Robust source analysis of oscillatory motor cortex activity with inherently variable phase delay

This study evaluated quantitatively the synchronization between the magnetoencephalography (MEG) and electromyography (EMG) signals and developed a novel method for the determination of the synchronization in order to increase the reliability of the source analysis of the oscillatory motor cortex activity. The new method is based on our observation that there are large variances in the time lag due to relatively low muscle-cortex synchronization which reduces the signal-to-noise ratio of the MEG signal when averaged in direct synchrony with the rectified EMG peaks. To improve the localization of the motor cortex activity, time-frequency analysis was performed for each epoch coinciding with an EMG peak to reject the weak oscillatory activity and artifacts. In addition, the MEG signals were shifted to maximize the coherence between MEG and rectified EMG by determining for each accepted epoch the time lag resulting in a maximum cross-correlation. An experiment was carried out using 30 subjects in order to determine the applicability of this method to a real situation. The synchronization and the results of the corresponding source analysis based on the novel method were compared with the data obtained using the non-phase-shift method and Hilbert approach detecting EMG phase. The results showed that the synchronization was significantly enhanced and the signal-to-noise ratio of the MEG signals improved, and that the localized dipoles of all subjects were well clustered at the motor cortex. This method, based on shifting the MEG epochs according to the simultaneously measured time lag, considerably improves the performance of the averaging and localization of the rhythmic activity of the motor cortex.     

Functional motor-cortex mapping using corticokinematic coherence

We present a novel method, corticokinematic coherence (CKC), for functional mapping of the motor cortex by computing coherence between cortical magnetoencephalographic (MEG) signals and the kinematics of voluntary movements. Ten subjects performed self-paced flexion-extensions of the right-hand fingers at about 3 Hz, with a three-axis accelerometer attached to the index finger. Cross-correlogram and coherence spectra were computed between 306 MEG channels and the accelerometer signals. In all subjects, accelerometer and coherence spectra showed peaks around 3-5 Hz and 6-10 Hz, corresponding to the movement frequencies. The coherence was statistically significant (P<0.05) in all subjects, with sources at the hand area of the primary motor cortex contralateral to the movement. CKC appears to be a promising and robust method for reliable and convenient functional mapping of the human motor cortex.     

Independent components of magnetoencephalography: single-trial response onset times

Werecently demonstrated that second-order blind identification (SOBI), an independent component analysis (ICA) method, can separate the mixture of neuronal and noise signals in magnetoencephalographic (MEG) data into neuroanatomically and neurophysiologically meaningful components. When the neuronal signals had relatively higher trial-to-trial variability, SOBI offered a particular advantage in identifying and localizing neuronal source activations with increased source detectability (A. C. Tang et al., 2002, Neural Comput. 14, 1827-1858). Here, we explore the utility of SOBI in the analysis of temporal aspects of neuromagnetic signals from MEG data. From SOBI components, we were able to measure single-trial response onset times of neuronal populations in visual, auditory, and somatosensory modalities during cognitive and sensory activation tasks, with a detection rate as high as 96% under optimal conditions. Comparing the SOBI-aided detection results with those obtained directly from the sensors, we found that with SOBI preprocessing, we were able to measure, among a greater proportion of trials, single-trial response onset times that are above background neuronal activity. We suggest that SOBI ICA can improve our current capability in measuring single-trial responses from human subjects using the noninvasive brain imaging method MEG.     

Quantification of the benefit from integrating MEG and EEG data in minimum l2-norm estimation

Source current estimation from electromagnetic (MEG and EEG) signals is an ill-posed problem that often produces blurry or inaccurately positioned estimates. The two modalities have distinct factors limiting the resolution, e.g., MEG cannot detect radially oriented sources, while EEG is sensitive to accuracy of the head model. This makes combined EEG + MEG estimation techniques desirable, but different acquisition noise statistics, complexity of the head models, and lack of pertinent metrics all complicate the assessment of the resulting improvements. We investigated analytically the effect of including EEG recordings in MEG studies versus the addition of new MEG channels when computing noise-normalized minimum ℓ₂-norm estimates. Three-compartment boundary-element forward models were constructed using structural MRI scans for four subjects. Singular value analysis of the resulting forward models predicted better performance of the EEG + MEG case in the form of higher matrix rank. MNE inverse operators for EEG, MEG and EEG + MEG were constructed using the sensor noise covariance estimated from data. Metrics derived from the resolution matrices predicted higher spatial resolution in EEG + MEG as compared to MEG due to decreased spread (lower spatial dispersion, higher resolution index) with no reduction in dipole localization error. The effect was apparent in all source locations, with increased magnitude for deep areas such as the cingulate cortex. We were also able to corroborate the results for the somatosensory cortex using median nerve responses.     

Stimuli of varying spatial scale induce gamma activity with distinct temporal characteristics in human visual cortex

Gamma activity to stationary grating stimuli was studied non-invasively using MEG recordings in humans. Using a spatial filtering technique, we localized gamma activity to primary visual cortex. We tested the hypothesis that spatial frequency properties of visual stimuli may be related to the temporal frequency characteristics of the associated cortical responses. We devised a method to assess temporal frequency differences between stimulus-related responses that typically exhibit complex spectral shapes. We applied this methodology to either single-trial (induced) or time-averaged (evoked) responses in four frequency ranges (0-40, 20-60, 40-80 and 60-100 Hz) and two time windows (either the entire duration of stimulus presentation or the first second following stimulus onset). Our results suggest that stimuli of varying spatial frequency induce responses that exhibit significantly different temporal frequency characteristics. These effects were particularly accentuated for induced responses in the classical gamma frequency band (20-60 Hz) analyzed over the entire duration of stimulus presentation. Strikingly, examining the first second of the responses following stimulus onset resulted in significant loss in stimulus specificity, suggesting that late signal components contain functionally relevant information. These findings advocate a functional role of gamma activity in sensory representation. We suggest that stimulus specific frequency characteristics of MEG signals can be mapped to processes of neuronal synchronization within the framework of coupled dynamical systems.     

Quantifying additive evoked contributions to the event-related potential

Event-related potentials (ERPs) are widely used in basic neuroscience and in clinical diagnostic procedures. In contrast, neurophysiological insights from ERPs have been limited, as several different mechanisms lead to ERPs. Apart from stereotypically repeated responses (additive evoked responses), these mechanisms are asymmetric amplitude modulations and phase-resetting of ongoing oscillatory activity. Therefore, a method is needed that differentiates between these mechanisms and moreover quantifies the stability of a response. We propose a constrained subspace independent component analysis that exploits the multivariate information present in the all-to-all relationship of recordings over trials. Our method identifies additive evoked activity and quantifies its stability over trials. We evaluate identification performance for biologically plausible simulation data and two neurophysiological test cases: Local field potential (LFP) recordings from a visuo-motor-integration task in the awake behaving macaque and magnetoencephalography (MEG) recordings of steady-state visual evoked fields (SSVEFs). In the LFPs we find additive evoked response contributions in visual areas V2/4 but not in primary motor cortex A4, although visually triggered ERPs were also observed in area A4. MEG-SSVEFs were mainly created by additive evoked response contributions. Our results demonstrate that the identification of additive evoked response contributions is possible both in invasive and in non-invasive electrophysiological recordings.     

Characterization of motor and somatosensory function for stroke patients

In a pilot study, stroke patients with a lesion related to the motor system were studied using magnetoencephalography (MEG) and electromyography (EMG). The patients performed sustained finger movements for 30 s followed by 30 s of rest and 20 repetitions of this sequence in total. Task-related cortical signals derived from MEG were observed here at very different frequency scales. Slow signals below 0.1 Hz were extracted by independent component analysis and are associated with the sustained activation of the motor cortex, the dcMEG motor activation. MEG-EMG coupling phenomena in the 10-30 Hz range were analyzed using the imaginary part of coherency and are attributed to cortico-muscular coupling driving the muscles. Additionally a signal from the somatosensory cortex due to an electrical stimulation at the wrist, the N20m, was recorded as a physiological marker. Field maps and time series associated with the three types of signals are presented for one patient and one control subject as the signal quality of the patient data was not sufficient to achieve a group result. The feasibility of a comprehensive electrophysiological measuring and analysis procedure of the motor function for stroke research is demonstrated by the results.     

Correlation of sensorimotor activation with functional magnetic resonance imaging and magnetoencephalography in presurgical functional imaging: a spatial analysis.

In this study we investigated the spatial heterotopy of MEG and fMRI localizations after sensory and motor stimulation tasks. Both methods are frequently used to study the topology of the primary and secondary motor cortex, as well as a tool for presurgical brain mapping. fMRI was performed with a 1.5T MR system, using echo-planar imaging with a motor and a sensory task. Somatosensory and motor evoked fields were recorded with a biomagnetometer. fMRI activation was determined with a cross-correlation analysis. MEG source localization was performed with a single equivalent current dipole model and a current density localization approach. Distances between MEG and fMRI activation sites were measured within the same anatomical 3-D-MR image set. The central region could be identified by MEG and fMRI in 33 of 34 cases. However, MEG and fMRI localization results showed significantly different activation sites for the motor and sensory task with a distance of 10 and 15 mm, respectively. This reflects the different neurophysiological mechanisms: direct neuronal current flow (MEG) and secondary changes in cerebral blood flow and oxygenation level of activated versus non activated brain structures (fMRI). The result of our study has clinical implications when MEG and fMRI localizations are used for pre- and intraoperative brain mapping. Although both modalities are useful for the estimation of the motor cortex, a single modality may err in the exact topographical labeling of the motor cortex. In some unclear cases a combination of both methods should be used in order to avoid neurological deficits.     

Localization of individual area neuronal activity

A family of methods, collectively known as independent component analysis (ICA), has recently been added to the array of methods designed to decompose a multi-channel signal into components. ICA methods have been applied to raw magnetoencephalography (MEG) and electroencephalography (EEG) signals to remove artifacts, especially when sources such as power line or cardiac activity generate strong components that dominate the signal. More recently, successful ICA extraction of stimulus-evoked responses has been reported from single-trial raw MEG and EEG signals. The extraction of weak components has often been erratic, depending on which ICA method is employed and even on what parameters are used. In this work, we show that if the emphasis is placed on individual "independent components," as is usually the case with standard ICA applications, differences in the results obtained for different components are exaggerated. We propose instead the reconstruction of regional brain activations by combining tomographic estimates of individual independent components that have been selected by appropriate spatial and temporal criteria. Such localization of individual area neuronal activity (LIANA) allows reliable semi-automatic extraction of single-trial regional activations from raw MEG data. We demonstrate the new method with three different ICA algorithms applied to both computer-generated signals and real data. We show that LIANA provides almost identical results with each ICA method despite the fact that each method yields different individual components.     

EEG signatures of auditory activity correlate with simultaneously recorded fMRI responses in humans

We recorded auditory-evoked potentials (AEPs) during simultaneous, continuous fMRI and identified trial-to-trial correlations between the amplitude of electrophysiological responses, characterised in the time domain and the time–frequency domain, and the hemodynamic BOLD response. Cortical AEPs were recorded from 30 EEG channels within the 3 T MRI scanner with and without the collection of simultaneous BOLD fMRI. Focussing on the Cz (vertex) EEG response, single-trial AEP responses were measured from time-domain waveforms. Furthermore, a novel method was used to characterise the single-trial AEP response within three regions of interest in the time–frequency domain (TF-ROIs). The latency and amplitude values of the N1 and P2 AEP peaks during fMRI scanning were not significantly different from the Control session (p > 0.16). BOLD fMRI responses to the auditory stimulation were observed in bilateral secondary auditory cortices as well as in the right precentral and postcentral gyri, anterior cingulate cortex (ACC) and supplementary motor cortex (SMC). Significant single-trial correlations were observed with a voxel-wise analysis, between (1) the magnitude of the EEG TF-ROI1 (70–800 ms post-stimulus, 1–5 Hz) and the BOLD response in right primary (Heschl's gyrus) and secondary (STG, planum temporale) auditory cortex; and (2) the amplitude of the P2 peak and the BOLD response in left pre- and postcentral gyri, the ACC and SMC. No correlation was observed with single-trial N1 amplitude on a voxel-wise basis. An fMRI-ROI analysis of functionally-identified auditory responsive regions identified further single-trial correlations of BOLD and EEG responses. The TF amplitudes in TF-ROI1 and TF-ROI2 (20–400 ms post-stimulus, 5–15 Hz) were significantly correlated with the BOLD response in all bilateral auditory areas investigated (planum temporale, superior temporal gyrus and Heschl's gyrus). However the N1 and P2 peak amplitudes, occurring at similar latencies did not show a correlation in these regions. N1 and P2 peak amplitude did correlate with the BOLD response in bilateral precentral and postcentral gyri and the SMC. Additionally P2 and TF-ROI1 both correlated with the ACC. TF-ROI3 (400–900 ms post-stimulus, 4–10 Hz) correlations were only observed in the ACC and SMC. Across the group, the subject-mean N1 peak amplitude correlated with the BOLD response amplitude in the primary and secondary auditory cortices bilaterally, as well as the right precentral gyrus and SMC. We confirm that auditory-evoked EEG responses can be recorded during continuous and simultaneous fMRI. We have presented further evidence of an empirical single-trial coupling between the EEG and BOLD fMRI responses, and show that a time–frequency decomposition of EEG signals can yield additional BOLD fMRI correlates, predominantly in auditory cortices, beyond those found using the evoked response amplitude alone.     

Unmasking motion-processing activity in human brain area V5/MT+ mediated by pathways that bypass primary visual cortex

Most models of the human visual system argue that higher-order motion-processing cortical regions receive their inputs only via the primary visual cortex (striate cortex), rather than also via direct projections from the thalamus that bypass primary visual cortex. However, recent evidence in non-human primates, along with some evidence in humans with damaged primary visual cortex (e.g., "blindsight" for motion in the blind visual hemifield), have argued for the existence of a direct thalamic-to-extrastriate projection for motion processing. This evidence remains controversial. Here we tested the idea that direct thalamic input to extrastriate motion processing areas exists in humans but might be masked in scalp recordings by activity from early visual areas. To do this, we employed stimuli that induced strong refractory effects in primary visual cortex--thereby creating a brief "reversable lesion" in primary visual cortex--immediately before the presentation of a motion stimulus. Under these conditions, we then assessed whether motion areas of cortex were still able to process the motion stimuli by recording event-related potentials (ERPs) and event-related magnetic fields (ERFs/MEG). We found robust motion-related activity in extrastriate motion processing areas in the ERP and MEG signals even when primary visual cortex was heavily suppressed by our manipulation. This finding provides evidence for a direct thalamic functional pathway to extrastriate visual cortical motion processing areas in the human that bypasses primary visual cortex.     

ICA-based spatiotemporal approach for single-trial analysis of postmovement MEG beta synchronization

The extraction of event-related oscillatory neuromagnetic activities from single-trial measurement is challenging due to the non-phase-locked nature and variability from trial to trial. The present study presents a method based on independent component analysis (ICA) and the use of a template-based correlation approach to extract Rolandic beta rhythm from magnetoencephalographic (MEG) measurements of right finger lifting. A single trial recording was decomposed into a set of coupled temporal independent components and corresponding spatial maps using ICA and the reactive beta frequency band for each trial identified using a two-spectrum comparison between the postmovement interval and a reference period. Task-related components survived dual criteria of high correlation with both the temporal and the spatial templates with an acceptance rate of about 80%. Phase and amplitude information for noise-free MEG beta activities were preserved not only for optimal calculation of beta rebound (event-related synchronization) but also for profound penetration into subtle dynamics across trials. Given the high signal-to-noise ratio (SNR) of this method, various methods of source estimation were used on reconstructed single-trial data and the source loci coherently anchored in the vicinity of the primary motor area. This method promises the possibility of a window into the intricate brain dynamics of motor control mechanisms and the cortical pathophysiology of movement disorder on a trial-by-trial basis.     

Developmental instability and the neural dynamics of the speed-intelligence relationship

Two of the most securely established findings in the biology of intelligence are the relationship between reaction time (RT) and intelligence, and the heritability of intelligence. To investigate why RT may related to intelligence, researchers have used a variety of techniques to subdivide RT into cognitive and motor components. In the current study, magnetoencephalographic (MEG) dipole latencies were used to examine the speed and timing of specific brain processing stages engaged during visually cued simple and choice reaction time tasks. Simple and choice reaction time and timing of MEG sources were considered in relation to fluid intelligence (as measured by the Raven's Advanced Progressive Matrices, RAPM). To address heritability of intelligence, developmental instability (DI) was assessed, measured here as fluctuating asymmetry. DI represents the degree to which an organism is susceptible to developmental stress arising from both environmental and genomic sources. Analyses showed that choice, but not simple reaction time was negatively correlated with RAPM score. MEG revealed a set of complex relationships between the timing of regional brain activations and psychometric intelligence. The neural component associated with integration of sensory and motor information was most associated with RAPM compared to other components. Higher values of fluctuating asymmetry predicted reduced psychometric intelligence, a result suggesting that some part of the variance of the heritability of intelligence reflects DI. Fluctuating asymmetry was significantly and negatively correlated with timing during all components of task completion. These observations suggest that fluid intelligence is primarily related to speed during processing associated with decision time, while fluctuating asymmetry predicted slower processing across all stages of information processing.     

Decoding natural grasp types from human ECoG

Electrocorticographic (ECoG) signals have been successfully used to provide information about arm movement direction, individual finger movements and even continuous arm movement trajectories. Thus, ECoG has been proposed as a potential control signal for implantable brain-machine interfaces (BMIs) in paralyzed patients. For the neuronal control of a prosthesis with versatile hand/arm functions, it is also necessary to successfully decode different types of grasping movements, such as precision grip and whole-hand grip. Although grasping is one of the most frequent and important hand movements performed in everyday life, until now, the decoding of ECoG activity related to different grasp types has not been systematically investigated. Here, we show that two different grasp types (precision vs. whole-hand grip) can be reliably distinguished in natural reach-to-grasp movements in single-trial ECoG recordings from the human motor cortex. Self-paced movement execution in a paradigm accounting for variability in grasped object position and weight was chosen to create a situation similar to everyday settings. We identified three informative signal components (low-pass-filtered component, low-frequency and high-frequency amplitude modulations), which allowed for accurate decoding of precision and whole-hand grips. Importantly, grasp type decoding generalized over different object positions and weights. Within the frontal lobe, informative signals predominated in the precentral motor cortex and could also be found in the right hemisphere's homologue of Broca's area. We conclude that ECoG signals are promising candidates for BMIs that include the restoration of grasping movements.

Highlights

? Two modes of natural grasping can be reliably discriminated from human ECoG. ? The decoding of grasp types generalizes over different object positions and weights. ? A low-frequency component and high-frequency amplitudes yield best decoding. ? ECoG may be used in BMIs for the restoration of grasping movements.     

Single-trial variability in early visual neuromagnetic responses: an explorative study based on the regional activation contributing to the N70m peak

Cortical activity evoked by repeated identical sensory stimulation is extremely variable. The source of this variability is often assigned to "random ongoing background activity" which is considered to be irrelevant to the processing of the stimuli and can therefore be eliminated by ensemble averaging. In this work, we studied the single-trial variability in neuromagnetic responses elicited by circular checkerboard pattern stimuli with radii of 1.8 degrees, 3.7 degrees, and 4.5 degrees. For most of the MEG sensors over the occipital areas, the averaged signal showed a clear early (N70m) response following the stimulus onset and this response was modulated by the checkerboard size. A data-driven spatial filter was used to extract one of the many possible composite time courses of single-trial activity corresponding to the complex of N70m generators. Pattern analysis principles were then employed to analyze, classify, and handle the extracted temporal patterns. We explored whether these patterns correspond to distinct response modes, which could characterize the evoked response better than the averaged signal and over an extended range of latencies around N70m. A novel scheme for detecting and organizing the structure in single-trial recordings was utilized. This served as a basis for comparisons between runs with different checkerboard sizes and provided a causal interpretation of variability in terms of regional dynamics, including the relatively weak activation in primary visual cortex. At the level of single trial activity, the polymorphic response to a simple stimulus is generated by a coupling of polymodal areas and cooperative activity in striate and extrastriate areas. Our results suggest a state-dependent response with a wide range of characteristic time scales and indicate the ongoing activity as a marker of the responsiveness state.     

1例偏瘫患者康复训练前后视觉指令食指伸屈运动脑磁图分析

目的观察偏瘫患者恢复期康复训练前后双手食指伸屈运动过程中脑磁图（MEG）变化。方法对1例偏瘫患者应用MEG记录双手食指伸屈运动时的脑电磁波并与MRI叠加形成磁源性影像（MSI）,对比康复训练前后MEG变化。结果两次MEG检测右侧半球皮质均无运动诱发出脑磁反应,左侧半球均有运动诱发反应;第1次和第2次潜伏期分别为-34.2ms和-61.7ms,部位向内前下移位,MSI显示兴奋的皮质位于中央前回,第2次激活的脑皮质体积（9569.6m3）明显大于第1次（2309.7m3）;第1次食指运动未诱发右半球体感反应;第2次食指运动诱发了右半球的体感反应,潜伏期为91.1ms,MSI显示兴奋的皮质位于中央后回。结论脑卒中患者皮质感觉功能先于运动功能恢复,未损半球经康复训练后功能明显增强。     

Improving MEG source localizations: an automated method for complete artifact removal based on independent component analysis

The major limitation for the acquisition of high-quality magnetoencephalography (MEG) recordings is the presence of disturbances of physiological and technical origins: eye movements, cardiac signals, muscular contractions, and environmental noise are serious problems for MEG signal analysis. In the last years, multi-channel MEG systems have undergone rapid technological developments in terms of noise reduction, and many processing methods have been proposed for artifact rejection. Independent component analysis (ICA) has already shown to be an effective and generally applicable technique for concurrently removing artifacts and noise from the MEG recordings. However, no standardized automated system based on ICA has become available so far, because of the intrinsic difficulty in the reliable categorization of the source signals obtained with this technique. In this work, approximate entropy (ApEn), a measure of data regularity, is successfully used for the classification of the signals produced by ICA, allowing for an automated artifact rejection. The proposed method has been tested using MEG data sets collected during somatosensory, auditory and visual stimulation. It was demonstrated to be effective in attenuating both biological artifacts and environmental noise, in order to reconstruct clear signals that can be used for improving brain source localizations.     

Modulation of motor-cortex oscillatory activity by painful Adelta- and C-fiber stimuli

Spontaneous approximately 20-Hz oscillations, arising predominantly from the primary motor cortex (MI), are readily observed by magnetoencephalography (MEG). Prior studies have indicated that the level of the approximately 20-Hz rhythm reflects the functional state of the MI cortex: increased 20-Hz level is associated with increased inhibition and suppression of the rhythm with excitation of MI. Close interaction is suggested between pain and the motor system by the association of chronic pain with motor dysfunction and by the alleviation of pain by motor-cortex stimulation. We therefore explored the effect of noxious input on motor-cortex functions by recording MEG signals from nine healthy subjects during selective laser stimulation of Adelta- and C-fibers of the hand. The approximately 20-Hz level was suppressed in the contralateral MI cortex in all nine subjects after painful Adelta- and C-fiber stimuli (P < 0.001). The suppression started 180 +/- 10 ms (mean +/- SEM) after Adelta-fiber stimuli and 820 +/- 30 ms after C-fiber stimuli, and peaked 160-170 ms later. Similar, but about 50% weaker, suppression of the approximately 20-Hz oscillations occurred in seven out of nine subjects in the ipsilateral MI. These results suggest automatic, lateralized, excitation of the MI cortex by noxious Adelta- and C-fiber input.     

Cortical representation of dermatomes: MEG-derived maps after tactile stimulation

Mechanical stimulation of skin receptors is known to evoke cortical responses arising from the somatosensory cortex. Here we present a magnetoencephalographic (MEG) study where dermatomal somatosensory-evoked fields (DSSEFs) were recorded after mechanical stimulation of sacral (S1), lumbar (L3), thoracic (Th7), and cervical (C4) dermatomes in three healthy volunteers. All MEG measurements were repeated in order to test the replicability of the results. DSSEFs were successfully measured and modeled in all three participants. The topography and temporal dynamics of cortical responses derived after stimulation of each dermatome are described. We found that cortical-evoked responses can be reliably recorded using MEG after mechanical stimulation of dermatomes when a sufficiently large skin region within the dermatome is stimulated. Primary sensory cortex response (SI) to each of the four dermatomes was replicable and showed stability over time. The MEG-derived individual maps of activation confirm the somatotopic representation of dermatomes in primary sensory cortex and the utility of MEG recordings in disentangling the interactions between primary and secondary sensory cortex during somatic perception.     

Magnetoencephalography: From SQUIDs to neuroscience. Neuroimage 20th anniversary special edition

Magnetoencephalography (MEG), with its direct view to the cortex through the magnetically transparent skull, has developed from its conception in physics laboratories to a powerful tool of basic and clinical neuroscience. MEG provides millisecond time resolution and allows real-time tracking of brain activation sequences during sensory processing, motor planning and action, cognition, language perception and production, social interaction, and various brain disorders. Current-day neuromagnetometers house hundreds of SQUIDs, superconducting quantum interference devices, to pick up signals generated by concerted action of cortical neurons. Complementary MEG measures of neuronal involvement include evoked responses, modulation of cortical rhythms, properties of the on-going neural activity, and interareal connectivity. Future MEG breakthroughs in understanding brain dynamics are expected through advanced signal analysis and combined use of MEG with hemodynamic imaging (fMRI). Methodological development progresses most efficiently when linked with insightful neuroscientific questions.