Subclinical depressive symptoms are associated with increased risk of Alzheimer’s disease (AD), but the brain mechanisms underlying this relationship are still unclear. We aimed to provide a comprehensive overview of the brain substrates of subclinical depressive symptoms in cognitively unimpaired older adults using complementary multimodal neuroimaging data. We included cognitively unimpaired older adults from the baseline data of the primary cohort Age-Well (n = 135), and from the replication cohort ADNI (n = 252). In both cohorts, subclinical depressive symptoms were assessed using the 15-item version of the Geriatric Depression Scale; based on this scale, participants were classified as having depressive symptoms (>0) or not (0). Voxel-wise between-group comparisons were performed to highlight differences in gray matter volume, glucose metabolism and amyloid deposition; as well as white matter integrity (only available in Age-Well). Age-Well participants with subclinical depressive symptoms had lower gray matter volume in the hippocampus and lower white matter integrity in the fornix and the posterior parts of the cingulum and corpus callosum, compared to participants without symptoms. Hippocampal atrophy was recovered in ADNI, where participants with subclinical depressive symptoms also showed glucose hypometabolism in the hippocampus, amygdala, precuneus/posterior cingulate cortex, medial and dorsolateral prefrontal cortex, insula, and temporoparietal cortex. Subclinical depressive symptoms were not associated with brain amyloid deposition in either cohort. Subclinical depressive symptoms in ageing are linked with neurodegeneration biomarkers in the frontolimbic network including brain areas particularly sensitive to AD. The relationship between depressive symptoms and AD may be partly underpinned by neurodegeneration in common brain regions.

Study objectives:To determine the polysomnography characteristics during sleep paralysis, false awakenings and lucid dreaming (which are states intermediate to REM sleep and wake but exceptionally observed in sleep laboratory). Methods:In 5 subjects, we captured 5 episodes of sleep paralysis (2 time-marked with the ocular left-right-left-right code normally used to signal lucid dreaming, 1 time-marked by an external noise and 2 retrospectively reported) and 2 episodes of false awakening. The sleep coding (using three seconds mini-epochs) and spectral EEG analysis were compared during these episodes and normal REM sleep as well as wakefulness in the same four among these five subjects, and vs. lucid REM sleep in four other patients with narcolepsy.Results: During episodes of sleep paralysis, 70.8 % of mini-epochs contained theta EEG rhythm, (vs. 89.7% in REM sleep and 21.2% in wakefulness), 93.8% contained chin muscle atonia (vs. 89.7% in REM sleep and 33.3% in wakefulness) and 6.9% contained rapid eye movements (vs. 11.9% in REM sleep and 8.1% in wakefulness). The EEG spectrum during sleep paralysis was intermediate between wakefulness and REM sleep in the alpha, theta and delta frequencies, whereas the beta frequencies were not different between sleep paralysis and normal REM sleep.The power spectrum during false awakening followed the same profile as in sleep paralysis. Conclusions:The predominant theta EEG rhythm during sleep paralysis and false awakenings (with rare and lower alpha rhythm) suggests that the brain during sleep paralysis is not in an awake but in a dreaming state. 4 BRIEF SUMMARYCurrent Knowledge/Study Rationale: Sleep paralysis is commonly viewed as an awake brain with a complete muscle atonia, but EEG analysis during sleep paralysis is exceptional.Study Impact: Our polysomnography and spectral analysis of time-marked sleep paralyses suggest that the brain during sleep paralysis is not in an awake but in a dreaming state. This may explain why hallucinations are often associated with sleep paralysis.