Human cortical responses to slow and fast binaural beats reveal multiple mechanisms of binaural hearing.

When two tones with slightly different frequencies are presented to both ears, they interact in the central auditory system and induce the sensation of a beating sound. At low difference frequencies, we perceive a single sound, which is moving across the head between the left and right ears. The percept changes to loudness fluctuation, roughness, and pitch with increasing beat rate. To examine the neural representations underlying these different perceptions, we recorded neuromagnetic cortical responses while participants listened to binaural beats at a continuously varying rate between 3 Hz and 60 Hz. Binaural beat responses were analyzed as neuromagnetic oscillations following the trajectory of the stimulus rate. Responses were largest in the 40-Hz gamma range and at low frequencies. Binaural beat responses at 3 Hz showed opposite polarity in the left and right auditory cortices. We suggest that this difference in polarity reflects the opponent neural population code for representing sound location. Binaural beats at any rate induced gamma oscillations. However, the responses were largest at 40-Hz stimulation. We propose that the neuromagnetic gamma oscillations reflect postsynaptic modulation that allows for precise timing of cortical neural firing. Systematic phase differences between bilateral responses suggest that separate sound representations of a sound object exist in the left and right auditory cortices. We conclude that binaural processing at the cortical level occurs with the same temporal acuity as monaural processing whereas the identification of sound location requires further interpretation and is limited by the rate of object representations.

Plasticity in neuromagnetic cortical responses suggests enhanced auditory object representation.

BACKGROUND: Auditory perceptual learning persistently modifies neural networks in the central nervous system. Central auditory processing comprises a hierarchy of sound analysis and integration, which transforms an acoustical signal into a meaningful object for perception. Based on latencies and source locations of auditory evoked responses, we investigated which stage of central processing undergoes neuroplastic changes when gaining auditory experience during passive listening and active perceptual training. Young healthy volunteers participated in a five-day training program to identify two pre-voiced versions of the stop-consonant syllable 'ba', which is an unusual speech sound to English listeners. Magnetoencephalographic (MEG) brain responses were recorded during two pre-training and one post-training sessions. Underlying cortical sources were localized, and the temporal dynamics of auditory evoked responses were analyzed. RESULTS: After both passive listening and active training, the amplitude of the P2m wave with latency of 200 ms increased considerably. By this latency, the integration of stimulus features into an auditory object for further conscious perception is considered to be complete. Therefore the P2m changes were discussed in the light of auditory object representation. Moreover, P2m sources were localized in anterior auditory association cortex, which is part of the antero-ventral pathway for object identification. The amplitude of the earlier N1m wave, which is related to processing of sensory information, did not change over the time course of the study. CONCLUSION: The P2m amplitude increase and its persistence over time constitute a neuroplastic change. The P2m gain likely reflects enhanced object representation after stimulus experience and training, which enables listeners to improve their ability for scrutinizing fine differences in pre-voicing time. Different trajectories of brain and behaviour changes suggest that the preceding effect of a P2m increase relates to brain processes, which are necessary precursors of perceptual learning. Cautious discussion is required when interpreting the finding of a P2 amplitude increase between recordings before and after training and learning.

Entrainment of somatosensory beta and gamma oscillations accompany improvement in tactile acuity after periodic and aperiodic repetitive sensory stimulation.

Previous research showed that repetitive sensory stimulation entrains neural oscillations at the stimulation rate, facilitates long-term potentiation like perceptual learning, and improves behavioural performance. For example, short-time repetitive tactile stimulation improved tactile acuity measured with two-point or spatial orientation discrimination tests. The behavioural gain was maximal for a stimulation rate of 20 Hz, the same frequency at which repetitive somatosensory stimulation elicits a steady-state response with maximum amplitude. The current study investigated whether sensory stimulation must be strictly periodic to induce perceptual learning and whether the 20-Hz steady-state response plays a crucial role in the neural mechanisms of perceptual learning. In a crossover-designed experiment, young, healthy adults received sensory stimulation to the fingertip on three subsequent days. The stimulation was either periodic or temporally randomized (aperiodic) with the same number of stimuli. Tactile acuity was assessed with a grating orientation discrimination task, and brain activity was measured with magnetoencephalography (MEG). Stimulus type-by-session interactions were found for behavioural and brain data. Tactile acuity improved more after a session with aperiodic than periodic stimulation. Beta-band 20-Hz steady-state responses were localized in the primary somatosensory cortex contralateral to the stimulated finger and had larger amplitudes after periodic than aperiodic stimulation. Both stimulus types also elicited gamma oscillations, which increased in amplitude more with aperiodic than periodic stimulation. Sensory stimuli caused a phase reset of sensorimotor beta oscillations phase-coupled to alpha oscillations. The system of stimulus-related oscillations was discussed as underlying temporal processing. Learning may result from facilitating the temporal code. More pronounced behavioural gain with aperiodic than periodic stimulation suggests beneficial effects of temporal stimulus variability for perceptual learning.

Sustained changes in somatosensory gamma responses after brief vibrotactile stimulation.

Short-time passive tactile stimulation at 20 Hz improves tactile discrimination acuity. We investigated whether sustained 20 Hz stimulation also modifies cortical responses and whether these changes are plastic as indicated by differences between subsequent recording sessions. Touch stimuli (20 Hz) were applied to the fingertip, and ß and γ oscillations at multiples of the stimulus frequency were recorded with magnetoencephalography. Neuromagnetic sources were found in the contralateral somatosensory cortex. ß Responses decreased within a session, but recovered after a break between two sessions. In contrast, γ responses were consistent across repeated blocks and increased between the sessions. The differences between ß and γ activities suggest that stimulus experience enhanced the temporal precision of the cortical stimulus representation, whereas the magnitude of the primary somatosensory response remained constant.

Neuromagnetic beta and gamma oscillations in the somatosensory cortex after music training in healthy older adults and a chronic stroke patient.

OBJECTIVE: Extensive rehabilitation training can lead to functional improvement even years after a stroke. Although neuronal plasticity is considered as a main origin of such ameliorations, specific subtending mechanisms need further investigation. Our aim was to obtain objective neuromagnetic measures sensitive to brain reorganizations induced by a music-supported training. METHODS: We applied 20-Hz vibrotactile stimuli to the index finger and the ring finger, recorded somatosensory steady-state responses with magnetoencephalography, and analyzed the cortical sources displaying oscillations synchronized with the external stimuli in two groups of healthy older adults before and after musical training or without training. In addition, we applied the same analysis for an anecdotic report of a single chronic stroke patient with hemiparetic arm and hand problems, who received music-supported therapy (MST). RESULTS: Healthy older adults showed significant finger separation within the primary somatotopic map. Beta dipole sources were more anterior located compared to gamma sources. An anterior shift of sources and increases in synchrony between the stimuli and beta and gamma oscillations were observed selectively after music training. In the stroke patient a normalization of somatotopic organization was observed after MST, with digit separation recovered after training and stimulus induced gamma synchrony increased. CONCLUSIONS: The proposed stimulation paradigm captures the integrity of primary somatosensory hand representation. Source position and synchronization between the stimuli and gamma activity are indices, sensitive to music-supported training. Responsiveness was also observed in a chronic stroke patient, encouraging for the music-supported therapy. Notably, changes in somatosensory responses were observed, even though the therapy did not involve specific sensory discrimination training. SIGNIFICANCE: The proposed protocol can be used for monitoring changes in neuronal organization during training and will improve the understanding of the brain mechanisms underlying rehabilitation.

Somatotopic finger mapping using MEG: toward an optimal stimulation paradigm.

OBJECTIVE: In non-invasive somatotopic mapping based on neuromagnetic source analysis, the recording time can be shortened and accuracy improved by applying simultaneously vibrotactile stimuli at different frequencies to multiple body sites and recording multiple steady-state responses. This study compared the reliability of sensory evoked responses, source localization performance, and reproducibility of digit maps for three different stimulation paradigms. METHODS: Vibrotactile stimuli were applied to the fingertip and neuromagnetic steady-state responses were recorded. Index and middle fingers were stimulated either sequentially in separate blocks, simultaneously at different frequencies, or in alternating temporal order within a block. RESULTS: Response amplitudes were largest and source localization was most accurate between 21 and 23 Hz. Separation of adjacent digits was significant for all paradigms in all participants. Suppressive interactions occurred between simultaneously applied stimuli. However, when frequently alternating between stimulus sites, the higher stimulus novelty resulted in increased amplitudes and superior localization performance. CONCLUSIONS: When receptive fields are strongly overlapping, the alternating stimulation is preferable over recording multiple steady state responses. SIGNIFICANCE: The new paradigm improved the measurement of the distance of somatotopic finger representation in human primary somatosensory cortex, which is an important metric for neuroplastic reorganization after learning and rehabilitation training.

Synchronization of ß and γ oscillations in the somatosensory evoked neuromagnetic steady-state response.

The sensory evoked neuromagnetic response consists of superimposition of an immediately stimulus-driven component and induced changes in the autonomous brain activity, each having distinct functional relevance. Commonly, the strength of phase locking in neural activities has been used to differentiate the different responses. The steady-state response is a strong oscillatory neural activity, which is evoked with rhythmic stimulation, and provides an effective tool to investigate oscillatory brain networks. In this case, both the sensory response and intrinsic activity, representing higher order processes, are highly synchronized to the stimulus. In this study we hypothesized that temporal dynamics of oscillatory activities would characterize the differences between the two types of activities and that beta and gamma oscillations are differently involved in this distinction. We used magnetoencephalography (MEG) for studying how ongoing steady-state responses elicited by a 20-Hz vibro-tactile stimulus to the right index finger were affected by a concurrent isolated touch stimulus to the same hand ring finger. SI source activity showed oscillations at multiples of 20 Hz with characteristic differences in the beta band and the gamma band. The response amplitudes were largest at 20 Hz (beta) and significantly reduced at 40 Hz and 60 Hz (gamma), although synchronization strength, indicated by inter-trial coherence (ITC), did not substantially differ between 20 Hz and 40 Hz. Moreover, the beta oscillations showed a fast onset, whereas the amplitude of gamma oscillations increased slowly and reached the steady state 400 ms after onset of the vibration stimulus. Most importantly, the pulse stimuli interacted only with gamma oscillations in a way that gamma oscillations decreased immediately after the concurrent stimulus onset and recovered slowly, resembling the initial slope. Such time course of gamma oscillations is similar to our previous observations in the auditory system. The time constant is in line with the time required for conscious perception of the sensory stimulus. Based on the observed different spectro-temporal dynamics, we propose that while beta activities likely relate to independent representation of the sensory input, gamma oscillation likely relates to binding of sensory information for higher order processing.

Precise mapping of the somatotopic hand area using neuromagnetic steady-state responses.

The body surface is represented in somatotopically organized maps in the primary somatosensory cortex. Estimating the size of the hand area with neuromagnetic source analysis has been used as a metric for monitoring neuroplastic changes related to training, learning, and brain injury. Commonly, results were significant as group statistics only because source localization accuracy was limited by factors such as residual noise and head motion. In this study we aimed to develop a robust method for obtaining the somatotopic map of the hand area in individuals using the bootstrap framework. Furthermore, a comprehensive analysis of the different factors affecting the accuracy of the obtained maps was provided. We applied vibrotactile touch stimuli to the tip of the index finger or the ring finger of the right hand and recorded the 22-Hz steady-state response using MEG. Single equivalent dipole sources were localized in contralateral left somatosensory cortex. Bootstrap resampling revealed the confidence intervals for the source coordinates using a single block of 5 min MEG recording. Residual noise in the averaged evoked response predominantly affected source localization, and the related confidence interval was reciprocally related to the signal-to-noise ratio. Apparently, head movements within a block of MEG recording contributed less to the variability of source localization in cooperative volunteers. The results of the current study indicate that significant separations of index finger and ring finger representations along the somatotopic map can be revealed in an individual using bootstrap framework.

Changes in neuromagnetic beta-band oscillation after music-supported stroke rehabilitation.

Precise timing of sound is crucial in music for both performing and listening. Indeed, listening to rhythmic sound sequences activates not only the auditory system but also the sensorimotor system. Previously, we showed the significance of neural beta-band oscillations (15-30 Hz) for the timing processing that involves such auditory-motor coordination. Thus, we hypothesized that motor rehabilitation training incorporating music playing will stimulate and enhance auditory-motor interaction in stroke patients. We examined three chronic patients who received Music-Supported Therapy following the protocols practiced by Schneider. Neuromagnetic beta-band activity was remarkably alike during passive listening to a metronome and during finger tapping, with or without the metronome, for either the paretic or nonparetic hand, suggesting a shared mechanism of the beta modulation. In the listening task, the magnitude of the beta decrease after the tone onset was more pronounced at the posttraining time point and was accompanied by improved arm and hand skills. The present case data give insight into the neural underpinnings of rehabilitation with music making and rhythmic auditory stimulation.

Realignment of magnetoencephalographic data for group analysis in the sensor domain.

Magnetoencephalography (MEG) is a neuroimaging modality with high temporal resolution for studying functional brain processes in relatively small neural assemblies on the time scale of <100 milliseconds and with synchrony and coherence in the recorded signals at high frequencies. Advanced MEG signal analysis gained importance for clinical applications, e.g., as a sensitive classifier for the diagnosis of neuropsychiatric disorders. Despite tremendous improvements in magnetic source imaging, MEG analysis often does not require explicit source estimation and can be performed in the sensor domain. However, group analysis of MEG sensor data is complicated by variable positioning of the sensor array relative to the head and needs realignment of the sensor configuration. Here, the authors provide an algorithm for transforming the magnetic field data as recorded at various sensor positions onto a common sensor array. Based on the measured magnetic field at the original sensor position, they estimate a source distribution and project it onto a virtual sensor array using the leadfield description of the magnetic forward solution. First, they analyzed the variation of sensor positioning in a typical MEG study and reported the impact on the leadfield matrix. Then they evaluated the realignment algorithm and reported its properties. Including efficient regularization to the inverse solution, they demonstrated that the introduced error is in the order of the sensor noise, and smoothing of data is limited to the set of smallest eigenvalues of the data. They demonstrated the performance of the algorithm with dipole source modeling on group averaged MEG data and comparison of grand averaged auditory evoked responses with and without sensor realignment.