A novel ANCOVA design for analysis of MEG data with application to a visual attention study

Theta oscillations linked to auditory informativeness and context disambiguation

Accurate predictions and the processing of prediction error signals can be important for efficient interaction with the auditory environment. In a reanalysis of data from Simal et al . (2021), who found that informative tones elicited increased N1 and P2 event-related potential components, we sought to identify electrophysiological indicators in the time-frequency domain associated with disambiguation of the hearing context and prediction of forthcoming stimulation. Participants heard two isochronous sequences of pure tones separated by a silent retention interval. A sequence could contain one, three, or five tones. Fifteen participants heard the three load conditions randomly intermixed. In this case, when sequence length was unknown, the second and fourth tone during encoding contained information allowing the prediction of another tone. Other participants heard the sequences blocked by sequence length, and the second and fourth tone of the sequences provided no new information (and hence were not informative). We used wavelet analysis and Hilbert transform methods to analyse the oscillatory activity related to tone informativeness. We found a significant increase in theta (4-7 Hz) amplitude following a tone that was informative and allowed prediction, in comparison with a tone that carried no predictive information. Previous work suggests increased theta amplitude is linked with task switching and an increase in cognitive control. We suggest informative tones recruit higher-level control processes involved in prediction of upcoming auditory events.

Long-Range α-Synchronization as Control Signal for BCI: A Feasibility Study

Shifts in spatial attention are associated with variations in α band (α, 8-14 Hz) activity, specifically in interhemispheric imbalance. The underlying mechanism is attributed to local α-synchronization, which regulates local inhibition of neural excitability, and frontoparietal synchronization reflecting long-range communication. The direction-specific nature of this neural correlate brings forward its potential as a control signal in brain-computer interfaces (BCIs). In the present study, we explored whether long-range α-synchronization presents lateralized patterns dependent on voluntary attention orienting and whether these neural patterns can be picked up at a single-trial level to provide a control signal for active BCI. We collected electroencephalography (EEG) data from a cohort of healthy adults (n = 10) while performing a covert visuospatial attention (CVSA) task. The data show a lateralized pattern of α-band phase coupling between frontal and parieto-occipital regions after target presentation, replicating previous findings. This pattern, however, was not evident during the cue-to-target orienting interval, the ideal time window for BCI. Furthermore, decoding the direction of attention trial-by-trial from cue-locked synchronization with support vector machines (SVMs) was at chance level. The present findings suggest EEG may not be capable of detecting long-range α-synchronization in attentional orienting on a single-trial basis and, thus, highlight the limitations of this metric as a reliable signal for BCI control.

Decreased brain network global efficiency after attachment memories retrieval in individuals with unresolved/disorganized attachment-related state of mind

The main aim of the study was to examine how brain network metrics change after retrieval of attachment memories in individuals with unresolved/disorganized (U/D) attachment-related state of mind and those with organized/resolved (O/R) state of mind. We focused on three main network metrics associated with integration and segregation: global (E_glob) efficiency for the first function, local (E_loc) efficiency and modularity for the second. We also examined assortativity and centrality metrics. Electroencephalography (EEG) recordings were performed before and after the Adult Attachment Interview (AAI) in a sample of 50 individuals previously assessed for parenting quality. Functional connectivity matrices were constructed by means of the exact Low-Resolution Electromagnetic Tomography (eLORETA) software and then imported into MATLAB to compute brain network metrics. Compared to individuals with O/R attachment-related state of mind, those with U/D show a significant decrease in beta E_glob after AAI. No statistically significant difference among groups emerged in E_loc and modularity metrics after AAI, neither in assortativity nor in betweenness centrality. These results may help to better understand the neurophysiological patterns underlying the disintegrative effects of retrieving traumatic attachment memories in individuals with disorganized state of mind in relation to attachment.

More than Meets the Mind's Eye? Preliminary Observations Hint at Heterogeneous Alpha Neuromarkers for Visual Attention

With their salient power distribution and privileged timescale for cognition and behavior, brainwaves within the 10 Hz band are special in human waking electroencephalography (EEG). From the inception of electroencephalographic technology, the contribution of alpha rhythm to attention is well-known: Its amplitude increases when visual attention wanes or visual input is removed. However, alpha is not alone in the 10 Hz frequency band. A number of other 10 Hz neuromarkers have function and topography clearly distinct from alpha. In small pilot studies, an activity that we named xi was found over left centroparietal scalp regions when subjects held their attention to spatially peripheral locations while maintaining their gaze centrally ("looking from the corner of the eyes"). I outline several potential functions for xi as a putative neuromarker of covert attention distinct from alpha. I review methodological aids to test and validate their functional role. They emphasize high spectral resolution, sufficient spatial resolution to provide topographical separation, and an acute attention to dynamics that caters to neuromarkers' transiency.

Overview of MEG

Magnetoencephalography (MEG) is a method to study electrical activity in the human brain by recording the neuromagnetic field outside the head. MEG, like electroencephalography (EEG), provides an excellent, millisecond-scale time resolution, and allows the estimation of the spatial distribution of the underlying activity, in favorable cases with a localization accuracy of a few millimeters. To detect the weak neuromagnetic signals, superconducting sensors, magnetically shielded rooms, and advanced signal processing techniques are used. The analysis and interpretation of MEG data typically involves comparisons between subject groups and experimental conditions using various spatial, temporal, and spectral measures of cortical activity and connectivity. The application of MEG to cognitive neuroscience studies is illustrated with studies of spoken language processing in subjects with normal and impaired reading ability. The mapping of spatiotemporal patterns of activity within networks of cortical areas can provide useful information about the functional architecture of the brain related to sensory and cognitive processing, including language, memory, attention, and perception.

A multivariate method for estimating cross-frequency neuronal interactions and correcting linear mixing in MEG data, using canonical correlations

BACKGROUND

Cross-frequency interactions between distinct brain areas have been observed in connection with a variety of cognitive tasks. With electro- and magnetoencephalography (EEG/MEG) data, typical connectivity measures between two brain regions analyze a single quantity from each region within a specific frequency band; given the wideband nature of EEG/MEG signals, many statistical tests may be required to identify true coupling. Furthermore, because of the poor spatial resolution of activity reconstructed from EEG/MEG, some interactions may actually be due to the linear mixing of brain sources.

NEW METHOD

In the present work, a method for the detection of cross-frequency functional connectivity in MEG data using canonical correlation analysis (CCA) is described. We demonstrate that CCA identifies correlated signals and also the frequencies that cause the correlation. We also implement a procedure to deal with linear mixing based on symmetry properties of cross-covariance matrices.

RESULTS

Our tests with both simulated and real MEG data demonstrate that CCA is able to detect interacting locations and the frequencies that cause them, while accurately discarding spurious coupling.

COMPARISON WITH EXISTING METHODS

Recent techniques look at time delays in the activity between two locations to discard spurious interactions, while we propose a linear mixing model and demonstrate its relationship with symmetry aspects of cross-covariance matrices.

CONCLUSIONS

Our tests indicate the benefits of the CCA approach in connectivity studies, as it allows the simultaneous evaluation of several possible combinations of cross-frequency interactions in a single statistical test.

Towards neural correlates of auditory stimulus processing: a simultaneous auditory evoked potentials and functional magnetic resonance study using an odd-ball paradigm

Background

The neural underpinnings of auditory information processing have often been investigated using the odd-ball paradigm, in which infrequent sounds (deviants) are presented within a regular train of frequent stimuli (standards). Traditionally, this paradigm has been applied using either high temporal resolution (EEG) or high spatial resolution (fMRI, PET). However, used separately, these techniques cannot provide information on both the location and time course of particular neural processes. The goal of this study was to investigate the neural correlates of auditory processes with a fine spatio-temporal resolution. A simultaneous auditory evoked potentials (AEP) and functional magnetic resonance imaging (fMRI) technique (AEP-fMRI), together with an odd-ball paradigm, were used.

Material/Methods

Six healthy volunteers, aged 20–35 years, participated in an odd-ball simultaneous AEP-fMRI experiment. AEP in response to acoustic stimuli were used to model bioelectric intracerebral generators, and electrophysiological results were integrated with fMRI data.

Results

fMRI activation evoked by standard stimuli was found to occur mainly in the primary auditory cortex. Activity in these regions overlapped with intracerebral bioelectric sources (dipoles) of the N1 component. Dipoles of the N1/P2 complex in response to standard stimuli were also found in the auditory pathway between the thalamus and the auditory cortex. Deviant stimuli induced fMRI activity in the anterior cingulate gyrus, insula, and parietal lobes.

Conclusions

The present study showed that neural processes evoked by standard stimuli occur predominantly in subcortical and cortical structures of the auditory pathway. Deviants activate areas non-specific for auditory information processing.

Interactions between visual and motor areas during the recognition of plausible actions as revealed by magnetoencephalography

Several studies have shown activation of the mirror neuron system (MNS), comprising the temporal, posterior parietal, and sensorimotor areas when observing plausible actions, but far less is known on how these cortical areas interact during the recognition of a plausible action. Here, we recorded neural activity with magnetoencephalography while subjects viewed point-light displays of biologically plausible and scrambled versions of actions. We were interested in modulations of oscillatory activity and, specifically, in coupling of oscillatory activity between visual and motor areas. Both plausible and scrambled actions elicited modulations of θ (5-7 Hz), α (7-13 Hz), β (13-35 Hz), and γ (55-100 Hz) power within visual and motor areas. When comparing between the two actions, we observed sequential and spatially distinct increases of γ (∼65 Hz), β (∼25 Hz), and α (∼11 Hz) power between 0.5 and 1.3 s in parieto-occipital, sensorimotor, and left temporal areas. In addition, significant clusters of γ (∼65 Hz) and α/β (∼15 Hz) power decrease were observed in right temporal and parieto-occipital areas between 1.3 and 2.0 s. We found β-power in sensorimotor areas to be positively correlated on a trial-by-trial basis with parieto-occipital γ and left temporal α-power for the plausible but not for the scrambled condition. These results provide new insights in the neuronal oscillatory activity of the areas involved in the recognition of plausible action movements and their interaction. The power correlations between specific areas underscore the importance of interactions between visual and motor areas of the MNS during the recognition of a plausible action.

Differential neural responses to acupuncture revealed by MEG using wavelet-based time-frequency analysis: a pilot study

Acupoint specificity, lying at the core of the Traditional Chinese Medicine, still faces many controversies. As previous neuroimaging studies on acupuncture mainly adopted relatively low time-resolution functional magnetic resonance imaging (fMRI) technology and inappropriate block-designed experimental paradigm due to sustained effect, in the current study, we employed a single block-designed paradigm together with high temporal-resolution magnetoencephalography (MEG) technology. We applied time-frequency analysis based upon Morlet wavelet transforming approach to detect differential oscillatory brain dynamics induced by acupuncture at Stomach Meridian 36 (ST36) using a nearby nonacupoint (NAP) as control condition. We observed that frequency power changes were mainly restricted to delta band for both ST36 group and NAP group. Consistently increased delta band power in contralateral temporal regions and decreased power in the counterparts of ipsilateral hemisphere were detected following stimulation at ST36 on the right leg. Compared with ST36, no significant delta ranges were found in temporal regions in NAP group, illustrating different oscillatory brain patterns. Our results may provide additional evidence to support the specificity of acupuncture modulation effects.

Dynamic activation of frontal, parietal, and sensory regions underlying anticipatory visual spatial attention

Although it is well established that multiple frontal, parietal, and occipital regions in humans are involved in anticipatory deployment of visual spatial attention, less is known about the electrophysiological signals in each region across multiple subsecond periods of attentional deployment. We used MEG measures of cortical stimulus-locked, signal-averaged (event-related field) activity during a task in which a symbolic cue directed covert attention to the relevant location on each trial. Direction-specific attention effects occurred in different cortical regions for each of multiple time periods during the delay between the cue and imperative stimulus. A sequence of activation from V1/V2 to extrastriate, parietal, and frontal regions occurred within 110 ms after cue, possibly related to extraction of cue meaning. Direction-specific activations ∼300 ms after cue in frontal eye field (FEF), lateral intraparietal area (LIP), and cuneus support early covert targeting of the cued location. This was followed by coactivation of a frontal-parietal system [superior frontal gyrus (SFG), middle frontal gyrus (MFG), LIP, anterior intraparietal sulcus (IPSa)] that may coordinate the transition from targeting the cued location to sustained deployment of attention to both space and feature in the last period. The last period involved direction-specific activity in parietal regions and both dorsal and ventral sensory regions [LIP, IPSa, ventral IPS, lateral occipital region, and fusiform gyrus], which was accompanied by activation that was not direction specific in right hemisphere frontal regions (FEF, SFG, MFG). Behavioral performance corresponded with the magnitude of attention-related activity in different brain regions at each time period during deployment. The results add to the emerging electrophysiological characterization of different cortical networks that operate during anticipatory deployment of visual spatial attention.

Brainstorm: a user-friendly application for MEG/EEG analysis

Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI).

Detection of event-related modulations of oscillatory brain activity with multivariate statistical analysis of MEG data

We describe a method to detect brain activation in cortically constrained maps of current density computed from magnetoencephalography (MEG) data using multivariate statistical inference. We apply time-frequency (wavelet) analysis to individual epochs to produce dynamic images of brain signal power on the cerebral cortex in multiple time-frequency bands. We form vector observations by concatenating the power in each frequency band, and fit them into separate multivariate linear models for each time band and cortical location with experimental conditions as predictor variables. The resulting Roy's maximum root statistic maps are thresholded for significance using permutation tests and the maximum statistic approach. A source is considered significant if it exceeds a statistical threshold, which is chosen to control the familywise error rate, or the probability of at least one false positive, across the cortical surface. We compare and evaluate the multivariate approach with existing univariate approaches to time-frequency MEG signal analysis, both on simulated data and experimental data from an MEG visuomotor task study. Our results indicate that the multivariate method is more powerful than the univariate approach in detecting experimental effects when correlations exist between power across frequency bands. We further describe protected F-tests and linear discriminant analysis to identify individual frequencies that contribute significantly to experimental effects.