Imaginary Part Of Coherency Research Articles

Differentiating ictal/subclinical spikes and waves in childhood absence epilepsy by spectral and network analyses: A pilot study

ObjectiveChildhood absence epilepsy (CAE) is a disease with distinct seizure semiology and electroencephalographic (EEG) features. Differentiating ictal and subclinical generalized spikes and waves discharges (GSWDs) in the EEG is challenging, since they appear to be identical upon visual inspection. Here, spectral and functional connectivity (FC) analyses were applied to routine EEG data of CAE patients, to differentiate ictal and subclinical GSWDs. MethodsTwelve CAE patients with both ictal and subclinical GSWDs were retrospectively selected for this study. The selected EEG epochs were subjected to frequency analysis in the range of 1–30 Hz. Further, FC analysis based on the imaginary part of coherency was used to determine sensor level networks. ResultsDelta, alpha and beta band frequencies during ictal GSWDs showed significantly higher power compared to subclinical GSWDs. FC showed significant network differences for all frequency bands, demonstrating weaker connectivity between channels during ictal GSWDs. ConclusionUsing spectral and FC analyses significant differences between ictal and subclinical GSWDs in CAE patients were detected, suggesting that these features could be used for machine learning classification purposes to improve EEG monitoring. SignificanceIdentifying differences between ictal and subclinical GSWDs using routine EEG, may improve understanding of this syndrome and the management of patients with CAE.

Clinical significance of electroencephalography power spectrum density and functional connection analysis in neonates with hypoxic-ischemic encephalopathy.

To investigate the effects of electroencephalography (EEG) power and functional connectivity analysis on cerebral cortex function in neonates with hypoxic-ischemic encephalopathy (HIE), and to identify the neurobiological indicators of neural development in HIE. We recruited 20 mild HIE neonates, 15 moderate HIE neonates, and 30 controls. EEG was performed about 72hr after birth. The power spectral density (PSD) and imaginary part of coherency (ICOH) were analyzed. Gesell developmental schedule (GDS) was used to evaluate the neural development in the mild and moderate HIE groups at 1year of age, and the correlation between the quantitative EEG results and the state of neural development was analyzed. Compared with the controls, FP1, FP2, C3, C4, Fz, Cz, Pz , F3, and P4 of moderate HIE neonates showed that the PSD of theta, alpha and beta bands decreased significantly. In terms of the mean whole-brain PSD, themoderate HIEgroupshowed a significant decrease in all frequency bands. ICOH of the moderate HIE group showed that functional connectivity was significantly less than that in the controls mainly in the delta band, and the functional connectivity of the delta, theta, alpha1, and alpha2 bands was markedly reduced compared with the mild HIE. GDS test at 1year old showed that two infants in the moderate HIE group had suspected neurological delay in gross motor and language. The developmental quotient(DQ) of gross motor, language, and personal-social ability in the moderate HIE group were significantly lower than in the mild HIE group. And there was a significant positive correlation between PSD in each EEG frequency band and GDS score in the moderate HIE group. PSD and ICOH can be used to evaluate brain function. PSD can detect the delayed neurological development in infants with moderate HIE, and can be a neurobiomarker of brain development in HIE.

Benchmarking metrics for inferring functional connectivity from multi-channel EEG and MEG: A simulation study.

I present a systematic evaluation of different types of metrics, for inferring magnitude, amplitude, or phase synchronization from the electroencephalogram (EEG) and the magnetoencephalogram (MEG). I used a biophysical model, generating EEG/MEG-like signals, together with a system of two coupled self-sustained chaotic oscillators, containing clear transitions from phase to amplitude synchronization solely modulated by coupling strength. Specifically, I compared metrics according to five benchmarks for assessing different types of reliability factors, including immunity to spatial leakage, test-retest reliability, and sensitivity to noise, coupling strength, and synchronization transition. My results delineate the heterogeneous reliability of widely used connectivity metrics, including two magnitude synchronization metrics [coherence (Coh) and imaginary part of coherence (ImCoh)], two amplitude synchronization metrics [amplitude envelope correlation (AEC) and corrected amplitude envelope correlation (AECc)], and three phase synchronization metrics [phase coherence (PCoh), phase lag index (PLI), and weighted PLI (wPLI)]. First, the Coh, AEC, and PCoh were prone to create spurious connections caused by spatial leakage. Therefore, they are not recommended to be applied to real EEG/MEG data. The ImCoh, AECc, PLI, and wPLI were less affected by spatial leakage. The PLI and wPLI showed the highest immunity to spatial leakage. Second, the PLI and wPLI showed higher test-retest reliability and higher sensitivity to coupling strength and synchronization transition than the ImCoh and AECc. Third, the AECc was less noisy than the ImCoh, PLI, and wPLI. In sum, my work shows that the choice of connectivity metric should be determined after a comprehensive consideration of the aforementioned five reliability factors.

Negative Correlation Between Functional Connectivity and Small-Worldness in the Alpha Frequency Band of a Healthy Brain.

The aim of the study was to analyze the relationship between resting state electroencephalographic (EEG) alpha functional connectivity (FC) and small-world organization. For that purpose, Pearson correlation was calculated between FC and small-worldness (SW). Three undirected FC measures were used: magnitude-squared coherence (MSC), imaginary part of coherency (ICOH), and synchronization likelihood (SL). As a result, statistically significant negative correlation occurred between FC and SW for all three FC measures. Small-worldness of MSC and SL were mostly above 1, but lower than 1 for ICOH, suggesting that functional EEG networks did not have small-world properties. Based on the results of the current study, we suggest that decreased alpha small-world organization is compensated with increased connectivity of alpha oscillations in a healthy brain.

Comparison of Phase Synchronization Measures for Identifying Stimulus-Induced Functional Connectivity in Human Magnetoencephalographic and Simulated Data.

Phase synchronization measures are widely used for investigating inter-regional functional connectivity (FC) of brain oscillations, but which phase synchronization measure should be chosen for a given experiment remains unclear. Using neuromagnetic brain signals recorded from healthy participants during somatosensory stimuli, we compared the performance of four phase synchronization measures, imaginary part of phase-locking value, imaginary part of coherency (ImCoh), phase lag index and weighted phase lag index (wPLI), for detecting stimulus-induced FCs between the contralateral primary and ipsilateral secondary somatosensory cortices. The analyses revealed that ImCoh exhibited the best performance for detecting stimulus-induced FCs, followed by the wPLI. We found that amplitude weighting, which is related to computing both ImCoh and wPLI, effectively attenuated the influence of noise contamination. A simulation study modeling noise-contaminated periodograms replicated these findings. The present results suggest that the amplitude-dependent measures, ImCoh followed by wPLI, may have the advantage in detecting stimulus-induced FCs.

Computational modeling of the effects of EEG volume conduction on functional connectivity metrics. Application to Alzheimer’s disease continuum

Objective. The aim of this study was to evaluate the effect of electroencephalographic (EEG) volume conduction in different measures of functional connectivity and to characterize the EEG coupling alterations at the different stages of dementia due to Alzheimer’s disease (AD). Approach. Magnitude squared coherence (MSCOH), imaginary part of coherence (iCOH), lagged coherence (lagCOH), amplitude envelope correlation (AEC), synchronization likelihood (SL), phase lag index (PLI), phase locking value (PLV), and corrected imaginary PLV (ciPLV) were applied to: (i) synthetic signals generated with a Kuramoto-based model of several coupled oscillators; and (ii) a resting-state EEG database of real recordings from 51 cognitively healthy controls, 51 mild cognitive impairment (MCI) subjects, 51 mild AD (ADmil) patients, 50 moderate AD (ADmod) patients, and 50 severe AD (ADsev) patients. Main results. Our results using synthetic signals showed that PLI was the least affected parameter by spurious influences in a simulated volume conduction environment. Results using real EEG recordings showed that spontaneous activity of MCI patients is characterized by a significant coupling increase in the band. As dementia progresses, this increase in the band became more pronounced, and a significant widespread decrease in band appeared at the last stage of dementia. Significance. Our results revealed that the estimation of functional EEG connectivity using PLI could reduce the bias introduced by the spurious influence of volume conduction, and it could increase the insight into the underlying brain dynamics at different stages of the AD continuum.

Oscillatory connectivity as a diagnostic marker of dementia due to Alzheimer’s disease

ObjectiveQuantitative EEG power has not been as effective in discriminating between healthy aging and Alzheimer’s disease as conventional biomarkers. But EEG coherence has shown promising results in small samples. The overall aim was to evaluate if EEG connectivity markers can discriminate between Alzheimer’s disease, mild cognitive impairment, and healthy aging and to explore the early underlying changes in coherence. MethodsEEGs were included in the analysis from 135 healthy controls, 117 patients with mild cognitive impairment, and 117 patients with Alzheimer’s disease from six Nordic memory clinics. Principal component analysis was performed before multinomial regression. ResultsWe found classification accuracies of above 95% based on coherence, imaginary part of coherence, and the weighted phase-lag index. The most prominent changes in coherence were decreased alpha coherence in Alzheimer’s disease, which was correlated to the scores of the 10-word test in the Consortium to Establish a Registry for Alzheimer’s Disease battery. ConclusionsThe diagnostic accuracies for EEG connectivity measures are higher than findings from studies investigating EEG power and conventional Alzheimer's disease biomarkers. Furthermore, decreased alpha coherence is one of the earliest changes in Alzheimer’s disease and associated with memory function. SignificanceEEG connectivity measures may be useful supplementary diagnostic classifiers.

Reliability of Magnetoencephalography and High-Density Electroencephalography Resting-State Functional Connectivity Metrics.

Resting-state connectivity, for example, based on magnetoencephalography (MEG) or electroencephalography (EEG), is a widely used method for characterizing brain networks and a promising imaging biomarker. However, there is no established standard as to which method, modality, and analysis variant is preferable and there is only limited knowledge on the reproducibility, an important prerequisite for clinical application. We conducted an MEG-/high-density (hd)-EEG-study on 22 young healthy adults, who were measured twice in a scan/rescan design after 7 ± 2 days. Reliability of resting-state (15 min, eyes-closed) connectivity in source space was calculated via intraclass correlation coefficient (ICC) in classical frequency bands (delta-gamma). We investigated the reliability of two commonly used connectivity metrics, namely the imaginary part of coherency and the weighted phase-lag index and the influence of frequency band, vigilance, and the number of trials. We found a strong increase of reliability with more trials and relatively mild effects of vigilance. Reliability was excellent in the alpha band for MEG, as well as hd-EEG (ICC >0.85); in the theta band, reliability was good for MEG and poor for EEG. Other frequency bands showed lower reliability, with delta band being the worst. Furthermore, we investigated the spatial reliability of resting-state connectivity in a vertex-based approach, which reached fair to good reliability (ICC up to 0.67) with 5 min of data. Our results indicate that excellent reliability of global connectivity is achievable in alpha band, and vertex-based connectivity was still fair to good. Moreover, electrophysiological resting-state studies could benefit from more data than used previously. MEG and hd-EEG were similar in their overall performance but showed frequency band-specific differences.

Altered Low-Frequency EEG Connectivity in Mild Cognitive Impairment as a Sign of Clinical Progression.

Mild cognitive impairment (MCI) is associated with clinical progression to Alzheimer's disease (AD) but not all patients with MCI convert to AD. However, it is important to have methods that can differentiate between patients with MCI who progress (pMCI) and those who remain stable (sMCI), i.e., for timely administration of disease-modifying drugs. In the current study, we wanted to investigate whether quantitative EEG coherence and imaginary part of coherency (iCoh) could be used to differentiate between pMCI and sMCI. 17 patients with AD, 27 patients with MCI, and 38 older healthy controls were recruited and followed for three years and 2nd year was used to determine progression. EEGs were recorded at baseline and coherence and iCoh were calculated after thorough preprocessing. Between pMCI and sMCI, the largest difference in total coherence was found in the theta and delta bands. Here, the significant differences for coherence and iCoh were found in the lower frequency bands involving the temporal-frontal connections for coherence and parietal-frontal connections for iCoh. Furthermore, we found a significant negative correlation between theta coherence and the Addenbrooke's Cognitive Examination (ACE) (p = 0.0378; rho = -0.2388). These findings suggest that low frequency coherence and iCoh can be used to determine, which patients with MCI will progress to AD and is associated with the ACE score. Low-frequency coherence has previously been associated with increased hippocampal atrophy and degeneration of the cholinergic system and may be an early marker of AD pathology.

P69. High-frequency (>500 Hz) recordings from deep brain macroelectrodes reveal local interactions of subcortical multi-unit activity

Introduction While low-frequency local field potentials (LFPs) are generated mainly by postsynaptic potentials, high-frequency (hf-) LFP-activity (>500 Hz) is thought to be a correlate of multi-unit spiking activity. Here, we explore hf-recordings from electrodes implanted for deep brain stimulation for the presence of local interactions of multi-unit activity. Methods LFPs were recorded postoperatively after implantation of deep brain stimulation electrodes (each featuring 4 linearly arranged contacts) in 2 patients (#1: globus pallidus internus for multisegmental dystonia; #2: subthalamic nucleus for Parkinson’s disease) using a dedicated low-noise signal amplifier at a sampling rate of 10 kHz. Imaginary part of coherency was calculated between electrodes as a measure of true brain interactions without interference from volume conduction effects. To rule out competing technical and biological mechanisms, the analysis was repeated on recordings from a dummy head and, resp., on surface-EMG recordings. Results The power spectral density of the recordings significantly deviated from a 1/f-characteristic exhibiting a plateau at 150–2.000 Hz. Moreover, at frequencies >500 Hz, imaginary part of coherency indicated the presence of ‘true’ interactions between neighboring pairs of bipolar chain derivations. These signal characteristics were neither present in the dummy-head recordings nor in the surface-EMG recordings. Discussion and significance LFP recordings from two different subcortical sites and in two different diseases indicated the detectability of locally interacting multi-unit activity using macroelectrodes. Major competing mechanisms could be ruled out in control measurements. Further recordings will address the general consistency of these findings and explore potential correlations with clinical data. Acknowledgement This work was supported by KFO 247 (DFG grant Ni 1308/1-2 ).

Increased Functional MEG Connectivity as a Hallmark of MRI-Negative Focal and Generalized Epilepsy.

Epilepsy is one of the most prevalent neurological diseases with a high morbidity. Accumulating evidence has shown that epilepsy is an archetypical neural network disorder. Here we developed a non-invasive cortical functional connectivity analysis based on magnetoencephalography (MEG) to assess commonalities and differences in the network phenotype in different epilepsy syndromes (non-lesional/cryptogenic focal and idiopathic/genetic generalized epilepsy). Thirty-seven epilepsy patients with normal structural brain anatomy underwent a 30-min resting state MEG measurement with eyes closed. We only analyzed interictal epochs without epileptiform discharges. The imaginary part of coherency was calculated as an indicator of cortical functional connectivity in five classical frequency bands. This connectivity measure was computed between all sources on individually reconstructed cortical surfaces that were surface-aligned to a common template. In comparison to healthy controls, both focal and generalized epilepsy patients showed widespread increased functional connectivity in several frequency bands, demonstrating the potential of elevated functional connectivity as a common pathophysiological hallmark in different epilepsy types. Furthermore, the comparison between focal and generalized epilepsies revealed increased network connectivity in bilateral mesio-frontal and motor regions specifically for the generalized epilepsy patients. Our study indicated that the surface-based normalization of MEG sources of individual brains enables the comparison of imaging findings across subjects and groups on a united platform, which leads to a straightforward and effective disclosure of pathological network characteristics in epilepsy. This approach may allow for the definition of more specific markers of different epilepsy syndromes, and increased MEG-based resting-state functional connectivity seems to be a common feature in MRI-negative epilepsy syndromes.

A Comparative Study of the Robustness of Frequency-Domain Connectivity Measures to Finite Data Length.

In this work we use numerical simulation to investigate how the temporal length of the data affects the reliability of the estimates of brain connectivity from EEG time-series. We assume that the neural sources follow a stable MultiVariate AutoRegressive model, and consider three connectivity metrics: imaginary part of coherency (IC), generalized partial directed coherence (gPDC) and frequency-domain granger causality (fGC). In order to assess the statistical significance of the estimated values, we use the surrogate data test by generating phase-randomized and autoregressive surrogate data. We first consider the ideal case where we know the source time courses exactly. Here we show how, expectedly, even exact knowledge of the source time courses is not sufficient to provide reliable estimates of the connectivity when the number of samples gets small; however, while gPDC and fGC tend to provide a larger number of false positives, the IC becomes less sensitive to the presence of connectivity. Then we proceed with more realistic simulations, where the source time courses are estimated using eLORETA, and the EEG signal is affected by biological noise of increasing intensity. Using the ideal case as a reference, we show that the impact of biological noise on IC estimates is qualitatively different from the impact on gPDC and fGC.

Directed Motor-Auditory EEG Connectivity Is Modulated by Music Tempo.

Beat perception is fundamental to how we experience music, and yet the mechanism behind this spontaneous building of the internal beat representation is largely unknown. Existing findings support links between the tempo (speed) of the beat and enhancement of electroencephalogram (EEG) activity at tempo-related frequencies, but there are no studies looking at how tempo may affect the underlying long-range interactions between EEG activity at different electrodes. The present study investigates these long-range interactions using EEG activity recorded from 21 volunteers listening to music stimuli played at 4 different tempi (50, 100, 150 and 200 beats per minute). The music stimuli consisted of piano excerpts designed to convey the emotion of “peacefulness”. Noise stimuli with an identical acoustic content to the music excerpts were also presented for comparison purposes. The brain activity interactions were characterized with the imaginary part of coherence (iCOH) in the frequency range 1.5–18 Hz (δ, θ, α and lower β) between all pairs of EEG electrodes for the four tempi and the music/noise conditions, as well as a baseline resting state (RS) condition obtained at the start of the experimental task. Our findings can be summarized as follows: (a) there was an ongoing long-range interaction in the RS engaging fronto-posterior areas; (b) this interaction was maintained in both music and noise, but its strength and directionality were modulated as a result of acoustic stimulation; (c) the topological patterns of iCOH were similar for music, noise and RS, however statistically significant differences in strength and direction of iCOH were identified; and (d) tempo had an effect on the direction and strength of motor-auditory interactions. Our findings are in line with existing literature and illustrate a part of the mechanism by which musical stimuli with different tempi can entrain changes in cortical activity.

Detecting atypical functioning of emotional processing in Colombian ex-combatants

The reincorporation process of Colombian ex-combatants is hindered by their chronic exposure to violence, which affects their Emotional Processing (EP). Characterizing their EP will contribute to their reinsertion. The objective of this work is to define an EEG-based brain connectivity approach to identify differences in EP between Colombian ex-combatants and individuals who were not directly exposed to the armed conflict. The proposed approach involves defining the Regions of Interest (ROI) and selecting one of five commonly used brain connectivity metrics: Correlation, Cross-Correlation, Coherence, Imaginary part of Coherency, and Phase-Lag Index. Significant differences were found in the positive valence stimuli in the Beta frequency band. These results support the previously reported trend in the literature regarding the difficulties ex-combatants have to process emotional information with positive valence.

Phase-coherence classification: A new wavelet-based method to separate local field potentials into local (in)coherent and volume-conducted components

BackgroundLocal field potentials (LFP) reflect the integrated electrophysiological activity of large neuron populations and may thus reflect the dynamics of spatially and functionally different networks. New methodWe introduce the wavelet-based phase-coherence classification (PCC), which separates LFP into volume-conducted, local incoherent and local coherent components. It allows to compute power spectral densities for each component associated with local or remote electrophysiological activity. ResultsWe use synthetic time series to estimate optimal parameters for the application to LFP from within the subthalamic nucleus of eight Parkinson patients. With PCC we identify multiple local tremor clusters and quantify the relative power of local and volume-conducted components. We analyze the electrophysiological response to an apomorphine injection during rest and hold. Here we show medication-induced significant decrease of incoherent activity in the low beta band and increase of coherent activity in the high beta band. On medication significant movement-induced changes occur in the high beta band of the local coherent signal. It increases during isometric hold tasks and decreases during phasic wrist movement. Comparison with existing methodsThe power spectra of local PCC components is compared to bipolar recordings. In contrast to bipolar recordings PCC can distinguish local incoherent and coherent signals. We further compare our results with classification based on the imaginary part of coherency and the weighted phase lag index. ConclusionsThe low and high beta band are more susceptible to medication- and movement-related changes reflected by incoherent and local coherent activity, respectively. PCC components may thus reflect functionally different networks.

EP 66. Cortico-subthalamic neural interactions: Relation to cognitive task performance and evidence for a novel interaction mode across multiple time scales in patients with Parkinson’s disease

Introduction Neural interactions between the cortex and the subthalamic nucleus (STN) are frequently studied in patients with Parkinson’s disease and were shown to relate to movement performance and the severity of motor symptoms. However, it is unknown whether cortico-subthalamic synchronization might also relate to non-motor, rather cognitive aspects of task performance, e.g., in language context. Furthermore, we do not know whether neural interactions between cortex and STN might be characterized by additional modes of communication across different time scales and frequency bands. Objectives Addressing these open questions we investigated (i) the relation between cortex-STN synchronization and lexical decisions in Parkinsonian patients. In contrast to previous studies we applied methods that optimize the extraction of neural synchronization from multi-channel recordings, while simultaneously excluding artifacts of volume conduction. Furthermore, we addressed (ii) for the first time the presence of cortico-subthalamic neural interactions across multiple time–frequency scales, by investigating the relation between long-range temporal correlations (across tens of seconds) in cortically dominant alpha oscillations and coherency (milliseconds) in subthalamic beta oscillations. Materials and methods Eight patients with Parkinson’s disease, operated for deep brain stimulation, performed a lexical decision task on dopaminergic medication. Simultaneous recordings were obtained from surface electroencephalography (EEG) and deep local field potentials (LFP) from the bilateral STN. In the continuous data cortico-subthalamic coherency was assessed by the maximized imaginary part of coherency (MIC), which simultaneously accounts for multi-channel recordings and is insensitive to volume conduction artifacts. Furthermore, cortical long-range temporal correlations (up to 20 s) were calculated in the amplitude envelope of EEG oscillations, and the detectability of the imaginary part of coherency (iCOHd) was calculated in STN-LFP. Results Cortico-subthalamic coherency in 14–35 Hz oscillations estimated by MIC was positively correlated with the accuracy in lexical decisions across patients. No significant correlation was present in 7–13 Hz oscillations, and MIC was not correlated with the reaction times in any of the frequency bands. Furthermore, we showed a significant positive correlation across patients between cortical long-range correlations (8–13 Hz, whole scalp) and subcortical coherency (iCOHd) selectively in 10–20 Hz oscillations in the left STN. Conclusions The present results suggest that the complementary usage of multivariate methods, such as MIC, might be effective in coherency studies for extracting neural synchronization from multi-channel recordings. Furthermore, cortico-subthalamic synchronization might be relevant also for cognitive aspects of task performance, here related to lexical decisions. Finally, our results provide evidence for novel, complex neural interaction modes across different time scales, reflected in the relation between neural dynamics in the most dominant rhythms in cortex and basal ganglia. Therefore, the present results contribute to further understanding of neural pathology in Parkinson’s disease on macroscopic network levels.

Response conflict processes' classification in 7 and 9 year old children using EEG brain connectivity measures.

Investigating cognitive development of children poses interesting challenges pertaining to emergence of children's' ability to think and understand. Psychological tasks that involve conflict, like the Flanker task, are widely used to understand development of response conflict processes. In this study, EEG signals were used to examine the coherence and imaginary part of coherency within the delta, theta, alpha and beta bands across different conditions of the Flanker task. Longitudinal data were collected from a group of typically developing children at ages of seven and nine. We found that the imaginary part of coherency was more helpful in distinguishing between stimuli - alpha and beta bands resulted in 90.90% classification rate in seven year old children. The beta and theta bands were found to be more effective for stimuli classification in nine year old children - more than 84.09% classification accuracy was achieved.

Phase to amplitude coupling as a potential biomarker for creative ideation: an EEG study.

The most consistent finding of creative ideation in the neuroscientific study of creativity is the increment of EEG α power. However, the majority of existing studies focused only on ERP experimental paradigms while only a few analyzed time-related changes of EEG α power patterns during the time unlocked creation of ideas. Here, we designed an experimental paradigm where the participants were asked to generate alternative uses of everyday objects (AU task). For the control task, we adopted an Object Characteristics (OC) task, for which participants were asked to list typical characteristics or properties of an object. We estimated relative power spectrum, global efficiency from brain networks constructed with the imaginary part of coherence and phase-to-amplitude coupling (PAC) as potential biomarkers of creativity. Both relative power spectrum and nodal global efficiency failed to reach significant level by comparing AU with OC. In contrast, statistically significant differences between AU and OC were detected with PAC estimated within sensors in frequency pairs θ-γ and α2-γ. Our results can be the ground for both detecting and designing a connectomic biomarker of creativity.

How reliable are MEG resting-state connectivity metrics?

MEG offers dynamic and spectral resolution for resting-state connectivity which is unavailable in fMRI. However, there are a wide range of available network estimation methods for MEG, and little in the way of existing guidance on which ones to employ. In this technical note, we investigate the extent to which many popular measures of stationary connectivity are suitable for use in resting-state MEG, localising magnetic sources with a scalar beamformer. We use as empirical criteria that network measures for individual subjects should be repeatable, and that group-level connectivity estimation shows good reproducibility. Using publically-available data from the Human Connectome Project, we test the reliability of 12 network estimation techniques against these criteria. We find that the impact of magnetic field spread or spatial leakage artefact is profound, creates a major confound for many connectivity measures, and can artificially inflate measures of consistency. Among those robust to this effect, we find poor test-retest reliability in phase- or coherence-based metrics such as the phase lag index or the imaginary part of coherency. The most consistent methods for stationary connectivity estimation over all of our tests are simple amplitude envelope correlation and partial correlation measures.

Upregulation of cortico-cerebellar functional connectivity after motor learning

Interactions between the cerebellum and primary motor cortex are crucial for the acquisition of new motor skills. Recent neuroimaging studies indicate that learning motor skills is associated with subsequent modulation of resting-state functional connectivity in the cerebellar and cerebral cortices. The neuronal processes underlying the motor-learning-induced plasticity are not well understood. Here, we investigate changes in functional connectivity in source-reconstructed electroencephalography (EEG) following the performance of a single session of a dynamic force task in twenty young adults. Source activity was reconstructed in 112 regions of interest (ROIs) and the functional connectivity between all ROIs was estimated using the imaginary part of coherence. Significant changes in resting-state connectivity were assessed using partial least squares (PLS). We found that subjects adapted their motor performance during the training session and showed improved accuracy but with slower movement times. A number of connections were significantly upregulated after motor training, principally involving connections within the cerebellum and between the cerebellum and motor cortex. Increased connectivity was confined to specific frequency ranges in the mu- and beta-bands. Post hoc analysis of the phase spectra of these cerebellar and cortico-cerebellar connections revealed an increased phase lag between motor cortical and cerebellar activity following motor practice. These findings show a reorganization of intrinsic cortico-cerebellar connectivity related to motor adaptation and demonstrate the potential of EEG connectivity analysis in source space to reveal the neuronal processes that underpin neural plasticity.