@article {69542, title = {Neural circuit basis of placebo pain relief.}, journal = {Nature}, year = {2024}, month = {2024 Jul 24}, abstract = {

Placebo effects are striking demonstrations of mind-body interactions . During pain perception, in the absence of any treatment, an expectation of pain relief can reduce the experience of pain, a phenomenon known as placebo analgesia . However, despite the strength of placebo effects and their impact on everyday human experience and failure of clinical trials for new therapeutics , the neural circuit basis of placebo effects has remained elusive. Here, we show that analgesia from the expectation of pain relief is mediated by rostral anterior cingulate cortex (rACC) neurons that project to the pontine nucleus (rACC{\textrightarrow}Pn), a pre-cerebellar nucleus with no established function in pain. We created a behavioral assay that generates placebo-like anticipatory pain relief in mice. In vivo calcium imaging of neural activity and electrophysiological recordings in brain slices showed that expectations of pain relief boost the activity of rACC{\textrightarrow}Pn neurons and potentiate neurotransmission in this pathway. Transcriptomic studies of Pn neurons revealed an abundance of opioid receptors, further suggesting a role in pain modulation. Inhibition of the rACC{\textrightarrow}Pn pathway disrupted placebo analgesia and decreased pain thresholds, whereas activation elicited analgesia in the absence of placebo conditioning. Finally, Purkinje cells exhibited activity patterns resembling those of rACC{\textrightarrow}Pn neurons during pain relief expectation, providing cellular-level evidence of a role for the cerebellum in cognitive pain modulation. These findings open the possibility of targeting this prefrontal cortico-ponto-cerebellar pathway with drugs or neurostimulation to treat pain.

}, issn = {1476-4687}, doi = {10.1038/s41586-024-07816-z}, author = {Chen, Chong and Niehaus, Jesse K and Dinc, Fatih and Huang, Karen L and Barnette, Alexander L and Tassou, Adrien and Shuster, S Andrew and Wang, Lihua and Lemire, Andrew and Menon, Vilas and Ritola, Kimberly and Hantman, Adam and Zeng, Hongkui and Schnitzer, Mark J and Scherrer, Gr{\'e}gory} } @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 {66008, title = {Survey of spiking in the mouse visual system reveals functional hierarchy.}, journal = {Nature}, volume = {592(7852):86-92}, year = {2021}, month = {2021 Jan 20}, pages = {86-92}, abstract = {

The anatomy of the mammalian visual system, from the retina to the neocortex, is organized hierarchically. However, direct observation of cellular-level functional interactions across this hierarchy is lacking due to the challenge of simultaneously recording activity across numerous regions. Here we describe a large, open dataset-part of the Allen Brain Observatory-that surveys spiking from tens of thousands of units in six cortical and two thalamic regions in the brains of mice responding to a battery of visual stimuli. Using cross-correlation analysis, we reveal that the organization of inter-area functional connectivity during visual stimulation mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas. We find that four classical hierarchical measures-response latency, receptive-field size, phase-locking to drifting gratings and response decay timescale-are all correlated with the hierarchy. Moreover, recordings obtained during a visual task reveal that the correlation between neural activity and behavioural choice also increases along the hierarchy. Our study provides a foundation for understanding coding and signal propagation across hierarchically organized cortical and thalamic visual areas.

}, issn = {1476-4687}, doi = {10.1038/s41586-020-03171-x}, author = {Siegle, Joshua H and Jia, Xiaoxuan and Durand, Severine and Gale, Sam and Bennett, Corbett and Graddis, Nile and Heller, Greggory and Ramirez, Tamina K and Choi, Hannah and Luviano, Jennifer A and Groblewski, Peter A and Ahmed, Ruweida and Arkhipov, Anton and Bernard, Amy and Billeh, Yazan N and Brown, Dillan and Buice, Michael A and Cain, Nicolas and Caldejon, Shiella and Casal, Linzy and Cho, Andrew and Chvilicek, Maggie and Cox, Timothy C and Dai, Kael and Denman, Daniel J and de Vries, Saskia E J and Dietzman, Roald and Esposito, Luke and Farrell, Colin and Feng, David and Galbraith, John and Garrett, Marina and Gelfand, Emily C and Hancock, Nicole and Harris, Julie A and Howard, Robert and Hu, Brian and Hytnen, Ross and Iyer, Ramakrishnan and Jessett, Erika and Johnson, Katelyn and Kato, India and Kiggins, Justin and Lambert, Sophie and Lecoq, Jerome and Ledochowitsch, Peter and Lee, Jung Hoon and Leon, Arielle and Li, Yang and Liang, Elizabeth and Long, Fuhui and Mace, Kyla and Melchior, Jose and Millman, Daniel and Mollenkopf, Tyler and Nayan, Chelsea and Ng, Lydia and Ngo, Kiet and Nguyen, Thuyahn and Nicovich, Philip R and North, Kat and Ocker, Gabriel Koch and Ollerenshaw, Doug and Oliver, Michael and Pachitariu, Marius and Perkins, Jed and Reding, Melissa and Reid, David and Robertson, Miranda and Ronellenfitch, Kara and Seid, Sam and Slaughterbeck, Cliff and Stoecklin, Michelle and Sullivan, David and Sutton, Ben and Swapp, Jackie and Thompson, Carol and Turner, Kristen and Wakeman, Wayne and Whitesell, Jennifer D and Williams, Derric and Williford, Ali and Young, Rob and Zeng, Hongkui and Naylor, Sarah and Phillips, John W and Reid, R Clay and Mihalas, Stefan and Olsen, Shawn R and Koch, Christof} } @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 {49495, title = {Shared and distinct transcriptomic cell types across neocortical areas.}, journal = {Nature}, volume = {563}, year = {2018}, month = {2018 Nov}, pages = {72-78}, abstract = {

The neocortex contains a multitude of cell types that are segregated into layers and functionally distinct areas. To investigate the diversity of cell types across the mouse neocortex, here we analysed 23,822 cells from two areas at distant poles of the mouse neocortex: the primary visual cortex and the anterior lateral motor cortex. We define 133 transcriptomic cell types by deep, single-cell RNA sequencing. Nearly all types of GABA (γ-aminobutyric acid)-containing neurons are shared across both areas, whereas most types of glutamatergic neurons were found in one of the two areas. By combining single-cell RNA sequencing and retrograde labelling, we match transcriptomic types of glutamatergic neurons to their long-range projection specificity. Our study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex.

}, issn = {1476-4687}, doi = {10.1038/s41586-018-0654-5}, author = {Tasic, Bosiljka and Yao, Zizhen and Graybuck, Lucas T and Smith, Kimberly A and Nguyen, Thuc Nghi and Bertagnolli, Darren and Goldy, Jeff and Garren, Emma and Economo, Michael N and Viswanathan, Sarada and Penn, Osnat and Bakken, Trygve and Menon, Vilas and Miller, Jeremy and Fong, Olivia and Hirokawa, Karla E and Lathia, Kanan and Rimorin, Christine and Tieu, Michael and Larsen, Rachael and Casper, Tamara and Barkan, Eliza and Kroll, Matthew and Parry, Sheana and Shapovalova, Nadiya V and Hirschstein, Daniel and Pendergraft, Julie and Sullivan, Heather A and Kim, Tae Kyung and Szafer, Aaron and Dee, Nick and Groblewski, Peter and Wickersham, Ian and Cetin, Ali and Harris, Julie A and Levi, Boaz P and Sunkin, Susan M and Madisen, Linda and Daigle, Tanya L and Looger, Loren and Bernard, Amy and Phillips, John and Lein, Ed and Hawrylycz, Michael and Svoboda, Karel and Jones, Allan R and Koch, Christof and Zeng, Hongkui} } @article {47894, title = {Adult mouse cortical cell taxonomy revealed by single cell transcriptomics.}, journal = {Nature neuroscience}, volume = {19}, year = {2016}, month = {2016 Feb}, pages = {335-46}, abstract = {

Nervous systems are composed of various cell types, but the extent of cell type diversity is poorly understood. We constructed a cellular taxonomy of one cortical region, primary visual cortex, in adult mice on the basis of single-cell RNA sequencing. We identified 49 transcriptomic cell types, including 23 GABAergic, 19 glutamatergic and 7 non-neuronal types. We also analyzed cell type-specific mRNA processing and characterized genetic access to these transcriptomic types by many transgenic Cre lines. Finally, we found that some of our transcriptomic cell types displayed specific and differential electrophysiological and axon projection properties, thereby confirming that the single-cell transcriptomic signatures can be associated with specific cellular properties.

}, keywords = {Animals, Cell Line, Cerebral Cortex, Classification, gamma-Aminobutyric Acid, Gene Library, Genetic Markers, Glutamic Acid, Interneurons, Male, Mice, Mice, Inbred C57BL, Mice, Transgenic, Neurons, RNA, Sequence Analysis, RNA, Transcriptome, Visual Cortex}, issn = {1546-1726}, doi = {10.1038/nn.4216}, author = {Tasic, Bosiljka and Menon, Vilas and Nguyen, Thuc Nghi and Kim, Tae Kyung and Jarsky, Tim and Yao, Zizhen and Levi, Boaz and Gray, Lucas T and Sorensen, Staci A and Dolbeare, Tim and Bertagnolli, Darren and Goldy, Jeff and Shapovalova, Nadiya and Parry, Sheana and Lee, Changkyu and Smith, Kimberly and Bernard, Amy and Madisen, Linda and Sunkin, Susan M and Hawrylycz, Michael and Koch, Christof and Zeng, Hongkui} } @article {47901, title = {Correlated gene expression and target specificity demonstrate excitatory projection neuron diversity.}, journal = {Cerebral Cortex (New York, N.Y. : 1991)}, volume = {25}, year = {2015}, month = {2015 Feb}, pages = {433-49}, abstract = {

The neocortex contains diverse populations of excitatory neurons segregated by layer and further definable by their specific cortical and subcortical projection targets. The current study describes a systematic approach to identify molecular correlates of specific projection neuron classes in mouse primary somatosensory cortex (S1), using a combination of in situ hybridization (ISH) data mining, marker gene colocalization, and combined retrograde labeling with ISH for layer-specific marker genes. First, we identified a large set of genes with specificity for each cortical layer, and that display heterogeneous patterns within those layers. Using these genes as markers, we find extensive evidence for the covariation of gene expression and projection target specificity in layer 2/3, 5, and 6, with individual genes labeling neurons projecting to specific subsets of target structures. The combination of gene expression and target specificity imply a great diversity of projection neuron classes that is similar to or greater than that of GABAergic interneurons. The covariance of these 2 phenotypic modalities suggests that these classes are both discrete and genetically specified.

}, keywords = {Animals, Atlases as Topic, Data Mining, Gene Expression, In Situ Hybridization, Fluorescence, Male, Mice, Mice, Inbred C57BL, Neural Pathways, Neuronal Tract-Tracers, Neurons, Somatosensory Cortex}, issn = {1460-2199}, doi = {10.1093/cercor/bht243}, author = {Sorensen, Staci A and Bernard, Amy and Menon, Vilas and Royall, Joshua J and Glattfelder, Katie J and Desta, Tsega and Hirokawa, Karla and Mortrud, Marty and Miller, Jeremy A and Zeng, Hongkui and Hohmann, John G and Jones, Allan R and Lein, Ed S} } @conference {47229, title = {Interactive exemplar-based segmentation toolkit for biomedical image analysis.}, booktitle = {International Symposium on Biomedical Imaging}, year = {2015}, month = {2015 Apr}, abstract = {

In the field of biomedical imaging analysis on single-cell level, reliable and fast segmentation of the cell nuclei from the background on three-dimensional images is highly needed for the further analysis. In this work we propose an interactive cell segmentation toolkit that first establishes a set of exemplar regions from user input through an easy and intuitive interface in both 2D and 3D in real-time, then
extracts the shape and intensity features from those exemplars. Based on a local contrast-constrained region growing scheme, each connected component in the whole image would be filtered by the features from exemplars, forming an {\textquotedblleft}exemplar-matching{\textquotedblright} group which passed the filtering and would be part of the final segmentation result, and a {\textquotedblleft}non-exemplar-matching{\textquotedblright} group in which components
would be further segmented by the gradient vector field based algorithm. The results of the filtering process are visualized back to the user in near real-time, thus enhancing the experience in exemplar selecting and parameter tuning. The toolkit is distributed as a plugin within the open source Vaa3D system (http://vaa3d.org).

}, author = {Li, Xiang and Zhou, Zhi and Keller, Philipp J and Zeng, Hongkui and Liu, Tianming and Peng, Hanchuan} } @article {45064, title = {Virtual finger boosts three-dimensional imaging and microsurgery as well as terabyte volume image visualization and analysis.}, journal = {Nature Communications}, volume = {5}, year = {2014}, month = {2014 Jul 11}, pages = {4342}, abstract = {

Three-dimensional (3D) bioimaging, visualization and data analysis are in strong need of powerful 3D exploration techniques. We develop virtual finger (VF) to generate 3D curves, points and regions-of-interest in the 3D space of a volumetric image with a single finger operation, such as a computer mouse stroke, or click or zoom from the 2D-projection plane of an image as visualized with a computer. VF provides efficient methods for acquisition, visualization and analysis of 3D images for roundworm, fruitfly, dragonfly, mouse, rat and human. Specifically, VF enables instant 3D optical zoom-in imaging, 3D free-form optical microsurgery, and 3D visualization and annotation of terabytes of whole-brain image volumes. VF also leads to orders of magnitude better efficiency of automated 3D reconstruction of neurons and similar biostructures over our previous systems. We use VF to generate from images of 1,107 Drosophila GAL4 lines a projectome of a Drosophila brain.

}, issn = {2041-1723}, doi = {10.1038/ncomms5342}, author = {Peng, Hanchuan and Tang, Jianyong and Xiao, Hang and Bria, Alessandro and Zhou, Jianlong and Butler, Victoria and Zhou, Zhi and Gonzalez-Bellido, Paloma T and Oh, Seung W and Chen, Jichao and Mitra, Ananya and Tsien, Richard W and Zeng, Hongkui and Ascoli, Giorgio A and Iannello, Giulio and Hawrylycz, Michael and Myers, Eugene and Long, Fuhui} } @article {4601, title = {A Cre-dependent GCaMP3 reporter mouse for neuronal imaging in vivo.}, journal = {The Journal of Neuroscience}, volume = {32}, year = {2012}, month = {2012 Feb 29}, pages = {3131-41}, abstract = {

Fluorescent calcium indicator proteins, such as GCaMP3, allow imaging of activity in genetically defined neuronal populations. GCaMP3 can be expressed using various gene delivery methods, such as viral infection or electroporation. However, these methods are invasive and provide inhomogeneous and nonstationary expression. Here, we developed a genetic reporter mouse, Ai38, which expresses GCaMP3 in a Cre-dependent manner from the ROSA26 locus, driven by a strong CAG promoter. Crossing Ai38 with appropriate Cre mice produced robust GCaMP3 expression in defined cell populations in the retina, cortex, and cerebellum. In the primary visual cortex, visually evoked GCaMP3 signals showed normal orientation and direction selectivity. GCaMP3 signals were rapid, compared with virally expressed GCaMP3 and synthetic calcium indicators. In the retina, Ai38 allowed imaging spontaneous calcium waves in starburst amacrine cells during development, and light-evoked responses in ganglion cells in adult tissue. Our results show that the Ai38 reporter mouse provides a flexible method for targeted expression of GCaMP3.

}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.4469-11.2012}, author = {Zariwala, Hatim A and Borghuis, Bart G and Hoogland, Tycho M and Madisen, Linda and Tian, Lin and De Zeeuw, Chris I and Zeng, Hongkui and Looger, Loren L and Svoboda, Karel and Chen, Tsai-Wen} } @article {6301, title = {A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing.}, journal = {Nature Neuroscience}, volume = {15}, year = {2012}, month = {2012 May}, pages = {793-802}, abstract = {

Cell type-specific expression of optogenetic molecules allows temporally precise manipulation of targeted neuronal activity. Here we present a toolbox of four knock-in mouse lines engineered for strong, Cre-dependent expression of channelrhodopsins ChR2-tdTomato and ChR2-EYFP, halorhodopsin eNpHR3.0 and archaerhodopsin Arch-ER2. All four transgenes mediated Cre-dependent, robust activation or silencing of cortical pyramidal neurons in vitro and in vivo upon light stimulation, with ChR2-EYFP and Arch-ER2 demonstrating light sensitivity approaching that of in utero or virally transduced neurons. We further show specific photoactivation of parvalbumin-positive interneurons in behaving ChR2-EYFP reporter mice. The robust, consistent and inducible nature of our ChR2 mice represents a significant advance over previous lines, and the Arch-ER2 and eNpHR3.0 mice are to our knowledge the first demonstration of successful conditional transgenic optogenetic silencing. When combined with the hundreds of available Cre driver lines, this optimized toolbox of reporter mice will enable widespread investigations of neural circuit function with unprecedented reliability and accuracy.

}, keywords = {Action Potentials, Animals, Archaeal Proteins, Brain, Electroporation, Halorhodopsins, Integrases, Light, Luminescent Proteins, Mice, Neurogenesis, Neurons, Optics and Photonics, Proteins, Rhodopsin, Transgenic, Wakefulness}, issn = {1546-1726}, doi = {10.1038/nn.3078}, author = {Madisen, Linda and Mao, Tianyi and Koch, Henner and Zhuo, Jia-min and Berenyi, Antal and Fujisawa, Shigeyoshi and Hsu, Yun-Wei A and Garcia, Alfredo J and Gu, Xuan and Zanella, Sebastien and Kidney, Jolene and Gu, Hong and Mao, Yimei and Hooks, Bryan M and Boyden, Edward S and Buzs{\'a}ki, Gy{\"o}rgy and Ramirez, Jan Marino and Jones, Allan R and Svoboda, Karel and Han, Xue and Turner, Eric E and Zeng, Hongkui} }