Consistency of magnetoencephalographic functional connectivity and network reconstruction using a template versus native MRI for co-registration

Covariance-based decoding reveals a category-specific functional connectivity network for imagined visual objects

The coordination of different brain regions is required for the visual imagery of complex objects (e.g., faces and places). Short-range connectivity within sensory areas is necessary to construct the mental image. Long-range connectivity between control and sensory areas is necessary to re-instantiate and maintain the mental image. While dynamic changes in functional connectivity are expected during visual imagery, it is unclear whether a category-specific network exists in which the strength and the spatial destination of the connections vary depending on the imagery target. In this magnetoencephalography study, we used a minimally constrained experimental paradigm wherein imagery categories were prompted using visual word cues only, and we decoded face versus place imagery based on their underlying functional connectivity patterns as estimated from the spatial covariance across brain regions. A subnetwork analysis further disentangled the contribution of different connections. The results show that face and place imagery can be decoded from both short-range and long-range connections. Overall, the results show that imagined object categories can be distinguished based on functional connectivity patterns observed in a category-specific network. Notably, functional connectivity estimates rely on purely endogenous brain signals suggesting that an external reference is not necessary to elicit such category-specific network dynamics.

Pseudo-MRI Engine for MRI-Free Electromagnetic Source Imaging

Structural head MRIs are a crucial ingredient in MEG/EEG source imaging; they are used to define a realistically shaped volume conductor model, constrain the source space, and visualize the source estimates. However, individual MRIs are not always available, or they may be of insufficient quality for segmentation, leading to the use of a generic template MRI, matched MRI, or the application of a spherical conductor model. Such approaches deviate the model geometry from the true head structure and limit the accuracy of the forward solution. Here, we implemented an easy-to-use tool, pseudo-MRI engine, which utilizes the head-shape digitization acquired during a MEG/EEG measurement for warping an MRI template to fit the subject's head. To this end, the algorithm first removes outlier digitization points, densifies the point cloud by interpolation if needed, and finally warps the template MRI and its segmented surfaces to the individual head shape using the thin-plate-spline method. To validate the approach, we compared the geometry of segmented head surfaces, cortical surfaces, and canonical brain regions in the real and pseudo-MRIs of 25 subjects. We also tested the MEG source reconstruction accuracy with pseudo-MRIs against that obtained with the real MRIs from individual subjects with simulated and real MEG data. We found that the pseudo-MRI enables comparable source localization accuracy to the one obtained with the subject's real MRI. The study indicates that pseudo-MRI can replace the need for individual MRI scans in MEG/EEG source imaging for applications that do not require subcentimeter spatial accuracy.

Left motor cortex contributes to auditory phonological discrimination

Evidence suggests that the articulatory motor system contributes to speech perception in a context-dependent manner. This study tested 2 hypotheses using magnetoencephalography: (i) the motor cortex is involved in phonological processing, and (ii) it aids in compensating for speech-in-noise challenges. A total of 32 young adults performed a phonological discrimination task under 3 noise conditions while their brain activity was recorded using magnetoencephalography. We observed simultaneous activation in the left ventral primary motor cortex and bilateral posterior-superior temporal gyrus when participants correctly identified pairs of syllables. This activation was significantly more pronounced for phonologically different than identical syllable pairs. Notably, phonological differences were resolved more quickly in the left ventral primary motor cortex than in the left posterior-superior temporal gyrus. Conversely, the noise level did not modulate the activity in frontal motor regions and the involvement of the left ventral primary motor cortex in phonological discrimination was comparable across all noise conditions. Our results show that the ventral primary motor cortex is crucial for phonological processing but not for compensation in challenging listening conditions. Simultaneous activation of left ventral primary motor cortex and bilateral posterior-superior temporal gyrus supports an interactive model of speech perception, where auditory and motor regions shape perception. The ventral primary motor cortex may be involved in a predictive coding mechanism that influences auditory-phonetic processing.

No Evidence of Musical Training Influencing the Cortical Contribution to the Speech-Frequency-Following Response and Its Modulation through Selective Attention

Musicians can have better abilities to understand speech in adverse condition such as background noise than non-musicians. However, the neural mechanisms behind such enhanced behavioral performances remain largely unclear. Studies have found that the subcortical frequency-following response to the fundamental frequency of speech and its higher harmonics (speech-FFR) may be involved since it is larger in people with musical training than in those without. Recent research has shown that the speech-FFR consists of a cortical contribution in addition to the subcortical sources. Both the subcortical and the cortical contribution are modulated by selective attention to one of two competing speakers. However, it is unknown whether the strength of the cortical contribution to the speech-FFR, or its attention modulation, is influenced by musical training. Here we investigate these issues through magnetoencephalographic (MEG) recordings of 52 subjects (18 musicians, 25 non-musicians, and 9 neutral participants) listening to two competing male speakers while selectively attending one of them. The speech-in-noise comprehension abilities of the participants were not assessed. We find that musicians and non-musicians display comparable cortical speech-FFRs and additionally exhibit similar subject-to-subject variability in the response. Furthermore, we also do not observe a difference in the modulation of the neural response through selective attention between musicians and non-musicians. Moreover, when assessing whether the cortical speech-FFRs are influenced by particular aspects of musical training, no significant effects emerged. Taken together, we did not find any effect of musical training on the cortical speech-FFR.

Examining cognition and brain networks using magnetoencephalography in paediatric autoimmune encephalitis and acute disseminated encephalomyelitis: a preliminary study

Paediatric autoimmune encephalitis, including acute disseminated encephalomyelitis, are inflammatory brain diseases presenting with cognitive deficits, psychiatric symptoms, seizures, MRI and EEG abnormalities. Despite improvements in disease recognition and early immunotherapy, long-term outcomes in paediatric autoimmune encephalitis remain poor. Our aim was to understand functional connectivity changes that could be associated with negative developmental outcomes across different types of paediatric autoimmune encephalitis using magnetoencephalography. Participants were children diagnosed with paediatric autoimmune encephalitis at least 18 months before testing and typically developing children. All completed magnetoencephalography recording at rest, T1 MRI scans and neuropsychology testing. Brain connectivity (specifically in delta and theta) was estimated with amplitude envelope correlation, and network efficiency was measured using graph measures (global efficiency, local efficiency and modularity). Twelve children with paediatric autoimmune encephalitis (11.2 ± 3.5 years, interquartile range 9 years; 5M:7F) and 12 typically developing controls (10.6 ± 3.2 years, interquartile range 7 years; 8M:4F) participated. Children with paediatric autoimmune encephalitis did not differ from controls in working memory (t(21) = 1.449; P = 0.162; d = 0.605) but had significantly lower processing speed (t(21) = 2.463; P = 0.023; Cohen's d = 1.028). Groups did not differ in theta network topology measures. The paediatric autoimmune encephalitis group had a significantly lower delta local efficiency across all thresholds tested (d = -1.60 at network threshold 14%). Theta modularity was associated with lower working memory (β = -0.781; t(8) = -2.588, P = 0.032); this effect did not survive correction for multiple comparisons (P(corr) = 0.224). Magnetoencephalography was able to capture specific network alterations in paediatric autoimmune encephalitis patients. This preliminary study demonstrates that magnetoencephalography is an appropriate tool for assessing children with paediatric autoimmune encephalitis and could be associated with cognitive outcomes.

Tracking EEG network dynamics through transitions between eyes-closed, eyes-open, and task states

Our study aimed to verify the possibilities of effectively applying chronnectomics methods to reconstruct the dynamic processes of network transition between three types of brain states, namely, eyes-closed rest, eyes-open rest, and a task state. The study involved dense EEG recordings and reconstruction of the source-level time-courses of the signals. Functional connectivity was measured using the phase lag index, and dynamic analyses concerned coupling strength and variability in alpha and beta frequencies. The results showed significant and dynamically specific transitions regarding processes of eyes opening and closing and during the eyes-closed-to-task transition in the alpha band. These observations considered a global dimension, default mode network, and central executive network. The decrease of connectivity strength and variability that accompanied eye-opening was a faster process than the synchronization increase during eye-opening, suggesting that these two transitions exhibit different reorganization times. While referring the obtained results to network studies, it was indicated that the scope of potential similarities and differences between rest and task-related networks depends on whether the resting state was recorded in eyes closed or open condition.

Alterations of Cortical Structure and Neurophysiology in Parkinson's Disease Are Aligned with Neurochemical Systems

OBJECTIVE

Parkinson's disease (PD) affects the structural integrity and neurophysiological signaling of the cortex. These alterations are related to the motor and cognitive symptoms of the disease. How these changes are related to the neurochemical systems of the cortex is unknown.

METHODS

We used T1-weighted magnetic resonance imaging (MRI) and magnetoencephalography (MEG) to measure cortical thickness and task-free neurophysiological activity in patients with idiopathic PD (nMEG = 79, nMRI = 65) and matched healthy controls (nMEG = 65, nMRI = 37). Using linear mixed-effects models, we examined the topographical alignment of cortical structural and neurophysiological alterations in PD with cortical atlases of 19 neurotransmitter receptor and transporter densities.

RESULTS

We found that neurophysiological alterations in PD occur primarily in brain regions rich in acetylcholinergic, serotonergic, and glutamatergic systems, with protective implications for cognitive and psychiatric symptoms. In contrast, cortical thinning occurs preferentially in regions rich in noradrenergic systems, and the strength of this alignment relates to motor deficits.

INTERPRETATION

This study shows that the spatial organization of neurophysiological and structural alterations in PD is relevant for nonmotor and motor impairments. The data also advance the identification of the neurochemical systems implicated. The approach uses novel nested atlas modeling methodology that is transferrable to research in other neurological and neuropsychiatric diseases and syndromes. ANN NEUROL 2024;95:802-816.

Using optically pumped magnetometers to replicate task-related responses in next generation magnetoencephalography

Optically pumped magnetometers (OPMs) offer a new wearable means to measure magnetoencephalography (MEG) signals, with many advantages compared to conventional systems. However, OPMs are an emerging technology, thus characterizing and replicating MEG recordings is essential. Using OPM-MEG and SQUID-MEG, this study investigated evoked responses, oscillatory power, and functional connectivity during emotion processing in 20 adults, to establish replicability across the two technologies. Five participants with dental fixtures were included to assess the validity of OPM-MEG recordings in those with irremovable metal. Replicable task-related evoked responses were observed in both modalities. Similar patterns of oscillatory power to faces were observed in both systems. Increased connectivity was found in SQUID-versus OPM-MEG in an occipital and parietal anchored network. Notably, high quality OPM-MEG data were retained in participants with metallic fixtures, from whom no useable data were collected using conventional MEG.

The Early Subcortical Response at the Fundamental Frequency of Speech Is Temporally Separated from Later Cortical Contributions

Most parts of speech are voiced, exhibiting a degree of periodicity with a fundamental frequency and many higher harmonics. Some neural populations respond to this temporal fine structure, in particular at the fundamental frequency. This frequency-following response to speech consists of both subcortical and cortical contributions and can be measured through EEG as well as through magnetoencephalography (MEG), although both differ in the aspects of neural activity that they capture: EEG is sensitive to both radial and tangential sources as well as to deep sources, whereas MEG is more restrained to the measurement of tangential and superficial neural activity. EEG responses to continuous speech have shown an early subcortical contribution, at a latency of around 9 msec, in agreement with MEG measurements in response to short speech tokens, whereas MEG responses to continuous speech have not yet revealed such an early component. Here, we analyze MEG responses to long segments of continuous speech. We find an early subcortical response at latencies of 4-11 msec, followed by later right-lateralized cortical activities at delays of 20-58 msec as well as potential subcortical activities. Our results show that the early subcortical component of the FFR to continuous speech can be measured from MEG in populations of participants and that its latency agrees with that measured with EEG. They furthermore show that the early subcortical component is temporally well separated from later cortical contributions, enabling an independent assessment of both components toward further aspects of speech processing.

A multidimensional model of memory complaints in older individuals and the associated hub regions

Memory complaints are highly prevalent among middle-aged and older adults, and they are frequently reported in individuals experiencing subjective cognitive decline (SCD). SCD has received increasing attention due to its implications for the early detection of dementia. This study aims to advance our comprehension of individuals with SCD by elucidating potential cognitive/psychologic-contributing factors and characterizing cerebral hubs within the brain network. To identify these potential contributing factors, a structural equation modeling approach was employed to investigate the relationships between various factors, such as metacognitive beliefs, personality, anxiety, depression, self-esteem, and resilience, and memory complaints. Our findings revealed that self-esteem and conscientiousness significantly influenced memory complaints. At the cerebral level, analysis of delta and theta electroencephalographic frequency bands recorded during rest was conducted to identify hub regions using a local centrality metric known as betweenness centrality. Notably, our study demonstrated that certain brain regions undergo changes in their hub roles in response to the pathology of SCD. Specifically, the inferior temporal gyrus and the left orbitofrontal area transition into hubs, while the dorsolateral prefrontal cortex and the middle temporal gyrus lose their hub function in the presence of SCD. This rewiring of the neural network may be interpreted as a compensatory response employed by the brain in response to SCD, wherein functional connectivity is maintained or restored by reallocating resources to other regions.

Attentional Modulation of the Cortical Contribution to the Frequency-Following Response Evoked by Continuous Speech

Selective attention to one of several competing speakers is required for comprehending a target speaker among other voices and for successful communication with them. It moreover has been found to involve the neural tracking of low-frequency speech rhythms in the auditory cortex. Effects of selective attention have also been found in subcortical neural activities, in particular regarding the frequency-following response related to the fundamental frequency of speech (speech-FFR). Recent investigations have, however, shown that the speech-FFR contains cortical contributions as well. It remains unclear whether these are also modulated by selective attention. Here we used magnetoencephalography to assess the attentional modulation of the cortical contributions to the speech-FFR. We presented both male and female participants with two competing speech signals and analyzed the cortical responses during attentional switching between the two speakers. Our findings revealed robust attentional modulation of the cortical contribution to the speech-FFR: the neural responses were higher when the speaker was attended than when they were ignored. We also found that, regardless of attention, a voice with a lower fundamental frequency elicited a larger cortical contribution to the speech-FFR than a voice with a higher fundamental frequency. Our results show that the attentional modulation of the speech-FFR does not only occur subcortically but extends to the auditory cortex as well.SIGNIFICANCE STATEMENT Understanding speech in noise requires attention to a target speaker. One of the speech features that a listener can use to identify a target voice among others and attend it is the fundamental frequency, together with its higher harmonics. The fundamental frequency arises from the opening and closing of the vocal folds and is tracked by high-frequency neural activity in the auditory brainstem and in the cortex. Previous investigations showed that the subcortical neural tracking is modulated by selective attention. Here we show that attention affects the cortical tracking of the fundamental frequency as well: it is stronger when a particular voice is attended than when it is ignored.

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.

Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: Early biomarkers along the Alzheimer's Disease continuum?

Alzheimer's disease (AD) pathological changes may begin up to decades earlier than the appearance of the first symptoms of cognitive decline. Subjective cognitive decline (SCD) could be the first pre-clinical sign of possible AD, which might be followed by mild cognitive impairment (MCI), the initial stage of clinical cognitive decline. However, the neural correlates of these prodromic stages are not completely clear yet. Recent studies suggest that EEG analysis tools characterizing the cortical activity as a whole, such as microstates and cortical regions connectivity, might support a characterization of SCD and MCI conditions. Here we test this approach by performing a broad set of analyses to identify the prominent EEG markers differentiating SCD (n = 57), MCI (n = 46) and healthy control subjects (HC, n = 19). We found that the salient differences were in the temporal structure of the microstates patterns, with MCI being associated with less complex sequences due to the altered transition probability, frequency and duration of canonic microstate C. Spectral content of EEG, network connectivity, and spatial arrangement of microstates were instead largely similar in the three groups. Interestingly, comparing properties of EEG microstates in different cerebrospinal fluid (CSF) biomarkers profiles, we found that canonic microstate C displayed significant differences in topography in AD-like profile. These results show that the progression of dementia might be associated with a degradation of the cortical organization captured by microstates analysis, and that this leads to altered transitions between cortical states. Overall, our approach paves the way for the use of non-invasive EEG recordings in the identification of possible biomarkers of progression to AD from its prodromal states.

Structural and neurophysiological alterations in Parkinson's disease are aligned with cortical neurochemical systems

Parkinson's disease (PD) affects cortical structures and neurophysiology. How these deviations from normative variants relate to the neurochemical systems of the cortex in a manner corresponding to motor and cognitive symptoms is unknown. We measured cortical thickness and spectral neurophysiological alterations from structural magnetic resonance imaging and task-free magnetoencephalography in patients with idiopathic PD (NMEG = 79; NMRI = 65), contrasted with similar data from matched healthy controls (NMEG = 65; NMRI = 37). Using linear mixed-effects models and cortical atlases of 19 neurochemical systems, we found that the structural and neurophysiological alterations of PD align with several receptor and transporter systems (acetylcholine, serotonin, glutamate, and noradrenaline) albeit with different implications for motor and non-motor symptoms. Some neurophysiological alignments are protective of cognitive functions: the alignment of broadband power increases with acetylcholinergic systems is related to better attention function. However, neurochemical alignment with structural and other neurophysiological alterations is associated with motor and psychiatric impairments, respectively. Collectively, the present data advance understanding of the association between the nature of neurophysiological and structural cortical alterations in PD and the symptoms that are characteristic of the disease. They also demonstrate the value of a new nested atlas modeling approach to advance research on neurological and neuropsychiatric diseases.

Reliable evaluation of functional connectivity and graph theory measures in source-level EEG: How many electrodes are enough?

OBJECTIVE

Using EEG to characterise functional brain networks through graph theory has gained significant interest in clinical and basic research. However, the minimal requirements for reliable measures remain largely unaddressed. Here, we examined functional connectivity estimates and graph theory metrics obtained from EEG with varying electrode densities.

METHODS

EEG was recorded with 128 electrodes in 33 participants. The high-density EEG data were subsequently subsampled into three sparser montages (64, 32, and 19 electrodes). Four inverse solutions, four measures of functional connectivity, and five graph theory metrics were tested.

RESULTS

The correlation between the results obtained with 128-electrode and the subsampled montages decreased as a function of the number of electrodes. As a result of decreased electrode density, the network metrics became skewed: mean network strength and clustering coefficient were overestimated, while characteristic path length was underestimated.

CONCLUSIONS

Several graph theory metrics were altered when electrode density was reduced. Our results suggest that, for optimal balance between resource demand and result precision, a minimum of 64 electrodes should be utilised when graph theory metrics are used to characterise functional brain networks in source-reconstructed EEG data.

SIGNIFICANCE

Characterisation of functional brain networks derived from low-density EEG warrants careful consideration.

Modified MRI Anonymization (De-Facing) for Improved MEG Coregistration

Localising the sources of MEG/EEG signals often requires a structural MRI to create a head model, while ensuring reproducible scientific results requires sharing data and code. However, sharing structural MRI data often requires the face go be hidden to help protect the identity of the individuals concerned. While automated de-facing methods exist, they tend to remove the whole face, which can impair methods for coregistering the MRI data with the EEG/MEG data. We show that a new, automated de-facing method that retains the nose maintains good MRI-MEG/EEG coregistration. Importantly, behavioural data show that this "face-trimming" method does not increase levels of identification relative to a standard de-facing approach and has less effect on the automated segmentation and surface extraction sometimes used to create head models for MEG/EEG localisation. We suggest that this trimming approach could be employed for future sharing of structural MRI data, at least for those to be used in forward modelling (source reconstruction) of EEG/MEG data.

A review of MEG dynamic brain network research

The dynamic description of neural networks has attracted the attention of researchers for dynamic networks may carry more information compared with resting-state networks. As a non-invasive electrophysiological data with high temporal and spatial resolution, magnetoencephalogram (MEG) can provide rich information for the analysis of dynamic functional brain networks. In this review, the development of MEG brain network was summarized. Several analysis methods such as sliding window, Hidden Markov model, and time-frequency based methods used in MEG dynamic brain network studies were discussed. Finally, the current research about multi-modal brain network analysis and their applications with MEG neurophysiology, which are prospected to be one of the research directions in the future, were concluded.

Spatio-temporal dynamics of large-scale electrophysiological networks during cognitive action control in healthy controls and Parkinson's disease patients

Among the cognitive symptoms that are associated with Parkinson's disease (PD), alterations in cognitive action control (CAC) are commonly reported in patients. CAC enables the suppression of an automatic action, in favor of a goal-directed one. The implementation of CAC is time-resolved and arguably associated with dynamic changes in functional brain networks. However, the electrophysiological functional networks involved, their dynamic changes, and how these changes are affected by PD, still remain unknown. In this study, to address this gap of knowledge, 10 PD patients and 10 healthy controls (HC) underwent a Simon task while high-density electroencephalography (HD-EEG) was recorded. Source-level dynamic connectivity matrices were estimated using the phase-locking value in the beta (12-25 Hz) and gamma (30-45 Hz) frequency bands. Temporal independent component analyses were used as a dimension reduction tool to isolate the task-related brain network states. Typical microstate metrics were quantified to investigate the presence of these states at the subject-level. Our results first confirmed that PD patients experienced difficulties in inhibiting automatic responses during the task. At the group-level, we found three functional network states in the beta band that involved fronto-temporal, temporo-cingulate and fronto-frontal connections with typical CAC-related prefrontal and cingulate nodes (e.g., inferior frontal cortex). The presence of these networks did not differ between PD patients and HC when analyzing microstates metrics, and no robust correlations with behavior were found. In the gamma band, five networks were found, including one fronto-temporal network that was identical to the one found in the beta band. These networks also included CAC-related nodes previously identified in different neuroimaging modalities. Similarly to the beta networks, no subject-level differences were found between PD patients and HC. Interestingly, in both frequency bands, the dominant network at the subject-level was never the one that was the most durably modulated by the task. Altogether, this study identified the dynamic functional brain networks observed during CAC, but did not highlight PD-related changes in these networks that might explain behavioral changes. Although other new methods might be needed to investigate the presence of task-related networks at the subject-level, this study still highlights that task-based dynamic functional connectivity is a promising approach in understanding the cognitive dysfunctions observed in PD and beyond.

Sharing individualised template MRI data for MEG source reconstruction: A solution for open data while keeping subject confidentiality

The increasing requirements for adoption of FAIR data management and sharing original research data from neuroimaging studies can be at odds with protecting the anonymity of the research participants due to the person-identifiable anatomical features in the data. We propose a solution to this dilemma for anatomical MRIs used in MEG source analysis. In MEG analysis, the channel-level data is reconstructed to the source-level using models derived from anatomical MRIs. Sharing data, therefore, requires sharing the anatomical MRI to replicate the analysis. The suggested solution is to replace the individual anatomical MRIs with individualised warped templates that can be used to carry out the MEG source analysis and that provide sufficient geometrical similarity to the original participants' MRIs. First, we demonstrate how the individualised template warping can be implemented with one of the leading open-source neuroimaging analysis toolboxes. Second, we compare results from four different MEG source reconstruction methods performed with an individualised warped template to those using the participant's original MRI. While the source reconstruction results are not numerically identical, there is a high similarity between the results for single dipole fits, dynamic imaging of coherent sources beamforming, and atlas-based virtual channel beamforming. There is a moderate similarity between minimum-norm estimates, as anticipated due to this method being anatomically constrained and dependent on the exact morphological features of the cortical sheet. We also compared the morphological features of the warped template to those of the original MRI. These showed a high similarity in grey matter volume and surface area, but a low similarity in the average cortical thickness and the mean folding index within cortical parcels. Taken together, this demonstrates that the results obtained by MEG source reconstruction can be preserved with the warped templates, whereas the anatomical and morphological fingerprint is sufficiently altered to protect the anonymity of research participants. In cases where participants consent to sharing anatomical MRI data, it remains preferable to share the original defaced data with an appropriate data use agreement. In cases where participants did not consent to share their MRIs, the individualised warped MRI template offers a good compromise in sharing data for reuse while retaining anonymity for research participants.

Sensitive and reproducible MEG resting-state metrics of functional connectivity in Alzheimer's disease

Background

Analysis of functional brain networks in Alzheimer’s disease (AD) has been hampered by a lack of reproducible, yet valid metrics of functional connectivity (FC). This study aimed to assess both the sensitivity and reproducibility of the corrected amplitude envelope correlation (AEC-c) and phase lag index (PLI), two metrics of FC that are insensitive to the effects of volume conduction and field spread, in two separate cohorts of patients with dementia due to AD versus healthy elderly controls.

Methods

Subjects with a clinical diagnosis of AD dementia with biomarker proof, and a control group of subjective cognitive decline (SCD), underwent two 5-min resting-state MEG recordings. Data consisted of a test (AD = 28; SCD = 29) and validation (AD = 29; SCD = 27) cohort. Time-series were estimated for 90 regions of interest (ROIs) in the automated anatomical labelling (AAL) atlas. For each of five canonical frequency bands, the AEC-c and PLI were calculated between all 90 ROIs, and connections were averaged per ROI. General linear models were constructed to compare the global FC differences between the groups, assess the reproducibility, and evaluate the effects of age and relative power. Reproducibility of the regional FC differences was assessed using the Mann-Whitney U tests, with correction for multiple testing using the false discovery rate (FDR).

Results

The AEC-c showed significantly and reproducibly lower global FC for the AD group compared to SCD, in the alpha (8–13 Hz) and beta (13–30 Hz) bands, while the PLI revealed reproducibly lower FC for the AD group in the delta (0.5–4 Hz) band and higher FC for the theta (4–8 Hz) band. Regionally, the beta band AEC-c showed reproducibility for almost all ROIs (except for 13 ROIs in the frontal and temporal lobes). For the other bands, the AEC-c and PLI did not show regional reproducibility after FDR correction. The theta band PLI was susceptible to the effect of relative power.

Conclusion

For MEG, the AEC-c is a sensitive and reproducible metric, able to distinguish FC differences between patients with AD dementia and cognitively healthy controls. These two measures likely reflect different aspects of neural activity and show differential sensitivity to changes in neural dynamics.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-022-00970-4.

Schizophrenia induces abnormal frequency-dependent patterns of dynamic brain network reconfiguration during an auditory oddball task

OBJECTIVE

Schizophrenia is a psychiatric disorder that has been shown to disturb the dynamic top-down processing of sensory information. Various imaging techniques have revealed abnormalities in brain activity associated with this disorder, both locally and between cerebral regions. However, there is increasing interest in investigating dynamic network response to novel and relevant events at the network level during an attention-demanding task with high-temporal-resolution techniques. The aim of the work was: (i) to test the capacity of a novel algorithm to detect recurrent brain meta-states from auditory oddball task recordings; and (ii) to evaluate how the dynamic activation and behavior of the aforementioned meta-states were altered in schizophrenia, since it has been shown to impair top-down processing of sensory information.

APPROACH

A novel unsupervised method for the detection of brain meta-states based on recurrence plots and community detection algorithms, previously tested on resting-state data, was used on auditory oddball task recordings. Brain meta-states and several properties related to their activation during target trials in the task were extracted from electroencephalography (EEG) data from patients with schizophrenia and cognitively healthy controls.

MAIN RESULTS

The methodology successfully detected meta-states during an auditory oddball task, and they appeared to show both frequency-dependent time-locked and non-time-locked activity with respect to the stimulus onset. Moreover, patients with schizophrenia displayed higher network diversity, and showed more sluggish meta-state transitions, reflected in increased dwell times, less complex meta-state sequences, decreased meta-state space speed, and abnormal ratio of negative meta-state correlations.

SIGNIFICANCE

Abnormal cognition in schizophrenia is also reflected in decreased brain flexibility at the dynamic network level, which may hamper top-down processing, possibly indicating impaired decision-making linked to dysfunctional predictive coding. Moreover, the results showed the ability of the methodology to find meaningful and task-relevant changes in dynamic connectivity and pathology-related group differences.

Generating diagnostic profiles of cognitive decline and dementia using magnetoencephalography

Accurate identification of the underlying cause(s) of cognitive decline and dementia is challenging due to significant symptomatic overlap between subtypes. This study presents a multi-class classification framework for subjects with subjective cognitive decline, mild cognitive impairment, Alzheimer's disease, dementia with Lewy bodies, fronto-temporal dementia and cognitive decline due to psychiatric illness, trained on source-localized resting-state magnetoencephalography data. Diagnostic profiles, describing probability estimates for each of the 6 diagnoses, were assigned to individual subjects. A balanced accuracy rate of 41% and multi-class area under the curve value of 0.75 were obtained for 6-class classification. Classification primarily depended on posterior relative delta, theta and beta power and amplitude-based functional connectivity in the beta and gamma frequency band. Dementia with Lewy bodies (sensitivity: 100%, precision: 20%) and Alzheimer's disease subjects (sensitivity: 51%, precision: 90%) could be classified most accurately. Fronto-temporal dementia subjects (sensitivity: 11%, precision: 3%) were most frequently misclassified. Magnetoencephalography biomarkers hold promise to increase diagnostic accuracy in a noninvasive manner. Diagnostic profiles could provide an intuitive tool to clinicians and may facilitate implementation of the classifier in the memory clinic.

Exploring the Interactions Between Neurophysiology and Cognitive and Behavioral Changes Induced by a Non-pharmacological Treatment: A Network Approach

Dementia due to Alzheimer's disease (AD) is a neurological syndrome which has an increasing impact on society, provoking behavioral, cognitive, and functional impairments. AD lacks an effective pharmacological intervention; thereby, non-pharmacological treatments (NPTs) play an important role, as they have been proven to ameliorate AD symptoms. Nevertheless, results associated with NPTs are patient-dependent, and new tools are needed to predict their outcome and to improve their effectiveness. In the present study, 19 patients with AD underwent an NPT for 83.1 ± 38.9 days (mean ± standard deviation). The NPT was a personalized intervention with physical, cognitive, and memory stimulation. The magnetoencephalographic activity was recorded at the beginning and at the end of the NPT to evaluate the neurophysiological state of each patient. Additionally, the cognitive (assessed by means of the Mini-Mental State Examination, MMSE) and behavioral (assessed in terms of the Dementia Behavior Disturbance Scale, DBD-13) status were collected before and after the NPT. We analyzed the interactions between cognitive, behavioral, and neurophysiological data by generating diverse association networks, able to intuitively characterize the relationships between variables of a different nature. Our results suggest that the NPT remarkably changed the structure of the association network, reinforcing the interactions between the DBD-13 and the neurophysiological parameters. We also found that the changes in cognition and behavior are related to the changes in spectral-based neurophysiological parameters. Furthermore, our results support the idea that MEG-derived parameters can predict NPT outcome; specifically, a lesser degree of AD neurophysiological alterations (i.e., neural oscillatory slowing, decreased variety of spectral components, and increased neural signal regularity) predicts a better NPT prognosis. This study provides deeper insights into the relationships between neurophysiology and both, cognitive and behavioral status, proving the potential of network-based methodology as a tool to further understand the complex interactions elicited by NPTs.

Does participant's age impact on tDCS induced fields? Insights from computational simulations

Objective: Understanding the induced current flow from transcranial direct current stimulation (tDCS) is essential for determining the optimal dose and treatment. Head tissue conductivities play a key role in the resulting electromagnetic fields. However, there exists a complicated relationship between skull conductivity and participant age, that remains unclear. We explored how variations in skull electrical conductivities, particularly as a suggested function of age, affected tDCS induced electric fields.Approach: Simulations were employed to compare tDCS outcomes for different intensities across head atlases of varying age. Three databases were chosen to demonstrate differing variability in skull conductivity with age and how this may affect induced fields. Differences in tDCS electric fields due to proposed age-dependent skull conductivity variation, as well as deviations in grey matter, white matter and scalp, were compared and the most influential tissues determined.Main results: tDCS induced peak electric fields significantly negatively correlated with age, exacerbated by employing proposed age-appropriate skull conductivity (according to all three datasets). Uncertainty in skull conductivity was the most sensitive to changes in peak fields with increasing age. These results were revealed to be directly due to changing skull conductivity, rather than head geometry alone. There was no correlation between tDCS focality and age.Significance: Accurate and individualised head anatomy andin vivoskull conductivity measurements are essential for modelling tDCS induced fields. In particular, age should be taken into account when considering stimulation dose to precisely predict outcomes.

Exploring the Alterations in the Distribution of Neural Network Weights in Dementia Due to Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder which has become an outstanding social problem. The main objective of this study was to evaluate the alterations that dementia due to AD elicits in the distribution of functional network weights. Functional connectivity networks were obtained using the orthogonalized Amplitude Envelope Correlation (AEC), computed from source-reconstructed resting-state eletroencephalographic (EEG) data in a population formed by 45 cognitive healthy elderly controls, 69 mild cognitive impaired (MCI) patients and 81 AD patients. Our results indicated that AD induces a progressive alteration of network weights distribution; specifically, the Shannon entropy (SE) of the weights distribution showed statistically significant between-group differences (p < 0.05, Kruskal-Wallis test, False Discovery Rate corrected). Furthermore, an in-depth analysis of network weights distributions was performed in delta, alpha, and beta-1 frequency bands to discriminate the weight ranges showing statistical differences in SE. Our results showed that lower and higher weights were more affected by the disease, whereas mid-range connections remained unchanged. These findings support the importance of performing detailed analyses of the network weights distribution to further understand the impact of AD progression on functional brain activity.

Mild traumatic brain injury is associated with dysregulated neural network functioning in children and adolescents

Mild traumatic brain injury is highly prevalent in paediatric populations, and can result in chronic physical, cognitive and emotional impairment, known as persistent post-concussive symptoms. Magnetoencephalography has been used to investigate neurophysiological dysregulation in mild traumatic brain injury in adults; however, whether neural dysrhythmia persists in chronic mild traumatic brain injury in children and adolescents is largely unknown. We predicted that children and adolescents would show similar dysfunction as adults, including pathological slow-wave oscillations and maladaptive, frequency-specific, alterations to neural connectivity. Using magnetoencephalography, we investigated regional oscillatory power and distributed brain-wide networks in a cross-sectional sample of children and adolescents in the chronic stages of mild traumatic brain injury. Additionally, we used a machine learning pipeline to identify the most relevant magnetoencephalography features for classifying mild traumatic brain injury and to test the relative classification performance of regional power versus functional coupling. Results revealed that the majority of participants with chronic mild traumatic brain injury reported persistent post-concussive symptoms. For neurophysiological imaging, we found increased regional power in the delta band in chronic mild traumatic brain injury, predominantly in bilateral occipital cortices and in the right inferior temporal gyrus. Those with chronic mild traumatic brain injury also showed dysregulated neuronal coupling, including decreased connectivity in the delta range, as well as hyper-connectivity in the theta, low gamma and high gamma bands, primarily involving frontal, temporal and occipital brain areas. Furthermore, our multivariate classification approach combined with functional connectivity data outperformed regional power in terms of between-group classification accuracy. For the first time, we establish that local and large-scale neural activity are altered in youth in the chronic phase of mild traumatic brain injury, with the majority presenting persistent post-concussive symptoms, and that dysregulated interregional neural communication is a reliable marker of lingering paediatric ‘mild’ traumatic brain injury.

Cognitive network hyperactivation and motor cortex decline correlate with ALS prognosis

We aimed to quantitatively characterize progressive brain network disruption in Amyotrophic Lateral Sclerosis (ALS) during cognition using the mismatch negativity (MMN), an electrophysiological index of attention switching. We measured the MMN using 128-channel EEG longitudinally (2-5 timepoints) in 60 ALS patients and cross-sectionally in 62 healthy controls. Using dipole fitting and linearly constrained minimum variance beamforming we investigated cortical source activity changes over time. In ALS, the inferior frontal gyri (IFG) show significantly lower baseline activity compared to controls. The right IFG and both superior temporal gyri (STG) become progressively hyperactive longitudinally. By contrast, the left motor and dorsolateral prefrontal cortices are initially hyperactive, declining progressively. Baseline motor hyperactivity correlates with cognitive disinhibition, and lower baseline IFG activities correlate with motor decline rate, while left dorsolateral prefrontal activity predicted cognitive and behavioural impairment. Shorter survival correlates with reduced baseline IFG and STG activity and later STG hyperactivation. Source-resolved EEG facilitates quantitative characterization of symptom-associated and symptom-preceding motor and cognitive-behavioral cortical network decline in ALS.

Abnormal meta-state activation of dynamic brain networks across the Alzheimer spectrum

The characterization of the distinct dynamic functional connectivity (dFC) patterns that activate in the brain during rest can help to understand the underlying time-varying network organization. The presence and behavior of these patterns (known as meta-states) have been widely studied by means of functional magnetic resonance imaging (fMRI). However, modalities with high-temporal resolution, such as electroencephalography (EEG), enable the characterization of fast temporally evolving meta-state sequences. Mild cognitive impairment (MCI) and dementia due to Alzheimer's disease (AD) have been shown to disrupt spatially localized activation and dFC between different brain regions, but not much is known about how they affect meta-state network topologies and their network dynamics. The main hypothesis of the study was that MCI and dementia due to AD alter normal meta-state sequences by inducing a loss of structure in their patterns and a reduction of their dynamics. Moreover, we expected that patients with MCI would display more flexible behavior compared to patients with dementia due to AD. Thus, the aim of the current study was twofold: (i) to find repeating, distinctly organized network patterns (meta-states) in neural activity; and (ii) to extract information about meta-state fluctuations and how they are influenced by MCI and dementia due to AD. To accomplish these goals, we present a novel methodology to characterize dynamic meta-states and their temporal fluctuations by capturing aspects based on both their discrete activation and the continuous evolution of their individual strength. These properties were extracted from 60-s resting-state EEG recordings from 67 patients with MCI due to AD, 50 patients with dementia due to AD, and 43 cognitively healthy controls. First, the instantaneous amplitude correlation (IAC) was used to estimate instantaneous functional connectivity with a high temporal resolution. We then extracted meta-states by means of graph community detection based on recurrence plots (RPs), both at the individual- and group-level. Subsequently, a diverse set of properties of the continuous and discrete fluctuation patterns of the meta-states was extracted and analyzed. The main novelty of the methodology lies in the usage of Louvain GJA community detection to extract meta-states from IAC-derived RPs and the extended analysis of their discrete and continuous activation. Our findings showed that distinct dynamic functional connectivity meta-states can be found on the EEG time-scale, and that these were not affected by the oscillatory slowing induced by MCI or dementia due to AD. However, both conditions displayed a loss of meta-state modularity, coupled with shorter dwell times and higher complexity of the meta-state sequences. Furthermore, we found evidence that meta-state sequencing is not entirely random; it shows an underlying structure that is partially lost in MCI and dementia due to AD. These results show evidence that AD progression is associated with alterations in meta-state switching, and a degradation of dynamic brain flexibility.

MEG-PLAN: a clinical and technical protocol for obtaining magnetoencephalography data in minimally verbal or nonverbal children who have autism spectrum disorder

BACKGROUND

Neuroimaging research on individuals who have autism spectrum disorder (ASD) has historically been limited primarily to those with age-appropriate cognitive and language performance. Children with limited abilities are frequently excluded from such neuroscience research given anticipated barriers like tolerating the loud sounds associated with magnetic resonance imaging and remaining still during data collection. To better understand brain function across the full range of ASD there is a need to (1) include individuals with limited cognitive and language performance in neuroimaging research (non-sedated, awake) and (2) improve data quality across the performance range. The purpose of this study was to develop, implement, and test the feasibility of a clinical/behavioral and technical protocol for obtaining magnetoencephalography (MEG) data. Participants were 38 children with ASD (8-12 years) meeting the study definition of minimally verbal/nonverbal language. MEG data were obtained during a passive pure-tone auditory task.

RESULTS

Based on stakeholder feedback, the MEG Protocol for Low-language/cognitive Ability Neuroimaging (MEG-PLAN) was developed, integrating clinical/behavioral and technical components to be implemented by an interdisciplinary team (clinicians, behavior specialists, scientists, and technologists). Using MEG-PLAN, a 74% success rate was achieved for acquiring MEG data, with a 71% success rate for evaluable and analyzable data. Exploratory analyses suggested nonverbal IQ and adaptive skills were related to reaching the point of acquirable data. No differences in group characteristics were observed between those with acquirable versus evaluable/analyzable data. Examination of data quality (evaluable trial count) was acceptable. Moreover, results were reproducible, with high intraclass correlation coefficients for pure-tone auditory latency.

CONCLUSIONS

Children who have ASD who are minimally verbal/nonverbal, and often have co-occurring cognitive impairments, can be effectively and comfortably supported to complete an electrophysiological exam that yields valid and reproducible results. MEG-PLAN is a protocol that can be disseminated and implemented across research teams and adapted across technologies and neurodevelopmental disorders to collect electrophysiology and neuroimaging data in previously understudied groups of individuals.

Consistency of local activation parameters at sensor- and source-level in neural signals

OBJECTIVE

Although magnetoencephalography and electroencephalography (M/EEG) signals at sensor level are robust and reliable, they suffer from different degrees of distortion due to changes in brain tissue conductivities, known as field spread and volume conduction effects. To estimate original neural generators from M/EEG activity acquired at sensor level, diverse source localisation algorithms have been proposed; however, they are not exempt from limitations and usually involve time-consuming procedures. Connectivity and network-based M/EEG analyses have been found to be affected by field spread and volume conduction effects; nevertheless, the influence of the aforementioned effects on widely used local activation parameters has not been assessed yet. The goal of this study is to evaluate the consistency of various local activation parameters when they are computed at sensor- and source-level.

APPROACH

Six spectral (relative power, median frequency, and individual alpha frequency) and non-linear parameters (Lempel-Ziv complexity, sample entropy, and central tendency measure) are computed from M/EEG signals at sensor- and source-level using four source inversion methods: weighted minimum norm estimate (wMNE), standardised low-resolution brain electromagnetic tomography (sLORETA), linear constrained minimum variance (LCMV), and dynamical statistical parametric mapping (dSPM).

MAIN RESULTS

Our results show that the spectral and non-linear parameters yield similar results at sensor- and source-level, showing high correlation values between them for all the source inversion methods evaluated and both modalities of signal, EEG and MEG. Furthermore, the correlation values remain high when performing coarse-grained spatial analyses.

SIGNIFICANCE

To the best of our knowledge, this is the first study analysing how field spread and volume conduction effects impact on local activation parameters computed from resting-state neural activity. Our findings evidence that local activation parameters are robust against field spread and volume conduction effects and provide equivalent information at sensor- and source-level even when performing regional analyses.

Local and large-scale beta oscillatory dysfunction in males with mild traumatic brain injury

Mild traumatic brain injury (mTBI) is impossible to detect with standard neuroradiological assessment such as structural magnetic resonance imaging (MRI). Injury does, however, disrupt the dynamic repertoire of neural activity indexed by neural oscillations. In particular, beta oscillations are reliable predictors of cognitive, perceptual, and motor system functioning, as well as correlating highly with underlying myelin architecture and brain connectivity-all factors particularly susceptible to dysregulation after mTBI. We measured local and large-scale neural circuit function by magnetoencephalography (MEG) with a data-driven model fit approach using the fitting oscillations and one-over f algorithm in a group of young adult men with mTBI and a matched healthy control group. We quantified band-limited regional power and functional connectivity between brain regions. We found reduced regional power and deficits in functional connectivity across brain areas, which pointed to the well-characterized thalamocortical dysconnectivity associated with mTBI. Furthermore, our results suggested that beta functional connectivity data reached the best mTBI classification performance compared with regional power and symptom severity [measured with Sport Concussion Assessment Tool 2 (SCAT2)]. The present study reveals the relevance of beta oscillations as a window into neurophysiological dysfunction in mTBI and also highlights the reliability of neural synchrony biomarkers in disorder classification.NEW & NOTEWORTHY Mild traumatic brain injury (mTBI) disrupts the dynamic repertoire of neural oscillations, but so far beta activity has not been studied. In mTBI, we found reductions in frontal beta and large-scale beta networks, indicative of thalamocortical dysconnectivity and disrupted information flow through cortico-basal ganglia-thalamic circuits. Relatively, connectivity more accurately classifies individual mTBI cases compared with regional power. We show the relevance of beta oscillations in mTBI and the reliability of these markers in classification.

Brain Network Integration in Patients with Migraine: A Magnetoencephalography Study

Migraine is a common disorder with high social and medical impact. Patients with migraine have a much higher chance of experiencing headache attacks compared with the general population. Recent neuroimaging studies have confirmed that pathophysiology in the brain is not only limited to the moment of the attack but is also present in between attacks, the interictal phase. In this study, we hypothesized that the topology of functional brain networks is also different in the interictal state, compared with people who are not affected by migraine. We also expected that the level of network disturbances scales with the number of years people have suffered from migraine. Functional connectivity between 78 cortical brain regions was estimated for source-level magnetoencephalography data by calculating the phase lag index, in five frequency bands (delta-beta), and compared between healthy controls (n = 24) and patients who had been suffering from migraine for longer than 6 years (n = 12) or shorter than 6 years (n = 12). Moreover, the topology of the functional networks was characterized using the minimum spanning tree. The migraine groups did not differ from each other in functional connectivity. However, the network topology was different compared with healthy controls. The results were frequency specific, and higher average nodal betweenness centrality was specifically evident in higher frequency bands in patients with longer disease duration, while an opposite trend was present for lower frequencies. This study shows that patients with migraine have a different network topology in the resting state compared with healthy controls, whereby specific brain areas have altered topological roles in a frequency-specific manner. Some alterations appear specifically in patients with long-term migraine, which might show the long-term effects of the disease.

Localization of Brain Networks Engaged by the Sustained Attention to Response Task Provides Quantitative Markers of Executive Impairment in Amyotrophic Lateral Sclerosis

Objective: To identify cortical regions engaged during the sustained attention to response task (SART) and characterize changes in their activity associated with the neurodegenerative condition amyotrophic lateral sclerosis (ALS). Methods: High-density electroencephalography (EEG) was recorded from 33 controls and 23 ALS patients during a SART paradigm. Differences in associated event-related potential peaks were measured for Go and NoGo trials. Sources active during these peaks were localized, and ALS-associated differences were quantified. Results: Go and NoGo N2 and P3 peak sources were localized to the left primary motor cortex, bilateral dorsolateral prefrontal cortex (DLPFC), and lateral posterior parietal cortex (PPC). NoGo trials evoked greater bilateral medial PPC activity during N2 and lesser left insular, PPC and DLPFC activity during P3. Widespread cortical hyperactivity was identified in ALS during P3. Changes in the inferior parietal lobule and insular activity provided very good discrimination (AUROC > 0.75) between patients and controls. Activation of the right precuneus during P3 related to greater executive function in ALS, indicative of a compensatory role. Interpretation: The SART engages numerous frontal and parietal cortical structures. SART–EEG measures correlate with specific cognitive impairments that can be localized to specific structures, aiding in differential diagnosis.

Kurtosis and skewness of high-frequency brain signals are altered in paediatric epilepsy

Intracranial studies provide solid evidence that high-frequency brain signals are a new biomarker for epilepsy. Unfortunately, epileptic (pathological) high-frequency signals can be intermingled with physiological high-frequency signals making these signals difficult to differentiate. Recent success in non-invasive detection of high-frequency brain signals opens a new avenue for distinguishing pathological from physiological high-frequency signals. The objective of the present study is to characterize pathological and physiological high-frequency signals at source levels by using kurtosis and skewness analyses. Twenty-three children with medically intractable epilepsy and age-/gender-matched healthy controls were studied using magnetoencephalography. Magnetoencephalographic data in three frequency bands, which included 2–80 Hz (the conventional low-frequency signals), 80–250 Hz (ripples) and 250–600 Hz (fast ripples), were analysed. The kurtosis and skewness of virtual electrode signals in eight brain regions, which included left/right frontal, temporal, parietal and occipital cortices, were calculated and analysed. Differences between epilepsy and controls were quantitatively compared for each cerebral lobe in each frequency band in terms of kurtosis and skewness measurements. Virtual electrode signals from clinical epileptogenic zones and brain areas outside of the epileptogenic zones were also compared with kurtosis and skewness analyses. Compared to controls, patients with epilepsy showed significant elevation in kurtosis and skewness of virtual electrode signals. The spatial and frequency patterns of the kurtosis and skewness of virtual electrode signals among the eight cerebral lobes in three frequency bands were also significantly different from that of the controls (2–80 Hz, P < 0.001; 80–250 Hz, P < 0.00001; 250–600 Hz, P < 0.0001). Compared to signals from non-epileptogenic zones, virtual electrode signals from epileptogenic zones showed significantly altered kurtosis and skewness (P < 0.001). Compared to normative data from the control group, aberrant virtual electrode signals were, for each patient, more pronounced in the epileptogenic lobes than in other lobes(kurtosis analysis of virtual electrode signals in 250–600 Hz; odds ratio = 27.9; P < 0.0001). The kurtosis values of virtual electrode signals in 80–250 and 250–600 Hz showed the highest sensitivity (88.23%) and specificity (89.09%) for revealing epileptogenic lobe, respectively. The combination of virtual electrode and kurtosis/skewness measurements provides a new quantitative approach to distinguishing pathological from physiological high-frequency signals for paediatric epilepsy. Non-invasive identification of pathological high-frequency signals may provide novel important information to guide clinical invasive recordings and direct surgical treatment of epilepsy.

Aberrant Whole-Brain Transitions and Dynamics of Spontaneous Network Microstates in Mild Traumatic Brain Injury

Dynamic Functional Connectivity (DFC) analysis is a promising approach for the characterization of brain electrophysiological activity. In this study, we investigated abnormal alterations due to mild Traumatic Brain Injury (mTBI) using DFC of the source reconstructed magnetoencephalographic (MEG) resting-state recordings. Brain activity in several well-known frequency bands was first reconstructed using beamforming of the MEG data to determine ninety anatomical brain regions of interest. A DFC graph was formulated using the imaginary part of phase-locking values, which were obtained from 30 mTBI patients and 50 healthy controls (HC). Subsequently, we estimated normalized Laplacian transformations of individual, statistically and topologically filtered quasi-static graphs. The corresponding eigenvalues of each node synchronization were then computed and through the neural-gas algorithm, we quantized the evolution of the eigenvalues resulting in distinct network microstates (NMstates). The discrimination level between the two groups was assessed using an iterative cross-validation classification scheme with features either the NMstates in each frequency band, or the combination of the so-called chronnectomics (flexibility index, occupancy time of NMstate, and Dwell time) with the complexity index over the evolution of the NMstates across all frequency bands. Classification performance based on chronnectomics showed 80% accuracy, 99% sensitivity, and 49% specificity. However, performance was much higher (accuracy: 91-97%, sensitivity: 100%, and specificity: 77-93%) when focusing on the microstates. Exploring the mean node degree within and between brain anatomical networks (default mode network, frontoparietal, occipital, cingulo-opercular, and sensorimotor), a reduced pattern occurred from lower to higher frequency bands, with statistically significant stronger degrees for the HC than the mTBI group. A higher entropic profile on the temporal evolution of the modularity index was observed for both NMstates for the mTBI group across frequencies. A significant difference in the flexibility index was observed between the two groups for the β frequency band. The latter finding may support a central role of the thalamus impairment in mTBI. The current study considers a complete set of frequency-dependent connectomic markers of mTBI-caused alterations in brain connectivity that potentially could serve as markers to assess the return of an injured subject back to normality.

New Horizons in Early Dementia Diagnosis: Can Cerebellar Stimulation Untangle the Knot?

Differentiating Mild Cognitive Impairment (MCI) from dementia and estimating the risk of MCI-to-dementia conversion (MDC) are challenging tasks. Thus, objective tools are mandatory to get early diagnosis and prognosis. About that, there is a growing interest on the role of cerebellum-cerebrum connectivity (CCC). The aim of this study was to differentiate patients with an early diagnosis of dementia and MCI depending on the effects of a transcranial magnetic stimulation protocol (intermittent theta-burst stimulation -iTBS) delivered on the cerebellum able to modify cortico-cortical connectivity. Indeed, the risk of MDC is related to the response to iTBS, being higher in non-responder individuals. All patients with MCI, but eight (labelled as MCI-), showed preserved iTBS aftereffect. Contrariwise, none of the patients with dementia showed iTBS aftereffects. None of the patients showed EEG aftereffects following a sham TBS protocol. Five among the MCI- patients converted to dementia at 6-month follow-up. Our data suggest that cerebellar stimulation by means of iTBS may support the differential diagnosis between MCI and dementia and potentially identify the individuals with MCI who may be at risk of MDC. These findings may help clinicians to adopt a better prevention/follow-up strategy in such patients.

Increased segregation of functional networks in developing brains

A growing literature conceptualises typical brain development from a network perspective. However, largely due to technical and methodological challenges inherent in paediatric functional neuroimaging, there remains an important gap in our knowledge regarding the typical development of functional brain networks in "preschool" childhood (i.e., children younger than 6 years of age). In this study, we recorded brain oscillatory activity using age-appropriate magnetoencephalography in 24 children, including 14 preschool children aged from 4 to 6 years and 10 school children aged from 7 to 12 years. We compared the topology of the resting-state brain networks in these children, estimated using minimum spanning tree (MST) constructed from phase synchrony between beamformer-reconstructed time-series, with that of 24 adults. Our results show that during childhood the MST topology shifts from a star-like (centralised) toward a more line-like (de-centralised) configuration, indicating the functional brain networks become increasingly segregated. In addition, the increasing global network segregation is frequency-independent and accompanied by decreases in centrality (or connectedness) of cortical regions with age, especially in areas of the default mode network. We propose a heuristic MST model of "network space", which posits a clear developmental trajectory for the emergence of complex brain networks. Our results not only revealed topological reorganisation of functional networks across multiple temporal and spatial scales in childhood, but also fill a gap in the literature regarding neurophysiological mechanisms of functional brain maturation during the preschool years of childhood.

Decreased integration of EEG source-space networks in disorders of consciousness

Increasing evidence links disorders of consciousness (DOC) with disruptions in functional connectivity between distant brain areas. However, to which extent the balance of brain network segregation and integration is modified in DOC patients remains unclear. Using high-density electroencephalography (EEG), the objective of our study was to characterize the local and global topological changes of DOC patients' functional brain networks.

Resting state high-density-EEG data were collected and analyzed from 82 participants: 61 DOC patients recovering from coma with various levels of consciousness (EMCS (n = 6), MCS+ (n = 29), MCS- (n = 17) and UWS (n = 9)), and 21 healthy subjects (i.e., controls). Functional brain networks in five different EEG frequency bands and the broadband signal were estimated using an EEG connectivity approach at the source level. Graph theory-based analyses were used to evaluate their relationship with decreasing levels of consciousness as well as group differences between healthy volunteers and DOC patient groups.

Results showed that networks in DOC patients are characterized by impaired global information processing (network integration) and increased local information processing (network segregation) as compared to controls. The large-scale functional brain networks had integration decreasing with lower level of consciousness.

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Long-distance communication between brain regions is altered in patients suffering from disorders of consciousness.

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Impaired consciousness is associated with disruptions in brain network integration.

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The left orbitofrontal and left precuneus were identified in all patients groups.

Abnormal maturation of the resting-state peak alpha frequency in children with autism spectrum disorder

Age-related changes in resting-state (RS) neural rhythms in typically developing children (TDC) but not children with autism spectrum disorder (ASD) suggest that RS measures may be of clinical use in ASD only for certain ages. The study examined this issue via assessing RS peak alpha frequency (PAF), a measure previous studies, have indicated as abnormal in ASD. RS magnetoencephalographic (MEG) data were obtained from 141 TDC (6.13-17.70 years) and 204 ASD (6.07-17.93 years). A source model with 15 regional sources projected the raw MEG surface data into brain source space. PAF was identified in each participant from the source showing the largest amplitude alpha activity (7-13 Hz). Given sex differences in PAF in TDC (females > males) and relatively few females in both groups, group comparisons were conducted examining only male TDC (N = 121) and ASD (N = 183). Regressions showed significant group slope differences, with an age-related increase in PAF in TDC (R²  = 0.32) but not ASD (R²  = 0.01). Analyses examining male children below or above 10-years-old (median split) indicated group effects only in the younger TDC (8.90 Hz) and ASD (9.84 Hz; Cohen's d = 1.05). In the older ASD, a higher nonverbal IQ was associated with a higher PAF. In the younger TDC, a faster speed of processing was associated with a higher PAF. PAF as a marker for ASD depends on age, with a RS alpha marker of more interest in younger versus older children with ASD. Associations between PAF and cognitive ability were also found to be age and group specific.

Psychological resilience correlates with EEG source-space brain network flexibility

We aimed at identifying the potential relationship between the dynamical properties of the human functional network at rest and one of the most prominent traits of personality, namely resilience. To tackle this issue, we used resting-state EEG data recorded from 45 healthy subjects. Resilience was quantified using the 10-item Connor-Davidson Resilience Scale (CD-RISC). By using a sliding windows approach, brain networks in each EEG frequency band (delta, theta, alpha, and beta) were constructed using the EEG source-space connectivity method. Brain networks dynamics were evaluated using the network flexibility, linked with the tendency of a given node to change its modular affiliation over time. The results revealed a negative correlation between the psychological resilience and the brain network flexibility for a limited number of brain regions within the delta, alpha, and beta bands. This study provides evidence that network flexibility, a metric of dynamic functional networks, is strongly correlated with psychological resilience as assessed from personality testing. Beyond this proof-of-principle that reliable EEG-based quantities representative of personality traits can be identified, this motivates further investigation regarding the full spectrum of personality aspects and their relationship with functional networks.

Characterizing hippocampal dynamics with MEG: A systematic review and evidence-based guidelines

The hippocampus, a hub of activity for a variety of important cognitive processes, is a target of increasing interest for researchers and clinicians. Magnetoencephalography (MEG) is an attractive technique for imaging spectro-temporal aspects of function, for example, neural oscillations and network timing, especially in shallow cortical structures. However, the decrease in MEG signal-to-noise ratio as a function of source depth implies that the utility of MEG for investigations of deeper brain structures, including the hippocampus, is less clear. To determine whether MEG can be used to detect and localize activity from the hippocampus, we executed a systematic review of the existing literature and found successful detection of oscillatory neural activity originating in the hippocampus with MEG. Prerequisites are the use of established experimental paradigms, adequate coregistration, forward modeling, analysis methods, optimization of signal-to-noise ratios, and protocol trial designs that maximize contrast for hippocampal activity while minimizing those from other brain regions. While localizing activity to specific sub-structures within the hippocampus has not been achieved, we provide recommendations for improving the reliability of such endeavors.

M/EEG-Based Bio-Markers to Predict the MCI and Alzheimer's Disease: A Review From the ML Perspective

This paper reviews the state-of-the-art neuromarkers development for the prognosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). The first part of this paper is devoted to reviewing the recently emerged machine learning (ML) algorithms based on electroencephalography (EEG) and magnetoencephalography (MEG) modalities. In particular, the methods are categorized by different types of neuromarkers. The second part of the review is dedicated to a series of investigations that further highlight the differences between these two modalities. First, several source reconstruction methods are reviewed and their source-level performances explored, followed by an objective comparison between EEG and MEG from multiple perspectives. Finally, a number of the most recent reports on classification of MCI/AD during resting state using EEG/MEG are documented to show the up-to-date performance for this well-recognized data collecting scenario. It is noticed that the MEG modality may be particularly effective in distinguishing between subjects with MCI and healthy controls, a high classification accuracy of more than 98% was reported recently; whereas the EEG seems to be performing well in classifying AD and healthy subjects, which also reached around 98% of the accuracy. A number of influential factors have also been raised and suggested for careful considerations while evaluating the ML-based diagnosis systems in the real-world scenarios.

Dysfunction of attention switching networks in amyotrophic lateral sclerosis

OBJECTIVE

To localise and characterise changes in cognitive networks in Amyotrophic Lateral Sclerosis (ALS) using source analysis of mismatch negativity (MMN) waveforms.

RATIONALE

The MMN waveform has an increased average delay in ALS. MMN has been attributed to change detection and involuntary attention switching. This therefore indicates pathological impairment of the neural network components which generate these functions. Source localisation can mitigate the poor spatial resolution of sensor-level EEG analysis by associating the sensor-level signals to the contributing brain sources. The functional activity in each generating source can therefore be individually measured and investigated as a quantitative biomarker of impairment in ALS or its sub-phenotypes.

METHODS

MMN responses from 128-channel electroencephalography (EEG) recordings in 58 ALS patients and 39 healthy controls were localised to source by three separate localisation methods, including beamforming, dipole fitting and exact low resolution brain electromagnetic tomography.

RESULTS

Compared with controls, ALS patients showed significant increase in power of the left posterior parietal, central and dorsolateral prefrontal cortices (false discovery rate = 0.1). This change correlated with impaired cognitive flexibility (rho = 0.45, 0.45, 0.47, p = .042, .055, .031 respectively). ALS patients also exhibited a decrease in the power of dipoles representing activity in the inferior frontal (left: p = 5.16 × 10-6, right: p = 1.07 × 10-5) and left superior temporal gyri (p = 9.30 × 10-6). These patterns were detected across three source localisation methods. Decrease in right inferior frontal gyrus activity was a good discriminator of ALS patients from controls (AUROC = 0.77) and an excellent discriminator of C9ORF72 expansion-positive patients from controls (AUROC = 0.95).

INTERPRETATION

Source localization of evoked potentials can reliably discriminate patterns of functional network impairment in ALS and ALS subgroups during involuntary attention switching. The discriminative ability of the detected cognitive changes in specific brain regions are comparable to those of functional magnetic resonance imaging (fMRI). Source analysis of high-density EEG patterns has excellent potential to provide non-invasive, data-driven quantitative biomarkers of network disruption that could be harnessed as novel neurophysiology-based outcome measures in clinical trials.

Delirium is associated with frequency band specific dysconnectivity in intrinsic connectivity networks: preliminary evidence from a large retrospective pilot case-control study

Background

Pathophysiological concepts in delirium are not sufficient to define objective biomarkers suited to improve clinical approaches. Advances in neuroimaging have revalued electroencephalography (EEG) as a tool to assess oscillatory network activity in neuropsychiatric disease. Yet, research in the field is limited to small populations and largely confined to postoperative delirium, which impedes generalizability of findings and planning of prospective studies in other populations. This study aimed to assess effect sizes of connectivity measures in a large mixed population to demonstrate that there are measurable EEG differences between delirium and control patients.

Methods

This retrospective pilot study investigated EEG measures as biomarkers in delirium using a case-control design including patients diagnosed with delirium (DSM-5 criteria) and age-/gender-matched controls drawn from a database of 9980 patients (n = 129 and 414, respectively). Assessors were not blinded for groups. Power spectra and connectivity estimates, using the weighted phase log index, of continuous EEG data were compared between conditions. Alterations of information flow through nodes of intrinsic connectivity networks (ICN; default mode, salience, and executive control network) were evaluated in source space using betweenness centrality. This was done frequency specific and network nodes were defined by the multimodal human cerebral cortex parcellation based on human connectome project data.

Results

Delirium and control patients exhibited distinct EEG power, connectivity, and network characteristics (F_(72,540) = 70.3, p < .001; F_(493,1079) = 2.69, p < .001; and F_(718,2159) = 1.14, p = .007, respectively). Connectivity analyses revealed global alpha and regional beta band disconnectivity that was accompanied by theta band hyperconnectivity in delirious patients. Source and network analyses yielded that these changes are not specific to single intrinsic connectivity networks but affect multiple nodes of networks engaged in level of consciousness, attention, working memory, executive control, and salience detection. Effect sizes were medium to strong in this mixed population of delirious patients.

Conclusions

We quantified effect sizes for EEG connectivity and network analyses to be expected in delirium. This study implicates that theta band hyperconnectivity and alpha band disconnectivity may be essential mechanisms in the pathophysiology of delirium. Upcoming prospective studies will build upon these results and evaluate the clinical utility of identified EEG measures as therapeutic and prognostic biomarkers.

Electronic supplementary material

The online version of this article (10.1186/s40814-018-0388-z) contains supplementary material, which is available to authorized users.

Brain functional networks become more connected as amyotrophic lateral sclerosis progresses: a source level magnetoencephalographic study

This study hypothesizes that the brain shows hyper connectedness as amyotrophic lateral sclerosis (ALS) progresses. 54 patients (classified as "early stage" or "advanced stage") and 25 controls underwent magnetoencephalography and MRI recordings. The activity of the brain areas was reconstructed, and the synchronization between them was estimated in the classical frequency bands using the phase lag index. Brain topological metrics such as the leaf fraction (number of nodes with degree of 1), the degree divergence (a measure of the scale-freeness) and the degree correlation (a measure of disassortativity) were estimated. Betweenness centrality was used to estimate the centrality of the brain areas. In all frequency bands, it was evident that, the more advanced the disease, the more connected, scale-free and disassortative the brain networks. No differences were evident in specific brain areas. Such modified brain topology is sub-optimal as compared to controls. Within this framework, our study shows that brain networks become more connected according to disease staging in ALS patients.