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The brain’s connectome continually rewires throughout the life of an organism. In this study, we sought to elucidate the operational principles of such rewiring by analyzing the functional connectomes in mouse primary motor cortex (M1) during a 14-session (day) lever-press task learning in response to an auditory cue. Specifically, we employed Calcium imaging recordings of L2/3 and L5 of M1 in awake mice to reconstruct and analyze functional connectomes across learning sessions. Our results show that functional connectomes in L2/3 and L5 follow a similar learning-induced rewiring trajectory. More specifically, the connectomes rewire in a biphasic manner, where functional connectivity increases over the first few learning sessions, and then, it is gradually pruned to return to a homeostatic level of network density. We demonstrated that the increase of network connectivity in L2/3 connectomes, but not in L5, generates neuronal co-firing activity that correlates with higher motor performance (shorter cue-to-reward time), while motor performance remains relatively stable throughout the pruning phase. The results show a biphasic rewiring principle that involves the maximization of reward / performance and maintenance of network density. Finally, we demonstrated that the connectome rewiring in L2/3 is clustered around a core set of movement-associated neurons that form a highly interconnected hub in the connectomes, and that the activity of these core neurons stably encodes movement throughout learning.Significance StatementConnectomes in the motor cortex rewire during motor skill learning, but the operational principle behind this rewiring is yet to be determined. Here, we characterized the rewiring dynamics of functional connectomes in L2/3 and L5 of M1 in mice engaging in a lever-press learning, using two-photon fluorescence microscopy data. We identified a universal biphasic rewiring trajectory across animals and layers in the motor cortex that reflects two objectives: an exploratory phase that increases functional connectivity and optimizes motor performance, and a pruning phase that brings connectivity back to a homeostatic level while maintaining motor performance. We found further that connectome rewiring during motor skill learning concentrates around a core set of highly interconnected neurons in L2/3 that reliably encode movements.

Objective. Neural decoding is an important tool in neural engineering and neural data analysis. Of various machine learning algorithms adopted for neural decoding, the recently introduced deep learning is promising to excel. Therefore, we sought to apply deep learning to decode movement trajectories from the activity of motor cortical neurons. Approach. In this paper, we assessed the performance of deep learning methods in three different decoding schemes, concurrent, time-delay, and spatiotemporal. In the concurrent decoding scheme where the input to the network is the neural activity coincidental to the movement, deep learning networks including artificial neural network (ANN) and long-short term memory (LSTM) were applied to decode movement and compared with traditional machine learning algorithms. Both ANN and LSTM were further evaluated in the time-delay decoding scheme in which temporal delays are allowed between neural signals and movements. Lastly, in the spatiotemporal decoding scheme, we trained convolutional neural network (CNN) to extract movement information from images representing the spatial arrangement of neurons, their activity, and connectomes (i.e. the relative strengths of connectivity between neurons) and combined CNN and ANN to develop a hybrid spatiotemporal network. To reveal the input features of the CNN in the hybrid network that deep learning discovered for movement decoding, we performed a sensitivity analysis and identified specific regions in the spatial domain. Main results. Deep learning networks (ANN and LSTM) outperformed traditional machine learning algorithms in the concurrent decoding scheme. The results of ANN and LSTM in the time-delay decoding scheme showed that including neural data from time points preceding movement enabled decoders to perform more robustly when the temporal relationship between the neural activity and movement dynamically changes over time. In the spatiotemporal decoding scheme, the hybrid spatiotemporal network containing the concurrent ANN decoder outperformed single-network concurrent decoders. Significance. Taken together, our study demonstrates that deep learning could become a robust and effective method for the neural decoding of behavior.

In recent years, awareness about the risk of particulate matter (PM) has increased both domestically and internationally, consequently, various policies have been implemented to reduce PM. Since citizens are both victim and cause of this risk, PM cannot be successfully decreased only through government policies. Therefore, citizens&rsquo, active participation is required to reduce PM and prevent its risks. However, a theoretical model about public behavior against PM has not been established. Therefore, we suggest the public behavior model about individuals&rsquo, response against PM, in which response actions are classified into four types based on the combinations of the public-personal domains and mitigation-adaptation actions: personal or public adaptations, and personal or public mitigations. We analyze how risk perception, risk communication, blame attribution factors influence the four types of responses against PM. The analysis results reveal that the receiver&rsquo, s ability, negative emotion, trust in government, and age influence personal mitigation behavior, personal adaptation, public mitigation, and public adaptation, respectively. As this study demonstrates the differences in the factors influencing each type of response actions against PM, evidence-based policy is needed that considers the differences in these influencing factors.

Early menarche is known to be associated with diabetes, however this association remains controversial. Our study aimed to investigate the possible association between age at menarche and diabetes prevalence in post-menopausal Korean women. This study included 3,254 post-menopausal Korean women aged 50-85 years from the Korea National Health and Nutrition Examination Survey IV (KNHANES 2007-2009). Logistic regression analyses were used to estimate odds ratios (ORs) for diabetes prevalence. Levels of biochemical markers were compared according to groups by age at menarche. Women in the earlier menarche age group (10-12 years) showed higher levels of fasting blood glucose (FBG) and scores of homeostatic model assessment in the insulin resistance (HOMA-IR) index than other groups (p

This study was designed to examine the effects of bitter melon (BM) on the plasma blood glucose and lipid levels in diabetic rats. Diabetes mellitus was induced in male Sprague-Dawley rats through ...