While it is widely acknowledged that different disciplines are associated with distinct characteristics that would require different learning strategies and approaches, how the disciplinary differences are manifested in students' daily learning process remain largely underexplored. The present study investigated the disciplinary differences from the framework of hard and soft disciplines, by recording thirty-four high-school students' EEGs simultaneously in real-classroom settings during their regular Chinese (a soft discipline) and Math (a hard discipline) courses across a whole semester. Inter-brain coupling analysis was conducted to identify the neural correlates of successful learning for different disciplines. One's theta-band inter-brain coupling to all their classmates during both the Chinese and the Math sessions was found to be positively correlated with their final exam scores of Math, while one's alpha-band inter-brain coupling to their excellent peers was positively correlated with their final exam scores of Chinese. These results demonstrated the value of inter-brain coupling for reflecting students' learning outcomes for both hard and soft disciplines and highlighted excellent peers as a good candidate to represent a successful learning process for soft disciplines. Our findings provided a piece of neuroscience evidence towards the understanding of disciplinary differences during real-world classroom learning.

The classroom is the primary site for learning. A vital feature of classroom learning is the division of educational content into various disciplines. While disciplinary differences could substantially influence the learning process toward success, little is known about the neural mechanism underlying successful disciplinary learning. In the present study, wearable EEG devices were used to record a group of high school students during their classes of a soft (Chinese) and a hard (Math) discipline throughout one semester. Inter-brain coupling analysis was conducted to characterize students’ classroom learning process. The students with higher scores in the Math final exam were found to have stronger inter-brain couplings to the class (i.e., all the other classmates), whereas the students with higher scores in Chinese were found to have stronger inter-brain couplings to the top students in the class. These differences in inter-brain couplings were also reflected in distinct dominant frequencies for the two disciplines. Our results illustrate disciplinary differences in the classroom learning from an inter-brain perspective, suggesting that an individual’s inter-brain coupling to the class and to the top students could serve as potential neural correlates for successful learning in hard and soft disciplines correspondingly.

Analysis of cell types is recognized as a major task in current single cell genotyping and phenotyping. In neuroscience, 3-D neuron morphologies are often reconstructed from multi-dimensional microscopic images. Recent studies indicate that neurons could form very complicated distributions in the feature space, and thus they can be explored using manifold analysis. We have developed manifold-classification toolkit (MCT) software to replace the conventional clustering analysis to discover cell subtypes from three state-of-the-art collections of single neurons’ 3-D morphologies that reconstructed from images. We have gathered 9,208 3-D spatially registered whole mouse brain neurons from three datasets with the highest quality to date generated by the single neuron morphology community. To explore manifold distribution, our method uses minimum spanning tree based principal skeletons to approximate locally linear embeddings, to explore the morphological feature spaces, which correspond to dendritic arbors, axonal arbors, or both categories of arborization patterns of neurons. We show manifold classification is a suitable approach for a majority of often referred cell types, each of which was also discovered to contain multiple subtypes. Our results show an initial effort to employ manifold-classification but not traditional clustering analysis as an alternative framework for analyzing 3-D neuron morphologies reconstructed from 3-D microscopic images. Availability Freely available at https://github.com/Mr-strlen/Cell_Pattern_Analysis_Tool.

We examined the distribution of pre-synaptic contacts in axons of mouse neurons and constructed whole-brain single-cell neuronal networks using an extensive dataset of 1891 fully reconstructed neurons. We found that bouton locations were not homogeneous throughout the axon and also among brain regions. As our algorithm was able to generate whole-brain single-cell connectivity matrices from full morphology reconstruction datasets, we further found that non-homogeneous bouton locations have a significant impact on network wiring, including degree distribution, triad census and community structure. By perturbing neuronal morphology, we further explored the link between anatomical details and network topology. In our in silico exploration, we found that dendritic and axonal tree span would have the greatest impact on network wiring, followed by synaptic contact deletion. Our results suggest that neuroanatomical details must be carefully addressed in studies of whole brain networks at the single cell level.