Decoupling astrocytes in adult mice impairs synaptic plasticity and spatial learning.

The mechanisms by which astrocytes modulate neural homeostasis, synaptic plasticity, and memory are still poorly explored. Astrocytes form large intercellular networks by gap junction coupling, mainly composed of two gap junction channel proteins, connexin 30 (Cx30) and connexin 43 (Cx43). To circumvent developmental perturbations and to test whether astrocytic gap junction coupling is required for hippocampal neural circuit function and behavior, we generate and study inducible, astrocyte-specific Cx30 and Cx43 double knockouts. Surprisingly, disrupting astrocytic coupling in adult mice results in broad activation of astrocytes and microglia, without obvious signs of pathology. We show that hippocampal CA1 neuron excitability, excitatory synaptic transmission, and long-term potentiation are significantly affected. Moreover, behavioral inspection reveals deficits in sensorimotor performance and a complete lack of spatial learning and memory. Together, our findings establish that astrocytic connexins and an intact astroglial network in the adult brain are vital for neural homeostasis, plasticity, and spatial cognition.

Deep learning-based behavioral analysis reaches human accuracy and is capable of outperforming commercial solutions.

To study brain function, preclinical research heavily relies on animal monitoring and the subsequent analyses of behavior. Commercial platforms have enabled semi high-throughput behavioral analyses by automating animal tracking, yet they poorly recognize ethologically relevant behaviors and lack the flexibility to be employed in variable testing environments. Critical advances based on deep-learning and machine vision over the last couple of years now enable markerless tracking of individual body parts of freely moving rodents with high precision. Here, we compare the performance of commercially available platforms (EthoVision XT14, Noldus; TSE Multi-Conditioning System, TSE Systems) to cross-verified human annotation. We provide a set of videos-carefully annotated by several human raters-of three widely used behavioral tests (open field test, elevated plus maze, forced swim test). Using these data, we then deployed the pose estimation software DeepLabCut to extract skeletal mouse representations. Using simple post-analyses, we were able to track animals based on their skeletal representation in a range of classic behavioral tests at similar or greater accuracy than commercial behavioral tracking systems. We then developed supervised machine learning classifiers that integrate the skeletal representation with the manual annotations. This new combined approach allows us to score ethologically relevant behaviors with similar accuracy to humans, the current gold standard, while outperforming commercial solutions. Finally, we show that the resulting machine learning approach eliminates variation both within and between human annotators. In summary, our approach helps to improve the quality and accuracy of behavioral data, while outperforming commercial systems at a fraction of the cost.