Summary Clinical evidence suggests a potentially causal interaction between sleep and affective brain function; nearly all mood disorders display co-occurring sleep abnormalities, commonly involving rapid-eye movement (REM) sleep [1–4]. Building on this clinical evidence, recent neurobiological frameworks have hypothesized a benefit of REM sleep in palliatively decreasing next-day brain reactivity to recent waking emotional experiences [5, 6]. Specifically, the marked suppression of central adrenergic neurotransmitters during REM (commonly implicated in arousal and stress), coupled with activation in amygdala-hippocampal networks that encode salient events, is proposed to (re)process and de-potentiate previous affective experiences, decreasing their emotional intensity [3]. In contrast, the failure of such adrenergic reduction during REM sleep has been described in anxiety disorders, indexed by persistent high-frequency electroencephalographic (EEG) activity (>30Hz) [7–10]; a candidate factor contributing to hyper-arousal and exaggerated amygdala reactivity [3, 11–13]. Despite these neurobiological frameworks, and their predictions, the proposed benefit of REM sleep physiology in de-potentiating neural and behavioral responsivity to prior emotional events remains unknown. Here, we demonstrate that REM sleep physiology is associated with an overnight dissipation of amygdala activity in response to previous emotional experiences, altering functional-connectivity and reducing next-day subjective emotionality.

In this paper we present a method to segment four brainstem structures (midbrain, pons, medulla oblongata and superior cerebellar peduncle) from 3D brain MRI scans. The segmentation method relies on a probabilistic atlas of the brainstem and its neighboring brain structures. To build the atlas, we combined a dataset of 39 scans with already existing manual delineations of the whole brainstem and a dataset of 10 scans in which the brainstem structures were manually labeled with a protocol that was specifically designed for this study. The resulting atlas can be used in a Bayesian framework to segment the brainstem structures in novel scans. Thanks to the generative nature of the scheme, the segmentation method is robust to changes in MRI contrast or acquisition hardware. Using cross validation, we show that the algorithm can segment the structures in previously unseen T1 and FLAIR scans with great accuracy (mean error under 1 mm) and robustness (no failures in 383 scans including 168 AD cases). We also indirectly evaluate the algorithm with a experiment in which we study the atrophy of the brainstem in aging. The results show that, when used simultaneously, the volumes of the midbrain, pons and medulla are significantly more predictive of age than the volume of the entire brainstem, estimated as their sum. The results also demonstrate that that the method can detect atrophy patterns in the brainstem structures that have been previously described in the literature. Finally, we demonstrate that the proposed algorithm is able to detect differential effects of AD on the brainstem structures. The method will be implemented as part of the popular neuroimaging package FreeSurfer.

Executive functions are often considered lynchpin “frontal lobe tasks”, despite accumulating evidence that a broad network of anterior and posterior brain structures supports them. Using a latent variable modeling approach, we assessed whether prefrontal grey matter volumes independently predict executive function performance when statistically differentiated from global atrophy and individual non-frontal lobar volume contributions. We further examined whether fronto-parietal white matter microstructure underlies and independently contributes to executive functions. We developed a latent variable model to decompose lobar grey matter volumes into a global grey matter factor and specific lobar volumes (i.e. prefrontal, parietal, temporal, occipital) that were independent of global grey matter. We then added mean fractional anisotropy (FA) for the superior longitudinal fasciculus (dorsal portion), corpus callosum, and cingulum bundle (dorsal portion) to models that included grey matter volumes related to cognitive variables in previous analyses. Results suggested that the 2-factor model (shifting/inhibition, updating/working memory) plus an information processing speed factor best explained our executive function data in a sample of 202 community dwelling older adults, and was selected as the base measurement model for further analyses. Global grey matter was related to the executive function and speed variables in all four lobar models, but independent contributions of the frontal lobes were not significant. In contrast, when assessing the effect of white matter microstructure, cingulum FA made significant independent contributions to all three executive function and speed variables and corpus callosum FA was independently related to shifting/inhibition and speed. Findings from the current study indicate that while prefrontal grey matter volumes are significantly associated with cognitive neuroscience measures of shifting/inhibition and working memory in healthy older adults, they do not independently predict executive function when statistically isolated from global atrophy and individual non-frontal lobar volume contributions. In contrast, better microstructure of fronto-parietal white matter, namely the corpus callosum and cingulum, continued to predict executive functions after accounting for global grey matter atrophy. These findings contribute to a growing literature suggesting that prefrontal contributions to executive functions cannot be viewed in isolation from more distributed grey and white matter effects in a healthy older adult cohort.