Research

Abstract

The effects of transcranial alternating current stimulation (tACS) frequency on brain oscillations and cortical excitability are still controversial. Therefore, this study investigated how different tACS frequencies differentially modulate cortical oscillation and inhibition. To do so, we first determined the optimal positioning of tACS electrodes through an electric field simulation con-structed from magnetic resonance images. Seven electrode configurations were tested on the electric field of the precentral gyrus (hand motor area). We determined that the Cz-CP1 config-uration was optimal, as it resulted in higher electric field values and minimized the in-tra-individual differences in the electric field. Therefore, tACS was delivered to the hand motor area through this arrangement at a fixed frequency of 10 Hz (alpha-tACS) or 20 Hz (beta-tACS) with a peak-to-peak amplitude of 0.6 mA for 20 min. We found that alpha- and beta-tACS resulted in larger alpha and beta oscillations, respectively, compared with the oscillations observed after sham-tACS. In addition, alpha- and beta-tACS decreased the amplitudes of conditioned motor evoked potentials and increased alpha and beta activity, respectively. Correspondingly, alpha- and beta-tACSs enhanced cortical inhibition. These results show that tACS frequency differentially affects motor cortex oscillation and inhibition.

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Abstract

Paired associative stimulation (PAS) increases and decreases cortical excitability in primary motor cortex (M1) neurons, depending on spike timing-dependent plasticity, i.e., long-term potentiation (LTP)- and long-term depression (LTD)-like plasticity, respectively. However, how PAS affects cortical circuits for agonist and antagonist muscles of M1 is unclear. Here, we investigated the changes in LTP- and LTD-like plasticity for agonist and antagonist muscles during PAS: 200 pairs of 0.25-Hz peripheral electric stimulation of the right median nerve at the wrist, followed by tran-scranial magnetic stimulation of the left M1 with an interstimulus interval of 25 ms (PAS-25ms) and 10 ms (PAS-10ms). The unconditioned motor evoked potential (MEP) amplitudes of agonist muscles were larger after PAS-25ms than after PAS-10ms, while those of antagonist muscles were smaller after PAS-25ms than after PAS-10ms. The γ-aminobutyric acid A (GABAA)- and GABAB-mediated cortical inhibition for agonist and antagonist muscles were higher after PAS-25ms than after PAS-10ms. The cortical excitability for agonist and antagonist muscles reciprocally and topographically increased and decreased after PAS, respectively; however, GABAA and GABAB-mediated cortical inhibitory functions for agonist and antagonist muscles were less topo-graphically decreased after PAS-10ms. Thus, PAS-25ms and PAS-10ms differentially affect LTP- and LTD-like plasticity in agonist and antagonist muscles.

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Abstract

Rhythmic passive movements are often used during rehabilitation to improve physical functions. Previous studies have explored oscillatory activities in the sensorimotor cortex during active movements; however, the relationship between movement rhythms and oscillatory activities during passive movements has not been substantially tested. Therefore, we aimed to quantitatively identify changes in cortical oscillations during rhythmic passive movements. Twenty healthy young adults participated in our study. We placed electroencephalography electrodes over a nine-position grid; the center was oriented on the transcranial magnetic stimulation hotspot of the biceps brachii muscle. Passive movements included elbow flexion and extension; the partic-ipants were instructed to perform rhythmic elbow flexion and extension in response to the blinking of 0.67 Hz light-emitting diode lamps. The coherence between high-beta and low-gamma oscillations near the hotspot of the biceps brachii muscle and passive movement rhythms was higher than that between alpha oscillation and passive movement rhythm. These results imply that alpha, beta, and gamma oscillations of the primary motor cortex are differently related to passive movement rhythm.

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Abstract

Reward expectation and time estimation are important for behavior and affect corticospinal excitability. This study investigated changes in corticospinal excitability during rewarded time-sensitive behavioral tasks. The rewarded time-sensitive task comprised three fixed-ratio (FR) schedules: FRA contained a reward stimulus after every response, FRB after every two responses, and FRC after every four responses. The participants were instructed to press a left button with the index finger as quickly as possible in response to the appearance of a red circle. Just after the left button press, the word “10-yen” (approximately $0.1) or “no pay” was presented as feedback. Then, the participant had to mentally estimate/wait for 2.5 s from pressing the left button to pressing the right button. One second after the reward stimulus, transcranial magnetic stimulation was delivered to the primary motor cortex at the hotspot of the first dorsal interosseous muscle. Each participant received items corresponding to the total monetary reward accumulated at the end of the experiment. The variability of motor evoked potential amplitudes transformed from a random process during the resting state into an autoregressive process during the rewarded time-sensitive behavioral task. Additionally, the random variation of motor evoked potential amplitudes in the FRC, FRB, and FRA schedules increased in a stepwise fashion. However, the magnitude of motor evoked potential amplitudes significantly increased for the FRB and FRC schedules compared to the FRA schedule. The time estimation lag was negative for the three FR schedules but there was no difference among the three FR schedules. The magnitude of corticospinal excitability increased in low reward probability, whereas the variability of corticospinal excitability transformed into an autoregressive process in high reward probability. These results imply that the magnitude and variability of expectation-related corticospinal excitabilities can be differentially altered by reward probability.

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Abstract

The simulation study investigated the relationship between the local linear trend model’s data-comparison accuracy, baseline-data variability, and changes in level and slope after introducing the N-of-1 intervention. Contour maps were constructed, which included baseline-data variability, change in level or slope, and percentage of non-overlapping data between the state and forecast values by the local linear trend model. Simulation results showed that baseline-data variability and changes in level and slope after intervention affect the data-comparison accuracy based on the local linear trend model. The field study investigated the intervention effects for actual field data using the local linear trend model, which confirmed 100% effectiveness of previous N-of-1 studies. These results imply that baseline-data variability affects the data-comparison accuracy using a local linear trend model, which could accurately predict the intervention effects. The local linear trend model may help assess the intervention effects of effective personalized interventions in precision rehabilitation.

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Abstract

This study aimed to use a convolutional neural network (CNN) to investigate the associations between the time of falling (TF) and multiple complicating factors (MCF), including age, dementia severity, lower extremity strength, and physical function, among nursing home residents with Alzheimer’s disease (AD). Forty-two people with AD were enrolled. We evaluated falling events from nursing home admission (baseline) to 300 days later. We assessed the knee extension strength (KES) and Functional Independence Measure (FIM) locomotion item and performed the Mini-Mental State Examination (MMS) at baseline. To predict falling, participants were categorized into three classes: those who fell within the first 150 (or 300) days from baseline or those who did not experience a fall within the study period. For each class, 1,000 bootstrap datasets were generated using 42 actual sample datasets and were used to propose a CNN algorithm and cross-validate the algorithm. Eight (19.0%), 11 (26.2%), and 31 participants (73.8%) fell within 150 or 300 days after the baseline assessment or did not fall until 300 days or later, respectively. The highest accuracy rate of the CNN classification was 0.647 in the factor combination extracted from the MMS score, KES, and FIM locomotion item score. A CNN based on MCF could predict the TF in nursing home residents with AD.

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