@article {68709, title = {BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.}, journal = {Nature Methods}, volume = {20}, year = {2023}, month = {2023 Jun 01}, abstract = {

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

}, issn = {1548-7105}, doi = {10.1038/s41592-023-01848-5}, author = {Manubens-Gil, Linus and Zhou, Zhi and Chen, Hanbo and Ramanathan, Arvind and Liu, Xiaoxiao and Liu, Yufeng and Bria, Alessandro and Gillette, Todd and Ruan, Zongcai and Yang, Jian and Radojevi{\'c}, Miroslav and Zhao, Ting and Cheng, Li and Qu, Lei and Liu, Siqi and Bouchard, Kristofer E and Gu, Lin and Cai, Weidong and Ji, Shuiwang and Roysam, Badrinath and Wang, Ching-Wei and Yu, Hongchuan and Sironi, Amos and Iascone, Daniel Maxim and Zhou, Jie and Bas, Erhan and Conde-Sousa, Eduardo and Aguiar, Paulo and Li, Xiang and Li, Yujie and Nanda, Sumit and Wang, Yuan and Muresan, Leila and Fua, Pascal and Ye, Bing and He, Hai-Yan and Staiger, Jochen F and Peter, Manuel and Cox, Daniel N and Simonneau, Michel and Oberlaender, Marcel and Jefferis, Gregory and Ito, Kei and Gonzalez-Bellido, Paloma and Kim, Jinhyun and Rubel, Edwin and Cline, Hollis T and Zeng, Hongkui and Nern, Aljoscha and Chiang, Ann-Shyn and Yao, Jianhua and Roskams, Jane and Livesey, Rick and Stevens, Janine and Liu, Tianming and Dang, Chinh and Guo, Yike and Zhong, Ning and Tourassi, Georgia and Hill, Sean and Hawrylycz, Michael and Koch, Christof and Meijering, Erik and Ascoli, Giorgio A and Peng, Hanchuan} } @article {66902, title = {Brain microvasculature has a common topology with local differences in geometry that match metabolic load.}, journal = {Neuron}, volume = {109}, year = {2021}, month = {2021 April 01}, pages = {1168}, abstract = {

The microvasculature underlies the supply networks that support neuronal activity within heterogeneous brain regions. What are common versus heterogeneous aspects of the connectivity, density, and orientation of capillary networks? To address this, we imaged, reconstructed, and analyzed the microvasculature connectome in whole adult mice brains with sub-micrometer resolution. Graph analysis revealed common network topology across the brain that leads to a shared structural robustness against the rarefaction of vessels. Geometrical analysis, based on anatomically accurate reconstructions, uncovered a scaling law that links length density, i.e., the length of vessel per volume, with tissue-to-vessel distances. We then derive a formula that connects regional differences in metabolism to differences in length density and, further, predicts a common value of maximum tissue oxygen tension across the brain. Last, the orientation of capillaries is weakly anisotropic with the exception of a few strongly anisotropic regions; this variation can impact the interpretation of fMRI data.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2021.02.006}, author = {Ji, Xiang and Ferreira, Tiago and Friedman, Beth and Liu, Rui and Liechty, Hannah and Bas, Erhan and Chandrashekar, Jayaram and Kleinfeld, David} } @article {65138, title = {Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain.}, journal = {Cell}, volume = {179}, year = {2019}, month = {2019 Sep 19}, pages = {268-81}, abstract = {

Neuronal\ cell\ types\ are the nodes of neural circuits that determine the flow of information within the\ brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of\ cell\ type and\ reveals\ how individual\ neurons\ route their output across the\ brain. Despite the importance of morphology, few\ projection\ neurons\ in the\ mouse\ brain\ have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the\ brain. We used this platform to reconstruct more than\ 1,000\ projection\ neurons\ in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed\ neurons\ constitute more than 85\ meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of\ projection\ neurons\ and suggest organizational principles of\ long-range\ connectivity.

}, issn = {00928674}, doi = {10.1016/j.cell.2019.07.042}, author = {Winnubst, Johan and Bas, Erhan and Ferreira, Tiago A. and Wu, Zhuhao and Economo, Michael N. and Edson, Patrick and Arthur, Ben J. and Bruns, Christopher and Rokicki, Konrad and Schauder, David and Olbris, Donald J. and Murphy, Sean D. and Ackerman, David G. and Arshadi, Cameron and Baldwin, Perry and Blake, Regina and Elsayed, Ahmad and Hasan, Mashtura and Ramirez, Daniel and Dos Santos, Bruno and Weldon, Monet and Zafar, Amina and Dudman, Joshua T. and Gerfen, Charles R. and Hantman, Adam W. and Korff, Wyatt and Sternson, Scott M. and Spruston, Nelson and Svoboda, Karel and Chandrashekar, Jayaram} } @article {64830, title = {Single-neuron axonal reconstruction: The search for a wiring diagram of the brain.}, journal = {The Journal of Comparative Neurology}, year = {2019}, month = {2019 Mar 12}, abstract = {

Reconstruction of the axonal projection patterns of single neurons has been an important tool for understanding both the diversity of cell types in the brain and the logic of information flow between brain regions. Innovative approaches now enable the complete reconstruction of axonal projection patterns of individual neurons with vastly increased throughput. Here we review how advances in genetic, imaging, and computational techniques have been exploited for axonal reconstruction. We also discuss how new innovations could enable the integration of genetic and physiological information with axonal morphology for producing a census of cell types in the mammalian brain at scale. This article is protected by copyright. All rights reserved.

}, issn = {1096-9861}, doi = {10.1002/cne.24674}, author = {Economo, Michael N and Winnubst, Johan and Bas, Erhan and Ferreira, Tiago A and Chandrashekar, Jayaram} } @article {49174, title = {Dissociable structural and functional hippocampal outputs via distinct subiculum cell classes.}, journal = {Cell}, volume = {173}, year = {2018}, month = {2018 Apr 10}, pages = {1280-92}, abstract = {

The mammalian hippocampus, comprised of serially connected subfields, participates in diverse behavioral and cognitive functions. It has been postulated that parallel circuitry embedded within hippocampal subfields may underlie such functional diversity. We sought to identify, delineate, and manipulate this putatively parallel architecture in the dorsal subiculum, the primary output subfield of the dorsal hippocampus. Population and single-cell RNA-seq revealed that the subiculum can be divided into two spatially adjacent subregions associated with prominent differences in pyramidal cell gene expression. Pyramidal cells occupying these two regions differed in their long-range inputs, local wiring, projection targets, and electrophysiological properties. Leveraging gene-expression differences across these regions, we use genetically restricted neuronal silencing to show that\ these regions differentially contribute to spatial working memory. This work provides a coherent molecular-, cellular-, circuit-, and behavioral-level demonstration that the hippocampus embeds structurally and functionally dissociable streams within its serial architecture.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2018.03.031}, author = {Cembrowski, Mark S and Phillips, Matthew G and DiLisio, Salvatore F and Shields, Brenda C and Winnubst, Johan and Chandrashekar, Jayaram and Bas, Erhan and Spruston, Nelson} } @article {49496, title = {Distinct descending motor cortex pathways and their roles in movement.}, journal = {Nature}, volume = {563}, year = {2018}, month = {2018 Nov}, pages = {79-84}, abstract = {

Activity in the motor cortex predicts movements, seconds before they are initiated. This preparatory activity has been observed across cortical layers, including in descending pyramidal tract neurons in layer 5. A key question is how preparatory activity is maintained without causing movement, and is ultimately converted to a motor command to trigger appropriate movements. Here, using single-cell transcriptional profiling and axonal reconstructions, we identify two types of pyramidal tract neuron. Both types project to several targets in the basal ganglia and brainstem. One type projects to thalamic regions that connect back to motor cortex; populations of these neurons produced early preparatory activity that persisted until the movement was initiated. The second type projects to motor centres in the medulla and mainly\ produced late preparatory activity and motor commands. These results indicate that two types of motor cortex output neurons have specialized roles in motor control.

}, issn = {1476-4687}, doi = {10.1038/s41586-018-0642-9}, author = {Economo, Michael N and Viswanathan, Sarada and Tasic, Bosiljka and Bas, Erhan and Winnubst, Johan and Menon, Vilas and Graybuck, Lucas T and Nguyen, Thuc Nghi and Smith, Kimberly A and Yao, Zizhen and Wang, Lihua and Gerfen, Charles R and Chandrashekar, Jayaram and Zeng, Hongkui and Looger, Loren L and Svoboda, Karel} } @article {6674, title = {Thalamocortical input onto layer 5 pyramidal neurons measured using quantitative large-scale array tomography.}, journal = {Frontiers in Neural Circuits}, volume = {7}, year = {2013}, month = {2013}, pages = {177}, abstract = {

The subcellular locations of synapses on pyramidal neurons strongly influences dendritic integration and synaptic plasticity. Despite this, there is little quantitative data on spatial distributions of specific types of synaptic input. Here we use array tomography (AT), a high-resolution optical microscopy method, to examine thalamocortical (TC) input onto layer 5 pyramidal neurons. We first verified the ability of AT to identify synapses using parallel electron microscopic analysis of TC synapses in layer 4. We then use large-scale array tomography (LSAT) to measure TC synapse distribution on L5 pyramidal neurons in a 1.00 {\texttimes} 0.83 {\texttimes} 0.21 mm(3) volume of mouse somatosensory cortex. We found that TC synapses primarily target basal dendrites in layer 5, but also make a considerable input to proximal apical dendrites in L4, consistent with previous work. Our analysis further suggests that TC inputs are biased toward certain branches and, within branches, synapses show significant clustering with an excess of TC synapse nearest neighbors within 5-15 μm compared to a random distribution. Thus, we show that AT is a sensitive and quantitative method to map specific types of synaptic input on the dendrites of entire neurons. We anticipate that this technique will be of wide utility for mapping functionally-relevant anatomical connectivity in neural circuits.

}, issn = {1662-5110}, doi = {10.3389/fncir.2013.00177}, author = {Rah, Jong-Cheol and Bas, Erhan and Colonell, Jennifer and Mishchenko, Yuriy and Karsh, Bill and Fetter, Richard D and Myers, Eugene W and Chklovskii, Dmitri B and Svoboda, Karel and Harris, Timothy D and Isaac, John T R} }