Functional and effective whole brain connectivity using magnetoencephalography to identify monozygotic twin pairs.

Resting-state functional connectivity patterns are highly stable over time within subjects. This suggests that such 'functional fingerprints' may have strong genetic component. We investigated whether the functional (FC) or effective (EC) connectivity patterns of one monozygotic twin could be used to identify the co-twin among a larger sample and determined the overlap in functional fingerprints within monozygotic (MZ) twin pairs using resting state magnetoencephalography (MEG). We included 32 cognitively normal MZ twin pairs from the Netherlands Twin Register who participate in the EMIF-AD preclinAD study (average age 68 years). Combining EC information across multiple frequency bands we obtained an identification rate over 75%. Since MZ twin pairs are genetically identical these results suggest a high genetic contribution to MEG-based EC patterns, leading to large similarities in brain connectivity patterns between two individuals even after 60 years of life or more.

Opportunities and methodological challenges in EEG and MEG resting state functional brain network research.

Electroencephalogram (EEG) and magnetoencephalogram (MEG) recordings during resting state are increasingly used to study functional connectivity and network topology. Moreover, the number of different analysis approaches is expanding along with the rising interest in this research area. The comparison between studies can therefore be challenging and discussion is needed to underscore methodological opportunities and pitfalls in functional connectivity and network studies. In this overview we discuss methodological considerations throughout the analysis pipeline of recording and analyzing resting state EEG and MEG data, with a focus on functional connectivity and network analysis. We summarize current common practices with their advantages and disadvantages; provide practical tips, and suggestions for future research. Finally, we discuss how methodological choices in resting state research can affect the construction of functional networks. When taking advantage of current best practices and avoid the most obvious pitfalls, functional connectivity and network studies can be improved and enable a more accurate interpretation and comparison between studies.

Structural degree predicts functional network connectivity: a multimodal resting-state fMRI and MEG study.

Communication between neuronal populations in the human brain is characterized by complex functional interactions across time and space. Recent studies have demonstrated that these functional interactions depend on the underlying structural connections at an aggregate level. Multiple imaging modalities can be used to investigate the relation between the structural connections between brain regions and their functional interactions at multiple timescales. We investigated if consistent modality-independent functional interactions take place between brain regions, and whether these can be accounted for by underlying structural properties. We used functional MRI (fMRI) and magnetoencephalography (MEG) recordings from a population of healthy adults together with a previously described structural network. A high overlap in resting-state functional networks was found in fMRI and especially alpha band MEG recordings. This overlap was characterized by a strongly interconnected functional core network in temporo-posterior brain regions. Anatomically realistically coupled neural mass models revealed that this strongly interconnected functional network emerges near the threshold for global synchronization. Most importantly, this functional core network could be explained by a trade-off between the product of the degrees of structurally-connected regions and the Euclidean distance between them. For both fMRI and MEG, the product of the degrees of connected regions was the most important predictor for functional network connectivity. Therefore, irrespective of the modality, these results indicate that a functional core network in the human brain is especially shaped by communication between high degree nodes of the structural network.

Neurodevelopmental retardation, as assessed clinically and with magnetoencephalography and electroencephalography, associated with perinatal dioxin exposure.

UNLABELLED: In 1980s Western Europe, human perinatal exposure to background levels of dioxins was rather high. We therefore evaluated the neurodevelopment of our cohort during the prepubertal period and in adolescence. At prepubertal age (7-12 years) 41 children were tested. Both neuromotor functioning and psychological testing were performed (Dutch version of the Wechsler Intelligence Scale for Children (WISC-R) and the Dutch version of the Child Behavior Checklist for ages 4-18 years (CBCL 4-18) and the Teacher Report Form (TRF)). Neurophysiological tests were performed using magnetoencephalography and electroencephalography. In adolescence (14-18 years) the behavior of 33 children was studied again (CBCL and TRF). And the levels of dioxins and dioxin-like PCBs (dl-PCBs) were measured in serum. RESULTS: At prepubertal age no association was found between perinatal dioxin exposure and verbal, performal and total IQ or with the Touwen's test for neuromotor development. There were behavioral problems associated with both prenatal and postnatal dioxin exposure. In adolescence there were problems associated with the current dioxin levels and dioxin-like-PCBs. Neurophysiological tests revealed clear negative dysfunction. An increase in latency time after a motion stimulus (N2b) of 13 ms (= a delay of 10%) is associated with the higher prenatal dioxin exposure. A similar delay was measured in testing cognitive ability by analyzing the odd ball measurements, N200 and P300, together with an amplitude decrease of 12 %. The delay is indicative of a defective myelinisation and the decrease in amplitude of a loss of neurons. CONCLUSION: We found effects on behavior in association with the perinatal dioxin exposure and in adolescence in association with the current dioxin levels. Neurophysiological testing is instrumental in the detection of effects of perinatal background levels of chemicals on brain development in normal, healthy children. The clinical, neurological and psychological tests commonly used are not sensitive enough to detect important effects.

Functional brain network analysis using minimum spanning trees in Multiple Sclerosis: an MEG source-space study.

Cognitive dysfunction in Multiple Sclerosis (MS) is closely related to altered functional brain network topology. Conventional network analyses to compare groups are hampered by differences in network size, density and suffer from normalization problems. We therefore computed the Minimum Spanning Tree (MST), a sub-graph of the original network, to counter these problems. We hypothesize that functional network changes analysed with MSTs are important for understanding cognitive changes in MS and that changes in MST topology also represent changes in the critical backbone of the original brain networks. Here, resting-state magnetoencephalography (MEG) recordings from 21 early MS patients and 17 age-, gender-, and education-matched controls were projected onto atlas-based regions-of-interest (ROIs) using beamforming. The phase lag index was applied to compute functional connectivity between regions, from which a graph and subsequently the MST was constructed. Results showed lower global integration in the alpha2 (10-13Hz) and beta (13-30Hz) bands in MS patients, whereas higher global integration was found in the theta band. Changes were most pronounced in the alpha2 band where a loss of hierarchical structure was observed, which was associated with poorer cognitive performance. Finally, the MST in MS patients as well as in healthy controls may represent the critical backbone of the original network. Together, these findings indicate that MST network analyses are able to detect network changes in MS patients, which may correspond to changes in the core of functional brain networks. Moreover, these changes, such as a loss of hierarchical structure, are related to cognitive performance in MS.

Feasibility of clinical magnetoencephalography (MEG) functional mapping in the presence of dental artefacts.

OBJECTIVE: To evaluate the viability of MEG source reconstruction in the presence of large interference due to orthodontic material. METHODS: We recorded the magnetic fields following a simple hand movement and following electrical stimulation of the median nerve (somatosensory evoked field -SEF). These two tasks were performed twice, once with and once without artificial dental artefacts. Temporal Signal Space Separation (tSSS) was applied to spatially filter the data and source reconstruction was performed according to standard procedures for pre-surgical mapping of eloquent cortex, applying dipole fitting to the SEF data and beamforming to the hand movement data. RESULTS: Comparing the data with braces to the data without braces, the observed distances between the activations following hand movement in the two conditions were on average 6.4 and 4.5 mm for the left and right hand, respectively, whereas the dipole localisation errors for the SEF were 4.1 and 5.4 mm, respectively. Without tSSS it was generally not possible to obtain reliable dipole fit or beamforming results when wearing braces. CONCLUSION: We confirm that tSSS is a required and effective pre-processing step for data recorded with the Elekta-MEG system. Moreover, we have shown that even the presence of large interference from orthodontic material does not significantly alter the results from dipole localisation or beamformer analysis, provided the data are spatially filtered by tSSS. SIGNIFICANCE: State-of-the-art signal processing techniques enable the use of MEG for pre-surgical evaluation in a much larger clinical population than previously thought possible.

Optimizing parameter choice for FSL-Brain Extraction Tool (BET) on 3D T1 images in multiple sclerosis.

BACKGROUND: Brain atrophy studies often use FSL-BET (Brain Extraction Tool) as the first step of image processing. Default BET does not always give satisfactory results on 3DT1 MR images, which negatively impacts atrophy measurements. Finding the right alternative BET settings can be a difficult and time-consuming task, which can introduce unwanted variability. AIM: To systematically analyze the performance of BET in images of MS patients by varying its parameters and options combinations, and quantitatively comparing its results to a manual gold standard. METHODS: Images from 159 MS patients were selected from different MAGNIMS consortium centers, and 16 different 3DT1 acquisition protocols at 1.5 T or 3T. Before running BET, one of three pre-processing pipelines was applied: (1) no pre-processing, (2) removal of neck slices, or (3) additional N3 inhomogeneity correction. Then BET was applied, systematically varying the fractional intensity threshold (the "f" parameter) and with either one of the main BET options ("B" - bias field correction and neck cleanup, "R" - robust brain center estimation, or "S" - eye and optic nerve cleanup) or none. For comparison, intracranial cavity masks were manually created for all image volumes. FSL-FAST (FMRIB's Automated Segmentation Tool) tissue-type segmentation was run on all BET output images and on the image volumes masked with the manual intracranial cavity masks (thus creating the gold-standard tissue masks). The resulting brain tissue masks were quantitatively compared to the gold standard using Dice overlap coefficient (DOC). Normalized brain volumes (NBV) were calculated with SIENAX. NBV values obtained using for SIENAX other BET settings than default were compared to gold standard NBV with the paired t-test. RESULTS: The parameter/preprocessing/options combinations resulted in 20,988 BET runs. The median DOC for default BET (f=0.5, g=0) was 0.913 (range 0.321-0.977) across all 159 native scans. For all acquisition protocols, brain extraction was substantially improved for lower values of "f" than the default value. Using native images, optimum BET performance was observed for f=0.2 with option "B", giving median DOC=0.979 (range 0.867-0.994). Using neck removal before BET, optimum BET performance was observed for f=0.1 with option "B", giving median DOC 0.983 (range 0.844-0.996). Using the above BET-options for SIENAX instead of default, the NBV values obtained from images after neck removal with f=0.1 and option "B" did not differ statistically from NBV values obtained with gold-standard. CONCLUSION: Although default BET performs reasonably well on most 3DT1 images of MS patients, the performance can be improved substantially. The removal of the neck slices, either externally or within BET, has a marked positive effect on the brain extraction quality. BET option "B" with f=0.1 after removal of the neck slices seems to work best for all acquisition protocols.

Dynamics underlying spontaneous human alpha oscillations: a data-driven approach.

Although the cognitive and clinical correlates of spontaneous human alpha oscillations as recorded with electroencephalography (EEG) or magnetoencephalography (MEG) are well documented, the dynamics underlying these oscillations is still a matter of debate. This study proposes a data-driven method to reveal the dynamics of these oscillations. It demonstrates that spontaneous human alpha oscillations as recorded with MEG can be viewed as noise-perturbed damped harmonic oscillations. This provides evidence for the hypothesis that these oscillations reflect filtered noise and hence do not possess limit-cycle dynamics. To illustrate the use of the model, we apply it to two data-sets in which a decrease in alpha power can be observed across conditions. The associated differences in the estimated model parameters show that observed decreases in alpha power are associated with different kinds of changes in the dynamics. Thus, the model parameters are useful dynamical biomarkers for spontaneous human alpha oscillations.

Cortico-spinal synchronization reflects changes in performance when learning a complex bimanual task.

Motor performance is accompanied by neural activity in various cortical and sub-cortical areas. This intricate network has to be delicately orchestrated. We analyzed the role of beta synchronization in motor learning using magneto-encephalography combined with electromyography. Cortico-spinal synchronization in the beta band was found to be of particular importance in establishing bimanual movement patterns in the context of a 3:2 polyrhythmic (isometric) force production task. Its dynamics correlated highly with the learning of this complex bimanual motor skill. We submit that the cortical dynamics entrains the spinal motor system by which cortico-spinal beta synchrony serves higher-level motor control functions as primary means of information transfer along the neural axis.

Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease.

In this study we examined changes in the large-scale structure of resting-state brain networks in patients with Alzheimer's disease compared with non-demented controls, using concepts from graph theory. Magneto-encephalograms (MEG) were recorded in 18 Alzheimer's disease patients and 18 non-demented control subjects in a no-task, eyes-closed condition. For the main frequency bands, synchronization between all pairs of MEG channels was assessed using a phase lag index (PLI, a synchronization measure insensitive to volume conduction). PLI-weighted connectivity networks were calculated, and characterized by a mean clustering coefficient and path length. Alzheimer's disease patients showed a decrease of mean PLI in the lower alpha and beta band. In the lower alpha band, the clustering coefficient and path length were both decreased in Alzheimer's disease patients. Network changes in the lower alpha band were better explained by a 'Targeted Attack' model than by a 'Random Failure' model. Thus, Alzheimer's disease patients display a loss of resting-state functional connectivity in lower alpha and beta bands even when a measure insensitive to volume conduction effects is used. Moreover, the large-scale structure of lower alpha band functional networks in Alzheimer's disease is more random. The modelling results suggest that highly connected neural network 'hubs' may be especially at risk in Alzheimer's disease.

Neural changes induced by learning a challenging perceptual-motor task.

We studied the neural changes accompanying the learning of a perceptual-motor task involving polyrhythmic bimanual force production. Motor learning was characterized by an increase in stability of performance. To assess after-effects in the corresponding neural network, magnetoencophalographic and electromyographic signals were recorded and analyzed in terms of (event-related) amplitude changes and synchronization patterns. The topology of the network was first identified using a beamformer analysis, which revealed differential effects of activation in cortical areas and cerebellar hemispheres. We found event-related (de-)synchronization of beta-activity in bilateral cortical motor areas and alpha-modulations in the cerebellum. The alpha-modulation increased after learning and, simultaneously, the bilateral M1 coupling increased around the movement frequency reflecting improved motor timing. Furthermore, the inter-hemispheric gamma-synchronization between primary motor areas decreased, which may reflect a reduced attentional demand after learning.

The influence of low-grade glioma on resting state oscillatory brain activity: a magnetoencephalography study.

PURPOSE: In the present MEG-study, power spectral analysis of oscillatory brain activity was used to compare resting state brain activity in both low-grade glioma (LGG) patients and healthy controls. We hypothesized that LGG patients show local as well as diffuse slowing of resting state brain activity compared to healthy controls and that particularly global slowing correlates with neurocognitive dysfunction. PATIENT AND METHODS: Resting state MEG recordings were obtained from 17 LGG patients and 17 age-, sex-, and education-matched healthy controls. Relative spectral power was calculated in the delta, theta, upper and lower alpha, beta, and gamma frequency band. A battery of standardized neurocognitive tests measuring 6 neurocognitive domains was administered. RESULTS: LGG patients showed a slowing of the resting state brain activity when compared to healthy controls. Decrease in relative power was mainly found in the gamma frequency band in the bilateral frontocentral MEG regions, whereas an increase in relative power was found in the theta frequency band in the left parietal region. An increase of the relative power in the theta and lower alpha band correlated with impaired executive functioning, information processing, and working memory. CONCLUSION: LGG patients are characterized by global slowing of their resting state brain activity and this slowing phenomenon correlates with the observed neurocognitive deficits.

Synchronization likelihood with explicit time-frequency priors.

Cognitive processing requires integration of information processed simultaneously in spatially distinct areas of the brain. The influence that two brain areas exert on each others activity is usually governed by an unknown function, which is likely to have nonlinear terms. If the functional relationship between activities in different areas is dominated by the nonlinear terms, linear measures of correlation may not detect the statistical interdependency satisfactorily. Therefore, algorithms for detecting nonlinear dependencies may prove invaluable for characterizing the functional coupling in certain neuronal systems, conditions or pathologies. Synchronization likelihood (SL) is a method based on the concept of generalized synchronization and detects nonlinear and linear dependencies between two signals (Stam, C.J., van Dijk, B.W., 2002. Synchronization likelihood: An unbiased measure of generalized synchronization in multivariate data sets. Physica D, 163: 236-241.). SL relies on the detection of simultaneously occurring patterns, which can be complex and widely different in the two signals. Clinical studies applying SL to electro- or magnetoencephalography (EEG/MEG) signals have shown promising results. In previous implementations of the algorithm, however, a number of parameters have lacked a rigorous definition with respect to the time-frequency characteristics of the underlying physiological processes. Here we introduce a rationale for choosing these parameters as a function of the time-frequency content of the patterns of interest. The number of parameters that can be arbitrarily chosen by the user of the SL algorithm is thereby decreased from six to two. Empirical evidence for the advantages of our proposal is given by an application to EEG data of an epileptic seizure and simulations of two unidirectionally coupled Hénon systems.

Resting state oscillatory brain dynamics in Parkinson's disease: an MEG study.

OBJECTIVE: The pathophysiological mechanisms of cognitive dysfunction and dementia in Parkinson's disease (PD) are still poorly understood. Altered resting state oscillatory brain activity may reflect underlying neuropathological changes. The present study using magneto encephalography (MEG) was set up to study differences in the pattern of resting state oscillatory brain activity in groups of demented and non-demented PD patients and healthy, elderly controls. METHODS: The pattern of MEG background oscillatory activity was studied in 13 demented PD patients, 13 non-demented PD patients and 13 healthy controls. Whole head MEG recordings were obtained in the morning in an eyes closed and an eyes open, resting state condition. Relative spectral power was calculated using Fast Fourier Transformation in delta, theta, alpha, beta and gamma frequency bands. RESULTS: In the non-demented PD patients, relative theta power was diffusely increased and beta power concomitantly decreased relative to controls. gamma Power was decreased in central and parietal channels. In the demented PD patients, a diffuse increase in relative delta and to lesser extent theta power and a decrease in relative alpha, beta and to lesser extent gamma power were found in comparison to the non-demented PD group. In addition, reactivity to eye opening was much reduced in the demented PD group. CONCLUSIONS: Parkinson's disease is characterized by a slowing of resting state brain activity involving theta, beta and gamma frequency bands. Dementia in PD is associated with a further slowing of resting state brain activity, additionally involving delta and alpha bands, as well as a reduction in reactivity to eye-opening. SIGNIFICANCE: The differential patterns of slowing of resting state brain activity in demented and non-demented PD patients suggests that, in conjunction with a progression of the pathological changes already present in non-demented patients, additional mechanisms are involved in the development of dementia in PD.

Magnetoencephalographic evaluation of resting-state functional connectivity in Alzheimer's disease.

Statistical interdependencies between magnetoencephalographic signals recorded over different brain regions may reflect the functional connectivity of the resting-state networks. We investigated topographic characteristics of disturbed resting-state networks in Alzheimer's disease patients in different frequency bands. Whole-head 151-channel MEG was recorded in 18 Alzheimer patients (mean age 72.1 years, SD 5.6; 11 males) and 18 healthy controls (mean age 69.1 years, SD 6.8; 7 males) during a no-task eyes-closed resting state. Pair-wise interdependencies of MEG signals were computed in six frequency bands (delta, theta, alpha1, alpha2, beta and gamma) with the synchronization likelihood (a nonlinear measure) and coherence and grouped into long distance (intra- and interhemispheric) and short distance interactions. In the alpha1 and beta band, Alzheimer patients showed a loss of long distance intrahemispheric interactions, with a focus on left fronto-temporal/parietal connections. Functional connectivity was increased in Alzheimer patients locally in the theta band (centro-parietal regions) and the beta and gamma band (occipito-parietal regions). In the Alzheimer group, positive correlations were found between alpha1, alpha2 and beta band synchronization likelihood and MMSE score. Resting-state functional connectivity in Alzheimer's disease is characterized by specific changes of long and short distance interactions in the theta, alpha1, beta and gamma bands. These changes may reflect loss of anatomical connections and/or reduced central cholinergic activity and could underlie part of the cognitive impairment.

MEG-compatible force sensor.

By use of an insulating material we constructed a strain gauge based sensor to measure isometric forces in parallel with magneto-encephalographic recordings (i.e. without interference). The sensor can be used in different geometries to measure force production in different dimensions. Furthermore, it can easily be adapted or modified for specific experimental applications. Finally, on-line processing of the recorded forces, e.g., for the purpose of feedback, can be realized using standard MEG equipment.

Detection of very high correlation in the alpha band between temporal regions of the human brain using MEG.

It is generally believed that alpha band (8-12 Hz) electric and magnetic activity in the area of the left and right temporal regions in the human brain are at best poorly correlated. There are no previous reports of very high alpha band correlation between left and right temporal regions by magnetoencephalography (MEG) or electroencephalography (EEG). We present whole head magnetoencephalography (MEG) results that demonstrate that, for temporal channels in the majority of healthy subjects tested, the alpha band signals are highly to very highly correlated and are antiparallel in direction. A correlation as high as -0.97 was found for a limited time in one subject. We suggest that the correlation found may be the consequence of strong direct or indirect coupling between homologue areas in left and right temporal regions rather than a common source. The correlation may provide a valuable index of loss of connectivity in the brain due to disease as well providing valuable insight to brain function and deserves further investigation.

A neural complexity measure applied to MEG data in Alzheimer's disease.

OBJECTIVE: A measure of neural complexity (C(N)) (Proc. Natl Acad. Sci. USA 91 (1994) 5033) was applied to magnetoencephalography (MEG) data to test the hypothesis that C(N) decreases when information processing in the brain is impaired, as is the case in patients with Alzheimer's Disease (AD). METHODS: One hundred and fifty-one channel MEGs were recorded in 20 AD patients and 20 healthy age-matched controls in a resting condition with eyes open (EO) and eyes closed (EC). Artifact-free epochs of 117 channels were selected for analysis. C(N) and D(2) were computed in different frequency bands, and correlated with the MMSE. RESULTS: The Group x Frequency band interaction was significant for both C(N) and D(2). C(N) was higher in AD, as compared with controls, in the 2-4 and 4-8Hz bands, and D(2) was higher in AD patients in the 14-20 and 20-30Hz bands. The C(N) was higher in the EC condition compared to the EO condition, whereas the D(2) was higher in the EO condition. CONCLUSIONS: The hypothesis of Tononi et al. (Proc. Natl Acad. Sci. USA 91 (1994) 5033) that the neural complexity decreases in AD patients has to be rejected. However, both neural complexity and the correlation dimension did show differences between controls and AD patients which depended on frequency band.

The localization of spontaneous brain activity: an efficient way to analyze large data sets.

An efficient solution is presented of the problem to localize the electric generators of spontaneous magnetoencephalography (MEG) and electroencephalography (EEG) data for large data sets. When a data set contains more than 100,000 samples standard methods fail or become impractical. The method presented here is useful, for example, for the localization of (pathological) brain rhythms or the analysis of single-trial data. The problem is defined as finding the good fitting dipoles using the single-dipole model applied on each time sample. First, the data is bandpass filtered to select the rhythm of interest. Next, the empirical relationship between data power and probability of a dipole with a high goodness of fit (g.o.f.) is used to preselect data points. Then a global search algorithm is applied, based on precomputed lead fields on a fixed grid, to obtain a good initial guess for the nonlinear dipole search. Finally, the dipole search is applied on those samples that have a low initial guess error. In a group of five patients, it is found that 50% of the dipoles with a g.o.f. of at least 90% can be found by disregarding 90% of the data samples. Those dipoles can be found efficiently by disregarding all sample points with an initial guess relative residual error of 15% or lower. Finally, a simple empirical expression is found for the optimal mesh size of the global search grid. The method is completely automatic and makes it possible to study simple generators of large MEG and EEG data sets on a routine basis.