The effects of AMPA receptor blockade on resting magnetoencephalography recordings

The ionotropic N-methyl-D-aspartate and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors of the glutamatergic neurotransmitter system are of fundamental importance to healthy brain function. Neuroimaging studies in humans have previously been conducted using various drugs that interact with N-methyl-D-aspartate glutamate receptors, but no such studies have investigated AMPA receptor signalling. The recent approval of perampanel (Fycompa) for use in humans provides a means to specifically study the role of AMPA receptors in the pharmacological basis of neuroimaging signals. Twenty male subjects participated in this placebo-controlled crossover study that consisted of two study days separated by a minimum two-week washout period. On one occasion participants ingested a 6 mg dose of perampanel, and on the other a placebo. Ten minutes of wakeful rest was recorded before and after each dose using magnetoencephalography. Subjective ratings of intoxication were significantly higher following drug than placebo. Cluster-based randomisation testing of sensor-level magnetoencephalography data showed significant drug-induced increases in low frequency power (1–4 Hz, 4–8 Hz, 8–13 Hz, 13–30 Hz), along with a significant decrease in the high gamma range (50–90 Hz). We also observed selective increases in functional connectivity in the alpha and beta bands. The findings are consistent with preclinical work and are similar to the spectral profile of other anti-epileptic drugs

Energy landscape of resting magnetoencephalography reveals frontoparietal network impairments in epilepsy

Juvenile myoclonic epilepsy (JME) is a form of idiopathic generalized epilepsy. It is yet unclear to what extent JME leads to abnormal network activation patterns. Here, we characterised statistical regularities in MEG resting-state networks and their differences between JME patients and controls, by combining a pairwise maximum entropy model (pMEM) and novel energy landscape analyses for MEG. First, we fitted the pMEM to the MEG oscillatory power in the frontoparietal network (FPN) and other resting-state networks, which provided a good estimation of the occurrence probability of network states. Then, we used energy values derived from the pMEM to depict an energy landscape, with a higher energy state corresponding to a lower occurrence probability. JME patients showed fewer local energy minima than controls and had elevated energy values for the FPN within the theta, beta and gamma-bands. Furthermore, simulations of the fitted pMEM showed that the proportion of time the FPN was occupied within the basins of energy minima was shortened in JME patients. These network alterations were highlighted by significant classification of individual participants employing energy values as multivariate features. Our findings suggested that JME patients had altered multi-stability in selective functional networks and frequency bands in the frontoparietal cortices

Recurrence Quantification Analysis of Dynamic Brain Networks

Evidence suggests that brain network dynamics is a key determinant of brain function and dysfunction. Here we propose a new framework to assess the dynamics of brain networks based on recurrence analysis. Our framework uses recurrence plots and recurrence quantification analysis to characterize dynamic networks. For resting-state magnetoencephalographic dynamic functional networks (dFNs), we have found that functional networks recur more quickly in people with epilepsy than healthy controls. This suggests that recurrence of dFNs may be used as a biomarker of epilepsy. For stereo electroencephalography data, we have found that dFNs involved in epileptic seizures emerge before seizure onset, and recurrence analysis allows us to detect seizures. We further observe distinct dFNs before and after seizures, which may inform neurostimulation strategies to prevent seizures. Our framework can also be used for understanding dFNs in healthy brain function and in other neurological disorders besides epilepsy.Comment: 77 pages, 11 figures; note: the acknowledgments section is the most complete in this arxiv version (compared to the published version in EJN

Juvenile myoclonic epilepsy shows increased posterior theta, and reduced sensorimotor beta resting connectivity

Background Widespread structural and functional brain network changes have been shown in Juvenile Myoclonic Epilepsy (JME) despite normal clinical neuroimaging. We sought to better define these changes using magnetoencephalography (MEG) and source space connectivity analysis for optimal neurophysiological and anatomical localisation. Methods We consecutively recruited 26 patients with JME who underwent resting state MEG recording, along with 26 age-and-sex matched controls. Whole brain connectivity was determined through correlation of Automated Anatomical Labelling (AAL) atlas source space MEG timeseries in conventional frequency bands of interest delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz) and gamma (40-60 Hz). We used a Linearly Constrained Minimum Variance (LCMV) beamformer to extract voxel wise time series of ‘virtual sensors’ for the desired frequency bands, followed by connectivity analysis using correlation between frequency- and node-specific power fluctuations, for the voxel maxima in each AAL atlas label, correcting for noise, potentially spurious connections and multiple comparisons. Results We found increased connectivity in the theta band in posterior brain regions, surviving statistical correction for multiple comparisons (corrected p < 0.05), and decreased connectivity in the beta band in sensorimotor cortex, between right pre- and post- central gyrus (p < 0.05) in JME compared to controls. Conclusions Altered resting-state MEG connectivity in JME comprised increased connectivity in posterior theta – the frequency band associated with long range connections affecting attention and arousal - and decreased beta-band sensorimotor connectivity. These findings likely relate to altered regulation of the sensorimotor network and seizure prone states in JME

The role of additive and diffusive coupling on the dynamics of neural populations

Dynamical models consisting of networks of neural masses commonly assume that the interactions between neural populations are via additive or diffusive coupling. When using the additive coupling, a population’s activity is affected by the sum of the activities of neighbouring populations. In contrast, when using the diffusive coupling a neural population is affected by the sum of the differences between its activity and the activity of its neighbours. These two coupling functions have been used interchangeably for similar applications. In this study, we show that the choice of coupling can lead to strikingly different brain network dynamics. We focus on a phenomenological model of seizure transitions that has been used both with additive and diffusive coupling in the literature. We consider small networks with two and three nodes, as well as large random and scale-free networks with 64 nodes. We further assess resting-state functional networks inferred from magnetoencephalography (MEG) from people with juvenile myoclonic epilepsy (JME) and healthy controls. To characterize the seizure dynamics on these networks, we use the escape time, the brain network ictogenicity (BNI) and the node ictogenicity (NI), which are measures of the network’s global and local ability to generate seizure activity. Our main result is that the level of ictogenicity of a network is strongly dependent on the coupling function. Overall, we show that networks with additive coupling have a higher propensity to generate seizures than those with diffusive coupling. We find that people with JME have higher additive BNI than controls, which is the hypothesized BNI deviation between groups, while the diffusive BNI provides opposite results. Moreover, we find that the nodes that are more likely to drive seizures in the additive coupling case are more likely to prevent seizures in the diffusive coupling case, and that these features correlate to the node’s number of connections. Consequently, previous results in the literature involving such models to interrogate functional or structural brain networks could be highly dependent on the choice of coupling. Our results on the MEG functional networks and evidence from the literature suggest that the additive coupling may be a better modeling choice than the diffusive coupling, at least for BNI and NI studies. Thus, we highlight the need to motivate and validate the choice of coupling in future studies involving network models of brain activity

A computational biomarker of photosensitive epilepsy from interictal EEG

People with photosensitive epilepsy (PSE) are prone to seizures elicited by visual stimuli. The possibility of inducing epileptiform activity in a reliable way makes PSE a useful model to understand epilepsy, with potential applications for the development of new diagnostic methods and new treatments for epilepsy. A relationship has been demonstrated between PSE and both occipital and more widespread cortical hyperexcitability using various types of stimulation. Here we aimed to test whether hyperexcitability could be inferred from resting interictal electroencephalographic (EEG) data without stimulation. We considered a cohort of 46 individuals with idiopathic generalized epilepsy who underwent EEG during intermittent photic stimulation: 26 had a photoparoxysmal response (PPR), the PPR group, and 20 did not, the non-PPR group. For each individual, we computed functional networks from the resting EEG data before stimulation. We then placed a computer model of ictogenicity into the networks and simulated the propensity of the network to generate seizures in silico [the brain network ictogenicity (BNI)]. Furthermore, we computed the node ictogenicity (NI), a measure of how much each brain region contributes to the overall ictogenic propensity. We used the BNI and NI as proxies for testing widespread and occipital hyperexcitability, respectively. We found that the BNI was not higher in the PPR group relative to the non-PPR group. However, we observed that the (right) occipital NI was significantly higher in the PPR group relative to the non-PPR group. Other regions did not have significant differences in NI values between groups

Effects of anti-seizure medication on sleep spindles and slow waves in drug-resistant epilepsy

There is a close bidirectional relationship between sleep and epilepsy. Anti-seizure medications (ASM) act to reduce seizure frequency but can also impact sleep; this remains a relatively unexplored field given the importance of sleep on seizure occurrence, memory consolidation, and quality of life. We compared the effect of poly-ASM treatment on a night of sleep compared to an unmedicated night in patients with drug-resistant epilepsy, where ASMs were withdrawn and later restored as part of their pre-surgical evaluation. Within-subject analysis between medicated and unmedicated nights showed ASMs increased spindle (11–16 Hz) power and decreased slow wave (0.1–2 Hz) amplitude. Spindles became less strongly coupled to slow waves in the ASM night compared to no-ASM night, with effects to both the phase and strength of coupling and correlated with slow wave reduction. These effects were not seen in age-matched controls from the same unit where ASMs were not changed between two nights. Overall, we found that ASM polytherapy not only changed specific sleep waveforms, but also the fine interplay of spindle/slow wave coupling. Since these sleep oscillations impact both seizure occurrence and memory consolidation, our findings provide evidence towards a decoupling impact of ASMs on sleep that should be considered in future studies of sleep and memory disruption in people with epileps

Tiagabine induced modulation of oscillatory connectivity and activity match PET-derived, canonical GABA-A receptor distributions

As the most abundant inhibitory neurotransmitter in the mammalian brain, γ-aminobutyric acid (GABA) plays a crucial role in shaping the frequency and amplitude of oscillations, which suggests a role for GABA in shaping the topography of functional connectivity and activity. This study explored the effects of pharmacologically blocking the reuptake of GABA (increasing local concentrations) using the GABA transporter 1 (GAT1) blocker, tiagabine (15 mg). In a placebo-controlled crossover design, we collected resting magnetoencephalography (MEG) recordings from 15 healthy individuals prior to, and at 1-, 3- and 5- hours post, administration of tiagabine and placebo. We quantified whole brain activity and functional connectivity in discrete frequency bands. Drug-by-session (2 × 4) analysis of variance in connectivity revealed interaction and main effects. Post-hoc permutation testing of each post-drug recording vs. respective pre-drug baseline revealed consistent reductions of a bilateral occipital network spanning theta, alpha and beta frequencies, across 1- 3- and 5- hour recordings following tiagabine only. The same analysis applied to activity revealed significant increases across frontal regions, coupled with reductions in posterior regions, across delta, theta, alpha and beta frequencies. Crucially, the spatial distribution of tiagabine-induced changes overlap with group-averaged maps of the distribution of GABAA receptors, from flumazenil (FMZ-VT) PET, demonstrating a link between GABA availability, GABAA receptor distribution, and low-frequency network oscillations. Our results indicate that the relationship between PET receptor distributions and MEG effects warrants further exploration, since elucidating the nature of this relationship may uncover electrophysiologically-derived maps of oscillatory activity as sensitive, time-resolved, and targeted receptor-mapping tools for pharmacological imaging

Significant reductions in human visual gamma frequency by the GABA reuptake inhibitor tiagabine revealed by robust peak frequency estimation

The frequency of visual gamma oscillations is determined by both the neuronal excitation–inhibition balance and the time constants of GABAergic processes. The gamma peak frequency has been linked to sensory processing, cognitive function, cortical structure, and may have a genetic contribution. To disentangle the intricate relationship among these factors, accurate and reliable estimates of peak frequency are required. Here, a bootstrapping approach that provides estimates of peak frequency reliability, thereby increasing the robustness of the inferences made on this parameter was developed. The method using both simulated data and real data from two previous pharmacological MEG studies of visual gamma with alcohol and tiagabine was validated. In particular, the study by Muthukumaraswamy et al. [2013a] (Neuropsychopharmacology 38(6):1105–1112), in which GABAergic enhancement by tiagabine had previously demonstrated a null effect on visual gamma oscillations, contrasting with strong evidence from both animal models and very recent human studies was re-evaluated. After improved peak frequency estimation and additional exclusion of unreliably measured data, it was found that the GABA reuptake inhibitor tiagabine did produce, as predicted, a marked decrease in visual gamma oscillation frequency. This result demonstrates the potential impact of objective approaches to data quality control, and provides additional translational evidence for the mechanisms of GABAergic transmission generating gamma oscillations in humans

GABAA receptor mapping in human using non-invasive electrophysiology

The non-invasive study of cortical oscillations provides a window onto neuronal processing. Temporal correlation of these oscillations between distinct anatomical regions is considered a marker of functional connectedness. As the most abundant inhibitory neurotransmitter in the mammalian brain, γ-aminobutyric acid (GABA) is thought to play a crucial role in shaping the frequency and amplitude of oscillations, which thereby suggests a role for GABA in shaping the topography of functional activity and connectivity. This study explored the effects of pharmacologically blocking the reuptake of GABA (increasing local concentrations) through oral administration of the GABA transporter 1 (GAT1) blocker tiagabine (15 mg). We show that the spatial distribution of tiagabine-induced activity changes, across the brain, corresponds to group-average flumazenil PET maps of GABAA receptor distribution. In a placebo-controlled crossover design, we collected resting magnetoencephalography (MEG) recordings from 15 healthy male individuals prior to, and at 1-, 3- and 5- hours post, administration of tiagabine and placebo pill. Using leakage-corrected amplitude envelope correlations (AECs), we quantified the functional connectivity in discrete frequency bands across the whole brain, using the 90-region Automatic Anatomical Labelling atlas (AAL90), as well as quantifying the average oscillatory activity across the brain. Analysis of variance in connectivity using a drug-by-session (2×4) design revealed interaction effects, accompanied by main effects of drug and session. Post-hoc permutation testing of each post-drug recording against the respective pre-drug baseline revealed consistent reductions of a bilateral occipital network spanning theta, alpha and beta frequencies, and across 1- 3- and 5- hour recordings following tiagabine, but not placebo. The same analysis applied to activity, across the brain, also revealed a significant interaction, with post-hoc permutation testing demonstrating significant increases in activity across frontal regions, coupled with reductions in activity in posterior regions, across the delta, theta, alpha and beta frequency bands. Crucially, we show that the spatial distribution of tiagabine-induced changes in oscillatory activity overlap significantly with group-averaged maps of the estimated distribution of GABAA receptors, derived from scaled flumazenil volume-of-distribution (FMZ-VT) PET, hence demonstrating a possible mechanistic link between GABA availability, GABAA receptor distribution, and low-frequency network oscillations. We therefore propose that electrophysiologically-derived maps of oscillatory connectivity and activity can be used as sensitive, time-resolved, and targeted receptor-mapping tools for pharmacological imaging at the group level, providing direct measures of target engagement and pharmacodynamics