Complete Whole-Brain Single Neuron Reconstruction Reveals Morphological Diversity in Molecularly Defined Claustral and Cortical Neuron Types

2019 ◽  
Author(s):  
Yun Wang ◽  
Peng Xie ◽  
Hui Gong ◽  
Zhi Zhou ◽  
Xiuli Kuang ◽  
...  
Keyword(s):  
Cortical Neuron ◽  
Single Neuron ◽  
Morphological Diversity ◽  
Whole Brain ◽  
Neuron Reconstruction

scholarly journals Brain-wide single neuron reconstruction reveals morphological diversity in molecularly defined striatal, thalamic, cortical and claustral neuron types

2020 ◽  
Author(s):  
Hanchuan Peng ◽  
Peng Xie ◽  
Lijuan Liu ◽  
Xiuli Kuang ◽  
Yimin Wang ◽  
...  
Keyword(s):  
Single Neuron ◽  
Morphological Diversity ◽  
Cell Types ◽  
Fluorescent Labeling ◽  
Computer Assisted ◽  
Large Set ◽  
Whole Brain ◽  
Wide Range ◽  
Neuronal Types ◽  
Organizational Rules

Abstract Ever since the seminal findings of Ramon y Cajal, dendritic and axonal morphology has been recognized as a defining feature of neuronal types. Yet our knowledge concerning the diversity of neuronal morphologies, in particular distal axonal projection patterns, is extremely limited. To systematically obtain single neuron full morphology on a brain-wide scale, we established a platform with five major components: sparse labeling, whole-brain imaging, reconstruction, registration, and classification. We achieved sparse, robust and consistent fluorescent labeling of a wide range of neuronal types by combining transgenic or viral Cre delivery with novel transgenic reporter lines. We acquired high-resolution whole-brain fluorescent images from a large set of sparsely labeled brains using fluorescence micro-optical sectioning tomography (fMOST). We developed a set of software tools for efficient large-volume image data processing, registration to the Allen Mouse Brain Common Coordinate Framework (CCF), and computer-assisted morphological reconstruction. We reconstructed and analyzed the complete morphologies of 1,708 neurons from the striatum, thalamus, cortex and claustrum. Finally, we classified these cells into multiple morphological and projection types and identified a set of region-specific organizational rules of long-range axonal projections at the single cell level. Specifically, different neuron types from different regions follow highly distinct rules in convergent or divergent projection, feedforward or feedback axon termination patterns, and between-cell homogeneity or heterogeneity. Major molecularly defined classes or types of neurons have correspondingly distinct morphological and projection patterns, however, we also identify further remarkably extensive morphological and projection diversity at more fine-grained levels within the major types that cannot presently be accounted for by preexisting transcriptomic subtypes. These insights reinforce the importance of full morphological characterization of brain cell types and suggest a plethora of ways different cell types and individual neurons may contribute to the function of their respective circuits.

scholarly journals Brain-wide single neuron reconstruction reveals morphological diversity in molecularly defined striatal, thalamic, cortical and claustral neuron types

2019 ◽  
Author(s):  
Hanchuan Peng ◽  
Peng Xie ◽  
Lijuan Liu ◽  
Xiuli Kuang ◽  
Yimin Wang ◽  
...  
Keyword(s):  
Single Neuron ◽  
Morphological Diversity ◽  
Cell Types ◽  
Fluorescent Labeling ◽  
Computer Assisted ◽  
Large Set ◽  
Whole Brain ◽  
Wide Range ◽  
Neuronal Types ◽  
Organizational Rules

ABSTRACTEver since the seminal findings of Ramon y Cajal, dendritic and axonal morphology has been recognized as a defining feature of neuronal types. Yet our knowledge concerning the diversity of neuronal morphologies, in particular distal axonal projection patterns, is extremely limited. To systematically obtain single neuron full morphology on a brain-wide scale, we established a platform with five major components: sparse labeling, whole-brain imaging, reconstruction, registration, and classification. We achieved sparse, robust and consistent fluorescent labeling of a wide range of neuronal types by combining transgenic or viral Cre delivery with novel transgenic reporter lines. We acquired high-resolution whole-brain fluorescent images from a large set of sparsely labeled brains using fluorescence micro-optical sectioning tomography (fMOST). We developed a set of software tools for efficient large-volume image data processing, registration to the Allen Mouse Brain Common Coordinate Framework (CCF), and computer-assisted morphological reconstruction. We reconstructed and analyzed the complete morphologies of 1,708 neurons from the striatum, thalamus, cortex and claustrum. Finally, we classified these cells into multiple morphological and projection types and identified a set of region-specific organizational rules of long-range axonal projections at the single cell level. Specifically, different neuron types from different regions follow highly distinct rules in convergent or divergent projection, feedforward or feedback axon termination patterns, and between-cell homogeneity or heterogeneity. Major molecularly defined classes or types of neurons have correspondingly distinct morphological and projection patterns, however, we also identify further remarkably extensive morphological and projection diversity at more fine-grained levels within the major types that cannot presently be accounted for by preexisting transcriptomic subtypes. These insights reinforce the importance of full morphological characterization of brain cell types and suggest a plethora of ways different cell types and individual neurons may contribute to the function of their respective circuits.

Automatic 3D Single Neuron Reconstruction with Exhaustive Tracing

2017 ◽  
Author(s):  
Zihao Tang ◽  
Donghao Zhang ◽  
Siqi Liu ◽  
Yang Song ◽  
Hanchuan Peng ◽  
...  
Keyword(s):  
Single Neuron ◽  
Neuron Reconstruction

Variability in somatosensory cortical neuron discharge: effects on capacity to signal different stimulus conditions using a mean rate code

1978 ◽  
Vol 41 (2) ◽  
pp. 338-349 ◽  
Author(s):  
R. C. Schreiner ◽  
G. K. Essick ◽  
B. L. Whitsel
Keyword(s):  
Firing Rate ◽  
Linear Equation ◽  
Cortical Neuron ◽  
Single Neuron ◽  
Rate Code ◽  
Firing Rates ◽  
Discrimination Criterion ◽  
The Mean ◽  
Change In Mean ◽  
The Relationship

1. The present study is based on the demonstration (8, 9) that the relationship between mean interval (MI) and standard deviation (SD) for stimulus-driven activity recorded from SI neurons is well fitted by the linear equation SD = a X MI + b and on the observations that the values of the slope (a) and y intercept (b) parameters of this relationship are independent of stimulus conditions and may vary widely from one neuron to the next (8). 2. A criterion for the discriminability of two different mean firing rates requiring that the mean intervals of their respective interspike interval (ISI) distributions be separated by a fixed interval (expressed in SD units) is developed and, on the basis of this criterion, a graphical display of the capacity of a neuron with a known SD-MI relationship to reflect a change in stimulus conditions with a change in mean firing rate is derived. Using this graphical approach, it is shown that the parameters of the SD-MI relationship for a single neuron determine a range of firing frequencies, within which that neuron exhibits the greatest capacity to signal differences in stimulus conditions using a frequency code. 3. The discrimination criterion is modified to incorporate the changes in the symmetry of the ISI distribution observed to accompany changes in mean firing rate. It is shown that, although the observed symmetry changes do influence the capacity of a cortical neuron to signal a change in stimulus conditions with a change in mean firing rate, they do not alter the range of firing rates (determined by the parameters of the SD-MI relationship) within which the capacity for discrimination is maximal. 4. The maximal number of firing levels that can be distinguished by a somatosensory cortical neuron (using the same discrimination criterion described above) discharging within a specified range of mean frequencies also is demonstrated to depend on the parameters of the linear equation which relates SD to MI. 5. Two approaches based on the t test for differences between two means are developed in an attempt to ascertain the minimum separation of the mean intervals of the ISI distributions necessary for two different mean firing rates to be discriminated with 80% certainty.

scholarly journals Cellular anatomy of the mouse primary motor cortex

Nature ◽  
2021 ◽  
Vol 598 (7879) ◽  
pp. 159-166
Author(s):  
Rodrigo Muñoz-Castañeda ◽  
Brian Zingg ◽  
Katherine S. Matho ◽  
Xiaoyin Chen ◽  
Quanxin Wang ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Brain Function ◽  
Single Neuron ◽  
Primary Motor Cortex ◽  
Projection Neuron ◽  
Mammalian Brain ◽  
Neuron Reconstruction ◽  
Multi Scale ◽  
Structural Framework ◽  
Cellular Resolution

AbstractAn essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input–output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture.

Nested U-Net Architecture Based Image Segmentation for 3D Neuron Reconstruction

2021 ◽  
Vol 11 (5) ◽  
pp. 1348-1356
Author(s):  
Jian Yang ◽  
Yong Zhang ◽  
Yuanlin Yu ◽  
Ning Zhong
Keyword(s):  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Scale Space ◽  
Neuronal Morphology ◽  
Reconstruction Algorithms ◽  
Working Mechanism ◽  
Neuron Morphology ◽  
Whole Brain ◽  
Neuron Reconstruction ◽  
Digital Reconstruction

Digital reconstruction of neurons is a critical step in studying neuronal morphology and exploring the working mechanism of the brain. In recent years, the focus of neuronal morphology reconstruction has gradually shifted from single neurons to multiple neurons in a whole brain. Microscopic images of a whole brain often have low signal-to-noise-ratio, discontinuous neuron fragments or weak neuron signals. It is very difficult to segment neuronal signals from the background of these images, which is the first step of most automatic reconstruction algorithms. In this study, we propose a Nested U-Net based Ultra-Tracer model (NUNU-Tracer) for better multiple neurons image segmentation and morphology reconstruction. The NUNU-Tracer utilizes nested U-Net (UNet++) deep network to segment 3D neuron images, reconstructs neuron morphologies under the framework of the Ultra-Tracer and prunes branches of noncurrent tracing neurons. The 3D UNet++ takes a 3D microscopic image as its input, and uses scale-space distance transform and linear fusion strategy to generate the segmentation maps for voxels in the image. It is capable of removing noise, repairing broken neurite patterns and enhancing neuronal signals. We evaluate the performance of the 3D UNet++ for image segmentation and NUNU-Tracer for neuron morphology reconstruction on image blocks and neurons, respectively. Experimental results show that they significantly improve the accuracy and length of neuron reconstructions.

scholarly journals Cross-Modality Coherent Registration of Whole Mouse Brains

2021 ◽  
Author(s):  
Hanchuan Peng ◽  
Lei Qu ◽  
Yuanyuan Li ◽  
Peng Xie ◽  
Lijuan Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Single Neuron ◽  
Cell Types ◽  
Brain Anatomy ◽  
Neuron Morphology ◽  
Brain Images ◽  
Whole Brain ◽  
Preparation Methods ◽  
Registration Method ◽  
3D Images

Abstract Recent whole brain mapping projects are collecting large-scale 3D images using powerful and informative modalities, such as STPT, fMOST, VISoR, or MRI. Registration of these multi-dimensional whole-brain images onto a standard atlas is essential for characterizing neuron types and constructing brain wiring diagrams. However, cross-modality image registration is challenging due to intrinsic variations of brain anatomy and artifacts resulted from different sample preparation methods and imaging modalities. We introduced a cross-modality registration method, called mBrainAligner, which uses coherent landmark mapping as well as deep neural networks to align whole mouse brain images to the standard Allen Common Coordinate Framework atlas. We also built a single cell resolution atlas using the fMOST modality, and used our method to generate whole brain map of 3D full single neuron morphology and neuron cell types.

scholarly journals Deep-learning-based whole-brain imaging at single-neuron resolution

2020 ◽  
Vol 11 (7) ◽  
pp. 3567
Author(s):  
Kefu Ning ◽  
Xiaoyu Zhang ◽  
Xuefei Gao ◽  
Tao Jiang ◽  
He Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Imaging ◽  
Single Neuron ◽  
Whole Brain
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