Manifold classification of neuron types from microscopic images.

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 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 9208 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 AND IMPLEMENTATION: Freely available at https://github.com/Mr-strlen/Cell_Pattern_Analysis_Tool.'

Non-homogenous axonal bouton distribution in whole-brain single-cell neuronal networks.

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 1,891 fully reconstructed neurons. We found that bouton locations were not homogeneous throughout the axon and 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.

Inter-brain coupling reflects disciplinary differences in 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.