Reorganization of functional and directed corticomuscular connectivity during precision grip from childhood to adulthood

How does the neural control of fine movements develop from childhood to adulthood? Here, we investigated developmental differences in functional corticomuscular connectivity using coherence analyses in 111 individuals from four different age groups covering the age range 8–30 y. EEG and EMG were recorded while participants performed a uni-manual force-tracing task requiring fine control of force in a precision grip with both the dominant and non-dominant hand. Using beamforming methods, we located and reconstructed source activity from EEG data displaying peak coherence with the EMG activity of an intrinsic hand muscle during the task. Coherent cortical sources were found anterior and posterior to the central sulcus in the contralateral hemisphere. Undirected and directed corticomuscular coherence was quantified and compared between age groups. Our results revealed that coherence was greater in adults (20–30 yo) than in children (8–10 yo) and that this difference was driven by greater magnitudes of descending (cortex-to-muscle), rather than ascending (muscle-to-cortex), coherence. We speculate that the age-related differences reflect maturation of corticomuscular networks leading to increased functional connectivity with age. We interpret the greater magnitude of descending oscillatory coupling as reflecting a greater degree of feedforward control in adults compared to children. The findings provide a detailed characterization of differences in functional sensorimotor connectivity for individuals at different stages of typical ontogenetic development that may be related to the maturational refinement of dexterous motor control.

Paradoxical fMRI overconnectivity upon chemogenetic silencing of the prefrontal cortex

While shaped and constrained by axonal connections, fMRI-based functional connectivity can reorganize in response to varying interareal input or pathological perturbations. However, the causal contribution of regional brain activity to whole-brain fMRI network organization remains unclear. Here we combine neural silencing, resting-state fMRI and in vivo electrophysiology to causally probe how inactivation of a cortical node affects brain-wide fMRI coupling in the mouse. We find that chronic suppression of the medial prefrontal cortex (PFC) via overexpression of a potassium channel paradoxically increases fMRI connectivity between the silenced area and its direct thalamo-cortical terminals. Acute chemogenetic inactivation of the PFC reproduces analogous patterns of fMRI overconnectivity, with increased fMRI coupling between polymodal thalamic regions and widespread cortical areas. Using multielectrode electrophysiological recordings, we further show that chemogenetic inactivation of the PFC results in enhanced slow (0.1 - 4 Hz) oscillatory coupling between fMRI overconnected areas, and that changes in δ band coherence are linearly correlated with corresponding increases in fMRI connectivity. These results provide causal evidence that cortical inactivation does not necessarily lead to reduced inter-areal coupling, but can counterintuitively increase fMRI connectivity via enhanced, less-localized slow oscillatory processes, with important implications for modelling and understanding fMRI overconnectivity in pathological states.

Age-specific modulation of intermuscular beta coherence during gait before and after experimentally induced fatigue

We examined the effects of age on intermuscular beta-band (15–35 Hz) coherence during treadmill walking before and after experimentally induced fatigue. Older (n = 12) and younger (n = 12) adults walked on a treadmill at 1.2 m/s for 3 min before and after repetitive sit-to-stand, rSTS, to induce muscle fatigability. We measured stride outcomes and coherence from 100 steps in the dominant leg for the synergistic (biceps femoris (BF)-semitendinosus, rectus femoris (RF)-vastus lateralis (VL), gastrocnemius lateralis (GL)-Soleus (SL), tibialis anterior (TA)-peroneus longus (PL)) and for the antagonistic (RF-BF and TA-GL) muscle pairs at late swing and early stance. Older vs. younger adults had 43–62% lower GL-SL, RF-VL coherence in swing and TA-PL and RF-VL coherence in stance. After rSTS, RF-BF coherence in late swing decreased by ~ 20% and TA-PL increased by 16% independent of age (p = 0.02). Also, GL-SL coherence decreased by ~ 23% and increased by ~ 23% in younger and older, respectively. Age affects the oscillatory coupling between synergistic muscle pairs, delivered presumably via corticospinal tracts, during treadmill walking. Muscle fatigability elicits age-specific changes in the common fluctuations in muscle activity, which could be interpreted as a compensation for muscle fatigability to maintain gait performance.

Precision Timing with α–β Oscillatory Coupling: Stopwatch or Motor Control?

Precise timing is crucial for many behaviors ranging from conversational speech to athletic performance. The precision of motor timing has been suggested to result from the strength of phase–amplitude coupling (PAC) between the phase of alpha oscillations (α, 8–12 Hz) and the power of beta activity (β, 14–30 Hz), herein referred to as α–β PAC. The amplitude of β oscillations has been proposed to code for temporally relevant information and the locking of β power to the phase of α oscillations to maintain timing precision. Motor timing precision has at least two sources of variability: variability of timekeeping mechanism and variability of motor control. It is ambiguous to which of these two factors α–β PAC should be ascribed: α–β PAC could index precision of stopwatch-like internal timekeeping mechanisms, or α–β PAC could index motor control precision. To disentangle these two hypotheses, we tested how oscillatory coupling at different stages of a time reproduction task related to temporal precision. Human participants encoded and subsequently reproduced a time interval while magnetoencephalography was recorded. The data show a robust α–β PAC during both the encoding and reproduction of a temporal interval, a pattern that cannot be predicted by motor control accounts. Specifically, we found that timing precision resulted from the trade-off between the strength of α–β PAC during the encoding and during the reproduction of intervals. These results support the hypothesis that α–β PAC codes for the precision of temporal representations in the human brain.

Brain Oscillatory Coupling during Motor Control as a Potential Biomarker for Autism Spectrum Disorders: a comparative study

Background Autism spectrum disorder (ASD) often involves dysfunction in general motor control and motor coordination, in addition to core symptoms. However, the neural mechanisms underlying motor dysfunction in ASD are poorly understood. To elucidate this issue, we focused on brain oscillations and their coupling in the primary motor cortex (M1). Methods We recorded magnetoencephalography in 18 children with autism spectrum disorder, aged 5 to 7 years, and 19 age- and IQ-matched typically-developing children while they pressed button during a video-game-like motor task. We measured motor-related gamma (70 to 90 Hz) and pre-movement beta oscillations (15 to 25 Hz) in the primary motor cortex. To determine the coupling between beta and gamma oscillations, we applied phase-amplitude coupling to calculate the statistical dependence between the amplitude of fast oscillations and the phase of slow oscillations. Results We observed a motor-related gamma increase and a pre-movement beta decrease in both groups. The autism spectrum disorder group exhibited a reduced motor-related gamma increase ( t(35) = 2.412, p = 0.021 ) and enhanced pre-movement beta decrease ( t(35) = 2.705, p = 0.010 ) in the ipsilateral primary motor cortex. We found the phase-amplitude coupling that the high-gamma activity modulated by the beta rhythm in the primary motor cortex. Phase-amplitude coupling in the ipsilateral primary motor cortex was reduced in the autism spectrum disorder group compared with the control group ( t(35) = 3.610, p = 0.001 ). Using oscillatory changes and their coupling, linear discriminant analysis classified autism spectrum disorder and control groups with high accuracy (area under the receiver operating characteristic curve 97.1%). Limitations Further studies with larger sample size and age range of data are warranted to confirm these effects. Conclusions The current findings revealed alterations in oscillations and oscillatory coupling reflecting the dysregulation of a motor gating mechanism in ASD. These results may be helpful for elucidating the neural mechanisms underlying motor dysfunction in ASD, suggesting the possibility of developing a biomarker for ASD diagnosis.