Biology subjects

Long, B.

Publications and source records attributed to Long, B..

3 recordsLinked to original sources

bioRxiv2018

Conserved cell types with divergent features between human and mouse cortex

Elucidating the cellular architecture of the human neocortex is central to understanding our cognitive abilities and susceptibility to disease. Here we applied single nucleus RNA-sequencing to perform a comprehensive analysis of cell types in the middle temporal gyrus of human cerebral cortex. We identify a highly diverse set of excitatory and inhibitory neuronal types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to a similar mouse cortex single cell RNA-sequencing dataset revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of human cell type properties. Despite this general conservation, we also find extensive differences between homologous human and mouse cell types, including dramatic alterations in proportions, laminar distributions, gene expression, and morphology. These species-specific features emphasize the importance of directly studying human brain.

neuroscience↗

bioRxiv2017

A mid-level organization of the ventral stream

Human object-selective cortex shows a large-scale organization characterized by the high-level properties of both animacy and object-size. To what extent are these neural responses explained by primitive perceptual features that distinguish animals from objects and big objects from small objects? To address this question, we used a texture synthesis algorithm to create a novel class of stimuli--texforms--which preserve some mid-level texture and form information from objects while rendering them unrecognizable. We found that unrecognizable texforms were sufficient to elicit the large-scale organizations of object-selective cortex along the entire ventral pathway. Further, the structure in the neural patterns elicited by texforms was well predicted by curvature features and by intermediate layers of a deep convolutional neural network, supporting the mid-level nature of the representations. These results provide clear evidence that a substantial portion of ventral stream organization can be accounted for by coarse texture and form information, without requiring explicit recognition of intact objects.\n\nSIGNIFICANCE STATEMENTWhile neural responses to object categories are remarkably systematic across human visual cortex, the nature of these responses been hotly debated for the past 20 years. In this paper, a new class of stimuli (\"texforms\") is used to examine how mid-level features contribute to the large-scale organization of the ventral visual stream. Despite their relatively primitive visual appearance, these unrecognizable texforms elicited the entire large-scale organizations of the ventral stream by animacy and object size. This work demonstrates that much of ventral stream organization can be explained by relatively primitive mid-level features, without requiring explicit recognition of the objects themselves.

neuroscience↗

bioRxiv2017

SmartScope2: Simultaneous Imaging and Reconstruction of Neuronal Morphology

Quantitative analysis of neuronal morphology is critical in cell type classification and for deciphering how structure gives rise to function in the brain. Most current approaches to imaging and tracing neuronal 3D morphology are data intensive. We introduce SmartScope2, the first open source, automated neuron reconstruction machine integrating online image analysis with automated multiphoton imaging. SmartScope2 takes advantage of a neurons sparse morphology to improve imaging speed and reduce image data stored, transferred and analyzed. We show that SmartScope2 is able to produce the complex 3D morphology of human and mouse cortical neurons with six-fold reduction in image data requirements and three times the imaging speed compared to conventional methods.

bioinformatics↗