Whole-brain connections of glutamatergic neurons in the mouse lateral habenula in both sexes

BACKGROUND

The lateral habenula (LHb) is an epithalamus nucleus that is evolutionarily conserved and involved in various physiological functions, such as encoding value signals, integrating emotional information, and regulating related behaviors. The cells in the LHb are predominantly glutamatergic and have heterogeneous functions in response to different stimuli. The circuitry connections of the LHb glutamatergic neurons play a crucial role in integrating a wide range of events. However, the circuitry connections of LHb glutamatergic neurons in both sexes have not been thoroughly investigated.

METHODS

In this study, we injected Cre-dependent retrograde trace virus and anterograde synaptophysin-labeling virus into the LHb of adult male and female Vglut2-ires-Cre mice, respectively. We then quantitatively analyzed the input and output of the LHb glutamatergic connections in both the ipsilateral and contralateral whole brain.

RESULTS

Our findings showed that the inputs to LHbvGlut2 neurons come from more than 30 brain subregions, including the cortex, striatum, pallidum, thalamus, hypothalamus, midbrain, pons, medulla, and cerebellum with no significant differences between males and females. The outputs of LHbvGlut2 neurons targeted eight large brain regions, primarily focusing on the midbrain and pons nuclei, with distinct features in presynaptic bouton across different brain subregions. While correlation and cluster analysis revealed differences in input and collateral projection features, the input-output connection pattern of LHbvGlut2 neurons in both sexes was highly similar.

CONCLUSIONS

This study provides a systematic and comprehensive analysis of the input and output connections of LHbvGlut2 neurons in male and female mice, shedding light on the anatomical architecture of these specific cell types in the mouse LHb. This structural understanding can help guide further investigations into the complex functions of the LHb.

Augmented Multicenter Graph Convolutional Network for COVID-19 Diagnosis

Chest computed tomography (CT) scans of coronavirus 2019 (COVID-19) disease usually come from multiple datasets gathered from different medical centers, and these images are sampled using different acquisition protocols. While integrating multicenter datasets increases sample size, it suffers from inter-center heterogeneity. To address this issue, we propose an augmented multicenter graph convolutional network (AM-GCN) to diagnose COVID-19 with steps as follows. First, we use a 3-D convolutional neural network to extract features from the initial CT scans, where a ghost module and a multitask framework are integrated to improve the network's performance. Second, we exploit the extracted features to construct a multicenter graph, which considers the intercenter heterogeneity and the disease status of training samples. Third, we propose an augmentation mechanism to augment training samples which forms an augmented multicenter graph. Finally, the diagnosis results are obtained by inputting the augmented multi-center graph into GCN. Based on 2223 COVID-19 subjects and 2221 normal controls from seven medical centers, our method has achieved a mean accuracy of 97.76%. The code for our model is made publicly.1.