Explore the vast range of titles available.

•Movement range did not affect MEF strength or beta modulation in SM1 cortex.•Movement range did not affect the CKC strength in SM1 cortex.•Studies or participants with moderate variation in movement range are comparable. Proprioception is the sense of body position and movement that relies on afference from the proprioceptors in muscles and joints. Proprioceptive responses in the primary sensorimotor (SM1) cortex can be elicited by stimulating the proprioceptors using evoked (passive) limb movements. In magnetoencephalography (MEG), proprioceptive processing can be quantified by recording the movement evoked fields (MEFs) and movement-induced beta power modulations or by computing corticokinematic coherence (CKC) between the limb kinematics and cortical activity. We examined whether cortical proprioceptive processing quantified with MEF peak strength, relative beta suppression and rebound power and CKC strength is affected by the movement range of the finger. MEG activity was measured from 16 right-handed healthy volunteers while movements were applied to their right-index finger metacarpophalangeal joint with an actuator. Movements were either intermittent, every 3000 ± 250 ms, to estimate MEF or continuous, at 3 Hz, to estimate CKC. In both cases, 4 different ranges of motion of the stimuli were investigated: 15, 18, 22 and 26 mm for MEF and 6, 7, 9 and 13 mm for CKC. MEF amplitude, relative beta suppression and rebound as well as peak CKC strength at the movement frequency were compared between the movement ranges in the source space. Inter-individual variation was also compared between the MEF and CKC strengths. As expected, MEF and CKC responses peaked at the contralateral SM1 cortex. MEF peak, beta suppression and rebound and CKC strengths were similar across all movement ranges. Furthermore, CKC strength showed a lower degree of inter-individual variation compared with MEF strength. Our result of absent modulation by movement range in cortical responses to passive movements of the finger indicates that variability in movement range should not hinder comparability between different studies or participants. Furthermore, our data indicates that CKC is less prone to inter-individual variability than MEFs, and thus more advantageous in what pertains to statistical power.

Electroencephalographic and magnetoencephalographic data have characterized two types of brain–body interactions observed during various types of motor actions, “corticokinematic” and “corticomuscular” coupling. Here, we review the literature on these interactions in healthy individuals, discuss several open debates, and outline current limitations and directions for future research. Corticokinematic coupling (commonly referred to as corticokinematic coherence) probes the relationship between activity of sensorimotor network nodes and various movement-related signals (e.g., speed, velocity, acceleration). It is mainly driven by movement rhythmicity during active, passive, and observed dynamic motor actions. It typically predominates at the primary sensorimotor cortex contralateral to the moving limb, occurs at movement frequency and its harmonics, and predominantly reflects the cortical processing of proprioceptive feedback driven by movement rhythmicity in a broad range of dynamic motor actions. Corticomuscular coupling (commonly referred to as corticomuscular coherence) probes the interaction between sensorimotor cortical rhythms and electromyographic (EMG) activity that mainly occurs during steady isometric muscle contraction. We will here focus on the ~20-Hz coupling that is observed during weak isometric contraction and is linked to the modulation of the descending motor command by the ~20-Hz sensorimotor rhythm. This review argues that corticokinematic and corticomuscular couplings have different neural bases. Corticokinematic coupling is mainly driven by afferent signals, while corticomuscular coupling is mainly (but not solely) driven by efferent signals. This distinction should be considered when investigating interactions between brain and body movements. •Two distinct types of brain–body interactions occur during motor actions.•Corticokinematic coupling (CKC) occurs at movement frequency and its harmonics.•CKC is mainly driven by proprioceptive spinocortical afferent signals.•Corticomuscular coupling (CMC) mainly occurs at ~20 Hz during weak contraction.•CMC is mainly driven by mu-rhythm-paced modulations in corticospinal efferent signals.

Maintaining an upright stance requires the integration of sensory inputs from the visual, vestibular and somatosensory-proprioceptive systems by the central nervous system to develop a corrective postural strategy. However, it is unclear whether and how the cerebral cortex monitors and controls postural sways. Here, we asked whether postural sways are encoded in ongoing cortical oscillations, giving rise to a form of corticokinematic coherence (CKC) in the context of standing balance. Center-of-pressure (CoP) fluctuations and electroencephalographic cortical activity were recorded as young healthy participants performed balance tasks during which sensory information was manipulated, by either removal or alteration. We found that postural sways are represented in ongoing cortical activity during challenging balance conditions, in the form of CKC at 1–6 Hz. Time delays between cortical activity and CoP features indicated that both afferent and efferent pathways contribute to CKC, wherein the brain would monitor the CoP velocity and control its position. Importantly, CKC was behaviorally relevant, as it predicted the increase in instability brought by alteration of sensory information. Our results suggest that human sensorimotor cortical areas take part in the closed-loop control of standing balance in challenging conditions. Importantly, CKC could serve as a neurophysiological marker of cortical involvement in maintaining balance.

During joint actions, people typically adjust their own actions according to the ongoing actions of the partner, which implies that the interaction modulates the behavior of both participants. However, the neural substrates of such mutual adaptation are still poorly understood. Here, we set out to identify the kinematics-related brain activity of leaders and followers performing hand actions. Sixteen participants as 8 pairs performed continuous, repetitive right-hand opening and closing actions with ~3-s cycles in a leader–follower task. Subjects played each role for 5min. Magnetoencephalographic (MEG) brain signals were recorded simultaneously from both partners with a dual-MEG setup, and hand kinematics was monitored with accelerometers. Modulation index, a cross-frequency coupling measure, was computed between the hand acceleration and the MEG signals in the alpha (7–13Hz) and beta (13–25Hz) bands. Regardless of the participants' role, the strongest alpha and beta modulations occurred bilaterally in the sensorimotor cortices. In the occipital region, beta modulation was stronger in followers than leaders; these oscillations originated, according to beamformer source reconstructions, in early visual cortices. Despite differences in the modulation indices, alpha and beta power did not differ between the conditions. Our results indicate that the beta modulation in the early visual cortices depends on the subject's role as a follower or leader in a joint hand-action task. This finding could reflect the different strategies employed by leaders and followers in integrating kinematics-related visual information to control their own actions. •Pairs of subjects performed hand movements as a leader and follower in a dual-MEG setup.•Alpha and beta powers did not differ between followers and leaders.•Alpha and beta modulation indices were strongest at bilateral sensorimotor cortices.•Beta modulation was stronger in leaders than followers in the early visual cortex.•The role might influence the integration of kinematics-related visual information to control one's own movements.

As humans, we seamlessly hold objects in our hands, and may even lose consciousness of these objects. This phenomenon raises the unsettled question of the involvement of the cerebral cortex, the core area for voluntary motor control, in dynamically maintaining steady muscle force. To address this issue, we measured magnetoencephalographic brain activity from healthy adults who maintained a steady pinch grip. Using a novel analysis approach, we uncovered fine-grained temporal modulations in the beta sensorimotor brain rhythm and its coupling with muscle activity, with respect to several aspects of muscle force (rate of increase/decrease or plateauing high/low). These modulations preceded changes in force features by ∼40 ms and possessed behavioral relevance, as less salient or absent modulation predicted a more stable force output. These findings have consequences for the existing theories regarding the functional role of cortico-muscular coupling, and suggest that steady muscle contractions are characterized by a stable rather than fluttering involvement of the sensorimotor cortex.

•The cortical tracking of speech (CTS) was assessed with wearable MEG.•Optically-pumped magnetometers (OPMs) were used to record brain activity.•Significant CTS was uncovered at phrasal, word and syllabic rates.•Levels of CTS were similar to those previously reported using classical MEG sensors.•This suggests that OPM-MEG is suitable to measure brain activity below 4 Hz. During continuous speech listening, brain activity tracks speech rhythmicity at frequencies matching with the repetition rate of phrases (0.2–1.5 Hz), words (2–4 Hz) and syllables (4–8 Hz). Here, we evaluated the applicability of wearable MEG based on optically-pumped magnetometers (OPMs) to measure such cortical tracking of speech (CTS). Measuring CTS with OPMs is a priori challenging given the complications associated with OPM measurements at frequencies below 4 Hz, due to increased intrinsic interference and head movement artifacts. Still, this represents an important development as OPM-MEG provides lifespan compliance and substantially improved spatial resolution compared with classical MEG. In this study, four healthy right-handed adults listened to continuous speech for 9 min. The radial component of the magnetic field was recorded simultaneously with 45–46 OPMs evenly covering the scalp surface and fixed to an additively manufactured helmet which fitted all 4 participants. We estimated CTS with reconstruction accuracy and coherence, and determined the number of dominant principal components (PCs) to remove from the data (as a preprocessing step) for optimal estimation. We also identified the dominant source of CTS using a minimum norm estimate. CTS estimated with reconstruction accuracy and coherence was significant in all 4 participants at phrasal and word rates, and in 3 participants (reconstruction accuracy) or 2 (coherence) at syllabic rate. Overall, close-to-optimal CTS estimation was obtained when the 3 (reconstruction accuracy) or 10 (coherence) first PCs were removed from the data. Importantly, values of reconstruction accuracy (~0.4 for 0.2–1.5-Hz CTS and ~0.1 for 2–8-Hz CTS) were remarkably close to those previously reported in classical MEG studies. Finally, source reconstruction localized the main sources of CTS to bilateral auditory cortices. In conclusion, t his study demonstrates that OPMs can be used for the purpose of CTS assessment. This finding opens new research avenues to unravel the neural network involved in CTS across the lifespan and potential alterations in, e.g., language developmental disorders. Data also suggest that OPMs are generally suitable for recording neural activity at frequencies below 4 Hz provided PCA is used as a preprocessing step; 0.2–1.5-Hz being the lowest frequency range successfully investigated here.

While the simultaneous degradation of muscle composition and postural stability in aging are independently highly investigated due to their association with fall risk, the interplay between the two has received little attention. Thus, the purpose of this study is to explore how age-related changes in muscle composition relate to postural stability. To that aim, we collected posturography measures and ultrasound images of the dominant Vastus Lateralis and Biceps Brachii from 32 young (18–35 year old) and 34 older (65–85 year old) participants. Muscle properties were quantified with echo-intensity and texture-based metrics derived from gray-level co-occurrence matrix analysis, and postural stability with the variability of the center of pressure during bipedal stance tasks. Ultrasound parameters revealed that young muscle possessed lower echo-intensity and higher homogeneity compared to the elderly. Echo-intensity and muscle thickness, and several texture-based parameters possessed outstanding young versus older classification performance. A canonical correlation analysis demonstrated a significant relationship between ultrasound and postural measures only within the young group ( r  = 0.53, p  < 0.002), where those with ‘better’ muscle composition displayed larger postural sways. Our results indicate that, in older individuals, postural stability and muscle composition, two common fall risk factors, are unrelated. In view of this decoupling, both may contribute independently to fall risk. Furthermore, our data support the view that texture-based parameters provide a robust alternative to echo-intensity in providing markers of muscle composition.

Altered neuromuscular strategies are suggested to contribute to age-related decreases in postural stability. Current approaches tend to overlook global (whole body) neuromuscular postural control strategies, potentially due to methodological constraints or residual influence from a longstanding, but outdated, biomechanical view in which postural sway is represented by a single-jointed inverted pendulum. In this study, we investigate age-related differences in postural strategies during upright static balance maintenance by assessing global neuromuscular control. We collected simultaneous posturography and electromyography (EMG) data from young (18–35 years, n = 32) and older (65–85 years, n = 33) participants while they stood upright on a force plate or on foam pads thereon, with eyes open or closed. Postural instability was assessed by the standard deviation and velocity of the center of pressure. EMG sensors recorded the activity of thirty muscles (15 on each hemibody). Co-contraction across all muscle pairs was measured with Falconer’s co-contraction index (CCI), and muscle synergy with non-negative matrix factorization. The older group possessed increased global co-contraction intensity, marked by more frequent use of a knee extensor synergy, and was more unstable than the younger group. Notably, advancing age modulated the variability of co-contraction intensity, where the oldest individuals consistently adopted a pure co-contraction strategy marked by the highest CCI values and lowest variability. Age-corrected correlations revealed that knee extensor CCI values were significantly related to postural instability. Taken together, global co-contraction appears to be a signature of elderly postural strategy and age-related instability may be directly related to the extent of knee extensor co-contraction. These results stress the importance of zooming out from classical agonist–antagonist muscle pair investigations in the endeavor to understand elderly postural control strategy.

The human brain is functionally organized into large-scale neural networks that are dynamically interconnected. Multiple short-lived states of resting-state functional connectivity (rsFC) identified transiently synchronized networks and cross-network integration. However, little is known about the way brain couplings covary as rsFC states wax and wane. In this magnetoencephalography study, we explore the synchronization structure among the spontaneous interactions of well-known resting-state networks (RSNs). To do so, we extracted modes of dynamic coupling that reflect rsFC synchrony and analyzed their spatio-temporal features. These modes identified transient, sporadic rsFC changes characterized by the widespread integration of RSNs across the brain, most prominently in the β band. This is in line with the metastable rsFC state model of resting-state dynamics, wherein our modes fit as state transition processes. Furthermore, the default-mode network (DMN) stood out as being structured into competitive cross-network couplings with widespread DMN-RSN interactions, especially among the β-band modes. These results substantiate the theory that the DMN is a core network enabling dynamic global brain integration in the β band. •We study synchronization among dynamic functional connectivity at rest.•We identify transient modes of dynamic coupling using an ICA of connectivity.•Some α-band modes are longer lived and β-band modes are more widespread.•Modes of the DMN are structured into competitive cross-network couplings.•Results concur with a metastable dynamics and the core network model of the DMN.

Speech understanding, while effortless in quiet conditions, is challenging in noisy environments. Previous studies have revealed that a feasible approach to supplement speech-in-noise (SiN) perception consists in presenting speech-derived signals as haptic input. In the current study, we investigated whether the presentation of a vibrotactile signal derived from the speech temporal envelope can improve SiN intelligibility in a multi-talker background for untrained, normal-hearing listeners. We also determined if vibrotactile sensitivity, evaluated using vibrotactile detection thresholds, modulates the extent of audio-tactile SiN improvement. In practice, we measured participants’ speech recognition in a multi-talker noise without (audio-only) and with (audio-tactile) concurrent vibrotactile stimulation delivered in three schemes: to the left or right palm, or to both. Averaged across the three stimulation delivery schemes, the vibrotactile stimulation led to a significant improvement of 0.41 dB in SiN recognition when compared to the audio-only condition. Notably, there were no significant differences observed between the improvements in these delivery schemes. In addition, audio-tactile SiN benefit was significantly predicted by participants’ vibrotactile threshold levels and unimodal (audio-only) SiN performance. The extent of the improvement afforded by speech-envelope-derived vibrotactile stimulation was in line with previously uncovered vibrotactile enhancements of SiN perception in untrained listeners with no known hearing impairment. Overall, these results highlight the potential of concurrent vibrotactile stimulation to improve SiN recognition, especially in individuals with poor SiN perception abilities, and tentatively more so with increasing tactile sensitivity. Moreover, they lend support to the multimodal accounts of speech perception and research on tactile speech aid devices.