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Sleep is a physiological state that plays important role in the recovery of fatigue. However, the relationship between the physiological status of sleep and subjective fatigue remains unknown. In the present study, we hypothesized that the non-recovery of fatigue at wake time due to non-restorative sleep might be ascribed to changes in specific parameters of electroencephalography (EEG) and heart rate variability (HRV) in poor sleepers. Twenty healthy female shift-working nurses participated in the study. Subjective fatigue was assessed using the visual analog scale (VAS) at bedtime and wake time. During sleep on the night between 2 consecutive day shifts, the EEG powers at the frontal pole, HRV based on electrocardiograms, and distal-proximal gradient of skin temperature were recorded and analyzed. The results indicated that the subjects with high fatigue on the VAS at wake time exhibited (1) a decrease in deep non-rapid eye movement (NREM) (stageN3) sleep duration in the first sleep cycle; (2) a decrease in REM latency; (3) a decrease in ultra-slow and delta EEG powers, particularly from 30 to 65 min after sleep onset; (4) a decrease in the total power of HRV, particularly from 0 to 30 min after sleep onset; (5) an increase in the very low frequency component of HRV; and (6) a smaller increase in the distal-proximal gradient of skin temperature, than those of the subjects with low fatigue levels. The correlational and structural equation modeling analyses of these parameters suggested that an initial decrease in the total power of HRV from 0 to 30 min after sleep onset might inhibit the recovery from fatigue during sleep (i.e., increase the VAS score at wake time) via its effects on the ultra-slow and delta powers from 30 to 65 min after sleep onset, stageN3 duration in the first sleep cycle, REM latency, and distal-proximal gradient of skin temperature. These findings suggest an important role of these physiological factors in recovery from fatigue during sleep, and that interventions to modify these physiological factors might ameliorate fatigue at wake time.

Although motor training programs have been applied to childhood apraxia of speech (AOS), the neural mechanisms of articulation learning are not well understood. To this aim, we recorded cerebral hemodynamic activity in the left hemisphere of healthy subjects (n = 15) during articulation learning. We used near-infrared spectroscopy (NIRS) while articulated voices were recorded and analyzed using spectrograms. The study consisted of two experimental sessions (modified and control sessions) in which participants were asked to repeat the articulation of the syllables “i-chi-ni” with and without an occlusal splint. This splint was used to increase the vertical dimension of occlusion to mimic conditions of articulation disorder. There were more articulation errors in the modified session, but number of errors were decreased in the final half of the modified session; this suggests that articulation learning took place. The hemodynamic NIRS data revealed significant activation during articulation in the frontal, parietal, and temporal cortices. These areas are involved in phonological processing and articulation planning and execution, and included the following areas: (i) the ventral sensory-motor cortex (vSMC), including the Rolandic operculum, precentral gyrus, and postcentral gyrus, (ii) the dorsal sensory-motor cortex, including the precentral and postcentral gyri, (iii) the opercular part of the inferior frontal gyrus (IFGoperc), (iv) the temporal cortex, including the superior temporal gyrus, and (v) the inferior parietal lobe (IPL), including the supramarginal and angular gyri. The posterior Sylvian fissure at the parietal–temporal boundary (area Spt) was selectively activated in the modified session. Furthermore, hemodynamic activity in the IFGoperc and vSMC was increased in the final half of the modified session compared with its initial half, and negatively correlated with articulation errors during articulation learning in the modified session. The present results suggest an essential role of the frontal regions, including the IFGoperc and vSMC, in articulation learning, with sensory feedback through area Spt and the IPL. The present study provides clues to the underlying pathology and treatment of childhood apraxia of speech.

Limb apraxia is a higher brain dysfunction that typically occurs after left hemispheric stroke and its cause cannot be explained by sensory disturbance or motor paralysis. The comparison of motor signals and visual feedback to generate errors, i.e., visuo-motor integration, is important in motor control and motor learning, which may be impaired in apraxia. However, in apraxia after stroke, it is unknown whether there is a specific deficit in visuo-motor temporal integration compared to visuo-tactile and visuo-proprioceptive temporal integration. We examined the precision of visuo-motor temporal integration and sensory-sensory (visuo-tactile and visuo-proprioception) temporal integration in apraxia after stroke by using a delayed visual feedback detection task with three different conditions (tactile, passive movement, and active movement). The delay detection threshold and the probability curve for delay detection obtained in this task were quantitative indicators of the respective temporal integration functions. In addition, we performed subtraction and voxel-based lesion-symptom mapping to identify the brain lesions responsible for apraxia and deficits in visuo-motor temporal integration. The behavioral experiments showed that the delay detection threshold was extended and that the probability curve for delay detection was less steep in apraxic patients compared to controls (pseudo-apraxic patients and unaffected patients), only for the active movement condition, and not for the tactile and passive movement conditions. Furthermore, the severity of apraxia was significantly correlated with the delay detection threshold and the steepness of the probability curve in the active movement condition. These results indicated that multisensory (i.e., visual, tactile, and proprioception) feedback was normally temporally integrated, but motor prediction and visual feedback were not correctly temporally integrated in apraxic patients. That is, apraxic patients had difficulties with visuo-motor temporal integration. Lesion analyses revealed that both apraxia and the distortion of visuo-motor temporal integration were associated with lesions in the fronto-parietal motor network, including the left inferior parietal lobule and left inferior frontal gyrus. We suppose that damage to the left inferior fronto-parietal network could cause deficits in motor prediction for visuo-motor temporal integration, but not for sensory-sensory (visuo-tactile and visuo-proprioception) temporal integration, leading to the distortion of visuo-motor temporal integration in patients with apraxia.