Penalized canonical correlation analysis reveals a relationship between temperament traits and brain oscillations during mind wandering.

INTRODUCTION: There has been a growing interest in studying brain activity under naturalistic conditions. However, the relationship between individual differences in ongoing brain activity and psychological characteristics is not well understood. We investigated this connection, focusing on the association between oscillatory activity in the brain and individually characteristic dispositional traits. Given the variability of unconstrained resting states among individuals, we devised a paradigm that could harmonize the state of mind across all participants. METHODS: We constructed task contrasts that included focused attention (FA), self-centered future planning, and rumination on anxious thoughts triggered by visual imagery. Magnetoencephalography was recorded from 28 participants under these 3 conditions for a duration of 16 min. The oscillatory power in the alpha and beta bands was converted into spatial contrast maps, representing the difference in brain oscillation power between the two conditions. We performed permutation cluster tests on these spatial contrast maps. Additionally, we applied penalized canonical correlation analysis (CCA) to study the relationship between brain oscillation patterns and behavioral traits. RESULTS: The data revealed that the FA condition, as compared to the other conditions, was associated with higher alpha and beta power in the temporal areas of the left hemisphere and lower alpha and beta power in the parietal areas of the right hemisphere. Interestingly, the penalized CCA indicated that behavioral inhibition was positively correlated, whereas anxiety was negatively correlated, with a pattern of high oscillatory power in the bilateral precuneus and low power in the bilateral temporal regions. This unique association was found in the anxious-thoughts condition when contrasted with the focused-attention condition. CONCLUSION: Our findings suggest individual temperament traits significantly affect brain engagement in naturalistic conditions. This research underscores the importance of considering individual traits in neuroscience and offers an effective method for analyzing brain activity and psychological differences.

Target of selective auditory attention can be robustly followed with MEG.

Selective auditory attention enables filtering of relevant acoustic information from irrelevant. Specific auditory responses, measurable by magneto- and electroencephalography (MEG/EEG), are known to be modulated by attention to the evoking stimuli. However, such attention effects have typically been studied in unnatural conditions (e.g. during dichotic listening of pure tones) and have been demonstrated mostly in averaged auditory evoked responses. To test how reliably we can detect the attention target from unaveraged brain responses, we recorded MEG data from 15 healthy subjects that were presented with two human speakers uttering continuously the words "Yes" and "No" in an interleaved manner. The subjects were asked to attend to one speaker. To investigate which temporal and spatial aspects of the responses carry the most information about the target of auditory attention, we performed spatially and temporally resolved classification of the unaveraged MEG responses using a support vector machine. Sensor-level decoding of the responses to attended vs. unattended words resulted in a mean accuracy of [Formula: see text] (N = 14) for both stimulus words. The discriminating information was mostly available 200-400 ms after the stimulus onset. Spatially-resolved source-level decoding indicated that the most informative sources were in the auditory cortices, in both the left and right hemisphere. Our result corroborates attention modulation of auditory evoked responses and shows that such modulations are detectable in unaveraged MEG responses at high accuracy, which could be exploited e.g. in an intuitive brain-computer interface.

Unsupervised representation learning of spontaneous MEG data with nonlinear ICA.

Resting-state magnetoencephalography (MEG) data show complex but structured spatiotemporal patterns. However, the neurophysiological basis of these signal patterns is not fully known and the underlying signal sources are mixed in MEG measurements. Here, we developed a method based on the nonlinear independent component analysis (ICA), a generative model trainable with unsupervised learning, to learn representations from resting-state MEG data. After being trained with a large dataset from the Cam-CAN repository, the model has learned to represent and generate patterns of spontaneous cortical activity using latent nonlinear components, which reflects principal cortical patterns with specific spectral modes. When applied to the downstream classification task of audio-visual MEG, the nonlinear ICA model achieves competitive performance with deep neural networks despite limited access to labels. We further validate the generalizability of the model across different datasets by applying it to an independent neurofeedback dataset for decoding the subject's attentional states, providing a real-time feature extraction and decoding mindfulness and thought-inducing tasks with an accuracy of around 70% at the individual level, which is much higher than obtained by linear ICA or other baseline methods. Our results demonstrate that nonlinear ICA is a valuable addition to existing tools, particularly suited for unsupervised representation learning of spontaneous MEG activity which can then be applied to specific goals or tasks when labelled data are scarce.

Mapping and decoding cortical engagement during motor imagery, mental arithmetic, and silent word generation using MEG.

Accurate quantification of cortical engagement during mental imagery tasks remains a challenging brain-imaging problem with immediate relevance to developing brain-computer interfaces. We analyzed magnetoencephalography (MEG) data from 18 individuals completing cued motor imagery, mental arithmetic, and silent word generation tasks. Participants imagined movements of both hands (HANDS) and both feet (FEET), subtracted two numbers (SUB), and silently generated words (WORD). The task-related cortical engagement was inferred from beta band (17-25 Hz) power decrements estimated using a frequency-resolved beamforming method. In the hands and feet motor imagery tasks, beta power consistently decreased in premotor and motor areas. In the word and subtraction tasks, beta-power decrements showed engagements in language and arithmetic processing within the temporal, parietal, and inferior frontal regions. A support vector machine classification of beta power decrements yielded high accuracy rates of 74 and 68% for classifying motor-imagery (HANDS vs. FEET) and cognitive (WORD vs. SUB) tasks, respectively. From the motor-versus-nonmotor contrasts, excellent accuracy rates of 85 and 80% were observed for hands-versus-word and hands-versus-sub, respectively. A multivariate Gaussian-process classifier provided an accuracy rate of 60% for the four-way (HANDS-FEET-WORD-SUB) classification problem. Individual task performance was revealed by within-subject correlations of beta-decrements. Beta-power decrements are helpful metrics for mapping and decoding cortical engagement during mental processes in the absence of sensory stimuli or overt behavioral outputs. Markers derived based on beta decrements may be suitable for rehabilitation purposes, to characterize motor or cognitive impairments, or to treat patients recovering from a cerebral stroke.

Activity level in left auditory cortex predicts behavioral performance in inhibition tasks in children.

Sensory processing during development is important for the emerging cognitive skills underlying goal-directed behavior. Yet, it is not known how auditory processing in children is related to their cognitive functions. Here, we utilized combined magneto- and electroencephalographic (M/EEG) measurements in school-aged children (6-14y) to show that child auditory cortical activity at ∼250 ms after auditory stimulation predicts the performance in inhibition tasks. While unaffected by task demands, the amplitude of the left-hemisphere activation pattern was significantly correlated with the variability of behavioral response time. Since this activation pattern is typically not present in adults, our results suggest divergent brain mechanisms in adults and children for consistent performance in auditory-based cognitive tasks. This difference can be explained as a shift in cortical resources for cognitive control from sensorimotor associations in the auditory cortex of children to top-down regulated control processes involving (pre)frontal and cingulate areas in adults.

Across-subject offline decoding of motor imagery from MEG and EEG.

Long calibration time hinders the feasibility of brain-computer interfaces (BCI). If other subjects' data were used for training the classifier, BCI-based neurofeedback practice could start without the initial calibration. Here, we compare methods for inter-subject decoding of left- vs. right-hand motor imagery (MI) from MEG and EEG. Six methods were tested on data involving MEG and EEG measurements of healthy participants. Inter-subject decoders were trained on subjects showing good within-subject accuracy, and tested on all subjects, including poor performers. Three methods were based on Common Spatial Patterns (CSP), and three others on logistic regression with l1 - or l2,1 -norm regularization. The decoding accuracy was evaluated using (1) MI and (2) passive movements (PM) for training, separately for MEG and EEG. With MI training, the best accuracies across subjects (mean 70.6% for MEG, 67.7% for EEG) were obtained using multi-task learning (MTL) with logistic regression and l2,1-norm regularization. MEG yielded slightly better average accuracies than EEG. With PM training, none of the inter-subject methods yielded above chance level (58.7%) accuracy. In conclusion, MTL and training with other subject's MI is efficient for inter-subject decoding of MI. Passive movements of other subjects are likely suboptimal for training the MI classifiers.

Comparing Features for Classification of MEG Responses to Motor Imagery.

BACKGROUND: Motor imagery (MI) with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG) noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest. METHODS: MEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD), Morlet wavelets, short-time Fourier transform (STFT), common spatial patterns (CSP), filter-bank common spatial patterns (FBCSP), spatio-spectral decomposition (SSD), and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject. RESULTS: The SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7%) and MI-vs-rest (mean 81.3%) classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%). There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results. CONCLUSIONS: We obtained good accuracy in sensor-level decoding of MI from single-trial MEG data. Feature extraction methods utilizing both the spatial and spectral profile of MI-related signals provided the best classification results, suggesting good performance of these methods in an online MEG neurofeedback system.

Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study.

We here compared results achieved by applying popular methods for reducing artifacts in magnetoencephalography (MEG) and electroencephalography (EEG) recordings of the auditory evoked Mismatch Negativity (MMN) responses in healthy adult subjects. We compared the Signal Space Separation (SSS) and temporal SSS (tSSS) methods for reducing noise from external and nearby sources. Our results showed that tSSS reduces the interference level more reliably than plain SSS, particularly for MEG gradiometers, also for healthy subjects not wearing strongly interfering magnetic material. Therefore, tSSS is recommended over SSS. Furthermore, we found that better artifact correction is achieved by applying Independent Component Analysis (ICA) in comparison to Signal Space Projection (SSP). Although SSP reduces the baseline noise level more than ICA, SSP also significantly reduces the signal-slightly more than it reduces the artifacts interfering with the signal. However, ICA also adds noise, or correction errors, to the waveform when the signal-to-noise ratio (SNR) in the original data is relatively low-in particular to EEG and to MEG magnetometer data. In conclusion, ICA is recommended over SSP, but one should be careful when applying ICA to reduce artifacts on neurophysiological data with relatively low SNR.

Human neuromagnetic steady-state responses to amplitude-modulated tones, speech, and music.

OBJECTIVES: Auditory steady-state responses that can be elicited by various periodic sounds inform about subcortical and early cortical auditory processing. Steady-state responses to amplitude-modulated pure tones have been used to scrutinize binaural interaction by frequency-tagging the two ears' inputs at different frequencies. Unlike pure tones, speech and music are physically very complex, as they include many frequency components, pauses, and large temporal variations. To examine the utility of magnetoencephalographic (MEG) steady-state fields (SSFs) in the study of early cortical processing of complex natural sounds, the authors tested the extent to which amplitude-modulated speech and music can elicit reliable SSFs. DESIGN: MEG responses were recorded to 90-s-long binaural tones, speech, and music, amplitude-modulated at 41.1 Hz at four different depths (25, 50, 75, and 100%). The subjects were 11 healthy, normal-hearing adults. MEG signals were averaged in phase with the modulation frequency, and the sources of the resulting SSFs were modeled by current dipoles. After the MEG recording, intelligibility of the speech, musical quality of the music stimuli, naturalness of music and speech stimuli, and the perceived deterioration caused by the modulation were evaluated on visual analog scales. RESULTS: The perceived quality of the stimuli decreased as a function of increasing modulation depth, more strongly for music than speech; yet, all subjects considered the speech intelligible even at the 100% modulation. SSFs were the strongest to tones and the weakest to speech stimuli; the amplitudes increased with increasing modulation depth for all stimuli. SSFs to tones were reliably detectable at all modulation depths (in all subjects in the right hemisphere, in 9 subjects in the left hemisphere) and to music stimuli at 50 to 100% depths, whereas speech usually elicited clear SSFs only at 100% depth.The hemispheric balance of SSFs was toward the right hemisphere for tones and speech, whereas SSFs to music showed no lateralization. In addition, the right lateralization of SSFs to the speech stimuli decreased with decreasing modulation depth. CONCLUSIONS: The results showed that SSFs can be reliably measured to amplitude-modulated natural sounds, with slightly different hemispheric lateralization for different carrier sounds. With speech stimuli, modulation at 100% depth is required, whereas for music the 75% or even 50% modulation depths provide a reasonable compromise between the signal-to-noise ratio of SSFs and sound quality or perceptual requirements. SSF recordings thus seem feasible for assessing the early cortical processing of natural sounds.

Implementation of a beam forming technique in real-time magnetoencephalography.

Real-time magnetoencephalography (rtMEG) is an emerging neurofeedback technology that could potentially benefit multiple areas of basic and clinical neuroscience. In the present study, we implemented voxel-based real-time coherence measurements in a rtMEG system in which we employed a beamformer to localize signal sources in the anatomical space prior to computing imaginary coherence. Our rtMEG experiment showed that a healthy subject could increase coherence between the parietal cortex and visual cortex when attending to a flickering visual stimulus. This finding suggests that our system is suitable for neurofeedback training and can be useful for practical brain-machine interface applications or neurofeedback rehabilitation.

Binaural interaction and the octave illusion.

The auditory octave illusion arises when dichotically presented tones, one octave apart, alternate rapidly between the ears. Most subjects perceive an illusory sequence of monaural tones: A high tone in the right ear (RE) alternates with a low tone, incorrectly localized to the left ear (LE). Behavioral studies suggest that the perceived pitch follows the RE input, and the perceived location the higher-frequency sound. To explore the link between the perceived pitches and brain-level interactions of dichotic tones, magnetoencephalographic responses were recorded to 4 binaural combinations of 2-min long continuous 400- and 800-Hz tones and to 4 monaural tones. Responses to LE and RE inputs were distinguished by frequency-tagging the ear-specific stimuli at different modulation frequencies. During dichotic presentation, ipsilateral LE tones elicited weaker and ipsilateral RE tones stronger responses than when both ears received the same tone. During the most paradoxical stimulus-high tone to LE and low tone to RE perceived as a low tone in LE during the illusion-also the contralateral responses to LE tones were diminished. The results demonstrate modified binaural interaction of dichotic tones one octave apart, suggesting that this interaction contributes to pitch perception during the octave illusion.

Characterization of neuromagnetic brain rhythms over time scales of minutes using spatial independent component analysis.

Independent component analysis (ICA) of electroencephalographic (EEG) and magnetoencephalographic (MEG) data is usually performed over the temporal dimension: each channel is one row of the data matrix, and a linear transformation maximizing the independence of component time courses is sought. In functional magnetic resonance imaging (fMRI), by contrast, most studies use spatial ICA: each time point constitutes a row of the data matrix, and independence of the spatial patterns is maximized. Here, we show the utility of spatial ICA in characterizing oscillatory neuromagnetic signals. We project the sensor data into cortical space using a standard minimum-norm estimate and apply a sparsifying transform to focus on oscillatory signals. The resulting method, spatial Fourier-ICA, provides a concise summary of the spatiotemporal and spectral content of spontaneous neuromagnetic oscillations in cortical source space over time scales of minutes. Spatial Fourier-ICA applied to resting-state and naturalistic stimulation MEG data from nine healthy subjects revealed consistent components covering the early visual, somatosensory and motor cortices with spectral peaks at ∼10 and ∼20 Hz. The proposed method seems valuable for inferring functional connectivity, stimulus-related modulation of rhythmic activity, and their commonalities across subjects from nonaveraged MEG data.

rtMEG: a real-time software interface for magnetoencephalography.

To date, the majority of studies using magnetoencephalography (MEG) rely on off-line analysis of the spatiotemporal properties of brain activity. Real-time MEG feedback could potentially benefit multiple areas of basic and clinical research: brain-machine interfaces, neurofeedback rehabilitation of stroke and spinal cord injury, and new adaptive paradigm designs, among others. We have developed a software interface to stream MEG signals in real time from the 306-channel Elekta Neuromag MEG system to an external workstation. The signals can be accessed with a minimal delay (≤45 ms) when data are sampled at 1000 Hz, which is sufficient for most real-time studies. We also show here that real-time source imaging is possible by demonstrating real-time monitoring and feedback of alpha-band power fluctuations over parieto-occipital and frontal areas. The interface is made available to the academic community as an open-source resource.

Sources of auditory brainstem responses revisited: contribution by magnetoencephalography.

Auditory brainstem responses provide diagnostic value in pathologies involving the early parts of the auditory pathway. Despite that, the neural generators underlying the various components of these responses have remained unclear. Direct electrical recordings in humans are possible only in limited time periods during surgery and from small regions of the diseased brains. The evidence of the generator sites is therefore fragmented and indirect, based strongly on lesion studies and animal models. Source modeling of EEG has been limited to grand averages across multiple subjects. Here, we employed magnetoencephalography (MEG) to shed more light on the neural origins of the auditory brainstem responses (ABR) and to test whether such deep brain structures are accessible by MEG. We show that the magnetic counterparts of the electric ABRs can be measured in 30 min and that they allow localization of some of the underlying neural sources in individual subjects. Many of the electric ABR components were present in our MEG data; however, the morphologies of the magnetic and electric responses were different, indicating that the MEG signals carry information complementary to the EEG data. The locations of the neural sources corresponding to the magnetic ABR deflections ranged from the auditory nerve to the inferior colliculus. The earliest cortical responses were detectable at the latency of 13 ms.

Early visual brain areas reflect the percept of an ambiguous scene.

When a visual scene allows multiple interpretations, the percepts may spontaneously alternate despite the stable retinal image and the invariant sensory input transmitted to the brain. To study the brain basis of such multi-stable percepts, we superimposed rapidly changing dynamic noise as regional tags to the Rubin vase-face figure and followed the corresponding tag-related cortical signals with magnetoencephalography. The activity already in the earliest visual cortical areas, the primary visual cortex included, varied with the perceptual states reported by the observers. These percept-related modulations most likely reflect top-down influences that accentuate the neural representation of the perceived object in the early visual cortex and maintain the segregation of objects from the background.

Speech-sound discrimination in neonates as measured with MEG.

Magnetic brain responses to speech sounds were measured in 10 healthy neonates. The stimulation consisted of a frequent vowel sound [a:] with a steady pitch contour, which was occasionally replaced by the vowel [i:] with a steady pitch, or the vowel [a:] with a rising pitch, manifesting a change of intonation. The magnetic mismatch-negativity response (MMNm) was obtained and successfully modelled to the speech sound quality change in all infants and to the intonation change in 6 infants. The present results indicate that auditory-cortex speech-sound discrimination may well be studied with magnetic recordings as early as in newborn infants.

Auditory magnetic responses of healthy newborns.

We recorded magnetic brain activity from healthy human newborns when they heard frequency changes in an otherwise repetitive sound stream. We were able to record the magnetic counterpart of the mismatch negativity (MMN) previously described only with electric recordings in infants. The results show that these recordings are possible, although still challenging due to the small head size and head movements. The modelling of the neural sources underlying the recorded responses suggests cortical sources in the temporal lobes.