Amplitude Envelope Correlation Research Articles

The cortical neurophysiological signature of amyotrophic lateral sclerosis.

The progressive loss of motor function characteristic of amyotrophic lateral sclerosis is associated with widespread cortical pathology extending beyond primary motor regions. Increasing muscle weakness reflects a dynamic, variably compensated brain network disorder. In the quest for biomarkers to accelerate therapeutic assessment, the high temporal resolution of magnetoencephalography is uniquely able to non-invasively capture micro-magnetic fields generated by neuronal activity across the entire cortex simultaneously. This study examined task-free magnetoencephalography to characterize the cortical oscillatory signature of amyotrophic lateral sclerosis for having potential as a pharmacodynamic biomarker. Eight to ten minutes of magnetoencephalography in the task-free, eyes-open state was recorded in amyotrophic lateral sclerosis (n = 36) and healthy age-matched controls (n = 51), followed by a structural MRI scan for co-registration. Extracted magnetoencephalography metrics from the delta, theta, alpha, beta, low-gamma, high-gamma frequency bands included oscillatory power (regional activity), 1/f exponent (complexity) and amplitude envelope correlation (connectivity). Groups were compared using a permutation-based general linear model with correction for multiple comparisons and confounders. To test whether the extracted metrics could predict disease severity, a random forest regression model was trained and evaluated using nested leave-one-out cross-validation. Amyotrophic lateral sclerosis was characterized by reduced sensorimotor beta band and increased high-gamma band power. Within the premotor cortex, increased disability was associated with a reduced 1/f exponent. Increased disability was more widely associated with increased global connectivity in the delta, theta and high-gamma bands. Intra-hemispherically, increased disability scores were particularly associated with increases in temporal connectivity and inter-hemispherically with increases in frontal and occipital connectivity. The random forest model achieved a coefficient of determination (R2) of 0.24. The combined reduction in cortical sensorimotor beta and rise in gamma power is compatible with the established hypothesis of loss of inhibitory, GABAergic interneuronal circuits in pathogenesis. A lower 1/f exponent potentially reflects a more excitable cortex and a pathology unique to amyotrophic lateral sclerosis when considered with the findings published in other neurodegenerative disorders. Power and complexity changes corroborate with the results from paired-pulse transcranial magnetic stimulation. Increased magnetoencephalography connectivity in worsening disability is thought to represent compensatory responses to a failing motor system. Restoration of cortical beta and gamma band power has significant potential to be tested in an experimental medicine setting. Magnetoencephalography-based measures have potential as sensitive outcome measures of therapeutic benefit in drug trials and may have a wider diagnostic value with further study, including as predictive markers in asymptomatic carriers of disease-causing genetic variants.

Minimum spanning tree analysis of EEG resting-state functional networks in schizophrenia

Schizophrenia is a serious and complex mental disease, known to be associated with various subtle structural and functional deviations in the brain. Recently, increased attention is given to the analysis of brain-wide, global mechanisms, strongly altering the communication of long-distance brain areas in schizophrenia. Data of 32 patients with schizophrenia and 28 matched healthy control subjects were analyzed. Two minutes long 64-channel EEG recordings were registered during resting, eyes closed condition. Average connectivity strength was estimated with Weighted Phase Lag Index (wPLI) in lower frequencies: delta and theta, and Amplitude Envelope Correlation with leakage correction (AEC-c) in higher frequencies: alpha, beta, lower gamma and higher gamma. To analyze functional network topology Minimum Spanning Tree (MST) algorithms were applied. Results show that patients have weaker functional connectivity in delta and alpha frequency bands. Concerning network differences, the result of lower diameter, higher leaf number, and also higher maximum degree and maximum betweenness centrality in patients suggest a star-like, and more random network topology in patients with schizophrenia. Our findings are in accordance with some previous findings based on resting-state EEG (and fMRI) data, suggesting that MST network structure in schizophrenia is biased towards a less optimal, more centralized organization.

Effects of one-week bilateral cerebellar iTBS on resting-state functional brain network and multi-task attentional performance in healthy individuals: A randomized, sham-controlled trial

Background:Cerebellar intermittent theta burst stimulation (iTBS) modulates the excitability of the cerebral cortex and may enhance attentional performance. To date, few studies have conducted iTBS on healthy subjects for one week and used electroencephalography (EEG) to investigate the effect of multiple stimulation sessions on resting-state functional brain networks and the daily stimulation effect on attentional performance. Methods:16 healthy subjects participated in a one-week experiment, receiving bilateral cerebellar iTBS or sham stimulation and engaging in multi-task attentional training. The primary measures were the one-week attentional performance and pre- and post-experiment resting-state EEG activities. Amplitude Envelope Correlation (AEC) was used to construct the functional connectivity in the eye-open (EO) and eye-closed (EC) phases. Results:At least three sessions of iTBS were required to enhance multi-task performance significantly, whereas only one or two sessions failed to elicit the improvement. Compared with the control group, iTBS induced significant changes in PSD, AEC functional connectivity, and AEC network properties during the EO phase, while it had little effect during the EC phase. During the EO phase, the network property changes of the iTBS subject were correlated with improved attentional performance. Conclusion:The multi-task performance requires multiple stimulations to enhance. iTBS affects the resting-state alpha band brain activities during the EO rather than the EC phase. The AEC network properties may serve as a biomarker to assess the attentional potential of healthy subjects.

Virtual neural network-guided optimization of non-invasive brain stimulation in Alzheimer's disease.

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique with potential for counteracting disrupted brain network activity in Alzheimer's disease (AD) to improve cognition. However, the results of tDCS studies in AD have been variable due to different methodological choices such as electrode placement. To address this, a virtual brain network model of AD was used to explore tDCS optimization. We compared a large, representative set of virtual tDCS intervention setups, to identify the theoretically optimized tDCS electrode positions for restoring functional network features disrupted in AD. We simulated 20 tDCS setups using a computational dynamic network model of 78 neural masses coupled according to human structural topology. AD network damage was simulated using an activity-dependent degeneration algorithm. Current flow modeling was used to estimate tDCS-targeted cortical regions for different electrode positions, and excitability of the pyramidal neurons of the corresponding neural masses was modulated to simulate tDCS. Outcome measures were relative power spectral density (alpha bands, 8-10 Hz and 10-13 Hz), total spectral power, posterior alpha peak frequency, and connectivity measures phase lag index (PLI) and amplitude envelope correlation (AEC). Virtual tDCS performance varied, with optimized strategies improving all outcome measures, while others caused further deterioration. The best performing setup involved right parietal anodal stimulation, with a contralateral supraorbital cathode. A clear correlation between the network role of stimulated regions and tDCS success was not observed. This modeling-informed approach can guide and perhaps accelerate tDCS therapy development and enhance our understanding of tDCS effects. Follow-up studies will compare the general predictions to personalized virtual models and validate them with tDCS-magnetoencephalography (MEG) in a clinical AD patient cohort.

Communicative signals during joint attention promote neural processes of infants and caregivers

Communicative signals such as eye contact increase infants’ brain activation to visual stimuli and promote joint attention. Our study assessed whether communicative signals during joint attention enhance infant-caregiver dyads’ neural responses to objects, and their neural synchrony. To track mutual attention processes, we applied rhythmic visual stimulation (RVS), presenting images of objects to 12-month-old infants and their mothers (n = 37 dyads), while we recorded dyads’ brain activity (i.e., steady-state visual evoked potentials, SSVEPs) with electroencephalography (EEG) hyperscanning. Within dyads, mothers either communicatively showed the images to their infant or watched the images without communicative engagement. Communicative cues increased infants’ and mothers’ SSVEPs at central-occipital-parietal, and central electrode sites, respectively. Infants showed significantly more gaze behaviour to images during communicative engagement. Dyadic neural synchrony (SSVEP amplitude envelope correlations, AECs) was not modulated by communicative cues. Taken together, maternal communicative cues in joint attention increase infants’ neural responses to objects, and shape mothers’ own attention processes. We show that communicative cues enhance cortical visual processing, thus play an essential role in social learning. Future studies need to elucidate the effect of communicative cues on neural synchrony during joint attention. Finally, our study introduces RVS to study infant-caregiver neural dynamics in social contexts.

Virtual brain model‐guided optimization of transcranial direct current stimulation in Alzheimer’s disease

AbstractBackgroundNon‐invasive brain stimulation techniques such as transcranial direct current stimulation (tDCS) can potentially counteract disrupted local and distributed brain network activity in Alzheimer’s disease (AD) with a beneficial effect on cognition. However, results of tDCS studies in AD up till now are inconsistent, probably partly due to different methodological choices such as electrode placement. We therefore explored tDCS optimization using a virtual brain network model of AD. Our aim was to compare a large, representative set of virtual tDCS intervention setups, to identify and potentially explain the optimal theoretical tDCS electrode positions for reducing or restoring functional network disruption.MethodWe simulated 20 tDCS setups in a computational macroscopic dynamic network model of 78 neural masses, coupled according to human structural topology. AD network damage was simulated using an activity‐dependent degeneration algorithm, which produces typical gradual oscillatory slowing and loss of connectivity. Current flow modeling was used to estimate tDCS‐targeted cortical regions for different electrode positions, and excitability of the pyramidal neurons of the corresponding neural masses was modulated to simulate tDCS. Outcome measures were relative power spectral density in the lower (8‐10 Hz) and upper (8‐13 Hz) alpha bands, total spectral power, posterior alpha peak frequency, and connectivity measures phase lag index (PLI) and amplitude envelope correlation (AEC).ResultThe performance of different tDCS strategies varied considerably, with best setups temporarily improving all spectral and connectivity outcome measures, while other setups had little to no effect, or even caused network deterioration. The best performing setup involved right parietal anodal stimulation, with a contralateral supraorbital cathode. A clear correlation between the network role of a region (e.g. hub) and its involvement in successful tDCS was not observed.ConclusionModeling tDCS produces specific, falsifiable predictions on how to counter disrupted brain dynamics in AD. In follow‐up studies, our general predictions will be compared to those of personalized virtual models, and then validated with tDCS‐magnetoencephalography (MEG) in a clinical AD patient cohort. This modeling‐informed approach can guide and perhaps accelerate tDCS therapy development, as well as enhance our understanding of tDCS effects.

Longitudinal resting‐state EEG along the Alzheimer’s disease continuum: Sample size considerations for clinical trials.

AbstractBackgroundTo enable successful inclusion of resting‐state electroencephalography (EEG) outcome measures in Alzheimer’s disease (AD) clinical trials, we (retrospectively) mapped the progression of EEG markers over time in amyloid‐positive subjects.MethodsResting‐state 21‐channel EEG was recorded in 173 subjects positive for amyloid deposition (subjective cognitive decline, n = 16; mild cognitive impairment, n = 94; dementia due to AD, n = 63). Two or more EEG recordings were available for all subjects. We computed whole‐brain and regional relative power (i.e., theta (4‐8Hz), alpha1 (8‐10Hz), alpha2 (10‐13Hz), beta (13‐30Hz)), peak frequency, signal variability (i.e., theta Permutation Entropy) and functional connectivity values (i.e., alpha and beta corrected Amplitude Envelope Correlation, theta Phase Lag Index, weighted Symbolic Mutual Information, inverted Joint Permutation Entropy). Whole‐group linear mixed effects models were used to model the development of EEG markers over time. Group‐wise analysis was performed to investigate potential differences in change trajectories between diagnostic groups. The longitudinal relation between EEG and cognition was evaluated. Finally, we estimated the minimum sample size required to detect a stabilizing treatment effect using EEG markers, in hypothetical clinical trials of 1‐ or 2‐year duration.ResultsWhole‐group analysis revealed significant regional and global oscillatory slowing over time (i.e., increased relative theta power, decreased alpha2 and beta power), with strongest effects for temporal and parieto‐occipital regions. Signal variability showed stronger decline on a global scale. We report no significant longitudinal changes in functional connectivity strength. Disease severity at baseline influenced EEG markers’ rates of change, with fastest deterioration in MCI subjects. A decrease of the parieto‐occipital peak frequency, temporal relative alpha2 and beta power, as well as an increase in temporal relative theta power, were significantly associated with a decline in Mini Mental State Examination score. We estimate that 1‐year trials, focusing on amyloid‐positive MCI subjects, require 148 or 438 subjects per arm to detect a stabilizing treatment effect on temporal relative theta‐ or beta power, respectively.ConclusionResting‐state EEG markers can facilitate early detection of target engagement in an objective, non‐invasive, low‐cost manner. Spectral measures, particularly recorded from temporal regions, present robust secondary endpoints for AD interventions.

Hub vulnerability and short path length to entorhinal cortex are associated with tau binding in Alzheimer’s disease: a combined MEG/PET study

AbstractBackgroundBrain network studies have revealed that highly connected ‘hub’ regions are particularly vulnerable to Alzheimer’s disease (AD) pathology. Additionally, the ‘‘transneuronal spread” hypothesis proposes that a toxic agent (tau) propagates along shortest paths through functionally connected neurons, driven by aberrant neuronal activity. We investigate whether network characteristics as determined using magnetoencephalography (MEG) are correlated to tau pathology in the different stages of AD.MethodWe analysed source‐reconstructed MEG data and dynamic 100‐minutes [18F]flortaucipir PET from 57 subjects positive for amyloid‐beta (Aβ)‐pathology (preclinical AD (n = 16)), mild cognitive impairment (MCI (n = 16)) and AD dementia (n = 25)). Cognitively healthy subjects without Aβ‐pathology were included as controls (n = 25). For each subject, we obtained a network based on the alpha band amplitude envelope correlation (AEC‐c, 8‐13Hz), and theta band phase lag index (PLI, 4‐8Hz), based on the Brainnettome atlas. For each node in the network, we obtained the following measures: weighted degree, degree, betweenness centrality, eccentricity (as measures of hubness); distance to left entorhinal cortex (measure of shortest path/transneuronal spread); hub disruption index for all previous hubness measures (selective hub damage) and [18F]flortaucipir BPND. Pearson’s correlations were calculated between group averages of network measures and tau‐PET binding (FDR‐corrected). Furthermore, we analysed whether network measures of the previous disease stage correlated with [18F]flortaucipir retention in the subsequent disease stage.ResultHub regions were predominantly found in the temporal areas (Fig‐1). Higher ‘hubness’ and shorter neuronal distance to epicentre were associated to higher tau binding in all stages of the AD continuum (Fig‐2a). Disproportional disruption to hub regions in the AEC‐c alpha band was associated with increased tau binding, but the opposite effect was found for the PLI theta band (Fig‐2b). Finally, high weighted degree, degree, and betweenness centrality in the preceding disease stage were correlated with increased tau binding in subsequent disease stages (Fig‐2c).ConclusionHub regions and transneuronal spreading are key mechanisms in the propagation of tau pathology across the AD brain. Furthermore, hubs are particularly vulnerable to tau. The insight that high neuronal activity might play a causal role in Alzheimer’s disease can have implications for early detection and interventional strategies.

EEG network functional connectivity during sleep and in presence of network hyperexcitability in Alzheimer’s disease

AbstractBackgroundFunctional connectivity (FC) studies in Alzheimer’s disease (AD) have focused primarily on the awake resting state. Yet, sleep disturbances in AD are common and occur early in the disease. Network hyperexcitability in AD also arises during sleep, resulting in epileptiform activity and seizures, and impacting memory consolidation. Hence, understanding the FC changes that occur during sleep in AD could provide important biological insights. To date, no studies have examined FC changes occurring during sleep in AD and their relationship with network hyperexcitability.MethodUsing ambulatory scalp EEG recordings, we assessed sleep FC in cognitively normal healthy controls (HC, n = 35), individuals with early clinical stages of AD with no history or risk factors for epilepsy (ADNoEp, n = 44), and individuals with early clinical stages of AD and epilepsy related to AD (ADEp, n = 14). We measured FC using corrected amplitude envelope correlation and assessed FC on different scales, including channel‐pair associations, regional associations, seed‐based connectivity, and graph theory metrics. Machine learning classifiers were developed to determine whether sleep FC measures provide accurate discrimination between HC, ADNoEp, and ADEp.ResultThere were no significant differences in age, sex, or education between HC, ADNoEp, and ADEp. Compared to HC, the ADNoEp group had 1) reduced FC during REM sleep in the alpha band in posterior regions; and 2) increased FC during NREM2 sleep in the gamma‐band in frontal regions. Compared to ADNoEp, the ADEp group had reduced FC in NREM2 sleep in the delta‐band in temporal and posterior regions. Graph theory metrics corroborated these sleep stage‐ and frequency‐specific findings between groups. Machine learning classifiers using graph theory metrics as input features distinguished ADNoEp from HC, and ADNoEp from ADEp, with high performance (AUC>0.85).ConclusionAD is associated with sleep stage‐, frequency band‐, and region‐specific changes in FC. Network hyperexcitability in ADEp is also associated with changes in sleep FC, which are distinct from those seen in ADNoEp. Our findings expand understanding of brain network changes in AD to the sleep state and demonstrate sleep‐stage specific alterations in FC that may have important implications for understanding AD neurophysiology and for disease monitoring and therapeutic development.

Longitudinal resting-state EEG in amyloid-positive patients along the Alzheimer’s disease continuum: considerations for clinical trials

BackgroundTo enable successful inclusion of electroencephalography (EEG) outcome measures in Alzheimer’s disease (AD) clinical trials, we retrospectively mapped the progression of resting-state EEG measures over time in amyloid-positive patients with mild cognitive impairment (MCI) or dementia due to AD.MethodsResting-state 21-channel EEG was recorded in 148 amyloid-positive AD patients (MCI, n = 88; dementia due to AD, n = 60). Two or more EEG recordings were available for all subjects. We computed whole-brain and regional relative power (i.e., theta (4-8 Hz), alpha1 (8-10 Hz), alpha2 (10-13 Hz), beta (13-30 Hz)), peak frequency, signal variability (i.e., theta permutation entropy), and functional connectivity values (i.e., alpha and beta corrected amplitude envelope correlation, theta phase lag index, weighted symbolic mutual information, inverted joint permutation entropy). Whole-group linear mixed effects models were used to model the development of EEG measures over time. Group-wise analysis was performed to investigate potential differences in change trajectories between the MCI and dementia subgroups. Finally, we estimated the minimum sample size required to detect different treatment effects (i.e., 50% less deterioration, stabilization, or 50% improvement) on the development of EEG measures over time, in hypothetical clinical trials of 1- or 2-year duration.ResultsWhole-group analysis revealed significant regional and global oscillatory slowing over time (i.e., increased relative theta power, decreased beta power), with strongest effects for temporal and parieto-occipital regions. Disease severity at baseline influenced the EEG measures’ rates of change, with fastest deterioration reported in MCI patients. Only AD dementia patients displayed a significant decrease of the parieto-occipital peak frequency and theta signal variability over time. We estimate that 2-year trials, focusing on amyloid-positive MCI patients, require 36 subjects per arm (2 arms, 1:1 randomization, 80% power) to detect a stabilizing treatment effect on temporal relative theta power.ConclusionsResting-state EEG measures could facilitate early detection of treatment effects on neuronal function in AD patients. Their sensitivity depends on the region-of-interest and disease severity of the study population. Conventional spectral measures, particularly recorded from temporal regions, present sensitive AD treatment monitoring markers.

Test-retest reliability of the human connectome: An OPM-MEG study

Abstract Magnetoencephalography with optically pumped magnetometers (OPM-MEG) offers a new way to record electrophysiological brain function, with significant advantages over conventional MEG, including adaptability to head shape/size, free movement during scanning, increased signal amplitude, and no reliance on cryogenics. However, OPM-MEG remains in its infancy, with significant questions to be answered regarding the optimal system design. Here, we present an open-source dataset acquired using a newly constructed OPM-MEG system with a triaxial sensor design, 168 channels, OPM-optimised magnetic shielding, and active background field control. We measure the test-retest reliability of the human connectome, which was computed using amplitude envelope correlation to measure whole-brain (parcellated) functional connectivity, in 10 individuals while they watch a 600 s move clip. Our results show high repeatability between experimental runs at the group level, with a correlation coefficient of 0.81 in the θ, 0.93 in α, and 0.94 in β frequency ranges. At the individual subject level, we found marked differences between individuals, but high within-subject robustness (correlations of 0.56 ± 0.25, 0.72 ± 0.15, and 0.78 ± 0.13 in α, θ, and β respectively). These results compare well to previous findings using conventional MEG and show that OPM-MEG is a viable way to robustly characterise connectivity.

Comparing individual and group-level simulated neurophysiological brain connectivity using the Jansen and Rit neural mass model.

Computational models are often used to assess how functional connectivity (FC) patterns emerge from neuronal population dynamics and anatomical brain connections. It remains unclear whether the commonly used group-averaged data can predict individual FC patterns. The Jansen and Rit neural mass model was employed, where masses were coupled using individual structural connectivity (SC). Simulated FC was correlated to individual magnetoencephalography-derived empirical FC. FC was estimated using phase-based (phase lag index (PLI), phase locking value (PLV)), and amplitude-based (amplitude envelope correlation (AEC)) metrics to analyze their goodness of fit for individual predictions. Individual FC predictions were compared against group-averaged FC predictions, and we tested whether SC of a different participant could equally well predict participants' FC patterns. The AEC provided a better match between individually simulated and empirical FC than phase-based metrics. Correlations between simulated and empirical FC were higher using individual SC compared to group-averaged SC. Using SC from other participants resulted in similar correlations between simulated and empirical FC compared to using participants' own SC. This work underlines the added value of FC simulations using individual instead of group-averaged SC for this particular computational model and could aid in a better understanding of mechanisms underlying individual functional network trajectories.

Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses

The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as “canonical” frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the “Connectivity-based Meta-Bands” (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the “canonical” frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity.

Variation in functional networks between clinical and subclinical discharges in childhood absence epilepsy: A multi-frequency MEG study

ObjectiveTwo types of spike-and-wave discharges (SWDs) exist in childhood absence epilepsy (CAE): clinical discharges are prolonged and manifest primarily as impaired consciousness, whereas subclinical discharges are brief with few objectively visible symptoms. This study aimed to compare neural functional network and default mode network (DMN) activity between clinical and subclinical discharges to better understand the underlying mechanism of CAE. MethodsUsing magnetoencephalography (MEG) data from 21 patients, we obtained 25 segments each of clinical discharges and subclinical discharges. Amplitude envelope correlation analysis was used to construct functional networks and graph theory was used to calculate network topological data. We then compared differences in functional connectivity within the DMN between clinical and subclinical discharges. All statistical comparisons were performed using paired-sample tests. ResultsCompared to subclinical discharges, the functional network of clinical discharges exhibited higher synchronization — particularly in the parahippocampal gyrus — as early as 10 s before the seizure. Additionally, the functional network of clinical SWDs presented an anterior shift of key nodes in the alpha frequency band. Regarding clinical discharge progression, there were gradual increases in the parameter node strengths (S), clustering coefficients (C), and global efficiency (E) of the functional networks, while the path lengths (L) decreased. These changes were most prominent at the onset of discharges and followed by some recovery in the high-frequency bands, but no significant change in the low-frequency bands. Furthermore, connections within the DMN during the discharge period were significantly stronger for clinical discharge compared to subclinical discharges. ConclusionsThese findings suggest that a more regular network before abnormal discharges in clinical discharges contributes to SWD explosion and that the parahippocampal gyrus plays an important role in maintaining oscillations. An absence seizure is a gradual process and the emergence of SWDs may be accompanied by initiation of inhibitory mechanisms. Enhanced functional connectivity among DMN brain regions may indicate that patients have entered a state of introspection, and functional abnormalities in the parahippocampal gyrus may be associated with patients' transient memory loss.

The influence of very preterm birth on adolescent EEG connectivity, network organization and long-term outcome

ObjectiveThe aim of this study was to explore differences in functional connectivity and network organization between very preterm born adolescents and term born controls and to investigate if these differences might explain the relation between preterm birth and adverse long-term outcome. MethodsForty-seven very preterm born adolescents (53% males) and 54 controls (54% males) with matching age, sex and parental educational levels underwent high-density electroencephalography (EEG) at 13 years of age. Long-term outcome was assessed by Intelligence Quotient (IQ), motor, attentional functioning and academic performance. Two minutes of EEG data were analysed within delta, theta, lower alpha, upper alpha and beta frequency bands. Within each frequency band, connectivity was assessed using the Phase Lag Index (PLI) and Amplitude Envelope Correlation, corrected for volume conduction (AEC-c). Brain networks were constructed using the minimum spanning tree method. ResultsVery preterm born adolescents had stronger beta PLI connectivity and less differentiated network organization. Beta AEC-c and differentiation of AEC-c based networks were negatively associated with long-term outcomes. EEG measures did not mediate the relation between preterm birth and outcomes. ConclusionsThis study shows that very preterm born adolescents may have altered functional connectivity and brain network organization in the beta frequency band. Alterations in measures of functional connectivity and network topologies, especially its differentiating characteristics, were associated with neurodevelopmental functioning. SignificanceThe findings indicate that EEG connectivity and network analysis is a promising tool for investigating underlying mechanisms of impaired functioning.

Non-reversibility outperforms functional connectivity in characterisation of brain states in MEG data

Characterising brain states during tasks is common practice for many neuroscientific experiments using electrophysiological modalities such as electroencephalography (EEG) and magnetoencephalography (MEG). Brain states are often described in terms of oscillatory power and correlated brain activity, i.e. functional connectivity. It is, however, not unusual to observe weak task induced functional connectivity alterations in the presence of strong task induced power modulations using classical time-frequency representation of the data. Here, we propose that non-reversibility, or the temporal asymmetry in functional interactions, may be more sensitive to characterise task induced brain states than functional connectivity. As a second step, we explore causal mechanisms of non-reversibility in MEG data using whole brain computational models. We include working memory, motor, language tasks and resting-state data from participants of the Human Connectome Project (HCP). Non-reversibility is derived from the lagged amplitude envelope correlation (LAEC), and is based on asymmetry of the forward and reversed cross-correlations of the amplitude envelopes. Using random forests, we find that non-reversibility outperforms functional connectivity in the identification of task induced brain states. Non-reversibility shows especially better sensitivity to capture bottom-up gamma induced brain states across all tasks, but also alpha band associated brain states. Using whole brain computational models we find that asymmetry in the effective connectivity and axonal conduction delays play a major role in shaping non-reversibility across the brain. Our work paves the way for better sensitivity in characterising brain states during both bottom-up as well as top-down modulation in future neuroscientific experiments.

Network Hyperexcitability in Early Alzheimer’s Disease: Is Functional Connectivity a Potential Biomarker?

Network hyperexcitability (NH) is an important feature of the pathophysiology of Alzheimer’s disease. Functional connectivity (FC) of brain networks has been proposed as a potential biomarker for NH. Here we use a whole brain computational model and resting-state MEG recordings to investigate the relation between hyperexcitability and FC. Oscillatory brain activity was simulated with a Stuart Landau model on a network of 78 interconnected brain regions. FC was quantified with amplitude envelope correlation (AEC) and phase coherence (PC). MEG was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Functional connectivity was determined with the corrected AECc and phase lag index (PLI), in the 4–8 Hz and the 8–13 Hz bands. The excitation/inhibition balance in the model had a strong effect on both AEC and PC. This effect was different for AEC and PC, and was influenced by structural coupling strength and frequency band. Empirical FC matrices of SCD and MCI showed a good correlation with model FC for AEC, but less so for PC. For AEC the fit was best in the hyperexcitable range. We conclude that FC is sensitive to changes in E/I balance. The AEC was more sensitive than the PLI, and results were better for the thetaband than the alpha band. This conclusion was supported by fitting the model to empirical data. Our study justifies the use of functional connectivity measures as surrogate markers for E/I balance.

Effect of channel density, inverse solutions and connectivity measures on EEG resting-state networks reconstruction: A simulation study

Along with the study of brain activity evoked by external stimuli, the past two decades witnessed an increased interest in characterizing the spontaneous brain activity occurring during resting conditions. The identification of connectivity patterns in this so-called "resting-state" has been the subject of a great number of electrophysiology-based studies, using the Electro/Magneto-Encephalography (EEG/MEG) source connectivity method. However, no consensus has been reached yet regarding a unified (if possible) analysis pipeline, and several involved parameters and methods require cautious tuning. This is particularly challenging when different analytical choices induce significant discrepancies in results and drawn conclusions, thereby hindering the reproducibility of neuroimaging research. Hence, our objective in this study was to shed light on the effect of analytical variability on outcome consistency by evaluating the implications of parameters involved in the EEG source connectivity analysis on the accuracy of resting-state networks (RSNs) reconstruction. We simulated, using neural mass models, EEG data corresponding to two RSNs, namely the default mode network (DMN) and dorsal attentional network (DAN). We investigated the impact of five channel densities (19, 32, 64, 128, 256), three inverse solutions (weighted minimum norm estimate (wMNE), exact low-resolution brain electromagnetic tomography (eLORETA), and linearly constrained minimum variance (LCMV) beamforming) and four functional connectivity measures (phase-locking value (PLV), phase-lag index (PLI), and amplitude envelope correlation (AEC) with and without source leakage correction), on the correspondence between reconstructed and reference networks. We showed that, with different analytical choices related to the number of electrodes, source reconstruction algorithm, and functional connectivity measure, high variability is present in the results. More specifically, our results show that a higher number of EEG channels significantly increased the accuracy of the reconstructed networks. Additionally, our results showed significant variability in the performance of the tested inverse solutions and connectivity measures. Such methodological variability and absence of analysis standardization represent a critical issue for neuroimaging studies that should be prioritized. We believe that this work could be useful for the field of electrophysiology connectomics, by increasing awareness regarding the challenge of variability in methodological approaches and its implications on reported results.

Non-invasive mapping of epileptogenic networks predicts surgical outcome.

Epilepsy is increasingly considered a disorder of brain networks. Studying these networks with functional connectivity can help identify hubs that facilitate the spread of epileptiform activity. Surgical resection of these hubs may lead patients who suffer from drug-resistant epilepsy to seizure freedom. Here, we aim to map non-invasively epileptogenic networks, through the virtual implantation of sensors estimated with electric and magnetic source imaging, in patients with drug-resistant epilepsy. We hypothesize that highly connected hubs identified non-invasively with source imaging can predict the epileptogenic zone and the surgical outcome better than spikes localized with conventional source localization methods (dipoles). We retrospectively analysed simultaneous high-density electroencephalography (EEG) and magnetoencephalography data recorded from 37 children and young adults with drug-resistant epilepsy who underwent neurosurgery. Using source imaging, we estimated virtual sensors at locations where intracranial EEG contacts were placed. On data with and without spikes, we computed undirected functional connectivity between sensors/contacts using amplitude envelope correlation and phase locking value for physiologically relevant frequency bands. From each functional connectivity matrix, we generated an undirected network containing the strongest connections within sensors/contacts using the minimum spanning tree. For each sensor/contact, we computed graph centrality measures. We compared functional connectivity and their derived graph centrality of sensors/contacts inside resection for good (n = 22, ILAE I) and poor (n = 15, ILAE II-VI) outcome patients, tested their ability to predict the epileptogenic zone in good-outcome patients, examined the association between highly connected hubs removal and surgical outcome and performed leave-one-out cross-validation to support their prognostic value. We also compared the predictive values of functional connectivity with those of dipoles. Finally, we tested the reliability of virtual sensor measures via Spearman's correlation with intracranial EEG at population- and patient-level. We observed higher functional connectivity inside than outside resection (P < 0.05, Wilcoxon signed-rank test) for good-outcome patients, on data with and without spikes across different bands for intracranial EEG and electric/magnetic source imaging and few differences for poor-outcome patients. These functional connectivity measures were predictive of both the epileptogenic zone and outcome (positive and negative predictive values ≥55%, validated using leave-one-out cross-validation) outperforming dipoles on spikes. Significant correlations were found between source imaging and intracranial EEG measures (0.4 ≤ rho ≤ 0.9, P < 0.05). Our findings suggest that virtual implantation of sensors through source imaging can non-invasively identify highly connected hubs in patients with drug-resistant epilepsy, even in the absence of frank epileptiform activity. Surgical resection of these hubs predicts outcome better than dipoles.

Reduced subgenual cingulate–dorsolateral prefrontal connectivity as an electrophysiological marker for depression

Major Depressive Disorder (MDD) is a widespread mental illness that causes considerable suffering, and neuroimaging studies are trying to reduce this burden by developing biomarkers that can facilitate detection. Prior fMRI- and neurostimulation studies suggest that aberrant subgenual Anterior Cingulate (sgACC)—dorsolateral Prefrontal Cortex (DLPFC) functional connectivity is consistently present within MDD. Combining the need for reliable depression markers with the electroencephalogram’s (EEG) high clinical utility, we investigated whether aberrant EEG sgACC–DLPFC functional connectivity could serve as a marker for depression. Source-space Amplitude Envelope Correlations (AEC) of 20 MDD patients and 20 matched controls were contrasted using non-parametric permutation tests. In addition, extracted AEC values were used to (a) correlate with characteristics of depression and (b) train a Support Vector Machine (SVM) to determine sgACC–DLPFC connectivity’s discriminative power. FDR-thresholded statistical maps showed reduced sgACC–DLPFC AEC connectivity in MDD patients relative to controls. This diminished AEC connectivity is located in the beta-1 (13–17 Hz) band and is associated with patients’ lifetime number of depressive episodes. Using extracted sgACC–DLPFC AEC values, the SVM achieved a classification accuracy of 84.6% (80% sensitivity and 89.5% specificity) indicating that EEG sgACC–DLPFC connectivity has promise as a biomarker for MDD.