Magnetoencephalogram Signals Research Articles

Autism Detection in Children using Common Spatial Patterns of MEG Signals.

Autism exhibits a wide range of developmental disabilities and is associated with aberrant anatomical and functional neural patterns. To detect autism in young children (4-7 years) in an automatic and non-invasive fashion, we have recorded magnetoencephalogram (MEG) signals from 30 autistic and 30 age-matched typically developing (TD) children. We have used a machine learning classification framework with common spatial pattern (CSP)-based logarithmic band power (LBP) features. When comparing the LBP feature to the conventional logarithmic variance (LV) spatial pattern, CSP + LBP (92.77%) has performed better than CSP + LV (90.66%) in the 1-100 Hz frequency range for distinguishing autistic children from TD children. In frequency band-wise analysis using our proposed method, the high gamma frequency band (50-100 Hz) has shown the highest classification accuracy (97.14%). Our findings reveal that the occipital lobe exhibits the most distinct spatial pattern in autistic children over the whole frequency range. This study shows that spatial brain activation patterns can be utilized as potential biomarkers of autism in young children. The improved performance signifies the clinical relevance of the work for autism detection using MEG signals.

Functional connectivity based machine learning approach for autism detection in young children using MEG signals

Objective. Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and identifying early autism biomarkers plays a vital role in improving detection and subsequent life outcomes. This study aims to reveal hidden biomarkers in the patterns of functional brain connectivity as recorded by the neuro-magnetic brain responses in children with ASD. Approach. We recorded resting-state magnetoencephalogram signals from thirty children with ASD (4–7 years) and thirty age and gender-matched typically developing (TD) children. We used a complex coherency-based functional connectivity analysis to understand the interactions between different brain regions of the neural system. The work characterizes the large-scale neural activity at different brain oscillations using functional connectivity analysis and assesses the classification performance of coherence-based (COH) measures for autism detection in young children. A comparative study has also been carried out on COH-based connectivity networks both region-wise and sensor-wise to understand frequency-band-specific connectivity patterns and their connections with autism symptomatology. We used artificial neural network (ANN) and support vector machine (SVM) classifiers in the machine learning framework with a five-fold CV technique. Main results. To classify ASD from TD children, the COH connectivity feature yields the highest classification accuracy of 91.66% in the high gamma (50–100 Hz) frequency band. In region-wise connectivity analysis, the second highest performance is in the delta band (1–4 Hz) after the gamma band. Combining the delta and gamma band features, we achieved a classification accuracy of 95.03% and 93.33% in the ANN and SVM classifiers, respectively. Using classification performance metrics and further statistical analysis, we show that ASD children demonstrate significant hyperconnectivity. Significance. Our findings support the weak central coherency theory in autism detection. Further, despite its lower complexity, we show that region-wise COH analysis outperforms the sensor-wise connectivity analysis. Altogether, these results demonstrate the functional brain connectivity patterns as an appropriate biomarker of autism in young children.

Network analysis of magnetoencephalogram signals in schizophrenia patients when viewing emotional facial stimuli.

Schizophrenia is a serious mental disorder. Network analysis of magnetoencephalogram signals may help to identify potential biomarkers of schizophrenia. The goal of this investigation was to identify potential biomarkers in the magnetoencephalogram signals of patients with schizophrenia, global brain connectivity measures was used for emotion recognition in discriminating the patients from controls. First, we employed a mutual information method to explore the topological characteristics of the brain network in patients with schizophrenia among different frequency bands in response to four different stimulus conditions. Second, multidimensional cross-recurrence quantification analysis was performed to investigate the differences in dynamic coupling among different frequencies of brain magnetic waves in patients with schizophrenia in response to four different stimulus conditions, as the major novel contribution of our study. We found that the differences in topological features of the brain network appear in different frequency bands under different stimulus conditions. The differences are evident in the alpha 1 (8-10 Hz) and beta (13-30 Hz) frequency bands in response to negative stimuli, in the alpha 1 (8-10 Hz) frequency band in response to positive stimuli, and in the theta (4-8 Hz) and alpha 1 (8-10 Hz) frequency bands in response to neutral and gray-cross stimuli. In addition, differences in dynamic coupling among pairs of frequency bands were the most prominent in response to positive stimuli. The characteristics identified by our methods may be potential markers of schizophrenia present in magnetoencephalogram data, which can facilitate the clinical identification of schizophrenia patients. Our method provides a comprehensive perspective of brain networks in patients with schizophrenia and has practical applications for the clinical diagnosis of this disease.

Recurrence network analysis of schizophrenia MEG under different stimulation states

The goal of this study was to investigate nonlinear dynamics in the magnetoencephalogram (MEG) signals of schizophrenia. In this study, the geometric characteristics of MEG signals of schizophrenia were studied in phase space with recurrence network analysis (RNA) under different stimulation states. First, during negative stimulation, the MEG signals of schizophrenia had a higher global recurrence rate, higher invariant objective trajectory and higher phase space separability in the alpha 1 band (8–10 Hz). However, the MEG signals had a lower global recurrence rate and a higher invariant objective trajectory in the beta band (13–30 Hz). Moreover, during positive stimulation and gray cross stimulation, the global recurrence rate and invariant objective trajectory of MEG signals in the alpha 1 band (8–10 Hz) were significantly higher in schizophrenia patients than in controls; and the phase space separability was significantly higher in schizophrenia patients than in controls. Overall, recurrence network parameters enable identification of specific properties of schizophrenia MEG. The abnormal information in the MEG signals of schizophrenia identified with the RNA method has the potential for the development of subbiomarkers for diagnosing schizophrenia.

Multiscale Weighted Permutation Entropy Analysis of Schizophrenia Magnetoencephalograms.

Schizophrenia is a neuropsychiatric disease that affects the nonlinear dynamics of brain activity. The primary objective of this study was to explore the complexity of magnetoencephalograms (MEG) in patients with schizophrenia. We combined a multiscale method and weighted permutation entropy to characterize MEG signals from 19 schizophrenia patients and 16 healthy controls. When the scale was larger than 42, the MEG signals of schizophrenia patients were significantly more complex than those of healthy controls (). The difference in complexity between patients with schizophrenia and the controls was strongest in the frontal and occipital areas (), and there was almost no difference in the central area. In addition, the results showed that the dynamic range of MEG complexity is wider in healthy individuals than in people with schizophrenia. Overall, the multiscale weighted permutation entropy method reliably quantified the complexity of MEG from schizophrenia patients, contributing to the development of potential magnetoencephalographic biomarkers for schizophrenia.

FNS-parameterization of human magnetoencephalograms for the diagnosis of photosensitive epilepsy

This work presents the results of parameterization of magnetoencephalogram signals from healthy subjects and a patient with photosensitive epilepsy. Diagnostic criteria were established during the extraction of resonant and high-frequency (chaotic) components of the initial time signals. It is shown that an increase in the intensity of the chaotic components of the studied signals in the high-frequency region leads to a violation of cross-correlation relationships and a decrease in the level of manifestation of frequency-phase synchronization. The discovered signs of photosensitive epilepsy will contribute to the development of new methods for the diagnosis and medical control of this disease based on Flicker-Noise Spectroscopy.

Multiscale multidimensional recurrence quantitative analysis for analysing MEG signals in patients with schizophrenia

Schizophrenia is a very serious chronic disease with very high hazards. Its pathogenesis is still unclear, and searching for its potential biomarkers has always been a popular issue in the field of schizophrenia research. Nonlinear dynamic analysis of magnetoencephalogram (MEG) signals in patients with schizophrenia is helpful to solve this problem. To quantitatively analyse multiscale recurrence information of MEG signals in patients with schizophrenia, this investigation propose a novel approach termed the multiscale multidimensional recurrence quantitative analysis (MsMdRQA). We first employed two sets of model series, which were generated by Lorenz and Rössler systems, to test the effectiveness of MsMdRQA in nonlinear analysis and to analyse the effects of dimension. Then we applied MsMdRQA to analyse MEG signals in schizophrenia. The test results suggest that both the Lorenz system and the Rössler system have different recurrence states at different scales; therefore, they effectively reflect the internal structure information of the Lorenz system and the Rössler system. Furthermore, we found that the recurrence values of the MEG signals in patients with schizophrenia were significantly lower (p < 0.001) than those of healthy control subjects. Overall, MsMdRQA provides a better analysis method for exploring the nonlinear characteristics of complex systems with different scales.

Scattering convolutional network based predictive model for cognitive activity of brain using empirical wavelet decomposition

Understanding the cognitive activity of brain is a challenging task in brain computer interface (BCI) applications. This work aims at exploring the capability of empirical wavelet transform in decoding the brain wave pattern acquired in response to a thought process and visual stimuli. Empirical wavelet transform (EWT), when combined with the wavelet scattering coefficients is found to efficiently decode the brain wave using recurrent neural network (RNN) based classifier. Electroencephalogram (EEG) and magnetoencephalogram (MEG) are the two modalities considered in this work. The proposed framework is assessed using three different RNN architectures namely long short term memory (LSTM), bi-directional long short term memory (Bi-LSTM), gated recurrent units (GRU). The experimental results show that wavelet scattering coefficients extracted from the dominant mode of EWT decomposition record better performance of 90.23 % and 84.25 % for EEG and MEG signals using GRU as classifier. Furthermore, the wavelet scattering network which involves no learning process achieves better classification at reduced time and computational complexities.

Frequency-phase synchronization in visually induced MEG signals of the human cerebral cortex

In paper, using the Memory Functions Formalism, we carry out a comparative analyzing the induced neuromagnetic responses of the cerebral cortex of healthy people to the two visual flickering stimuli. At the first stage, based on the correlation coefficient, we determined the regions whose interaction (on average for the group) for different stimuli differs most strongly. Next, we analyze the phase portraits of dynamic orthogonal variables and the memory function power spectra. We have found the differences in dynamic, spectral and stochastic characteristics of the magnetoencephalogram signals of the cerebral cortex under the influence of the considered stimuli. The findings provide an opportunity for a deeper understanding of the processes of the spread of excitation in the cerebral cortex with visual stimuli.

Complexity changes in preclinical Alzheimer’s disease: An MEG study of subjective cognitive decline and mild cognitive impairment

ObjectiveTo analyse magnetoencephalogram (MEG) signals with Lempel-Ziv Complexity (LZC) to identify the regions of the brain showing changes related to cognitive decline and Alzheimeŕs Disease (AD). MethodsLZC was used to study MEG signals in the source space from 99 participants (36 male, 63 female, average age: 71.82 ± 4.06) in three groups (33 subjects per group): healthy (control) older adults, older adults with subjective cognitive decline (SCD), and adults with mild cognitive impairment (MCI). Analyses were performed in broadband (2–45 Hz) and in classic narrow bands (theta (4–8 Hz), alpha (8–12 Hz), low beta (12–20 Hz), high beta (20–30 Hz), and, gamma (30–45 Hz)). ResultsLZC was significantly lower in subjects with MCI than in those with SCD. Moreover, subjects with MCI had significantly lower MEG complexity than controls and SCD subjects in the beta frequency band. Lower complexity was correlated with smaller hippocampal volumes. ConclusionsBrain complexity – measured with LZC – decreases in MCI patients when compared to SCD and healthy controls. This decrease is associated with a decrease in hippocampal volume, a key feature in AD progression. SignificanceThis is the first study to date characterising the changes of brain activity complexity showing the specific spatial pattern of the alterations as well as the morphological correlations throughout preclinical stages of AD.

Sex Differences in the Complexity of Healthy Older Adults' Magnetoencephalograms.

The analysis of resting-state brain activity recording in magnetoencephalograms (MEGs) with new algorithms of symbolic dynamics analysis could help obtain a deeper insight into the functioning of the brain and identify potential differences between males and females. Permutation Lempel-Ziv complexity (PLZC), a recently introduced non-linear signal processing algorithm based on symbolic dynamics, was used to evaluate the complexity of MEG signals in source space. PLZC was estimated in a broad band of frequencies (2–45 Hz), as well as in narrow bands (i.e., theta (4–8 Hz), alpha (8–12 Hz), low beta (12–20 Hz), high beta (20–30 Hz), and gamma (30–45 Hz)) in a sample of 98 healthy elderly subjects (49 males, 49 female) aged 65–80 (average age of 72.71 ± 4.22 for males and 72.67 ± 4.21 for females). PLZC was significantly higher for females than males in the high beta band at posterior brain regions including the precuneus, and the parietal and occipital cortices. Further statistical analyses showed that higher complexity values over highly overlapping regions than the ones mentioned above were associated with larger hippocampal volumes only in females. These results suggest that sex differences in healthy aging can be identified from the analysis of magnetoencephalograms with novel signal processing methods.

Switch or stay? Automatic classification of internal mental states in bistable perception

The human brain goes through numerous cognitive states, most of these being hidden or implicit while performing a task, and understanding them is of great practical importance. However, identifying internal mental states is quite challenging as these states are difficult to label, usually short-lived, and generally, overlap with other tasks. One such problem pertains to bistable perception, which we consider to consist of two internal mental states, namely, transition and maintenance. The transition state is short-lived and represents a change in perception while the maintenance state is comparatively longer and represents a stable perception. In this study, we proposed a novel approach for characterizing the duration of transition and maintenance states and classified them from the neuromagnetic brain responses. Participants were presented with various types of ambiguous visual stimuli on which they indicated the moments of perceptual switches, while their magnetoencephalogram (MEG) data were recorded. We extracted different spatio-temporal features based on wavelet transform, and classified transition and maintenance states on a trial-by-trial basis. We obtained a classification accuracy of 79.58% and 78.40% using SVM and ANN classifiers, respectively. Next, we investigated the temporal fluctuations of these internal mental representations as captured by our classifier model and found that the accuracy showed a decreasing trend as the maintenance state was moved towards the next transition state. Further, to identify the neural sources corresponding to these internal mental states, we performed source analysis on MEG signals. We observed the involvement of sources from the parietal lobe, occipital lobe, and cerebellum in distinguishing transition and maintenance states. Cross-conditional classification analysis established generalization potential of wavelet features. Altogether, this study presents an automatic classification of endogenous mental states involved in bistable perception by establishing brain-behavior relationships at the single-trial level.

Integrating EEG and MEG Signals to Improve Motor Imagery Classification in Brain-Computer Interface.

We adopted a fusion approach that combines features from simultaneously recorded electroencephalogram (EEG) and magnetoencephalogram (MEG) signals to improve classification performances in motor imagery-based brain-computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of noninvasive BCIs.

Complexity Changes in Brain Activity in Healthy Ageing: A Permutation Lempel-Ziv Complexity Study of Magnetoencephalograms.

Maturation and ageing, which can be characterised by the dynamic changes in brain morphology, can have an impact on the physiology of the brain. As such, it is possible that these changes can have an impact on the magnetic activity of the brain recorded using magnetoencephalography. In this study changes in the resting state brain (magnetic) activity due to healthy ageing were investigated by estimating the complexity of magnetoencephalogram (MEG) signals. The main aim of this study was to identify if the complexity of background MEG signals changed significantly across the human lifespan for both males and females. A sample of 177 healthy participants (79 males and 98 females aged between 21 and 80 and grouped into 3 categories i.e., early-, mid- and late-adulthood) was used in this investigation. This investigation also extended to evaluating if complexity values remained relatively stable during the 5 min recording. Complexity was estimated using permutation Lempel-Ziv complexity, a recently introduced complexity metric, with a motif length of 5 and a lag of 1. Effects of age and gender were investigated in the MEG channels over 5 brain regions, i.e., anterior, central, left lateral, posterior, and, right lateral, with highest complexity values observed in the signals recorded by the channels over the anterior and central regions of the brain. Results showed that while changes due to age had a significant effect on the complexity of the MEG signals recorded over 5 brain regions, gender did not have a significant effect on complexity values in all age groups investigated. Moreover, although some changes in complexity were observed between the different minutes of recording, due to the small magnitude of the changes it was concluded that practical significance might outweigh statistical significance in this instance. The results from this study can contribute to form a fingerprint of the characteristics of healthy ageing in MEGs that could be useful when investigating changes to the resting state activity due to pathology.

A framework to reconcile frequency scaling measurements, from intracellular recordings, local-field potentials, up to EEG and MEGsignals.

In this viewpoint article, we discuss the electric properties of the medium around neurons, which are important to correctly interpret extracellular potentials or electric field effects in neural tissue. We focus on how these electric properties shape the frequency scaling of brain signals at different scales, such as intracellular recordings, the local field potential (LFP), the electroencephalogram (EEG) or the magnetoencephalogram (MEG). These signals display frequency-scaling properties which are not consistent with resistive media. The medium appears to exert a frequency filtering scaling as 1/f, which is the typical frequency scaling of ionic diffusion. Such a scaling was also found recently by impedance measurements in physiological conditions. Ionic diffusion appears to be the only possible explanation to reconcile these measurements and the frequency-scaling properties found in different brain signals. However, other measurements suggest that the extracellular medium is essentially resistive. To resolve this discrepancy, we show new evidence that metal-electrode measurements can be perturbed by shunt currents going through the surface of the brain. Such a shunt may explain the contradictory measurements, and together with ionic diffusion, provides a framework where all observations can be reconciled. Finally, we propose a method to perform measurements avoiding shunting effects, thus enabling to test the predictions of this framework.

Macroscopic neural mass model constructed from a current-based network model of spiking neurons.

Neural mass models (NMMs) are efficient frameworks for describing macroscopic cortical dynamics including electroencephalogram and magnetoencephalogram signals. Originally, these models were formulated on an empirical basis of synaptic dynamics with relatively long time constants. By clarifying the relations between NMMs and the dynamics of microscopic structures such as neurons and synapses, we can better understand cortical and neural mechanisms from a multi-scale perspective. In a previous study, the NMMs were analytically derived by averaging the equations of synaptic dynamics over the neurons in the population and further averaging the equations of the membrane-potential dynamics. However, the averaging of synaptic current assumes that the neuron membrane potentials are nearly time invariant and that they remain at sub-threshold levels to retain the conductance-based model. This approximation limits the NMM to the non-firing state. In the present study, we newly propose a derivation of a NMM by alternatively approximating the synaptic current which is assumed to be independent of the membrane potential, thus adopting a current-based model. Our proposed model releases the constraint of the nearly constant membrane potential. We confirm that the obtained model is reducible to the previous model in the non-firing situation and that it reproduces the temporal mean values and relative power spectrum densities of the average membrane potentials for the spiking neurons. It is further ensured that the existing NMM properly models the averaged dynamics over individual neurons even if they are spiking in the populations.

The effect of saccadic eye movements on the sensor-level magnetoencephalogram.

We used a combination of simulation and recordings from human subjects to characterize how saccadic eye movements affect the magnetoencephalogram (MEG). We used simulated saccadic eye movements to generate simulated MEG signals. We also recorded the MEG signals from three healthy adults to 5° magnitude saccades that were vertical up and down, and horizontal left and right. The signal elicited by the rotating eye dipoles is highly dependent on saccade direction, can cover a large area, can sometimes have a non-intuitive trajectory, but does not significantly extend above approximately 30Hz in the frequency domain. In contrast, the saccadic spikes (which are primarily monophasic pulses, but can be biphasic) are highly localized to the lateral frontal regions for all saccade directions, and in the frequency domain extend up past 60Hz. Gamma band saccadic artifact is spatially localized to small regions regardless of saccade direction, but beta band and lower frequency saccadic artifact have broader spatial extents that vary strongly as a function of saccade direction. We have here characterized the MEG saccadic artifact in both the spatial and the frequency domains for saccades of different directions. This could be important in ruling in or ruling out artifact in MEG recordings.

Advances in modern mental chronometry.

Advances in modern mental chronometry.

Cortical activity associated with the detection of temporal gaps in tones: a magnetoencephalography study.

We used magnetoencephalogram (MEG) in two experiments to investigate spatio-temporal profiles of brain responses to gaps in tones. Stimuli consisted of leading and trailing markers with gaps between the two markers of 0, 30, or 80 ms. Leading and trailing markers were 300 ms pure tones at 800 or 3200 Hz.Two conditions were examined: the within-frequency (WF) condition in which the leading and trailing markers had identical frequencies, and the between-frequency (BF) condition in which they had different frequencies. Using minimum norm estimates (MNE), we localized the source activations at the time of the peak response to the trailing markers. Results showed that MEG signals in response to 800 and 3200 Hz tones were localized in different regions within the auditory cortex, indicating that the frequency pathways activated by the two markers were spatially represented.The time course of regional activity (RA) was extracted from each localized region for each condition. In Experiment 1, which used a continuous tone for the WF 0-ms stimulus, the N1m amplitude for the trailing marker in the WF condition differed depending on gap duration but not tonal frequency. In contrast, N1m amplitude in BF conditions differed depending on the frequency of the trailing marker. In Experiment 2, in which the 0-ms gap stimulus in the WF condition was made from two markers and included an amplitude reduction in the middle, the amplitude in WF and BF conditions changed depending on frequency, but not gap duration.The difference in temporal characteristics betweenWF and BF conditions could be observed in the RA.

Changes in the MEG background activity in patients with positive symptoms of schizophrenia: spectral analysis and impact of age

The frequency spectrum of the magnetoencephalogram (MEG) background activity was analysed in 15 schizophrenia (SCH) patients with predominant positive symptoms and 17 age-matched healthy control subjects using the following variables: median frequency (MF), spectral entropy (SpecEn) and relative power in delta (RPδ), theta (RPθ), lower alpha (RPα1), upper alpha (RPα2), beta (RPβ) and gamma (RPγ) bands. We found significant differences between the two subject groups in the average level of MF and RPγ in some regions of the scalp. Additionally, the MF, SpecEn, RPβ and RPγ values of SCH patients with positive symptoms had a different dependence on age as compared with the results of control subjects, suggesting that SCH affects the way in which the brain activity evolves with age. Moreover, we also classified the MEG signals by means of a cross-validated feature selection process followed by a logistic regression. The subjects were classified with 71.3% accuracy and an area under the ROC curve of 0.741. Thus, the spectral and classification analysis of the MEG in SCH may provide insights into how this condition affects the brain activity and may help in its early detection.