Ageing-related changes in nap neuroscillatory activity are mediated and moderated by grey matter volume

Cerebral Gray Matter May Not Explain Sleep Slow-Wave Characteristics after Severe Brain Injury

Sleep slow waves are the hallmark of deeper non-rapid eye movement sleep. It is generally assumed that gray matter properties predict slow-wave density, morphology, and spectral power in healthy adults. Here, we tested the association between gray matter volume (GMV) and slow-wave characteristics in 27 patients with moderate-to-severe traumatic brain injury (TBI, 32.0 ± 12.2 years old, eight women) and compared that with 32 healthy controls (29.2 ± 11.5 years old, nine women). Participants underwent overnight polysomnography and cerebral MRI with a 3 Tesla scanner. A whole-brain voxel-wise analysis was performed to compare GMV between groups. Slow-wave density, morphology, and spectral power (0.4-6 Hz) were computed, and GMV was extracted from the thalamus, cingulate, insula, precuneus, and orbitofrontal cortex to test the relationship between slow waves and gray matter in regions implicated in the generation and/or propagation of slow waves. Compared with controls, TBI patients had significantly lower frontal and temporal GMV and exhibited a subtle decrease in slow-wave frequency. Moreover, higher GMV in the orbitofrontal cortex, insula, cingulate cortex, and precuneus was associated with higher slow-wave frequency and slope, but only in healthy controls. Higher orbitofrontal GMV was also associated with higher slow-wave density in healthy participants. While we observed the expected associations between GMV and slow-wave characteristics in healthy controls, no such associations were observed in the TBI group despite lower GMV. This finding challenges the presumed role of GMV in slow-wave generation and morphology.

Neural Mechanisms of Nonauditory Effects of Noise Exposure on Special Populations

Due to the abnormal structure and function of brain neural networks in special populations, such as children, elderly individuals, and individuals with mental disorders, noise exposure is more likely to have negative psychological and cognitive nonauditory effects on these individuals. There are unique and complex neural mechanisms underlying this phenomenon. For individuals with mental disorders, there are anomalies such as structural atrophy and decreased functional activation in brain regions involved in emotion and cognitive processing, such as the prefrontal cortex (PFC). Noise exposure can worsen these abnormalities in relevant brain regions, further damaging neural plasticity and disrupting normal connections and the transmission of information between the PFC and other brain areas by causing neurotransmitter imbalances. In the case of children, in a noisy environment, brain regions such as the left inferior frontal gyrus and PFC, which are involved in growth and development, are more susceptible to structural and functional changes, leading to neurodegenerative alterations. Furthermore, noise exposure can interrupt auditory processing neural pathways or impair inhibitory functions, thus hindering children's ability to map sound to meaning in neural processes. For elderly people, age-related shrinkage of brain regions such as the PFC, as well as deficiencies in hormone, neurotransmitter, and nutrient levels, weakens their ability to cope with noise. Currently, it is feasible to propose and apply coping strategies to improve the nonauditory effects of noise exposure on special populations based on the plasticity of the human brain.

Targeted memory reactivation during post-learning sleep does not enhance motor memory consolidation in older adults

Targeted memory reactivation (TMR) during sleep enhances memory consolidation in young adults by modulating electrophysiological markers of neuroplasticity. Interestingly, older adults exhibit deficits in motor memory consolidation, an impairment that has been linked to age-related degradations in the same sleep features sensitive to TMR. We hypothesised that TMR would enhance consolidation in older adults via the modulation of these markers. A total of 17 older participants were trained on a motor task involving two auditory-cued sequences. During a post-learning nap, two auditory cues were played: one associated to a learned (i.e., reactivated) sequence and one control. Performance during two delayed re-tests did not differ between reactivated and non-reactivated sequences. Moreover, both associated and control sounds modulated brain responses, yet there were no consistent differences between the auditory cue types. Our results collectively demonstrate that older adults do not benefit from specific reactivation of a motor memory trace by an associated auditory cue during post-learning sleep. Based on previous research, it is possible that auditory stimulation during post-learning sleep could have boosted motor memory consolidation in a non-specific manner.

In vivo marker of brainstem myelin is associated to quantitative sleep parameters in healthy young men

The regional integrity of brain subcortical structures has been implicated in sleep-wake regulation, however, their associations with sleep parameters remain largely unexplored. Here, we assessed association between quantitative Magnetic Resonance Imaging (qMRI)-derived marker of the myelin content of the brainstem and the variability in the sleep electrophysiology in a large sample of 18-to-31 years healthy young men (N = 321; ~ 22 years). Separate Generalized Additive Model for Location, Scale and Shape (GAMLSS) revealed that sleep onset latency and slow wave energy were significantly associated with MTsat estimates in the brainstem (pcorrected ≤ 0.03), with overall higher MTsat value associated with values reflecting better sleep quality. The association changed with age, however (MTsat-by-age interaction-pcorrected ≤ 0.03), with higher MTsat value linked to better values in the two sleep metrics in the younger individuals of our sample aged ~ 18 to 20 years. Similar associations were detected across different parts of the brainstem (pcorrected ≤ 0.03), suggesting that the overall maturation and integrity of the brainstem was associated with both sleep metrics. Our results suggest that myelination of the brainstem nuclei essential to regulation of sleep is associated with inter-individual differences in sleep characteristics during early adulthood. They may have implications for sleep disorders or neurological diseases related to myelin.

Aging-Related Changes in Cortical Sources of Sleep Oscillatory Neural Activity Following Motor Learning Reflect Contributions of Cortical Thickness and Pre-sleep Functional Activity

Oscillatory neural activity during sleep, such as that in the delta and sigma bands, is important for motor learning consolidation. This activity is reduced with typical aging, and this reduction may contribute to aging-related declines in motor learning consolidation. Evidence suggests that brain regions involved in motor learning contribute to oscillatory neural activity during subsequent sleep. However, aging-related differences in regional contributions to sleep oscillatory activity following motor learning are unclear. To characterize these differences, we estimated the cortical sources of consolidation-related oscillatory activity using individual anatomical information in young and older adults during non-rapid eye movement sleep after motor learning and analyzed them in light of cortical thickness and pre-sleep functional brain activation. High-density electroencephalogram was recorded from young and older adults during a midday nap, following completion of a functional magnetic resonance imaged serial reaction time task as part of a larger experimental protocol. Sleep delta activity was reduced with age in a left-weighted motor cortical network, including premotor cortex, primary motor cortex, supplementary motor area, and pre-supplementary motor area, as well as non-motor regions in parietal, temporal, occipital, and cingulate cortices. Sleep theta activity was reduced with age in a similar left-weighted motor network, and in non-motor prefrontal and middle cingulate regions. Sleep sigma activity was reduced with age in left primary motor cortex, in a non-motor right-weighted prefrontal-temporal network, and in cingulate regions. Cortical thinning mediated aging-related sigma reductions in lateral orbitofrontal cortex and frontal pole, and partially mediated delta reductions in parahippocampal, fusiform, and lingual gyri. Putamen, caudate, and inferior parietal cortex activation prior to sleep predicted frontal and motor cortical contributions to sleep delta and theta activity in an age-moderated fashion, reflecting negative relationships in young adults and positive or absent relationships in older adults. Overall, these results support the local sleep hypothesis that brain regions active during learning contribute to consolidation-related neural activity during subsequent sleep and demonstrate that sleep oscillatory activity in these regions is reduced with aging.

Ageing-related changes in nap neuroscillatory activity are mediated and moderated by grey matter volume

Ageing‐related changes in grey matter result in changes in the intensity and topography of sleep neural activity. However, it is unclear whether these findings can be explained by ageing‐related differences in sleep pressure or circadian influence. The current study used high‐density electroencephalography to assess how grey matter volume differences between young and older adults mediate and moderate neuroscillatory activity differences during a midday nap following a motor sequencing task. Delta, theta, and sigma amplitude were reduced in older relative to young adults, especially over frontocentral scalp, leading to increases in relative delta frontality and relative sigma lateral centroposteriority. Delta reductions in older adults were mediated by grey matter loss in frontal medial cortex, primary motor cortex, thalamus, caudate, putamen, and pallidum, and were moderated by putamen grey matter volume. Theta reductions were mediated by grey matter loss in primary motor cortex, thalamus, and caudate, and were moderated by putamen and pallidum grey matter volume. Sigma changes were moderated by putamen and pallidum grey matter volume. Moderation results suggested that across frequencies, young adults with more grey matter had increased activity, whereas older adults with more grey matter had unchanged or decreased activity. These results provide a critical extension of previous findings from overnight sleep in a midday nap, indicating that they are not driven by sleep pressure or circadian confounds. Moreover, these results suggest brain regions associated with motor sequence learning contribute to sleep neural activity following a motor sequencing task.

Encoding and consolidation of motor sequence learning in young and older adults

Sleep benefits motor memory consolidation in young adults, but this benefit is reduced in older adults. Here we sought to understand whether differences in the neural bases of encoding between young and older adults contribute to aging-related differences in sleep-dependent consolidation of an explicit variant of the serial reaction time task (SRTT). Seventeen young and 18 older adults completed two sessions (nap, wake) one week apart. In the MRI, participants learned the SRTT. Following an afternoon interval either awake or with a nap (recorded with high-density polysomnography), performance on the SRTT was reassessed in the MRI. Imaging and behavioral results from SRTT performance showed clear sleep-dependent consolidation of motor sequence learning in older adults after a daytime nap, compared to an equal interval awake. Young adults, however, showed brain activity and behavior during encoding consistent with high SRTT performance prior to the sleep interval, and did not show further sleep-dependent performance improvements. Young adults did show reduced cortical activity following sleep, suggesting potential systems-level consolidation related to automatization. Sleep physiology data showed that sigma activity topography was affected by hippocampal and cortical activation prior to the nap in both age groups, and suggested a role of theta activity in sleep-dependent automatization in young adults. These results suggest that previously observed aging-related sleep-dependent consolidation deficits may be driven by aging-related deficiencies in fast learning processes. Here we demonstrate that when sufficient encoding strength is reached with additional training, older adults demonstrate intact sleep-dependent consolidation of motor sequence learning.