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Astrocytes play crucial roles in brain homeostasis and are regulatory elements of neuronal and synaptic physiology. Astrocytic alterations have been found in Major Depressive Disorder (MDD) patients; however, the consequences of astrocyte Ca2+ signaling in MDD are poorly understood. Here, we found that corticosterone-treated mice (Cort-mice) showed altered astrocytic Ca2+ dynamics in mPFC both in resting conditions and during social interactions, in line with altered mice behavior. Additionally, Cort-mice displayed reduced serotonin (5-HT)-mediated Ca2+ signaling in mPFC astrocytes, and aberrant 5-HT-driven synaptic plasticity in layer 2/3 mPFC neurons. Downregulation of astrocyte Ca2+ signaling in naïve animals mimicked the synaptic deficits found in Cort-mice. Remarkably, boosting astrocyte Ca2+ signaling with Gq-DREADDS restored mood and cognitive deficits in Cort-mice to control levels. This study highlights the important role of astrocyte Ca2+ signaling for homeostatic control of brain circuits and behavior, but also reveals its potential therapeutic value for depressive-like states.

Background: Identifying the structured distribution (or lack thereof) of a given feature over a point cloud is a general research question. In the neuroscience field, this problem arises while investigating representations over neural manifolds (e.g., spatial coding), in the analysis of neurophysiological signals (e.g., auditory coding) or in anatomical image segmentation. New method: We introduce the Structure Index (SI) as a graph-based topological metric to quantify the distribution of feature values projected over data in arbitrary D-dimensional spaces (neurons, time stamps, pixels). The SI is defined from the overlapping distribution of data points sharing similar feature values in a given neighborhood. Results: Using model data clouds we show how the SI provides quantification of the degree of local versus global organization of feature distribution. SI can be applied to both scalar and vectorial features permitting quantification of the relative contribution of related variables. When applied to experimental studies of head-direction cells, it is able to retrieve consistent feature structure from both the high- and low-dimensional representations. Finally, we provide two general-purpose examples (sound and image categorization), to illustrate the potential application to arbitrary dimensional spaces. Comparison with existing methods: Most methods for quantifying structure depend on cluster analysis, which are suboptimal for continuous features and non-discrete data clouds. SI unbiasedly quantifies structure from continuous data in any dimensional space. Conclusions: The method provides versatile applications in the neuroscience and data science fields.

Sleep disturbances are common during aging. Compared to young animals, old mice show altered sleep structure, with changes in both slow and fast electrocorticographic (ECoG) activity and fewer transitions between sleep and wake stages. Insulin-like growth factor I (IGF-I), which is involved in adaptive changes during aging, was previously shown to increase ECoG activity in young mice and monkeys. Furthermore, IGF-I shapes sleep architecture by modulating the activity of mouse orexin neurons in the lateral hypothalamus (LH). We now report that both ECoG activation and excitation of orexin neurons by systemic IGF-I are abrogated in old mice. Moreover, orthodromical responses of LH neurons are facilitated by either systemic or local IGF-I in young mice, but not in old ones. As orexin neurons of old mice show dysregulated IGF-I receptor (IGF-IR) expression, suggesting disturbed IGF-I sensitivity, we treated old mice with AIK3a305, a novel IGF-IR sensitizer, and observed restored responses to IGF-I and rejuvenation of sleep patterns. Thus, disturbed sleep structure in aging mice may be related to impaired IGF-I signaling onto orexin neurons, reflecting a broader loss of IGF-I activity in the aged mouse brain., This work was funded by a grant from Ciberned and is part of the project SAF2016-76462 funded by MCIN/AEI/https://doi.org/10.13039/501100011033. J.A. ZegarraValdivia acknowledges the fnancial support of the National Council of Science, Technology and Technological Innovation (CONCYTEC, Perú) through the National Fund for Scientifc and Technological Development (FONDECYT, Perú). J. Fernandes received a post-doc fellowship from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP: # 2017/14742–0; # 2019/03368–5).