Motor sequence learning-induced corticospinal plasticity is biased towards sensorimotor mu rhythm peak phases.

Motor cortical (M1) transcranial magnetic stimulation (TMS) increases corticospinal output and improves motor learning when delivered during sensorimotor mu rhythm trough but not peak phases, suggesting that mechanisms supporting motor learning may be most active during mu trough phases. If so, learning-related corticospinal plasticity should be most evident during mu trough phases. Healthy adults were assigned to either a sequence or control group. Participants in the sequence group practiced the implicit serial reaction time task (SRTT), which contained an embedded, repeating 12-item sequence. Participants in the control group practiced a version of the SRTT that contained no sequence. We measured mu phase-independent and phase-dependent MEP amplitudes using EEG-informed single-pulse TMS before, immediately, and 30 minutes after the SRTT in both groups. All participants performed a retention test one hour after SRTT acquisition. In both groups, mu phase-independent MEP amplitudes increased following SRTT acquisition, but the pattern of mu phase-dependent MEP amplitude increases after SRTT acquisition differed between groups. MEP amplitude changes from baseline to 30 minutes after SRTT acquisition more strongly differed across phases in the control relative to the sequence group, with the control group showing smaller increases in peak- than trough-specific MEPs. Contrary to our original hypothesis, results revealed that sequence learning recruits peak- rather than trough-specific neurophysiological mechanisms. Overall, these findings suggest that mu peak phases may provide protected time windows for motor memory consolidation and demonstrate the presence of a mu phase-dependent motor learning mechanism in the human brain. Significance statement: Recent work suggests that the neurophysiological mechanisms supporting motor learning may be most active during sensorimotor mu rhythm trough phases. Here, we evaluated this possibility by measuring mu phase-dependent corticospinal plasticity induced by motor sequence learning. Results provide first evidence that motor sequence learning produced corticospinal plasticity that was more pronounced during mu peak than trough phases, demonstrating the presence of a phase-dependent learning mechanism within the human motor system.

Modeling the electrical resistivity of polymer composites with segregated structures.

Hybrid carbon nanotube composites with two different types of fillers have attracted considerable attention for various advantages. The incorporation of micro-scale secondary fillers creates an excluded volume that leads to the increase in the electrical conductivity. By contrast, nano-scale secondary fillers shows a conflicting behavior of the decreased electrical conductivity with micro-scale secondary fillers. Although several attempts have been made in theoretical modeling of secondary-filler composites, the knowledge about how the electrical conductivity depends on the dimension of secondary fillers was not fully understood. This work aims at comprehensive understanding of the size effect of secondary particulate fillers on the electrical conductivity, via the combination of Voronoi geometry induced from Swiss cheese models and the underlying percolation theory. This indicates a transition in the impact of the excluded volume, i.e., the adjustment of the electrical conductivity was measured in cooperation with loading of second fillers with different sizes.