Clinical Application of Motor Imagery Training

Motor imagery training is applied to a rehabilitation program based on previous studies regarding neuroscience and behavioral science. Motor imagery training is useful because it can be applied to almost all patients in clinical settings. However, because motor imagery training has some shortcoming, clinicians need to consider its shortcoming. The objective of this chapter is to promote understanding about using motor imagery effectively

Clinical Study Immediate Beneficial Effects of Mental Rotation Using Foot Stimuli on Upright Postural Stability in Healthy Participants

The present study was designed to investigate whether an intervention during which participants were involved in mental rotation (MR) of a foot stimulus would have immediate beneficial effects on postural stability (Experiment 1) and to confirm whether it was the involvement of MR of the foot, rather than simply viewing foot stimuli, that could improve postural stability (Experiment 2). Two different groups of participants ( = 16 in each group) performed MR intervention of foot stimuli in each of the two experiments. Pre-and postmeasurements of postural stability during unipedal and bipedal standing were made using a force plate for the intervention. Consistently, postural sway values for unipedal standing, but not for bipedal standing, were decreased immediately after the MR intervention using the foot stimuli. Such beneficial effects were not observed after the MR intervention using car stimuli (Experiment 1) or when participants observed the same foot stimuli during a simple reaction task (Experiment 2). These findings suggest that the MR intervention using the foot stimuli could contribute to improving postural stability, at least when it was measured immediately after the intervention, under a challenging standing condition (i.e., unipedal standing)

A Gas Giant Planet in the OGLE-2006-BLG-284L Stellar Binary System

We present the analysis of microlensing event OGLE-2006-BLG-284, which has a lens system that consists of two stars and a gas giant planet with a mass ratio of $q_p = (1.26\pm 0.19) \times 10^{-3}$ to the primary. The mass ratio of the two stars is $q_s = 0.289\pm 0.011$, and their projected separation is $s_s = 2.1\pm 0.7\,$AU, while the projected separation of the planet from the primary is $s_p = 2.2\pm 0.8\,$AU. For this lens system to have stable orbits, the three-dimensional separation of either the primary and secondary stars or the planet and primary star must be much larger than that these projected separations. Since we do not know which is the case, the system could include either a circumbinary or a circumstellar planet. Because there is no measurement of the microlensing parallax effect or lens system brightness, we can only make a rough Bayesian estimate of the lens system masses and brightness. We find host star and planet masses of $M_{L1} = 0.35^{+0.30}_{-0.20}\,M_\odot$, $M_{L2} = 0.10^{+0.09}_{-0.06}\,M_\odot$, and $m_p = 144^{+126}_{-82}\,M_\oplus$, and the $K$-band magnitude of the combined brightness of the host stars is $K_L = 19.7^{+0.7}_{-1.0}$. The separation between the lens and source system will be $\sim 90\,$mas in mid-2020, so it should be possible to detect the host system with follow-up adaptive optics or Hubble Space Telescope observations

A Gas Giant Planet in the OGLE-2006-BLG-284L Stellar Binary System

We present the analysis of microlensing event OGLE-2006-BLG-284, which has a lens system that consists of two stars and a gas giant planet with a mass ratio of q_p = (1.26 ± 0.19) × 10⁻³ to the primary. The mass ratio of the two stars is q_s = 0.289 ± 0.011, and their projected separation is s_s = 2.1 ± 0.7 au, while the projected separation of the planet from the primary is s_p = 2.2 ± 0.8 au. For this lens system to have stable orbits, the three-dimensional separation of either the primary and secondary stars or the planet and primary star must be much larger than the projected separations. Since we do not know which is the case, the system could include either a circumbinary or a circumstellar planet. Because there is no measurement of the microlensing parallax effect or lens system brightness, we can only make a rough Bayesian estimate of the lens system masses and brightness. We find host star and planet masses of, M_(L1) = 0.35^(+0.30)_(−0.20) M⊙, M_(L2) = 0.10^(+0.09)_(−0.06) M⊙, and m_p = 144^(+126)_(−82) M⊕, and the K-band magnitude of the combined brightness of the host stars is K_L = 19.7^(+0.7)_(−1.0). The separation between the lens and source system will be ~90 mas in mid-2020, so it should be possible to detect the host system with follow-up adaptive optics or Hubble Space Telescope observations

Efficacy of Verbally Describing One’s Own Body Movement in Motor Skill Acquisition

The present study examined whether (a) verbally describing one’s own body movement can be potentially effective for acquiring motor skills, and (b) if the effects are related to motor imagery. The participants in this study were 36 healthy young adults (21.2 ± 0.7 years), randomly assigned into two groups (describing and control). They performed a ball rotation activity, with the describing group being asked by the examiner to verbally describe their own ball rotation, while the control group was asked to read a magazine aloud. The participants’ ball rotation performances were measured before the intervention, then again immediately after, five minutes after, and one day after. In addition, participants’ motor imagery ability (mental chronometry) of their upper extremities was measured. The results showed that the number of successful ball rotations (motor smoothness) and the number of ball drops (motor error) significantly improved in the describing group. Moreover, improvement in motor skills had a significant correlation with motor imagery ability. This suggests that verbally describing an intervention is an effective tool for learning motor skills, and that motor imagery is a potential mechanism for such verbal descriptions

Figshare_Data.docx

<p>1. Characteristics of groups of participants; <i><u></u></i> </p><p>2. (a) Mean times required for five rotations; (b) Mean improvement rates for the time required for five rotations (Mean ± SD); and (c) Number of times a ball was dropped for each observation condition and session (Mean ± SD); </p><p>3. Diagram for the experiment; </p><p>4. The diagram shows a comparison between the three groups in the time required for five rotations in four sessions; </p><p><br></p

Flow diagram for the experiment

3 Fig. 1 Flow diagram for the experimen