observed ictal onset and semiology were in agreement with previous video- and stereo-EEG recordings.Overall, OPM data were very much comparable to those obtained with a cryogenic system: OPMs outperformed SQUIDs for two of the four patients with IEDs, with either a higher spike yield, or an ability to detect IEDs that were not observable in the SQUID data. For three patients the SNRs of IEDs were (slightly) lower in the OPM data than in the SQUID data, but with negligible effect sizes for two of these patients. The relatively cheap technology, in combination with reduced running and maintenance costs, means that OPM-based MEG could be used more widely than current MEG systems, and may become an affordable alternative to scalp EEG, with the potential benefits of increased spatial accuracy, reduced sensitivity to volume conduction/field spread, and increased sensitivity to deep sources. Wearable MEG thus provides an unprecedented opportunity for epilepsy, and given its patient-friendliness, we envisage that it will not only be used for presurgical evaluation of epilepsy patients, but also for diagnosis after a first seizure.

Understanding recovery of consciousness and elucidating its underlying mechanism is believed to be crucial in the field of basic neuroscience and medicine. Ideas such as the global neuronal workspace (GNW) and the mesocircuit theory hypothesize that failure of recovery in conscious states coincide with loss of connectivity between subcortical and frontoparietal areas, a loss of the repertoire of functional networks states and metastable brain activation. We adopted a time-resolved functional connectivity framework to explore these ideas and assessed the repertoire of functional network states as a potential marker of consciousness and its potential ability to tell apart patients in the unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). In addition, the prediction of these functional network states by underlying hidden spatial patterns in the anatomical network, that is so-called eigenmodes, was supplemented as potential markers. By analysing time-resolved functional connectivity from functional MRI data, we demonstrated a reduction of metastability and functional network repertoire in UWS compared to MCS patients. This was expressed in terms of diminished dwell times and loss of nonstationarity in the default mode network and subcortical fronto-temporoparietal network in UWS compared to MCS patients. We further demonstrated that these findings co-occurred with a loss of dynamic interplay between structural eigenmodes and emerging time-resolved functional connectivity in UWS. These results are, amongst others, in support of the GNW theory and the mesocircuit hypothesis, underpinning the role of time-resolved thalamo-cortical connections and metastability in the recovery of consciousness.

BACKGROUND Glioma is associated with pathologically high peritumoral neuronal activity, which associates with faster tumor progression. Concurrently, glioma patients have local and widespread disturbances of the functional brain network as measured with magnetoencephalography (MEG), such as higher network clustering (the extent to which regions connected to a particular area are also connected to each other) and locally altered integrative connectivity (for instance assessed with a centrality measure). How local neuronal activity and nodal network properties relate to each other has yet to be investigated. METHODS We obtained eyes-closed resting-state MEG in 95 de novo glioma patients and 57 matched healthy controls (HCs). The offset of the power spectrum was calculated for 210 cortical atlas regions as a proxy for neuronal activity. Regional clustering coefficient (CC) and eigenvector centrality (EC) were calculated in the delta, theta and alpha bands. Offset and network values were then averaged across peritumoral regions, contralateral homologue regions and all non-peritumoral regions. Linear mixed models were used to relate nodal CC and EC to local offset in patients and HCs. RESULTS The peritumoral area was significantly more active than the non-peritumoral homologue in patients, and showed pathologically high activity in comparison to HCs. However, patients’ functional network was disturbed globally, showing higher clustering and lower centrality than HCs. Whereas high activity related to high centrality in HCs and patients alike, high activity seemed to relate to low clustering in non-peritumoral regions in patients, but not HCs. CONCLUSION We find that the relationship between neuronal activity and functional network properties is disturbed in a complex manner in glioma patients. Our results further underline the importance of investigating how local activity may impact global network topology in order to understand how neuron-glioma interactions shape brain functioning.

BACKGROUND Many patients with glioma suffer from poorly understood executive functioning deficits before and/or after tumor resection. In the past decade, it has become increasingly clear that there is a complex interplay between glioma and both local and global functional activity and connectivity. Moreover, multilayer network integration of the frontoparietal network across multiple modalities has recently been found to explain individual differences in executive functioning in healthy subjects. OBJECTIVE To test whether multilayer frontoparietal network integration relates to individual differences in executive functioning before and after glioma resection. METHODS Patients with glioma (n = 37) underwent neuropsychological tests assessing word fluency, inhibition, and set shifting as components of executive functioning, and resting-state magnetoencephalography at T1 (at diagnosis, before resection) and T2 (1 year after resection). We constructed binary multilayer networks comprising six layers, with each layer representing frequency-specific functional connectivity between source-localized time series of 78 cortical regions. Average frontoparietal network multilayer eigenvector centrality, a measure for network integration, was calculated at both time points. Regression analyses were used to investigate associations with executive functioning. RESULTS At T1, lower multilayer integration (p = .017) and having epilepsy (p = .006) associated with poorer set shifting. Decreasing multilayer integration (p = .022) between T1 and T2 and not undergoing chemotherapy at T2 (p = .004) related to deteriorating set shifting. No significant associations were found for word fluency or inhibition. CONCLUSION As hypothesized, our results establish multilayer integration of the frontoparietal network as a cross-sectional and longitudinal correlate of executive functioning in glioma patients, further building upon the idea that glioma impacts the whole brain network and general cognitive functioning. However, multilayer integration did not significantly predict postoperative changes in executive functioning, limiting the direct clinical relevance of this measure.

It is unclear why exactly gliomas show preferential occurrence in certain brain areas. Increased spiking activity around gliomas leads to faster tumour growth in animal models, while higher non-invasively measured brain activity is related to shorter survival in patients. However, it is unknown how regional intrinsic brain activity, as measured in healthy controls, relates to glioma occurrence. We first investigated whether gliomas occur more frequently in regions with intrinsically higher brain activity. Second, we explored whether intrinsic cortical activity at individual patients’ tumour locations relates to tumour and patient characteristics. Across three cross-sectional cohorts, 413 patients were included. Individual tumour masks were created. Intrinsic regional brain activity was assessed through resting-state magnetoencephalography acquired in healthy controls and source-localized to 210 cortical brain regions. Brain activity was operationalized as: (i) broadband power; and (ii) offset of the aperiodic component of the power spectrum, which both reflect neuronal spiking of the underlying neuronal population. We additionally assessed (iii) the slope of the aperiodic component of the power spectrum, which is thought to reflect the neuronal excitation/inhibition ratio. First, correlation coefficients were calculated between group-level regional glioma occurrence, as obtained by concatenating tumour masks across patients, and group-averaged regional intrinsic brain activity. Second, intrinsic brain activity at specific tumour locations was calculated by overlaying patients’ individual tumour masks with regional intrinsic brain activity of the controls and was associated with tumour and patient characteristics. As proposed, glioma preferentially occurred in brain regions characterized by higher intrinsic brain activity in controls as reflected by higher offset. Second, intrinsic brain activity at patients’ individual tumour locations differed according to glioma subtype and performance status: the most malignant isocitrate dehydrogenase-wild-type glioblastoma patients had the lowest excitation/inhibition ratio at their individual tumour locations as compared to isocitrate dehydrogenase-mutant, 1p/19q-codeleted glioma patients, while a lower excitation/inhibition ratio related to poorer Karnofsky Performance Status, particularly in codeleted glioma patients. In conclusion, gliomas more frequently occur in cortical brain regions with intrinsically higher activity levels, suggesting that more active regions are more vulnerable to glioma development. Moreover, indices of healthy, intrinsic excitation/inhibition ratio at patients’ individual tumour locations may capture both tumour biology and patients’ performance status. These findings contribute to our understanding of the complex and bidirectional relationship between normal brain functioning and glioma growth, which is at the core of the relatively new field of ‘cancer neuroscience’.

Deep brain stimulation (DBS) of the central thalamus is an experimental treatment for restoration of impaired consciousness in patients with severe acquired brain injury. Previous results of experimental DBS are heterogeneous, but significant improvements in consciousness have been reported. However, the mechanism of action of DBS remains unknown. We used magnetoencephalography to study the direct effects of DBS of the central thalamus on oscillatory activity and functional connectivity throughout the brain in a patient with a prolonged minimally conscious state. Different DBS settings were used to improve consciousness, including two different stimulation frequencies (50 Hz and 130 Hz) with different effective volumes of tissue activation within the central thalamus. While both types of DBS resulted in a direct increase in arousal, we found that DBS with a lower frequency (50 Hz) and larger volume of tissue activation was associated with a stronger increase in functional connectivity and neural variability throughout the brain. Moreover, this form of DBS was associated with improvements in visual pursuit, a reduction in spasticity, and improvement of swallowing, eight years after loss of consciousness. However, after DBS, all neurophysiological markers remained significantly lower than in healthy controls and objective increases in consciousness remained limited. Our findings provide new insights on the mechanistic understanding of neuromodulatory effects of DBS of the central thalamus in humans and suggest that DBS can re-activate dormant functional brain networks, but that the severely injured stimulated brain still lacks the ability to serve cognitive demands.

Understanding recovery of consciousness and elucidating its underlying mechanism is believed to be crucial in the field of basic neuroscience and medicine. Ideas such as the global neuronal workspace and the mesocircuit theory hypothesize that failure of recovery in conscious states coincide with loss of connectivity between subcortical and frontoparietal areas, a loss of the repertoire of functional networks states and metastable brain activation. We adopted a time-resolved functional connectivity framework to explore these ideas and assessed the repertoire of functional network states as a potential marker of consciousness and its potential ability to tell apart patients in the unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). In addition, prediction of these functional network states by underlying hidden spatial patterns in the anatomical network, i.e. so-called eigenmodes, were supplemented as potential markers. By analysing time-resolved functional connectivity from fMRI data, we demonstrated a reduction of metastability and functional network repertoire in UWS compared to MCS patients. This was expressed in terms of diminished dwell times and loss of nonstationarity in the default mode network and fronto-parietal subcortical network in UWS compared to MCS patients. We further demonstrated that these findings co-occurred with a loss of dynamic interplay between structural eigenmodes and emerging time-resolved functional connectivity in UWS. These results are, amongst others, in support of the global neuronal workspace theory and the mesocircuit hypothesis, underpinning the role of time-resolved thalamo-cortical connections and metastability in the recovery of consciousness.

Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients. However, seizure-freedom is currently achieved in only 2/3 of the patients after surgery. In this study we have developed an individualized computational model based on MEG brain networks to explore seizure propagation and the efficacy of different virtual resections. Eventually, the goal is to obtain individualized models to optimize resection strategy and outcome. We have modelled seizure propagation as an epidemic process using the susceptible-infected (SI) model on individual brain networks derived from presurgical MEG. We included 10 patients who had received epilepsy surgery and for whom the surgery outcome at least one year after surgery was known. The model parameters were tuned in in order to reproduce the patient-specific seizure propagation patterns as recorded with invasive EEG. We defined a personalized search algorithm that combined structural and dynamical information to find resections that maximally decreased seizure propagation for a given resection size. The optimal resection for each patient was defined as the smallest resection leading to at least a 90% reduction in seizure propagation. The individualized model reproduced the basic aspects of seizure propagation for 9 out of 10 patients when using the resection area as the origin of epidemic spreading, and for 10 out of 10 patients with an alternative definition of the seed region. We found that, for 7 patients, the optimal resection was smaller than the resection area, and for 4 patients we also found that a resection smaller than the resection area could lead to a 100% decrease in propagation. Moreover, for two cases these alternative resections included nodes outside the resection area. Epidemic spreading models fitted with patient specific data can capture the fundamental aspects of clinically observed seizure propagation, and can be used to test virtual resections in silico. Combined with optimization algorithms, smaller or alternative resection strategies, that are individually targeted for each patient, can be determined with the ultimate goal to improve surgery outcome. MEG-based networks can provide a good approximation of structural connectivity for computational models of seizure propagation, and facilitate their clinical use.

Executive functioning is a higher-order cognitive process that is thought to depend on a brain network organization facilitating network integration across specialized subnetworks. The frontoparietal network (FPN), a subnetwork that has diverse connections to other brain modules, seems pivotal to this integration, and a more central role of regions in the FPN has been related to better executive functioning. Brain networks can be constructed using different modalities: diffusion MRI (dMRI) can be used to reconstruct structural networks, while resting-state fMRI (rsfMRI) and magnetoencephalography (MEG) yield functional networks. These networks are often studied in a unimodal way, which cannot capture potential complementary or synergistic modal information. The multilayer framework is a relatively new approach that allows for the integration of different modalities into one ‘network of networks’. It has already yielded promising results in the field of neuroscience, having been related to e.g. cognitive dysfunction in Alzheimer’s disease. Multilayer analyses thus have the potential to help us better understand the relation between brain network organization and executive functioning. Here, we hypothesized a positive association between centrality of the FPN and executive functioning, and we expected that multimodal multilayer centrality would supersede unilayer centrality in explaining executive functioning. We used dMRI, rsfMRI, MEG, and neuropsychological data obtained from 33 healthy adults (age range 22-70 years) to construct eight modality-specific unilayer networks (dMRI, fMRI, and six MEG frequency bands), as well as a multilayer network comprising all unilayer networks. Interlayer links in the multilayer network were present only between a node’s counterpart across layers. We then computed and averaged eigenvector centrality of the nodes within the FPN for every uni- and multilayer network and used multiple regression models to examine the relation between uni- or multilayer centrality and executive functioning. We found that higher multilayer FPN centrality, but not unilayer FPN centrality, was related to better executive functioning. To further validate multilayer FPN centrality as a relevant measure, we assessed its relation with age. Network organization has been shown to change across the life span, becoming increasingly efficient up to middle age and regressing to a more segregated topology at higher age. Indeed, the relation between age and multilayer centrality followed an inverted-U shape. These results show the importance of FPN integration for executive functioning as well as the value of a multilayer framework in network analyses of the brain. Multilayer network analysis may particularly advance our understanding of the interplay between different brain network aspects in clinical populations, where network alterations differ across modalities.Highlights:Multimodal neuroimaging and neurophysiology data were collected in healthy adultsMultilayer frontoparietal centrality was positively associated with executive functioningUnilayer (unimodal) centralities were not associated with executive functioningThere was an inverted-U relationship between multilayer centrality and age

Non-invasively measured brain activity is related to progression-free survival in glioma patients, suggesting its potential as a marker of glioma progression. We therefore assessed the relationship between brain activity and increasing tumor volumes on routine clinical magnetic resonance imaging (MRI) in glioma patients. Postoperative magnetoencephalography (MEG) was recorded in 45 diffuse glioma patients. Brain activity was estimated using three measures (absolute broadband power, offset and slope) calculated at three spatial levels: global average, averaged across the peritumoral areas, and averaged across the homologues of these peritumoral areas in the contralateral hemisphere. Tumors were segmented on MRI. Changes in tumor volume between the two scans surrounding the MEG were calculated and correlated with brain activity. Brain activity was compared between patient groups classified into having increasing or stable tumor volume. Results show that brain activity was significantly increased in the tumor hemisphere in general, and in peritumoral regions specifically. However, none of the measures and spatial levels of brain activity correlated with changes in tumor volume, nor did they differ between patients with increasing versus stable tumor volumes. Longitudinal studies in more homogeneous subgroups of glioma patients are necessary to further explore the clinical potential of non-invasively measured brain activity.

Aim. The diagnostic process for epilepsy can be lengthy and stressful, which may delay the start of treatment. The objective of this study was to determine the benefit of routine magnetoencephalography (MEG) with regard to diagnostic gain, compared to routine electroencephalography (EEG), EEG following sleep deprivation (EEGsd), and 24-hour EEG.Methods. In this prospective study, patients were included from two centres (Academic Centre for Epileptology Kempenhaeghe, Heeze and Elisabeth-Twee Steden Hospital, Tilburg) and MEG recording took place at a single centre (Amsterdam University Medical Centre, Vrije Universiteit Amsterdam) in The Netherlands. Consecutively referred patients from peripheral hospitals were included between August 2013 and March 2016. Patients were offered routine MEG in addition to EEG examination and MRI for the diagnosis of epilepsy. The final clinical diagnosis was based on all available clinical data and test results at the end of the diagnostic process. Sensitivity, specificity, and positive and negative predictive values were calculated for routine EEG, routine EEG plus additional EEG and MEG. In addition, diagnostic gain associated with MEG, relative to the other modalities, was calculated. Secondary outcome was congruence of localization of epileptiform discharges between MEG and MRI or final clinical diagnosis.Results. Based on a cohort of 138 patients, sensitivity and specificity was shown to be 31.6% and 78.4% for routine MEG, 31.6% and 100% for routine EEG, and 52.6% and 97.3% for routine EEG plus additional EEG, respectively. Routine MEG demonstrated a diagnostic gain of 16.8% compared to routine EEG and 9.5% compared to routine EEG plus additional EEG. In 35.7% of patients with a lesion on MRI that was consistent with the final clinical diagnosis, MEG showed epileptiform discharges in the same area.Conclusion. Routine MEG may provide additional value during the initial diagnosis of epilepsy.

Background: Neurophysiological measures of brain function, such as magnetoencephalography (MEG), are widely used in clinical neurology and have strong relations with cognitive impairment and dementia but are still underdeveloped in multiple sclerosis (MS). Objectives: To demonstrate the value of clinically applicable MEG-measures in evaluating cognitive impairment in MS. Methods: In eyes-closed resting-state, MEG data of 83 MS patients and 34 healthy controls (HCs) peak frequencies and relative power of six canonical frequency bands for 78 cortical and 10 deep gray matter (DGM) areas were calculated. Linear regression models, correcting for age, gender, and education, assessed the relation between cognitive performance and MEG biomarkers. Results: Increased alpha1 and theta power was strongly associated with impaired cognition in patients, which differed between cognitively impaired (CI) patients and HCs in bilateral parietotemporal cortices. CI patients had a lower peak frequency than HCs. Oscillatory slowing was also widespread in the DGM, most pronounced in the thalamus. Conclusion: There is a clinically relevant slowing of neuronal activity in MS patients in parietotemporal cortical areas and the thalamus, strongly related to cognitive impairment. These measures hold promise for the application of resting-state MEG as a biomarker for cognitive disturbances in MS in a clinical setting.

Introduction: Studies using functional connectivity and network analyses based on magnetoencephalography (MEG) with source localization are rapidly emerging in neuroscientific literature. However, these analyses currently depend on the availability of costly and sometimes burdensome individual MR scans for co-registration. We evaluated the consistency of these measures when using a template MRI, instead of native MRI, for the analysis of functional connectivity and network topology. Methods: Seventeen healthy participants underwent resting-state eyes-closed MEG and anatomical MRI. These data were projected into source space using an atlas-based peak voxel and a centroid beamforming approach either using (1) participants’ native MRIs or (2) the Montreal Neurological Institute's template. For both methods, time series were reconstructed from 78 cortical atlas regions. Relative power was determined in six classical frequency bands per region and globally averaged. Functional connectivity (phase lag index) between each pair of regions was calculated. The adjacency matrices were then used to reconstruct functional networks, of which regional and global metrics were determined. Intraclass correlation coefficients were calculated and Bland–Altman plots were made to quantify the consistency and potential bias of the use of template versus native MRI. Results: Co-registration with the template yielded largely consistent relative power, connectivity, and network estimates compared to native MRI. Discussion: These findings indicate that there is no (systematic) bias or inconsistency between template and native MRI co-registration of MEG. They open up possibilities for retrospective and prospective analyses to MEG datasets in the general population that have no native MRIs available. Hum Brain Mapp, 2017.

Alzheimer's disease (AD) is accompanied by functional brain changes that can be detected in imaging studies, including electromagnetic activity recorded with magnetoencephalography (MEG). Here, we systematically review the studies that have examined resting-state MEG changes in AD and identify areas that lack scientific or clinical progress. Three levels of MEG analysis will be covered: (i) single-channel signal analysis, (ii) pairwise analyses over time series, which includes the study of interdependencies between two time series and (iii) global network analyses. We discuss the findings in the light of other functional modalities, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Overall, single-channel MEG results show consistent changes in AD that are in line with EEG studies, but the full potential of the high spatial resolution of MEG and advanced functional connectivity and network analysis has yet to be fully exploited. Adding these features to the current knowledge will potentially aid in uncovering organizational patterns of brain function in AD and thereby aid the understanding of neuronal mechanisms leading to cognitive deficits.

Rapid reconfigurations of brain activity support efficient neuronal communication and flexible behaviour. Suboptimal brain dynamics impair this adaptability, possibly leading to functional deficiencies. We hypothesize that impaired flexibility in brain activity can lead to motor and cognitive symptoms of Parkinson’s disease (PD). To test this hypothesis, we studied the ‘functional repertoire’ – the number of distinct configurations of neural activity – using source-reconstructed magnetoencephalography in PD patients and controls. We found stereotyped brain dynamics and reduced flexibility in PD. The intensity of this reduction was proportional to symptoms severity, which can be explained by beta-band hyper-synchronization. Moreover, the basal ganglia were prominently involved in the abnormal patterns of brain activity. Our findings support the hypotheses that: symptoms in PD reflect impaired brain flexibility, this impairment preferentially involves the basal ganglia, and beta-band hypersynchronization is associated with reduced brain flexibility. These findings highlight the importance of extensive functional repertoires for behaviour and motor.

Some patients with severe brain injury show short-term neurological improvements, such as recovery of consciousness, motor function, or speech after administering zolpidem, a GABA receptor agonist. The working mechanism of this paradoxical phenomenon remains unknown. In this study, we used electroencephalography and magnetoencephalography to investigate a spectacular zolpidem-induced awakening, including the recovery of functional communication and the ability to walk in a patient with severe hypoxic-ischemic brain injury. We show that cognitive deficits, speech loss, and motor impairments after severe brain injury are associated with stronger beta band connectivity throughout the brain and suggest that neurological recovery after zolpidem occurs with the restoration of beta band connectivity. This exploratory work proposes an essential role for beta rhythms in goal-directed behavior and cognition. It advocates further fundamental and clinical research on the role of increased beta band connectivity in the development of neurological deficits after severe brain injury.

In this chapter we will describe recent developments in magnetoencephalography (MEG) network analysis, where we will focus on the rationale behind, and application in clinical cohorts, of an atlas-based beamforming approach. This approach contains three main components, namely, (i) the reconstruction of time series of neuronal activation through beamforming; (ii) the use of a standard atlas, which enables comparisons across studies and modalities; and (iii) the estimation of functional connectivity using the phase lag index (PLI), a measure that is insensitive to the effects of field spread/volume conduction. Moreover, we will discuss the use of the minimum spanning tree (MST), which allows for a biasfree characterization of the topology of the reconstructed functional networks. Application of this approach will be illustrated through examples from recent studies in patients with gliomas, Parkinson's disease, and multiple sclerosis.

OBJECTIVE: High frequency oscillations (HFOs, >80Hz) are biomarkers for epileptogenic cortex in invasive and non-invasive electroencephalography (EEG). Identification of HFOs in magnetoencephalography (MEG) is hindered by noise. Computing spatial filters using beamforming to reconstruct time series for selected brain regions, so-called virtual sensors (VS), can increase the signal-to-noise ratio. We identified HFOs in MEG in time domain using VS.METHODS: Fifteen minutes of MEG data were selected from 12 patients. VS were placed around the epileptic spikes (affected region) and in the contralateral hemisphere. VS and physical sensors were reviewed for HFOs and spikes. HFO locations were compared to spikes and other clinical parameters.RESULTS: Eight patients showed 78 time points with 575 HFOs in VS, 513 were in the affected region. HFOs could not be identified in physical sensors for 61 of the 78 VS time points. HFOs overlapped with presumed epileptogenic areas and were also visible in unfiltered VS signals.CONCLUSION: Beamformer-based VS analysis can help to identify epileptic HFOs that are not discernable in physical MEG sensors.SIGNIFICANCE: This approach can be extended to enable localization of non-invasively recorded HFOs. This would help surgical planning and reduce the need for invasive diagnostics.

There is general agreement that the neuropathological processes leading to Alzheimer’s disease (AD) begin decades before the clinical onset. In order to detect early topological changes, we applied functional connectivity and network analysis to magnetoencephalographic (MEG) data obtained from 16 patients with amnestic Mild Cognitive Impairment (aMCI), a prodromal stage of AD, and 16 matched healthy control (HCs). Significant differences between the two groups were found in the theta band, which is associated with memory processes, in both temporal poles (TPs). In aMCI, the degree and betweenness centrality (BC) were lower in the left superior TP, whereas in the right middle TP the BC was higher. A statistically significant negative linear correlation was found between the BC of the left superior TP and a delayed recall score, a sensitive marker of the “hippocampal memory” deficit in early AD. Our results suggest that the TPs, which are involved early in AD pathology and belong to the memory circuitry, have an altered role in the functional network in aMCI.

Introduction Diffuse gliomas have local and global effects on neurophysiological brain functioning, which are often seen as ‘passive’ consequences of the tumor. However, seminal preclinical work has shown a prominent role for neuronal activity in glioma growth: mediated by neuroligin-3 (NLGN3), increased neuronal activity causes faster glioma growth. It is unclear whether the same holds true in patients. Here, we investigate whether lower levels of oscillatory brain activity relate to lower NLGN3 expression and predict longer progression free survival (PFS) in diffuse glioma patients. Methods Twenty-four newly diagnosed patients with diffuse glioma underwent magnetoencephalography and subsequent tumor resection. Oscillatory brain activity was approximated by calculating broadband power (0.5–48 Hz) of the magnetoencephalography. NLGN3 expression in glioma tissue was semi-quantitatively assessed by immunohistochemistry. Peritumor and global oscillatory brain activity was then compared between different levels of NLGN3 expression with Kruskal–Wallis tests. Cox proportional hazards analyses were performed to estimate the predictive value of oscillatory brain activity for PFS. Results Patients with low expression of NLGN3 had lower levels of global oscillatory brain activity than patients with higher NLGN3 expression (P

Highlights • The main principles of magnetoencephalography (MEG) and the value of combined MEG and EEG are discussed. • Established and some potential future clinical applications of MEG are reviewed. • Practical guidelines for clinical MEG examinations are presented., Magnetoencephalography (MEG) records weak magnetic fields outside the human head and thereby provides millisecond-accurate information about neuronal currents supporting human brain function. MEG and electroencephalography (EEG) are closely related complementary methods and should be interpreted together whenever possible. This manuscript covers the basic physical and physiological principles of MEG and discusses the main aspects of state-of-the-art MEG data analysis. We provide guidelines for best practices of patient preparation, stimulus presentation, MEG data collection and analysis, as well as for MEG interpretation in routine clinical examinations. In 2017, about 200 whole-scalp MEG devices were in operation worldwide, many of them located in clinical environments. Yet, the established clinical indications for MEG examinations remain few, mainly restricted to the diagnostics of epilepsy and to preoperative functional evaluation of neurosurgical patients. We are confident that the extensive ongoing basic MEG research indicates potential for the evaluation of neurological and psychiatric syndromes, developmental disorders, and the integrity of cortical brain networks after stroke. Basic and clinical research is, thus, paving way for new clinical applications to be identified by an increasing number of practitioners of MEG.

Multiple sclerosis (MS) is a neuroinflammatory and neurodegenerative disease that affects the central nervous system. There is a clinico-radiological paradox in MS: A discrepancy between clinical symptoms and the amount of focal brain lesions. In this chapter we explore how new sophisticated neuroimaging approaches could help elucidate the clinico-radiological paradox, as they quantify structural and functional pathology beyond focal MRI-visible white matter lesions. The observed triad of structural MS pathology (focal lesions, diffuse changes and brain atrophy) throughout the grey and white matter seems to induce highly complex functional network changes that are currently understudied. The current debate on beneficial and maladaptive functional changes remains ongoing. The high variability in all forms of structural and functional pathology in MS highlights the need for a more holistic, network-based approach to study the disease. Hopefully, such future studies could then provide the much-needed missing links essential to unravelling the clinico-radiological paradox.

Recently, there has been a growing interest in the use of brain activity for biometric systems. However, so far these studies have focused mainly on basic features of the Electroencephalography. In this study we propose an approach based on phase synchronization, to investigate personal distinctive brain network organization. To this end, the importance, in terms of centrality, of different regions was determined on the basis of EEG recordings. We hypothesized that nodal centrality enables the accurate identification of individuals. EEG signals from a cohort of 109 64-channels EEGs were band-pass filtered in the classical frequency bands and functional connectivity between the sensors was estimated using the Phase Lag Index. The resulting connectivity matrix was used to construct a weighted network, from which the nodal Eigenvector Centrality was computed. Nodal centrality was successively used as feature vector. Highest recognition rates were observed in the gamma band (equal error rate ( ${\rm EER}) = 0.044$ ) and high beta band ( ${\rm EER} = 0.102$ ). Slightly lower recognition rate was observed in the low beta band ( ${\rm EER} = 0.144$ ), while poor recognition rates were observed for the others frequency bands. The reported results show that resting-state functional brain network topology provides better classification performance than using only a measure of functional connectivity, and may represent an optimal solution for the design of next generation EEG based biometric systems. This study also suggests that results from biometric systems based on high-frequency scalp EEG features should be interpreted with caution.

Recent work has revealed frequency-dependent global patterns of information flow by a network analysis of magnetoencephalography data of the human brain. However, it is unknown which properties on a small subgraph-scale of those functional brain networks are dominant at different frequencies bands. Motifs are the building blocks of networks on this level and have previously been identified as important features for healthy and abnormal brain function. In this study, we present a network construction that enables us to search and analyze motifs in different frequency bands. We give evidence that the bi-directional two-hop path is the most important motif for the information flow in functional brain networks. A clustering based on this motif exposes a spatially coherent yet frequency-dependent sub-division between the posterior, occipital and frontal brain regions.

Subjects with mild cognitive impairment (MCI) have an increased risk of developing Alzheimer's disease (AD), and their functional brain networks are presumably already altered. To test this hypothesis, we compared magnetoencephalography (MEG) eyes-closed resting-state recordings from 29 MCI subjects and 29 healthy elderly subjects in the present exploratory study. Functional connectivity in different frequency bands was assessed with the phase lag index (PLI) in source space. Normalized weighted clustering coefficient (normalized Cw) and path length (normalized Lw), as well as network measures derived from the minimum spanning tree [MST; i.e., betweenness centrality (BC) and node degree], were calculated. First, we found altered PLI values in the lower and upper alpha bands in MCI patients compared to controls. Thereafter, we explored network differences in these frequency bands. Normalized Cw and Lw did not differ between the groups, whereas BC and node degree of the MST differed, although these differences did not survive correction for multiple testing using the False Discovery Rate (FDR). As an exploratory study, we may conclude that: (1) the increases and decreases observed in PLI values in lower and upper alpha bands in MCI patients may be interpreted as a dual pattern of disconnection and aberrant functioning; (2) network measures are in line with connectivity findings, indicating a lower efficiency of the brain networks in MCI patients; (3) the MST centrality measures are more sensitive to detect subtle differences in the functional brain networks in MCI than traditional graph theoretical metrics.

Resective neurosurgery carries the risk of postoperative cognitive deterioration. The concept of ‘hub (over)load’, caused by (over)use of the most important brain regions, has been theoretically postulated in relation to symptomatology and neurological disease course, but lacks experimental confirmation. We investigated functional hub load and postsurgical cognitive deterioration in patients undergoing lesion resection. Patients (n = 28) underwent resting-state magnetoencephalography and neuropsychological assessments preoperatively and 1-year after lesion resection. We calculated stationary hub load score (SHub) indicating to what extent brain regions linked different subsystems; high SHub indicates larger processing pressure on hub regions. Dynamic hub load score (DHub) assessed its variability over time; low values, particularly in combination with high SHub values, indicate increased load, because of consistently high usage of hub regions. Hypothetically, increased SHub and decreased DHub relate to hub overload and thus poorer/deteriorating cognition. Between time points, deteriorating verbal memory performance correlated with decreasing upper alpha DHub. Moreover, preoperatively low DHub values accurately predicted declining verbal memory performance. In summary, dynamic hub load relates to cognitive functioning in patients undergoing lesion resection: postoperative cognitive decline can be tracked and even predicted using dynamic hub load, suggesting it may be used as a prognostic marker for tailored treatment planning.

Thalamic atrophy is known to be one of the most important predictors for clinical dysfunction in multiple sclerosis (MS). As the thalamus is highly connected to many cortical areas, this suggests that thalamic atrophy is associated with disruption of cortical functional networks. We investigated this thalamo-cortical system to explain the presence of physical and cognitive problems in MS. Functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) were performed in 86 MS patients and 21 healthy subjects. We computed cortical functional networks for fMRI and MEG by respectively the Pearson's correlation coefficient and the phase lag index using the same automated anatomical labeling atlas for both modalities. Thalamo-cortical functional connectivity was only estimated using fMRI. We computed conventional network metrics such as clustering coefficient and path length and analyzed the minimum spanning tree (MST), a subnetwork and backbone of the original network. MS patients showed reduced thalamic volumes and increased thalamo-cortical connectivity. MEG cortical functional networks showed a lower level of integration in MS in terms of the MST, whereas fMRI cortical networks did not differ between groups. Lower integration of MEG cortical functional networks was both related to thalamic atrophy as well as to increased thalamo-cortical functional connectivity in fMRI and to worse cognitive and clinical status. This study demonstrated for the first time that thalamic atrophy is associated with global disruption of cortical functional networks in MS and this global disruption of network activity was related to worse cognitive and clinical function in MS. Hum Brain Mapp 36:603-618, 2015. © 2014 Wiley Periodicals, Inc.

Both gray matter atrophy and disruption of functional networks are important predictors for physical disability and cognitive impairment in multiple sclerosis (MS), yet their relationship is poorly understood. Graph theory provides a modality invariant framework to analyze patterns of gray matter morphology and functional coactivation. We investigated, how gray matter and functional networks were affected within the same MS sample and examined their interrelationship. Magnetic resonance imaging and magnetoencephalography (MEG) were performed in 102 MS patients and 42 healthy controls. Gray matter networks were computed at the group-level based on cortical thickness correlations between 78 regions across subjects. MEG functional networks were computed at the subject level based on the phase-lag index between time-series of regions in source-space. In MS patients, we found a more regular network organization for structural covariance networks and for functional networks in the theta band, whereas we found a more random network organization for functional networks in the alpha2 band. Correlation analysis revealed a positive association between covariation in thickness and functional connectivity in especially the theta band in MS patients, and these results could not be explained by simple regional gray matter thickness measurements. This study is a first multimodal graph analysis in a sample of MS patients, and our results suggest that a disruption of gray matter network topology is important to understand alterations in functional connectivity in MS as regional gray matter fails to take into account the inherent connectivity structure of the brain.

In recent years there has been a shift in focus from the study of local, mostly task-related activation to the exploration of the organization and functioning of large-scale structural and functional complex brain networks. Progress in the interdisciplinary field of modern network science has introduced many new concepts, analytical tools and models which allow a systematic interpretation of multivariate data obtained from structural and functional MRI, EEG and MEG. However, progress in this field has been hampered by the absence of a simple, unbiased method to represent the essential features of brain networks, and to compare these across different conditions, behavioural states and neuropsychiatric/neurological diseases. One promising solution to this problem is to represent brain networks by a minimum spanning tree (MST), a unique acyclic subgraph that connects all nodes and maximizes a property of interest such as synchronization between brain areas. We explain how the global and local properties of an MST can be characterized. We then review early and more recent applications of the MST to EEG and MEG in epilepsy, development, schizophrenia, brain tumours, multiple sclerosis and Parkinson's disease, and show how MST characterization performs compared to more conventional graph analysis. Finally, we illustrate how MST characterization allows representation of observed brain networks in a space of all possible tree configurations and discuss how this may simplify the construction of simple generative models of normal and abnormal brain network organization.

Objective Integrity of resting-state functional brain networks (RSNs) is important for proper cognitive functioning. In type 1 diabetes mellitus (T1DM) cognitive decrements are commonly observed, possibly due to alterations in RSNs, which may vary according to microvascular complication status. Thus, we tested the hypothesis that functional connectivity in RSNs differs according to clinical status and correlates with cognition in T1DM patients, using an unbiased approach with high spatio-temporal resolution functional network. Methods Resting-state magnetoencephalographic (MEG) data for T1DM patients with (n = 42) and without (n = 41) microvascular complications and 33 healthy participants were recorded. MEG time-series at source level were reconstructed using a recently developed atlas-based beamformer. Functional connectivity within classical frequency bands, estimated by the phase lag index (PLI), was calculated within eight commonly found RSNs. Neuropsychological tests were used to assess cognitive performance, and the relation with RSNs was evaluated. Results Significant differences in terms of RSN functional connectivity between the three groups were observed in the lower alpha band, in the default-mode (DMN), executive control (ECN) and sensorimotor (SMN) RSNs. T1DM patients with microvascular complications showed the weakest functional connectivity in these networks relative to the other groups. For DMN, functional connectivity was higher in patients without microangiopathy relative to controls (all p, Highlights • MEG RSN functional connectivity was estimated among T1DM+ and T1DM− patients and controls. • Lower alpha band in DMN, ECN and SMN significantly differed among groups. • Functional connectivity may play a key role in T1DM-related cognitive dysfunction.

Op 22 februari 2018 promoveerde Nicole van Klink aan de Universiteit van Utrecht op haar proefschrift High frequency oscillations: towards clinical application. Nicole onderzocht het nut van hoog-frequente oscillaties in zowel oppervlakte-EEG, magneto-encephalogram en intra-operatief elektrocorticogram voor diagnostiek en behandeling van epilepsie en kwam hierbij tot een aantal relevante conclusies.

Background Early detection of pathological hippocampal hyperactivity in persons with Mild Cognitive Impairment (MCI) and positive AD biomarkers (CSF/PET) is desirable, but no method is available. Here, we test MEG-based detection of hippocampal activity in MCI patients with positive biomarkers for AD, hypothesizing that their levels will be higher than in controls and AD patients. Method We used resting-state MEG recordings from 18 AD patients (age 63.8 ± 6.5, 9 females, mean MMSE 17.9), 18 amyloid-positive amnestic MCI patients (age 64.1 ± 6.2, 10 females, mean MMSE 25.8) and 18 persons with subjective cognitive decline (SCD, age 64.2 ± 6.1, 10 females, mean MMSE 27.6). Group and regional differences in spectral power and peak frequency were identified. Patient data was compared with computational model simulations of hyperactivity. Results Peak frequency was similar in SCD and MCI groups, but lower in AD patients. MCI showed a strong increase in absolute and relative lower alpha (8–10 Hz) power, strongest in the (right) hippocampus. Gamma (30–45 Hz) relative power decreased in MCI. In all groups, absolute hippocampal power was larger than the mean, especially in the (right) hippocampus. Group classification was most accurate using hippocampal power in the gamma band. Hyperactivity simulations produced changes fitting the MCI data, supporting hyperactivity as interpretation. Conclusion Our results support pathological neuronal activity increases in preclinical AD. Significant changes in the hippocampus and gamma band show the additional value of MEG. Besides diagnostic and trial patient selection relevance, this finding is valuable for activity-targeting therapy research in AD.

Increasing evidence from neuroimaging and modeling studies suggests that local lesions can give rise to global network changes in the human brain. These changes are often attributed to the disconnection of the lesioned areas. However, damaged brain areas may still be active, although the activity is altered. Here, we hypothesize that empirically observed global decreases in functional connectivity in patients with brain lesions can be explained by specific alterations of local neural activity that are the result of damaged tissue. We simulated local polymorphic delta activity (PDA), which typically characterizes EEG/MEG recordings of patients with cerebral lesions, in a realistic model of human brain activity. 78 neural masses were coupled according to the human structural brain network. Lesions were created by altering the parameters of individual neural masses in order to create PDA (i.e. simulating acute focal brain damage); combining this PDA with weakening of structural connections (i.e. simulating brain tumors), and fully deleting structural connections (i.e. simulating a full resection). Not only structural disconnection but also PDA in itself caused a global decrease in functional connectivity, similar to the observed alterations in MEG recordings of patients with PDA due to brain lesions. Interestingly, connectivity between regions that were not lesioned directly also changed. The impact of PDA depended on the network characteristics of the lesioned region in the structural connectome. This study shows for the first time that locally disturbed neural activity, i.e. PDA, may explain altered functional connectivity between remote areas, even when structural connections are unaffected. We suggest that focal brain lesions and the corresponding altered neural activity should be considered in the framework of the full functionally interacting brain network, implying that the impact of lesions reaches far beyond focal damage.

Although alterations in resting-state functional connectivity between brain regions have previously been reported in Parkinson's disease, the spatial organization of these changes remains largely unknown. Here, we longitudinally studied brain network topology in Parkinson's disease in relation to clinical measures of disease progression, using magnetoencephalography and concepts from graph theory. We characterized whole-brain functional networks by means of a standard graph analysis approach, measuring clustering coefficient and shortest path length, as well as the construction of a minimum spanning tree, a novel approach that allows a unique and unbiased characterization of brain networks. We observed that brain networks in early stage untreated patients displayed lower local clustering with preserved path length in the delta frequency band in comparison to controls. Longitudinal analysis over a 4-year period in a larger group of patients showed a progressive decrease in local clustering in multiple frequency bands together with a decrease in path length in the alpha2 frequency band. In addition, minimum spanning tree analysis revealed a decentralized and less integrated network configuration in early stage, untreated Parkinson's disease that also progressed over time. Moreover, the longitudinal changes in network topology identified with both techniques were associated with deteriorating motor function and cognitive performance. Our results indicate that impaired local efficiency and network decentralization are very early features of Parkinson's disease that continue to progress over time, together with reductions in global efficiency. As these network changes appear to reflect clinically relevant phenomena, they hold promise as markers of disease progression. © 2013 The Author.

Clinical and cognitive dysfunction in Multiple Sclerosis (MS) is insufficiently explained by structural damage as identified by traditional magnetic resonance imaging (MRI) of the brain, indicating the need for reliable functional measures in MS. We investigated whether altered resting-state oscillatory power could be related to clinical and cognitive dysfunction in MS. MEG recordings were acquired using a 151-channel whole-head MEG system from 21 relapsing remitting MS patients and 17 healthy age-, gender-, and education-matched controls, using an eyes-closed no-task condition. Relative spectral power was estimated for 78 regions of interest, using an atlas-based beamforming approach, for classical frequency bands; delta, theta, alpha1, alpha2, beta and gamma. These cortical power estimates were compared between groups by means of permutation analysis and correlated with clinical disability (Expanded Disability Status Scale: EDSS), cognitive performance and MRI measures of atrophy and lesion load. Patients showed increased power in the alpha1 band and decreased power in the alpha2 band, compared to controls, mainly in occipital, parietal and temporal areas, confirmed by a lower alpha peak-frequency. Increased power in the alpha1 band was associated with worse overall cognition and especially with information processing speed. Our quantitative relative power analysis of MEG recordings showed abnormalities in oscillatory brain dynamics in MS patients in the alpha band. By applying source-space analyses, this study provides a detailed topographical view of abnormal brain activity in MS patients, especially localized to occipital areas. Interestingly, poor cognitive performance was related to high resting-state alpha1 power indicating that changes in oscillatory activity might be of value as an objective measure of disease burden in MS patients., Highlights • MEG was recorded in relapsing remitting MS patients and healthy controls. • Atlas-based MEG beamformer was used for anatomical mapping of neuronal activity. • Increased power in alpha1 band in patients, associated with cognitive dysfunction. • Decreased power in alpha2 band in patients, confirmed by lower alpha peak-frequency.

Parkinson's disease (PD) related dementia (PDD) develops in up to 80% of PD patients. The present study was performed to further unravel the underlying pathophysiological mechanisms by applying a new analysis approach that uses an atlas-based MEG beamformer to provide a detailed anatomical mapping of cortical rhythms and functional interactions. Importantly, we used the phase lag index (PLI) as a measure of functional connectivity to avoid any biases due to effects of volume conduction. MEG recordings were obtained in 13 PDD and 13 non-demented PD patients. Beamforming was used to estimate spectral power and PLI in delta, theta, alpha, beta and gamma frequency bands. Compared to PD patients, PDD patients had more delta and theta power in parieto-occipital and fronto-parietal areas, respectively. The PDD patients had less alpha and beta power in parieto-temporo-occipital and frontal areas, respectively. Compared to PD patients, PDD patients had lower mean PLI values in the delta and alpha bands in fronto-temporal and parieto-temporo-occipital areas, respectively. In addition, in PDD patients connectivity between pairs of regions of interest (Brodmann areas) was stronger in the theta band and weaker in the delta, alpha and beta bands. The added value of the present results over previous studies analysing frequency-specific changes in neuronal activity in PD patients, is the anatomical framework in which we demonstrated a slowing in neuronal activity and a reduction in functional connectivity in PD related dementia. Moreover, this study shows a widespread reduction in functional connectivity between different regions in PDD., Highlights ► MEG was recorded in demented (PDD) and non-demented (PD) Parkinson patients. ► Atlas-based MEG beamformer was used for anatomical mapping of neuronal activity. ► Slowing of neuronal activity (power) in PDD compared to PD. ► Reduction in functional connectivity (Phase Lag Index) in PDD compared to PD.

Network science has widely studied the properties of brain networks. Recent work has observed a global back-to-front pattern of information flow for higher frequency bands in magnetoencephalography data. However, the effective connectivity at a local level remains yet to be analyzed. On a local level, the building blocks of all networks are motifs. In this study, we exploit the measure of dPTE to analyze motifs of the estimated effective connectivity networks. We find that some 3- and 4-motifs, the bidirectional two-hop path and its extended 4-node versions, are significantly overexpressed in the analyzed networks in comparison with random networks. With a recently developed motif-based clustering algorithm we separate the effective connectivity network in two main clusters which reveal its higher-order organization with a strong information flow between posterior hubs and anterior regions.

The interplay between structural connections and emerging information flow in the human brain remains an open research problem. A recent study observed global patterns of directional information flow in empirical data using the measure of transfer entropy. For higher frequency bands, the overall direction of information flow was from posterior to anterior regions whereas an anterior-to-posterior pattern was observed in lower frequency bands. In this study, we applied a simple Susceptible-Infected-Susceptible (SIS) epidemic spreading model on the human connectome with the aim to reveal the topological properties of the structural network that give rise to these global patterns. We found that direct structural connections induced higher transfer entropy between two brain regions and that transfer entropy decreased with increasing distance between nodes (in terms of hops in the structural network). Applying the SIS model, we were able to confirm the empirically observed opposite information flow patterns and posterior hubs in the structural network seem to play a dominant role in the network dynamics. For small time scales, when these hubs acted as strong receivers of information, the global pattern of information flow was in the posterior-to-anterior direction and in the opposite direction when they were strong senders. Our analysis suggests that these global patterns of directional information flow are the result of an unequal spatial distribution of the structural degree between posterior and anterior regions and their directions seem to be linked to different time scales of the spreading process.

Neuronal oscillations exist across a broad frequency spectrum, and are thought to provide a mechanism of interaction between spatially separated brain regions. Since ongoing mental activity necessitates the simultaneous formation of multiple networks, it seems likely that the brain employs interactions within multiple frequency bands, as well as cross-frequency coupling, to support such networks. Here, we propose a multi-layer network framework that elucidates this pan-spectral picture of network interactions. Our network consists of multiple layers (frequency-band specific networks) that influence each other via inter-layer (cross-frequency) coupling. Applying this model to MEG resting-state data and using envelope correlations as connectivity metric, we demonstrate strong dependency between within layer structure and inter-layer coupling, indicating that networks obtained in different frequency bands do not act as independent entities. More specifically, our results suggest that frequency band specific networks are characterised by a common structure seen across all layers, superimposed by layer specific connectivity, and inter-layer coupling is most strongly associated with this common mode. Finally, using a biophysical model, we demonstrate that there are two regimes of multi-layer network behaviour; one in which different layers are independent and a second in which they operate highly dependent. Results suggest that the healthy human brain operates at the transition point between these regimes, allowing for integration and segregation between layers. Overall, our observations show that a complete picture of global brain network connectivity requires integration of connectivity patterns across the full frequency spectrum.

Magnetoencephalography (MEG) can be used to reconstruct neuronal activity with high spatial and temporal resolution. However, this reconstruction problem is ill-posed, and requires the use of prior constraints in order to produce a unique solution. At present there are a multitude of inversion algorithms, each employing different assumptions, but one major problem when comparing the accuracy of these different approaches is that often the true underlying electrical state of the brain is unknown. In this study, we explore one paradigm, retinotopic mapping in the primary visual cortex (V1), for which the ground truth is known to a reasonable degree of accuracy, enabling the comparison of MEG source reconstructions with the true electrical state of the brain. Specifically, we attempted to localize, using a beanforming method, the induced responses in the visual cortex generated by a high contrast, retinotopically varying stimulus. Although well described in primate studies, it has been an open question whether the induced gamma power in humans due to high contrast gratings derives from V1 rather than the prestriate cortex (V2). We show that the beanformer source estimate in the gamma and theta bands does vary in a manner consistent with the known retinotopy of V1. However, these peak locations, although retinotopically organized, did not accurately localize to the cortical surface. We considered possible causes for this discrepancy and suggest that improved MEG/magnetic resonance imaging co-registration and the use of more accurate source models that take into account the spatial extent and shape of the active cortex may, in future, improve the accuracy of the source reconstructions.

BACKGROUND: Isocitrate dehydrogenase (IDH) mutation (IDH-mut) is an important marker for survival in diffuse glioma patients. Patients with an IDH-mut glioma have a better prognosis but more often suffer from epilepsy. Patients with a wildtype glioma (IDH-wt) are generally older and more often have cognitive deficits. Regardless of IDH status, cognitive deficits in glioma patients are associated with altered functional connectivity throughout the brain as calculated through statistical correlations between regional neurophysiological activity patterns measured with magnetoencephalography (MEG). Cognitive variation in glioma patients is specifically related to altered theta (4–8 Hz) and alpha (8–13 Hz) band functional connectivity. We investigated whether global brain functional connectivity differs between patients with an IDH-mut and IDH-wt diffuse glioma, and whether this measure reflects variations in cognitive functioning in these subpopulations beyond the associated differences in age and epilepsy. MATERIAL AND METHODS: IDH mutation status was determined in 54 diffuse glioma patients via immunohistochemistry (31 IDH-mut, 23 IDH-wt). We recorded MEG and patients underwent cognitive testing. Global functional connectivity between 78 atlas regions spanning the entire cortex was calculated in the theta and alpha frequency bands. Group differences in cognition and global functional connectivity were tested. Next, the association between functional connectivity and cognitive performance was tested. RESULTS: As expected, patients with IDH-mut glioma were younger (P = 0.001) and more often had epilepsy (P = 0.010). Patients with IDH-wt glioma showed poorer verbal memory (P = 0.027, corrected for age, education and presence of epilepsy). More importantly, patients with IDH-wt glioma had lower functional connectivity in the alpha band than patients with IDH-mut glioma (P = 0.015, corrected for age and presence of epilepsy). Alpha band functional connectivity was positively associated with executive functioning (P = 0.049) and information processing speed (P = 0.009), regardless of IDH status and corrected for age, education, and presence of epilepsy. CONCLUSION: These results suggest that global functional connectivity is lower in patients with IDH-wt diffuse glioma compared to patients with IDH-mut diffuse glioma. Moreover, functional connectivity relates to cognitive performance in both IDH-wt and IDH-mut diffuse glioma patients, regardless of age, education and presence of epilepsy. These findings contribute to our understanding of cognitive functioning in patients with diffuse glioma in general, and cognitive deficits in patients with IDH-wt glioma specifically.

In recent years, the use of beamformers for source localisation has significantly improved the spatial accuracy of magnetoencephalography. In this paper, we examine techniques by which to optimise experimental design, and ensure that the application of beamformers yields accurate results. We show that variation in the experimental duration, or variation in the bandwidth of a signal of interest, can significantly affect the accuracy of a beamformer reconstruction of source power. Specifically, power will usually be underestimated if covariance windows are made too short, or bandwidths too narrow. The accuracy of spatial localisation may also be reduced. We conclude that for optimum accuracy, experimenters should aim to collect as much data as possible, and use a bandwidth spanning the entire frequency distribution of the signal of interest. This minimises distortion to reconstructed source images, time courses and power estimation. In the case where experimental duration is short, and small covariance windows are therefore used, we show that accurate power estimation can be achieved by matrix regularisation. However, large amounts of regularisation cause a loss in the spatial resolution of the MEG beamformer, hence regularisation should be used carefully, particularly if multiple sources in close proximity are expected.