Altered temporal stability in dynamic neural networks underlies connectivity changes in neurodevelopment

The effect of L-dopa and DBS on cortical oscillations in Parkinson's disease analyzed by hidden Markov model algorithm

BACKGROUND

Parkinson's disease (PD) is a movement disorder caused by dopaminergic neurodegeneration. Both Levodopa (L-dopa) and Subthalamic Deep Brain Stimulation (STN-DBS) effectively alleviate symptoms, yet their cerebral effects remain under-explored. Understanding these effects is essential for optimizing treatment strategies and assessing disease severity. Magnetoencephalogram (MEG) data provide a continuous time series signal that reflects the dynamic changes in brain activity. The hidden Markov model (HMM) can capture and model the temporal features and underlying states of the MEG signal to extract potential brain states and monitor dynamic changes. In this study, we employed HMM to investigate the cortical mechanism underlying the treatment of PD patients using MEG recordings.

METHODS

21 PD patients treated with medication underwent MEG recording in both L-dopa medoff and medon conditions. Additionally, 11 PD patients receiving STN-DBS treatment underwent MEG recording in both dbsoff and dbson conditions. The MEG data were segmented into four states by Time-delay embedded Hidden Markov Model (TDE-HMM) algorithm. The state parameters including Fractional Occupancy (FO), Interval Times (IT), and Life Time (LT) for each state and power spectrum of β band were analyzed to study the effects of L-dopa and STN-DBS treatment respectively.

RESULTS

L-dopa significantly increased the motor state of HMM and power in the motor area of both high β (21-35 Hz) and low β (13-20 Hz); the motor state of high β in medoff were correlated with the Unified Parkinson's Disease Rating Scale III (UPDRS III). Conversely, DBS significantly diminishes the motor state of HMM and power in motor area of high β oscillations. The score changes of tremor and limb rigidity after DBS treatment were significantly correlated with the changes of motor state of high β.

CONCLUSIONS

This study demonstrates that L-dopa and STN-DBS exert differing effects on β oscillations in the motor cortex of PD patients, primarily in high β band. Understanding these distinct neurophysiological impacts can provide valuable insights for refining therapeutic approaches in motor control for PD patients.

Clustering Electrophysiological Predisposition to Binge Drinking: An Unsupervised Machine Learning Analysis

BACKGROUND

The demand for fresh strategies to analyze intricate multidimensional data in neuroscience is increasingly evident. One of the most complex events during our neurodevelopment is adolescence, where our nervous system suffers constant changes, not only in neuroanatomical traits but also in neurophysiological components. One of the most impactful factors we deal with during this time is our environment, especially when encountering external factors such as social behaviors or substance consumption. Binge drinking (BD) has emerged as an extended pattern of alcohol consumption in teenagers, not only affecting their future lifestyle but also changing their neurodevelopment. Recent studies have changed their scope into finding predisposition factors that may lead adolescents into this kind of patterns of consumption.

METHODS

In this article, using unsupervised machine learning (UML) algorithms, we analyze the relationship between electrophysiological activity of healthy teenagers and the levels of consumption they had 2 years later. We used hierarchical agglomerative UML techniques based on Ward's minimum variance criterion to clusterize relations between power spectrum and functional connectivity and alcohol consumption, based on similarity in their correlations, in frequency bands from theta to gamma.

RESULTS

We found that all frequency bands studied had a pattern of clusterization based on anatomical regions of interest related to neurodevelopment and cognitive and behavioral aspects of addiction, highlighting the dorsolateral and medial prefrontal, the sensorimotor, the medial posterior, and the occipital cortices. All these patterns, of great cohesion and coherence, showed an abnormal electrophysiological activity, representing a dysregulation in the development of core resting-state networks. The clusters found maintained not only plausibility in nature but also robustness, making this a great example of the usage of UML in the analysis of electrophysiological activity-a new perspective into analysis that, while contributing to classical statistics, can clarify new characteristics of the variables of interest.

Adolescent alcohol consumption predicted by differences in electrophysiological functional connectivity and neuroanatomy

Alcohol consumption during adolescence has been associated with neuroanatomical abnormalities and the appearance of future disorders. However, the latest advances in this field point to the existence of risk profiles which may lead to some individuals into an early consumption. To date, some studies have established predictive models of consumption based on sociodemographic, behavioral, and anatomical-functional variables using MRI. However, the neuroimaging variables employed are usually restricted to local and hemodynamic phenomena. Given the potential of connectome approaches, and the high temporal dynamics of electrophysiology, we decided to explore the relationship between future alcohol consumption and electrophysiological connectivity measured by MEG in a cohort of 83 individuals aged 14 to 16. As a result, we found a positive correlation between alcohol consumption and the functional connectivity in frontal, parietal, and frontoparietal connections. Once this relationship was described, multivariate linear regression analyses were used to evaluate the predictive capacity of functional connectivity in conjunction with other neuroanatomical and behavioral variables described in the literature. Finally, the multivariate linear regression analysis determined the importance of anatomical and functional variables in the prediction of alcohol consumption but failed to find associations with impulsivity, sensation seeking, and executive function scales. In conclusion, the predictive traits obtained in these models were closely associated with changes occurring during adolescence, suggesting the existence of different paths in neurodevelopment that have the potential to influence adolescents' relationship with alcohol consumption.

From preparation to post-processing: Insights into evoked and induced cortical activity during pre-cued motor reactions in children and adolescents

INTRODUCTION

The motor system undergoes significant development throughout childhood and adolescence. The contingent negative variation (CNV), a brain response reflecting preparation for upcoming actions, offers valuable insights into these changes. However, previous CNV studies of motor preparation have primarily focused on adults, leaving a gap in our understanding of how cortical activity related to motor planning and execution matures in children and adolescents.

METHODS

The study addresses this gap by investigating the maturation of motor preparation, pre-activation, and post-processing in 46 healthy, right-handed children and adolescents aged 5-16 years. To overcome the resolution limitations of previous studies, we combined 64-electrode high-density Electroencephalography (EEG) and advanced analysis techniques, such as event-related potentials (ERPs), mu-rhythm desynchronization as well as source localization approaches. The combined analyses provided an in-depth understanding of cortical activity during motor control.

RESULTS

Our data showed that children exhibited prolonged reaction times, increased errors, and a distinct pattern of cortical activation compared to adolescents. The findings suggest that the supplementary motor area (SMA) plays a progressively stronger role in motor planning and response evaluation as children age. Additionally, we observe a decrease in sensory processing and post-movement activity with development, potentially reflecting increased efficiency. Interestingly, adolescent subjects, unlike young adults in previous studies, did not yet show contralateral activation of motor areas during the motor preparation phase (late CNV).

CONCLUSION

The progressive increase in SMA activation and distinct cortical activation patterns in younger participants suggest immature motor areas. These immature regions might be a primary cause underlying the age-related increase in motor action control efficiency. Additionally, the study demonstrates a prolonged maturation of cortical motor areas, extending well into early adulthood, challenging the assumption that motor control is fully developed by late adolescence. This research, extending fundamental knowledge of motor control development, offers valuable insights that lay the foundation for understanding and treating motor control difficulties.

Tracking the neurodevelopmental trajectory of beta band oscillations with optically pumped magnetometer-based magnetoencephalography

Neural oscillations mediate the coordination of activity within and between brain networks, supporting cognition and behaviour. How these processes develop throughout childhood is not only an important neuroscientific question but could also shed light on the mechanisms underlying neurological and psychiatric disorders. However, measuring the neurodevelopmental trajectory of oscillations has been hampered by confounds from instrumentation. In this paper, we investigate the suitability of a disruptive new imaging platform - optically pumped magnetometer-based magnetoencephalography (OPM-MEG) - to study oscillations during brain development. We show how a unique 192-channel OPM-MEG device, which is adaptable to head size and robust to participant movement, can be used to collect high-fidelity electrophysiological data in individuals aged between 2 and 34 years. Data were collected during a somatosensory task, and we measured both stimulus-induced modulation of beta oscillations in sensory cortex, and whole-brain connectivity, showing that both modulate significantly with age. Moreover, we show that pan-spectral bursts of electrophysiological activity drive task-induced beta modulation, and that their probability of occurrence and spectral content change with age. Our results offer new insights into the developmental trajectory of beta oscillations and provide clear evidence that OPM-MEG is an ideal platform for studying electrophysiology in neurodevelopment.

Sex-specific Differences in Resting Oscillatory Dynamics in Children with Prenatal Alcohol Exposure

At rest children with prenatal alcohol exposure (PAE) exhibit impaired static and dynamic functional connectivity, along with decreased alpha oscillations. Sex-specific information regarding the impact of PAE on whole-brain resting-state gamma spectral power remains unknown. Eyes-closed and eyes-open MEG resting-state data were examined in 83 children, ages 6-13 years of age. Using a matched design, the sample consisted of 42 typically developing children (TDC) (22 males/20 females) and 41 children with PAE and/or a fetal alcohol spectrum disorders (FASD) diagnosis (21 males/20 females). Whole-brain source resting-state spectral power was examined to determine group and sex specific relationships. Within gamma, we found sex and group specific changes such that female participants with PAE/FASD had increased gamma power when compared to female TDC and male participants with PAE/FASD. These differences were detected in most source regions analyzed during both resting-states, and were observed across the age spectrum examined. Within delta, we found sex and group specific changes such that female participants with PAE/FASD had decreased delta power when compared to female TDC and male participants with PAE/FASD. The reduced delta oscillations in female participants with PAE/FASD were detected in several source regions during eyes-closed rest and were evident at younger ages. These results indicate PAE alters neural oscillations during rest in a sex-specific manner, with females with PAE/FASD showing the largest perturbations. These results further demonstrate PAE has global effects on resting-state spectral power and connectivity, creating long-term consequences by potentially disrupting the excitation/inhibition balance in the brain, interrupting normative neurodevelopment.

Motor learning- and consolidation-related resting state fast and slow brain dynamics across wake and sleep

Motor skills dynamically evolve during practice and after training. Using magnetoencephalography, we investigated the neural dynamics underpinning motor learning and its consolidation in relation to sleep during resting-state periods after the end of learning (boost window, within 30 min) and at delayed time scales (silent 4 h and next day 24 h windows) with intermediate daytime sleep or wakefulness. Resting-state neural dynamics were investigated at fast (sub-second) and slower (supra-second) timescales using Hidden Markov modelling (HMM) and functional connectivity (FC), respectively, and their relationship to motor performance. HMM results show that fast dynamic activities in a Temporal/Sensorimotor state network predict individual motor performance, suggesting a trait-like association between rapidly recurrent neural patterns and motor behaviour. Short, post-training task re-exposure modulated neural network characteristics during the boost but not the silent window. Re-exposure-related induction effects were observed on the next day, to a lesser extent than during the boost window. Daytime naps did not modulate memory consolidation at the behavioural and neural levels. These results emphasise the critical role of the transient boost window in motor learning and memory consolidation and provide further insights into the relationship between the multiscale neural dynamics of brain networks, motor learning, and consolidation.

Brain connectivity patterns derived from aging-related alterations in dynamic brain functional networks and their potential as features for brain age classification

Objective.Recent studies have demonstrated that the analysis of brain functional networks (BFNs) is a powerful tool for exploring brain aging and age-related neurodegenerative diseases. However, investigating the mechanism of brain aging associated with dynamic BFN is still limited. The purpose of this study is to develop a novel scheme to explore brain aging patterns by constructing dynamic BFN using resting-state functional magnetic resonance imaging data.Approach.A dynamic sliding-windowed non-negative block-diagonal representation (dNBDR) method is proposed for constructing dynamic BFN, based on which a collection of dynamic BFN measures are suggested for examining age-related differences at the group level and used as features for brain age classification at the individual level.Results.The experimental results reveal that the dNBDR method is superior to the sliding time window with Pearson correlation method in terms of dynamic network structure quality. Additionally, significant alterations in dynamic BFN structures exist across the human lifespan. Specifically, average node flexibility and integration coefficient increase with age, while the recruitment coefficient shows a decreased trend. The proposed feature extraction scheme based on dynamic BFN achieved the highest accuracy of 78.7% in classifying three brain age groups.Significance. These findings suggest that dynamic BFN measures, dynamic community structure metrics in particular, play an important role in quantitatively assessing brain aging.

The neurodevelopmental trajectory of beta band oscillations: an OPM-MEG study

Neural oscillations mediate the coordination of activity within and between brain networks, supporting cognition and behaviour. How these processes develop throughout childhood is not only an important neuroscientific question but could also shed light on the mechanisms underlying neurological and psychiatric disorders. However, measuring the neurodevelopmental trajectory of oscillations has been hampered by confounds from instrumentation. In this paper, we investigate the suitability of a disruptive new imaging platform - Optically Pumped Magnetometer-based magnetoencephalography (OPM-MEG) - to study oscillations during brain development. We show how a unique 192-channel OPM-MEG device, which is adaptable to head size and robust to participant movement, can be used to collect high-fidelity electrophysiological data in individuals aged between 2 and 34 years. Data were collected during a somatosensory task, and we measured both stimulus-induced modulation of beta oscillations in sensory cortex, and whole-brain connectivity, showing that both modulate significantly with age. Moreover, we show that pan-spectral bursts of electrophysiological activity drive task-induced beta modulation, and that their probability of occurrence and spectral content change with age. Our results offer new insights into the developmental trajectory of beta oscillations and provide clear evidence that OPM-MEG is an ideal platform for studying electrophysiology in neurodevelopment.

State-transition dynamics of resting-state functional magnetic resonance imaging data: model comparison and test-to-retest analysis

Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals.

Brain-wide network analysis of resting-state neuromagnetic data

The present study performed a brain-wide network analysis of resting-state magnetoencephalograms recorded from 53 healthy participants to visualize elaborate brain maps of phase- and amplitude-derived graph-theory metrics at different frequencies. To achieve this, we conducted a vertex-wise computation of threshold-independent graph metrics by combining proportional thresholding and a conjunction analysis and applied them to a correlation analysis of age and brain networks. Source power showed a frequency-dependent cortical distribution. Threshold-independent graph metrics derived from phase- and amplitude-based connectivity showed similar or different distributions depending on frequency. Vertex-wise age-brain correlation maps revealed that source power at the beta band and the amplitude-based degree at the alpha band changed with age in local regions. The present results indicate that a brain-wide analysis of neuromagnetic data has the potential to reveal neurophysiological network features in the human brain in a resting state.

Hampered gamma oscillations induced by sad emotion underlying suicide attempt in major depressive disorder

AIM

Major depressive disorder (MDD) is associated with high suicidality, especially for those with suicide attempt (SA). Although impaired oscillatory activity has been previously reported in patients with SA, little is known about precise temporal-spatial variability of its neural dynamics. To solve this, the current study probed the spectral power and network interactions underlying SA in MDD.

METHODS

The present study recruited 104 subjects including 56 subjects with MDD (30 with SA and 26 without SA) and 48 healthy controls, who performed sad expressions recognition task during magnetoencephalography (MEG) recording. By investigating source-reconstructed MEG-data, brain states representing different task stages were estimated from a Hidden Markov model. Spectrum power and network connectivity were compared via Gaussian Mixture Models, and fractional occupancy (FO) of states were compared via an independent F-test.

RESULTS

Brain states were corresponding to various frequencies (theta/beta/low gamma/ high gamma). In low gamma band (35-45 Hz), the early visual state exhibited increased activation and hyper inter-network connectivity between visual regions and the limbic system, while the middle fronto-parietal state exhibited attenuated activation and decreased intra-network connectivity within fronto-parietal regions in SA group. Crucially, FO values of these two states were significantly correlated with the suicide risks.

CONCLUSIONS

Suicide behavior of patients with MDD was significantly associated with aberrant oscillations in low gamma band. Elevated oscillations in occipital cortices and attenuated oscillations in fronto-parietal cortices were significantly associated with SA. Manifesting sadness indulging and reckless decision-making, the hampered temporal characteristics could help explain the neural-electric basis of SA.

Preliminary Observations of Resting-State Magnetoencephalography in Nonmedicated Children with Obsessive-Compulsive Disorder

Background: Cortico-striato-thalamo-cortical (CSTC) network alterations are hypothesized to contribute to symptoms of obsessive-compulsive disorder (OCD). To date, very few studies have examined whether CSTC network alterations are present in children with OCD, who are medication naive. Medication-naive pediatric imaging samples may be optimal to study neural correlates of illness and identify brain-based markers, given the proximity to illness onset. Methods: Magnetoencephalography (MEG) data were analyzed at rest, in 18 medication-naive children with OCD (M = 12.1 years ±2.0 standard deviation [SD]; 10 M/8 F) and 13 typically developing children (M = 12.3 years ±2.2 SD; 6 M/7 F). Whole-brain MEG-derived resting-state functional connectivity (rs-fc), for alpha- and gamma-band frequencies were compared between OCD and typically developing (control) groups. Results: Increased MEG-derived rs-fc across alpha- and gamma-band frequencies was found in the OCD group compared to the control group. Increased MEG-derived rs-fc at alpha-band frequencies was evident across a number of regions within the CSTC circuitry and beyond, including the cerebellum and limbic regions. Increased MEG-derived rs-fc at gamma-band frequencies was restricted to the frontal and temporal cortices. Conclusions: This MEG study provides preliminary evidence of altered alpha and gamma networks, at rest, in medication-naive children with OCD. These results support prior findings pointing to the relevance of CSTC circuitry in pediatric OCD and further support accumulating evidence of altered connectivity between regions that extend beyond this network, including the cerebellum and limbic regions. Given the substantial portion of children and youth whose OCD symptoms do not respond to conventional treatments, our findings have implications for future treatment innovation research aiming to target and track whether brain patterns associated with having OCD may change with treatment and/or predict treatment response.

Dynamic connectivity alterations in anterior cingulate cortex associated with suicide attempts in bipolar disorders with a current major depressive episode

OBJECTIVE

Considering that the physiological mechanism of the anterior cingulate cortex (ACC) in suicide brain remains elusive for bipolar disorder (BD) patients. The study aims to investigate the intrinsic relevance between ACC and suicide attempts (SA) through transient functional connectivity (FC).

METHODS

We enrolled 50 un-medicated BD patients with at least one SA, 67 none-suicide attempt patients (NSA) and 75 healthy controls (HCs). The sliding window approach was utilized to study the dynamic FC of ACC via resting-state functional MRI data. Subsequently, we probed into the temporal properties of dynamic FC and then estimated the relationship between dynamic characteristics and clinical variables using the Pearson correlation.

RESULTS

We found six distinct FC states in all populations, with one of them being more associated with SA. Compared with NSA and HCs, the suicide-related functional state showed significantly reduced dwell time in SA patients, accompanied by a significantly increased FC strength between the right ACC and the regions within the subcortical (SubC) network. In addition, the number of transitions was significantly increased in SA patients relative to other groups. All these altered indicators were significantly correlated with the suicide risk.

CONCLUSIONS

The results suggested that the dysfunction of ACC was relevant to SA from a dynamic FC perspective in BD patients. It highlights the temporal properties in dynamic FC of ACC that could be used as a putative target of suicide risk assessment for BD patients.

Resting state fast brain dynamics predict interindividual variability in motor performance

Motor learning features rapid enhancement during practice then offline post-practice gains with the reorganization of related brain networks. We hypothesised that fast transient, sub-second variations in magnetoencephalographic (MEG) network activity during the resting-state (RS) reflect early learning-related plasticity mechanisms and/or interindividual motor variability in performance. MEG RS activity was recorded before and 20 min after motor learning. Hidden Markov modelling (HMM) of MEG power envelope signals highlighted 8 recurrent topographical states. For two states, motor performance levels were associated with HMM temporal parameters both in pre- and post-learning resting-state sessions. However, no association emerged with offline changes in performance. These results suggest a trait-like relationship between spontaneous transient neural dynamics at rest and interindividual variations in motor abilities. On the other hand, transient RS dynamics seem not to be state-dependent, i.e., modulated by learning experience and reflect neural plasticity, at least on the short timescale.

Developmental trajectory of MEG resting-state oscillatory activity in children and adolescents: a longitudinal reliability study

Neural oscillations may be sensitive to aspects of brain maturation such as myelination and synaptic density changes. Better characterization of developmental trajectories and reliability is necessary for understanding typical and atypical neurodevelopment. Here, we examined reliability in 110 typically developing children and adolescents (aged 9-17 years) across 2.25 years. From 10 min of magnetoencephalography resting-state data, normalized source spectral power and intraclass correlation coefficients were calculated. We found sex-specific differences in global normalized power, with males showing age-related decreases in delta and theta, along with age-related increases in beta and gamma. Females had fewer significant age-related changes. Structural magnetic resonance imaging revealed that males had more total gray, subcortical gray, and cortical white matter volume. There were significant age-related changes in total gray matter volume with sex-specific and frequency-specific correlations to normalized power. In males, increased total gray matter volume correlated with increased theta and alpha, along with decreased gamma. Split-half reliability was excellent in all frequency bands and source regions. Test-retest reliability ranged from good (alpha) to fair (theta) to poor (remaining bands). While resting-state neural oscillations can have fingerprint-like quality in adults, we show here that neural oscillations continue to evolve in children and adolescents due to brain maturation and neurodevelopmental change.

The "Neural Shift" of Sleep Quality and Cognitive Aging: A Resting-State MEG Study of Transient Neural Dynamics

Sleep quality changes dramatically from young to old age, but its effects on brain dynamics and cognitive functions are not yet fully understood. We tested the hypothesis that a shift in brain networks dynamics relates to sleep quality and cognitive performance across the lifespan. Network dynamics were assessed using Hidden Markov Models (HMMs) in resting-state MEG data from a large cohort of population-based adults (N = 564, aged 18-88). Using multivariate analyses of brain-sleep profiles and brain-cognition profiles, we found an age-related "neural shift," expressed as decreased occurrence of "lower-order" brain networks coupled with increased occurrence of "higher-order" networks. This "neural shift" was associated with both increased sleep dysfunction and decreased fluid intelligence, and this relationship was not explained by age, sex or other covariates. These results establish the link between poor sleep quality, as evident in aging, and a behavior-related shift in neural dynamics.

Spontaneous transient states of fronto-temporal and default-mode networks altered by suicide attempt in major depressive disorder

Major depressive disorder (MDD) is associated with increased suicidality, and it's still challenging to identify suicide in clinical practice. Although suicide attempt (SA) is the most relevant precursor with multiple functional abnormalities reported from neuroimaging studies, little is known about how the spontaneous transient activated patterns organize and coordinate brain networks underlying SA. Thus, we obtained resting-state magnetoencephalography data for two MDD subgroups of 44 non-suicide patients and 34 suicide-attempted patients, together with 49 matched health-controls. For the source-space signals, Hidden Markov Model (HMM) helped to capture the sub-second dynamic activity via a hidden sequence of finite number of states. Temporal parameters and spectral activation were acquired for each state and then compared between groups. Here, HMM states characterized the spatiotemporal signatures of eight networks. The activity of suicide attempters switches more frequently into the fronto-temporal network, as the time spent occupancy of fronto-temporal state is increased and interval time is decreased compared with the non-suicide patients. Moreover, these changes are significantly correlated with Nurses' Global Assessment of Suicide Risk scores. Suicide attempters also exhibit increased state-wise activations in the theta band (4-8 Hz) in the posterior default mode network centered on posterior cingulate cortex, which can't be detected in the static spectral analysis. These alternations may disturb the time allocations of cognitive control regulations and cause inflexible decision making to SA. As the better sensitivity of dynamic study in reflecting SA diathesis than the static is validated, dynamic stability could serve as a potential neuronal marker for SA.

Microstates and power envelope hidden Markov modeling probe bursting brain activity at different timescales

State modeling of whole-brain electroencephalography (EEG) or magnetoencephalography (MEG) allows to investigate transient, recurring neurodynamical events. Two widely-used techniques are the microstate analysis of EEG signals and hidden Markov modeling (HMM) of MEG power envelopes. Both reportedly lead to similar state lifetimes on the 100 ms timescale, suggesting a common neural basis. To investigate whether microstates and power envelope HMM states describe the same neural dynamics, we used simultaneous MEG/EEG recordings at rest and compared the spatial signature and temporal activation dynamics of microstates and power envelope HMM states obtained separately from EEG and MEG. Results showed that microstates and power envelope HMM states differ both spatially and temporally. Microstates reflect sharp events of neural synchronization, whereas power envelope HMM states disclose network-level activity with 100–200 ms lifetimes. Further, MEG microstates do not correspond to the canonical EEG microstates but are better interpreted as split HMM states. On the other hand, both MEG and EEG HMM states involve the (de)activation of similar functional networks. Microstate analysis and power envelope HMM thus appear sensitive to neural events occurring over different spatial and temporal scales. As such, they represent complementary approaches to explore the fast, sub-second scale bursting electrophysiological dynamics in spontaneous human brain activity.

Age of onset modulates resting-state brain network dynamics in Friedreich Ataxia

This magnetoencephalography (MEG) study addresses (i) how Friedreich ataxia (FRDA) affects the sub‐second dynamics of resting‐state brain networks, (ii) the main determinants of their dynamic alterations, and (iii) how these alterations are linked with FRDA‐related changes in resting‐state functional brain connectivity (rsFC) over long timescales. For that purpose, 5 min of resting‐state MEG activity were recorded in 16 FRDA patients (mean age: 27 years, range: 12–51 years; 10 females) and matched healthy subjects. Transient brain network dynamics was assessed using hidden Markov modeling (HMM). Post hoc median‐split, nonparametric permutations and Spearman rank correlations were used for statistics. In FRDA patients, a positive correlation was found between the age of symptoms onset (ASO) and the temporal dynamics of two HMM states involving the posterior default mode network (DMN) and the temporo‐parietal junctions (TPJ). FRDA patients with an ASO <11 years presented altered temporal dynamics of those two HMM states compared with FRDA patients with an ASO > 11 years or healthy subjects. The temporal dynamics of the DMN state also correlated with minute‐long DMN rsFC. This study demonstrates that ASO is the main determinant of alterations in the sub‐second dynamics of posterior associative neocortices in FRDA patients and substantiates a direct link between sub‐second network activity and functional brain integration over long timescales.

Transient neural network dynamics in cognitive ageing

It is important to maintain cognitive function in old age, yet the neural substrates that support successful cognitive ageing remain unclear. One factor that might be crucial, but has been overlooked due to limitations of previous data and methods, is the ability of brain networks to flexibly reorganize and coordinate over a millisecond time-scale. Magnetoencephalography (MEG) provides such temporal resolution, and can be combined with Hidden Markov Models (HMMs) to characterise transient neural states. We applied HMMs to resting-state MEG data from a large cohort (N=595) of population-based adults (aged 18-88), who also completed a range of cognitive tasks. Using multivariate analysis of neural and cognitive profiles, we found that decreased occurrence of "lower-order" brain networks, coupled with increased occurrence of "higher-order" networks, was associated with both increasing age and decreased fluid intelligence. These results favour theories of age-related reductions in neural efficiency over current theories of age-related functional compensation, and suggest that this shift might reflect a stable property of the ageing brain.

Frontoparietal network and neuropsychological measures in typically developing children

Resting-state activity has been used to gain a broader understanding of typical and aberrant developmental changes. However, the developmental trajectory of resting-state activity in relation to cognitive performance has not been studied in detail. The present study assessed spectral characteristics of theta (5-8 Hz) and alpha (9-13 Hz) frequency bands during resting-state in a priori selected regions of the frontoparietal network (FPN). We also examined the relationship between resting-state activity and cognitive performance in typically developing children. We hypothesized that older children and children with high attentional scores would have higher parietal alpha activity and frontal theta activity while at rest compared to young children and those with lower attentional scores. MEG data were collected in 65 typically developing children, ages 9-14 years, as part of the Developmental Chronnecto-Genomics study. Resting-state data were collected during eyes open and eyes closed for 5 min. Participants completed the NIH Toolbox Flanker Inhibitory Control (FICA) and Attention Test and Dimensional Change Card Sort Test (DCCS) to assess top-down attentional control. Spectral power density was used to characterize the FPN. We found during eyes open and eyes closed, all participants had higher theta and alpha power in parietal regions relative to frontal regions. The group with high attentional scores had higher alpha power during resting-state eyes closed compared to those with low attentional scores. However, there were no significant differences between age groups, suggesting changes in the maturation of neural oscillations in theta and alpha are not evident among children in the 9-14-year age range.

Modelling state-transition dynamics in resting-state brain signals by the hidden Markov and Gaussian mixture models

Recent studies have proposed that one can summarize brain activity into dynamics among a relatively small number of hidden states and that such an approach is a promising tool for revealing brain function. Hidden Markov models (HMMs) are a prevalent approach to inferring such neural dynamics among discrete brain states. However, the impact of assuming Markovian structure in neural time series data has not been sufficiently examined. Here, to address this situation and examine the performance of the HMM, we compare the model with the Gaussian mixture model (GMM), which is with no temporal regularization and thus a statistically simpler model than the HMM, by applying both models to synthetic time series generated from empirical resting‐state functional magnetic resonance imaging (fMRI) data. We compared the GMM and HMM for various sampling frequencies, lengths of recording per participant, numbers of participants and numbers of independent component signals. We find that the HMM attains a better accuracy of estimating the hidden state than the GMM in a majority of cases. However, we also find that the accuracy of the GMM is comparable to that of the HMM under the condition that the sampling frequency is reasonably low (e.g., TR = 2.88 or 3.60 s) or the data are relatively short. These results suggest that the GMM can be a viable alternative to the HMM for investigating hidden‐state dynamics under this condition.

The developing relations between networks of cortical myelin and neurophysiological connectivity

Recent work identified that patterns of distributed brain regions sharing similar myeloarchitecture are related to underlying functional connectivity, demonstrating cortical myelin's plasticity to changes in functional demand. However, the changing relations between functional and structural architecture throughout child and adulthood are poorly understood. We show that structural covariance connectivity (T1-weighted/T2-weighted ratio) and functional connectivity (magnetoencephalography) exhibit nonlinear developmental changes. We then show significant relations between structural and functional connectivity, which have shared and distinct characteristics dependent on the neural oscillatory frequency. Increases in structure-function coupling are visible during the protracted myelination observed throughout childhood and adolescence and are followed by decreases near the onset of adulthood. Our work lays the foundation for understanding the mechanisms by which myeloarchitecture supports brain function, enabling future investigations into how clinical populations may deviate from normative patterns.

Dependence of Connectivity on the Logarithm of Geometric Distance in Brain Networks

Physical connections between nodes in a complex network are constrained by limiting factors, such as the cost of establishing links and maintaining them, which can hinder network capability in terms of signal propagation speed and processing power. Trade-off mechanisms between cost constraints and performance requirements are reflected in the topology of a network and, ultimately, on the dependence of connectivity on geometric distance. This issue, though rarely addressed, is crucial in neuroscience, where physical links between brain regions are associated with a metabolic cost. In this work we investigate brain connectivity-estimated by means of a recently developed method that evaluates time scales of cross-correlation observability-and its dependence on geometric distance by analyzing resting state magnetoencephalographic recordings collected from a large set of healthy subjects. We identify three regimes of distance each showing a specific behavior of connectivity. This identification makes up a new tool to study the mechanisms underlying network formation and sustainment, with possible applications to the investigation of neuroscientific issues, such as aging and neurodegenerative diseases.

Alterations in resting-state network dynamics along the Alzheimer's disease continuum

Human brain activity is intrinsically organized into resting-state networks (RSNs) that transiently activate or deactivate at the sub-second timescale. Few neuroimaging studies have addressed how Alzheimer's disease (AD) affects these fast temporal brain dynamics, and how they relate to the cognitive, structural and metabolic abnormalities characterizing AD. We aimed at closing this gap by investigating both brain structure and function using magnetoencephalography (MEG) and hybrid positron emission tomography-magnetic resonance (PET/MR) in 10 healthy elders, 10 patients with subjective cognitive decline (SCD), 10 patients with amnestic mild cognitive impairment (aMCI) and 10 patients with typical Alzheimer's disease with dementia (AD). The fast activation/deactivation state dynamics of RSNs were assessed using hidden Markov modeling (HMM) of power envelope fluctuations at rest measured with MEG. Correlations were sought between temporal properties of HMM states and participants' cognitive test scores, whole hippocampal grey matter volume and regional brain glucose metabolism. The posterior default-mode network (DMN) was less often activated and for shorter durations in AD patients than matched healthy elders. No significant difference was found in patients with SCD or aMCI. The time spent by participants in the activated posterior DMN state did not correlate significantly with cognitive scores, nor with the whole hippocampal volume. However, it correlated positively with the regional glucose consumption in the right dorsolateral prefrontal cortex (DLPFC). AD patients present alterations of posterior DMN power activation dynamics at rest that identify an additional electrophysiological correlate of AD-related synaptic and neural dysfunction. The right DLPFC may play a causal role in the activation of the posterior DMN, possibly linked to the occurrence of mind wandering episodes. As such, these data might suggest a neural correlate of the decrease in mind wandering episodes reported in pathological aging.

Alpha connectivity and inhibitory control in adults with autism spectrum disorder

BACKGROUND

Individuals with autism spectrum disorder (ASD) often report difficulties with inhibition in everyday life. During inhibition tasks, adults with ASD show reduced activation of and connectivity between brain areas implicated in inhibition, suggesting impairments in inhibitory control at the neural level. Our study further investigated these differences by using magnetoencephalography (MEG) to examine the frequency band(s) in which functional connectivity underlying response inhibition occurs, as brain functions are frequency specific, and whether connectivity in certain frequency bands differs between adults with and without ASD.

METHODS

We analysed MEG data from 40 adults with ASD (27 males; 26.94 ± 6.08 years old) and 39 control adults (27 males; 27.29 ± 5.94 years old) who performed a Go/No-go task. The task involved two blocks with different proportions of No-go trials: Inhibition (25% No-go) and Vigilance (75% No-go). We compared whole-brain connectivity in the two groups during correct No-go trials in the Inhibition vs. Vigilance blocks between 0 and 400 ms.

RESULTS

Despite comparable performance on the Go/No-go task, adults with ASD showed reduced connectivity compared to controls in the alpha band (8-14 Hz) in a network with a main hub in the right inferior frontal gyrus. Decreased connectivity in this network predicted more self-reported difficulties on a measure of inhibition in everyday life.

LIMITATIONS

Measures of everyday inhibitory control were not available for all participants, so this relationship between reduced network connectivity and inhibitory control abilities may not be necessarily representative of all adults with ASD or the larger ASD population. Further research with independent samples of adults with ASD, including those with a wider range of cognitive abilities, would be valuable.

CONCLUSIONS

Our findings demonstrate reduced functional brain connectivity during response inhibition in adults with ASD. As alpha-band synchrony has been linked to top-down control mechanisms, we propose that the lack of alpha synchrony observed in our ASD group may reflect difficulties in suppressing task-irrelevant information, interfering with inhibition in real-life situations.

Neural generators involved in visual cue processing in children with attention-deficit/hyperactivity disorder (ADHD)

Event-related potentials (ERP) studies report alterations in the ongoing visuo-attentional processes in children with attention-deficit/hyperactivity disorder (ADHD). We hypothesized that the neural generators progressively recruited after a cue stimulus imply executive-related areas well before engagement in executive processing in children with ADHD compared to typically developed children (TDC). We computed source localization (swLORETA) of the ERP and ERSP evoked by the Cue stimulus during a visual Cue-Go/Nogo paradigm in 15 ADHD compared to 16 TDC. A significant difference in N200/P200 amplitude over the right centro-frontal regions was observed between ADHD and TDC, supported by a stronger contribution of the left visuo-motor coordination area, premotor cortex, and prefrontal cortex in ADHD. In addition, we recorded a greater beta power spectrum in ADHD during the 80-230 ms interval, which was explained by increased activity in occipito-parieto-central areas and lower activity in the left supramarginal gyrus and prefrontal areas in ADHD. Successive analysis of the ERP generators (0-500 ms with successive periods of 50 ms) revealed significant differences beginning at 50 ms, with higher activity in the ventral anterior cingulate cortex, premotor cortex, and fusiform gyrus, and ending at 400-500 ms with higher activity of the dorsolateral prefrontal cortex and lower activity of the posterior cingulate cortex in ADHD compared to TDC. The areas contributing to ERP in ADHD and TDC differ from the early steps of visuo-attentional processing and reveal an overinvestment of the executive networks interfering with the activity of the dorsal attention network in children with ADHD.

Measuring robust functional connectivity from resting-state MEG using amplitude and entropy correlation across frequency bands and temporal scales

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MRVE measures the dynamic variability of MEG signals at a range of temporal scales.

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MRVE correlation and AEC detected robust resting state functional connectivity.

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The most robust patterns were found for f_S=75Hz MRVE correlation and beta band AEC.

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Connectivity strength negatively correlates with local MRVE at fine time scales.

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Eye movement affects resting state connectivity measurements across frequencies.

Recent studies have shown how MEG can reveal spatial patterns of functional connectivity using frequency-specific oscillatory coupling measures and that these may be modified in disease. However, there is a need to understand both how repeatable these patterns are across participants and how these measures relate to the moment-to-moment variability (or ‘irregularity) of neural activity seen in healthy brain function. In this study, we used Multi-scale Rank-Vector Entropy (MRVE) to calculate the dynamic timecourses of signal variability over a range of temporal scales. The correlation of MRVE timecourses was then used to detect functional connections in resting state MEG recordings that were robust over 183 participants and varied with temporal scale. We compared these MRVE connectivity patterns to those derived using the more conventional method of oscillatory amplitude envelope correlation (AEC) using methods designed to quantify the consistency of these patterns across participants. Using AEC, the most consistent connectivity patterns, across the cohort, were seen in the alpha and beta frequency bands. At fine temporal scales (corresponding to ‘scale frequencies, fS = 30-150Hz), MRVE correlation detected mostly occipital and parietal connections. These showed high similarity with the networks identified by AEC in the alpha and beta frequency bands. The most consistent connectivity profiles between participants were given by MRVE correlation at fS = 75Hz and AEC in the beta band. The physiological relevance of MRVE was also investigated by examining the relationship between connectivity strength and local variability. It was found that local activity at frequencies fS≳ 10Hz becomes more regular when a region exhibits high levels of resting state connectivity, as measured by fine scale MRVE correlation (fS∼ 30-150Hz) and by alpha and beta band AEC. Analysis of the EOG recordings also revealed that eye movement affected both connectivity measures. Higher levels of eye movement were associated with stronger frontal connectivity, as measured by MRVE correlation. More eye movement was also associated with reduced occipital and parietal connectivity strength for both connectivity measures, although this was not significant after correction for multiple comparisons.

Changes in electrophysiological static and dynamic human brain functional architecture from childhood to late adulthood

This magnetoencephalography study aimed at characterizing age-related changes in resting-state functional brain organization from mid-childhood to late adulthood. We investigated neuromagnetic brain activity at rest in 105 participants divided into three age groups: children (6-9 years), young adults (18-34 years) and healthy elders (53-78 years). The effects of age on static resting-state functional brain integration were assessed using band-limited power envelope correlation, whereas those on transient functional brain dynamics were disclosed using hidden Markov modeling of power envelope activity. Brain development from childhood to adulthood came with (1) a strengthening of functional integration within and between resting-state networks and (2) an increased temporal stability of transient (100-300 ms lifetime) and recurrent states of network activation or deactivation mainly encompassing lateral or medial associative neocortical areas. Healthy aging was characterized by decreased static resting-state functional integration and dynamic stability within the primary visual network. These results based on electrophysiological measurements free of neurovascular biases suggest that functional brain integration mainly evolves during brain development, with limited changes in healthy aging. These novel electrophysiological insights into human brain functional architecture across the lifespan pave the way for future clinical studies investigating how brain disorders affect brain development or healthy aging.

Variety Is the Spice of Life: Positive and Negative Effects of Noise in Electrical Stimulation of the Nervous System

Noisy stimuli may hold the key for optimal electrical stimulation of the nervous system. Possible mechanisms of noise’s impact upon neuronal function are discussed, including intracellular, extracellular, and systems-level mechanisms. Specifically, channel resonance, stochastic resonance, high conductance states, and network binding are investigated. These mechanisms are examined and possible directions of growth for the field are discussed, with examples of applications provided from the fields of deep brain stimulation or spinal cord injury. Together, this review highlights the theoretical basis and evidence base for the use of noise to enhance current stimulation paradigms of the nervous system.

Using multiple short epochs optimises the stability of infant EEG connectivity parameters

Atypicalities in connectivity between brain regions have been implicated in a range of neurocognitive disorders. We require metrics to assess stable individual differences in connectivity in the developing brain, while facing the challenge of limited data quality and quantity. Here, we examine how varying core processing parameters can optimise the test–retest reliability of EEG connectivity measures in infants. EEG was recorded twice with a 1-week interval between sessions in 10-month-olds. EEG alpha connectivity was measured across different epoch lengths and numbers, with the phase lag index (PLI) and debiased weighted PLI (dbWPLI), for both whole-head connectivity and graph theory metrics. We calculated intra-class correlations between sessions for infants with sufficient data for both sessions (N’s = 19–41, depending on the segmentation method). Reliability for the whole brain dbWPLI was higher across many short epochs, whereas reliability for the whole brain PLI was higher across fewer long epochs. However, the PLI is confounded by the number of available segments. Reliability was higher for whole brain connectivity than graph theory metrics. Thus, segmenting available data into a high number of short epochs and calculating the dbWPLI is most appropriate for characterising connectivity in populations with limited availability of EEG data.

Cortical electrophysiological evidence for individual-specific temporal organization of brain functional networks

The human brain has been demonstrated to rapidly and continuously form and dissolve networks on a subsecond timescale, offering effective and essential substrates for cognitive processes. Understanding how the dynamic organization of brain functional networks on a subsecond level varies across individuals is, therefore, of great interest for personalized neuroscience. However, it remains unclear whether features of such rapid network organization are reliably unique and stable in single subjects and, therefore, can be used in characterizing individual networks. Here, we used two sets of 5‐min magnetoencephalography (MEG) resting data from 39 healthy subjects over two consecutive days and modeled the spontaneous brain activity as recurring networks fast shifting between each other in a coordinated manner. MEG cortical maps were obtained through source reconstruction using the beamformer method and subjects' temporal structure of recurring networks was obtained via the Hidden Markov Model. Individual organization of dynamic brain activity was quantified with the features of the network‐switching pattern (i.e., transition probability and mean interval time) and the time‐allocation mode (i.e., fractional occupancy and mean lifetime). Using these features, we were able to identify subjects from the group with significant accuracies (~40% on average in 0.5–48 Hz). Notably, the default mode network displayed a distinguishable pattern, being the least frequently visited network with the longest duration for each visit. Together, we provide initial evidence suggesting that the rapid dynamic temporal organization of brain networks achieved in electrophysiology is intrinsic and subject specific.

Intrinsic connectome organization across temporal scales: New insights from cross-modal approaches

The discovery of a stable, whole-brain functional connectivity organization that is largely independent of external events has drastically extended our view of human brain function. However, this discovery has been primarily based on functional magnetic resonance imaging (fMRI). The role of this whole-brain organization in fast oscillation-based connectivity as measured, for example, by electroencephalography (EEG) and magnetoencephalography (MEG) is only beginning to emerge. Here, we review studies of intrinsic connectivity and its whole-brain organization in EEG, MEG, and intracranial electrophysiology with a particular focus on direct comparisons to connectome studies in fMRI. Synthesizing this literature, we conclude that irrespective of temporal scale over four orders of magnitude, intrinsic neurophysiological connectivity shows spatial similarity to the connectivity organization commonly observed in fMRI. A shared structural connectivity basis and cross-frequency coupling are possible mechanisms contributing to this similarity. Acknowledging that a stable whole-brain organization governs long-range coupling across all timescales of neural processing motivates researchers to take "baseline" intrinsic connectivity into account when investigating brain-behavior associations, and further encourages more widespread exploration of functional connectomics approaches beyond fMRI by using EEG and MEG modalities.

The role of transient spectral 'bursts' in functional connectivity: A magnetoencephalography study

Neural oscillations dominate electrophysiological measures of macroscopic brain activity and fluctuations in these rhythms offer an insightful window on cortical excitation, inhibition, and connectivity. However, in recent years the 'classical' picture of smoothly varying oscillations has been challenged by the idea that many 'oscillations' may actually be formed from the recurrence of punctate high-amplitude bursts in activity, whose spectral composition intersects the traditionally defined frequency ranges (e.g. alpha/beta band). This finding offers a new interpretation of measurable brain activity, however neither the methodological means to detect bursts, nor their link to other findings (e.g. connectivity) have been settled. Here, we use a new approach to detect bursts in magnetoencephalography (MEG) data. We show that a time-delay embedded Hidden Markov Model (HMM) can be used to delineate single-region bursts which are in agreement with existing techniques. However, unlike existing techniques, the HMM looks for specific spectral patterns in timecourse data. We characterise the distribution of burst duration, frequency of occurrence and amplitude across the cortex in resting state MEG data. During a motor task we show how the movement related beta decrease and post movement beta rebound are driven by changes in burst occurrence. Finally, we show that the beta band functional connectome can be derived using a simple measure of burst overlap, and that coincident bursts in separate regions correspond to a period of heightened coherence. In summary, this paper offers a new methodology for burst identification and connectivity analysis which will be important for future investigations of neural oscillations.

Individual differences in motor development during early childhood: An MEG study

In a previous study, we reported the first measurements of pre-movement and sensorimotor cortex activity in preschool age children (ages 3-5 years) using a customized pediatric magnetoencephalographic system. Movement-related activity in the sensorimotor cortex differed from that typically observed in adults, suggesting that maturation of cortical motor networks was still incomplete by late preschool age. Here we compare these earlier results to a group of school age children (ages 6-8 years) including seven children from the original study measured again two years later, and a group of adults (mean age 31.1 years) performing the same task. Differences in movement-related brain activity were observed both longitudinally within children in which repeated measurements were made, and cross-sectionally between preschool age children, school age children, and adults. Movement-related mu (8-12 Hz) and beta (15-30 Hz) oscillations demonstrated linear increases in amplitude and mean frequency with age. In contrast, movement-evoked gamma synchronization demonstrated a step-like transition from low (30-50 Hz) to high (70-90 Hz) narrow-band oscillations, and this occurred at different ages in different children. Notably, pre-movement activity ('readiness fields') observed in adults was absent in even the oldest children. These are the first direct observations of brain activity accompanying motor responses throughout early childhood, confirming that maturation of this activity is still incomplete by mid-childhood. In addition, individual children demonstrated markedly different developmental trajectories in movement-related brain activity, suggesting that individual differences need to be taken into account when studying motor development across age groups.

Reduced parietal alpha power and psychotic symptoms: Test-retest reliability of resting-state magnetoencephalography in schizophrenia and healthy controls

BACKGROUND

Despite increased reporting of resting-state magnetoencephalography (MEG), reliability of those measures remains scarce and predominately reported in healthy controls (HC). As such, there is limited knowledge on MEG resting-state reliability in schizophrenia (SZ).

METHODS

To address test-retest reliability in psychosis, a reproducibility study of 26 participants (13-SZ, 13-HC) was performed. We collected eyes open and eyes closed resting-state data during 4 separate instances (2 Visits, 2 runs per visit) to estimate spectral power reliability (power, normalized power, alpha reactivity) across one hour and one week. Intraclass correlation coefficients (ICCs) were calculated. For source modeling, we applied an anatomically constrained linear estimation inverse model known as dynamic statistical parametric mapping (MNE dSPM) and source-based connectivity using the weighted phase lag index.

RESULTS

Across one week there was excellent test-retest reliability in global spectral measures in theta-gamma bands (HC ICC_Avg = 0.87, SZ ICC_Avg = 0.87), regional spectral measures in all bands (HC ICC_Avg = 0.86, SZ ICC_Avg = 0.80), and parietal alpha measures (HC ICC_Avg = 0.90, SZ ICC_Avg = 0.84). Conversely, functional connectivity had poor reliability, as did source spectral power across one hour for SZ. Relative to HC, SZ also had reduced parietal alpha normalized power during eyes closed only, reduced alpha reactivity, and an association between higher PANSS positive scores and lower parietal alpha power.

CONCLUSIONS

There was excellent to good test-retest reliability in most MEG spectral measures with a few exceptions in the schizophrenia patient group. Overall, these findings encourage the use of resting-state MEG while emphasizing the importance of determining reliability in clinical populations.

How Sensitive Are Conventional MEG Functional Connectivity Metrics With Sliding Windows to Detect Genuine Fluctuations in Dynamic Functional Connectivity?

Despite advances in the field of dynamic connectivity, fixed sliding window approaches for the detection of fluctuations in functional connectivity are still widely used. The use of conventional connectivity metrics in conjunction with a fixed sliding window comes with the arbitrariness of the chosen window lengths. In this paper we use multivariate autoregressive and neural mass models with a priori defined ground truths to systematically analyze the sensitivity of conventional metrics in combination with different window lengths to detect genuine fluctuations in connectivity for various underlying state durations. Metrics of interest are the coherence, imaginary coherence, phase lag index, phase locking value and the amplitude envelope correlation. We performed analysis for two nodes and at the network level. We demonstrate that these metrics show indeed higher variability for genuine temporal fluctuations in connectivity compared to a static connectivity state superimposed by noise. Overall, the error of the connectivity estimates themselves decreases for longer state durations (order of seconds), while correlations of the connectivity fluctuations with the ground truth was higher for longer state durations. In general, metrics, in combination with a sliding window, perform poorly for very short state durations. Increasing the SNR of the system only leads to a moderate improvement. In addition, at the network level, only longer window widths were sufficient to detect plausible resting state networks that matched the underlying ground truth, especially for the phase locking value, amplitude envelope correlation and coherence. The length of these longer window widths did not necessarily correspond to the underlying state durations. For short window widths resting state network connectivity patterns could not be retrieved. We conclude that fixed sliding window approaches for connectivity can detect modulations of connectivity, but mostly if the underlying dynamics operate on moderate to slow timescales. In practice, this can be a drawback, as state durations can vary significantly in empirical data.

Spatial and spectral trajectories in typical neurodevelopment from childhood to middle age

Detailed characterization of typical human neurodevelopment is key if we are to understand the nature of mental and neurological pathology. While research on the cellular processes of neurodevelopment has made great advances, in vivo human imaging is crucial to understand our uniquely human capabilities, as well as the pathologies that affect them. Using magnetoencephalography data in the largest normative sample currently available (324 participants aged 6-45 years), we assess the developmental trajectory of resting-state oscillatory power and functional connectivity from childhood to middle age. The maturational course of power, indicative of local processing, was found to both increase and decrease in a spectrally dependent fashion. Using the strength of phase-synchrony between parcellated regions, we found significant linear and nonlinear (quadratic and logarithmic) trajectories to be characterized in a spatially heterogeneous frequency-specific manner, such as a superior frontal region with linear and nonlinear trajectories in theta and gamma band respectively. Assessment of global efficiency revealed similar significant nonlinear trajectories across all frequency bands. Our results link with the development of human cognitive abilities; they also highlight the complexity of neurodevelopment and provide quantitative parameters for replication and a robust footing from which clinical research may map pathological deviations from these typical trajectories.

Development of human electrophysiological brain networks

Functional activity in the human brain is intrinsically organized into independently active, connected brain regions. These networks include sensorimotor systems, as well as higher-order cognitive networks such as the default mode network (DMN), which dominates activity when the brain is at rest, and the frontoparietal (FPN) and salience (SN) networks, which are often engaged during demanding tasks. Evidence from functional magnetic resonance imaging (fMRI) suggests that although sensory systems are mature by the end of childhood, the integrity of the FPN and SN develops throughout adolescence. There has been little work to corroborate these findings with electrophysiology. Using magnetoencephalography (MEG) recordings of 48 participants (aged 9–25 yr) at rest, we find that beta-band functional connectivity within the FPN, SN, and DMN continues to increase through adolescence, whereas connectivity in the visual system is mature by late childhood. In contrast to fMRI results, but replicating the MEG findings of Schäfer et al. (Schäfer CB, Morgan BR, Ye AX, Taylor MJ, Doesburg SM. Hum Brain Mapp 35: 5249–5261, 2014), we also see that connectivity between networks increases rather than decreases with age. This suggests that the development of coordinated beta-band oscillations within and between higher-order cognitive networks through adolescence might contribute to the developing abilities of adolescents to focus their attention and coordinate diverse aspects of mental activity.

NEW & NOTEWORTHY Using magnetoencephalography to assess beta frequency oscillations, we show that functional connectivity within higher-order cognitive networks increases from childhood, reaching adult values by age 20 yr. In contrast, connectivity within a primary sensory (visual) network reaches adult values by age 14 yr. In contrast to functional MRI findings, connectivity between cognitive networks matures at a rate similar to within-network connectivity, suggesting that coordination of beta oscillations both within and between networks is associated with maturation of cognitive skills.