堀尾 佳世

Body movements influence brain-wide neuronal activities. In the sensory cortex, thalamocortical bottom-up inputs and motor-sensory top-down inputs are thought to affect the dynamics of membrane potentials (V_m) of neurons and change their processing of sensory information during movements. However, direct perturbation of the axons projecting to the sensory cortex from other remote areas during movements has remained unassessed, and therefore the interareal circuits generating motor-related signals in sensory cortices remain unclear. Using a G_i-coupled opsin, eOPN3, we here inhibited interareal signals incoming to the whisker primary somatosensory barrel cortex (wS1) of awake male mice and tested their effects on whisking-related changes in neuronal activities in wS1. Spontaneous whisking in air induced the changes in spike rates of a fraction of wS1 neurons, which were accompanied by depolarization and substantial reduction of slow-wave oscillatory fluctuations of V_m. Despite an extensive innervation, inhibition of inputs from the whisker primary motor cortex (wM1) to wS1 did not alter the spike rates and V_mdynamics of wS1 neurons during whisking. In contrast, inhibition of axons from the whisker-related thalamus (wTLM) and the whisker secondary somatosensory cortex (wS2) to wS1 largely attenuated the whisking-related supra- and sub-threshold V_mdynamics of wS1 neurons. Notably, silencing inputs from wTLM markedly decreased the modulation depth of whisking phase-tuned neurons, while inhibiting wS2 inputs did not impact the whisking variable tuning of wS1 neurons. Thus, sensorimotor integration in wS1 during spontaneous whisking is predominantly facilitated by direct synaptic inputs from wTLM and wS2 rather than from wM1. Significance statementThe traditional viewpoint underscores the importance of motor-sensory projections in shaping movement-induced neuronal activity within sensory cortices. However, this study challenges such established views. We reveal that the synaptic inputs from the whisker primary motor cortex do not alter the activity patterns and membrane potential dynamics of neurons in the whisker primary somatosensory cortex (wS1) during spontaneous whisker movements. Furthermore, we make a novel observation that inhibiting inputs from the whisker secondary somatosensory cortex (wS2) substantially curtails movement-related activities in wS1, leaving the tuning to whisking variables unaffected. These findings provoke a reconsideration of the role of motor-sensory projections in sensorimotor integration and bring to light a new function for wS2-to-wS1 projections.

OBJECTIVE: The JT interval of the myocardial repolarization time can be divided into Jpoint to T-peak interval (JTp) and T-peak to T-end interval (Tpe). It is well known that the JT interval is dependent on the heart rate, but little is known regarding heart rate dependence for JTp and Tpe. The aim of the present study was to clarify the heart rate dependence of JTp and Tpe and to elucidate the interference of autonomic nervous activity with these parameters. METHODS: We evaluated 50 prepubertal children (mean age: 6.4 ± 0.5 years; male:female, 22:28) without heart disease. JTp, Tpe, and the preceding RR intervals were measured using 120 consecutive beats (lead CM5). First, the relationships between the RR interval and JTp and Tpe were evaluated by Pearson's correlation coefficient. Second, to evaluate autonomic interference with JTp and Tpe, the degree of coherence between RR interval variability and JTp or Tpe variability was calculated using spectral analysis. RESULTS: Significant positive correlations were observed between the RR interval and JTp (y = 0.116x + 105.5; r = 0.594, p < 0.001) and between the RR interval and Tpe (y = 0.037x + 44.7; r = 0.432, p < 0.001). Tpe variability had a lower degree of coherence with RR interval variability (range: 0.039-0.5 Hz) than with JTp variability (0.401 [interquartile range, 0.352-0.460] vs. 0.593 [0.503-0.664], respectively; p < 0.001). CONCLUSIONS: Tpe had lower heart rate dependence and a lower degree of autonomic nervous interference than did JTp.

The QT variability index (QTVI), which measures the instability of myocardial repolarization, is usually calculated from a single electrocardiogram (ECG) recording and can be easily applied in children. It is well known that frequency analysis of heart rate variability (HRV) can detect autonomic balance, but it is not clear whether QTVI is correlated with autonomic tone. Therefore, we evaluated the association between QTVI and HRV to elucidate whether QTVI is correlated with autonomic nerve activity. Apparently, healthy 320 children aged 0-7 years who visited Fujita Health University Hospital for heart checkup examinations were included. The RR and QT intervals of 60 continuous heart beats were measured, and the QTVI was calculated using the formula of Berger et al. Frequency analysis of HRV, including the QTVI analysis region, was conducted for 2 min and the ratio of low-frequency (LF) components to high-frequency (HF) components (LF/HF) and HF/(LF + HF) ratio was calculated as indicators of autonomic nerve activity. Then, the correlations between QTVI and these parameters were assessed. QTVI showed a significant positive correlation with LF/HF ratio (r = 0.45, p < 0.001) and negative correlation with HF/(LF + HF) ratio (r = -0.429, p < 0.001). These correlations remained after adjustment for sex and age. QTVI, which is calculated from non-invasive ECG and can detect abnormal myocardial repolarization, is significantly correlated with frequency analysis of HRV parameters. QTVI reflects autonomic nerve balance in children.