@article {68379, title = {The Neurodata Without Borders ecosystem for neurophysiological data science.}, journal = {eLife}, volume = {11}, year = {2022}, month = {2022 Oct 04}, abstract = {

The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB{\textquoteright}s impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.

}, keywords = {Data Science, Ecosystem, Humans, Metadata, Neurophysiology, Software}, issn = {2050-084X}, doi = {10.7554/eLife.78362}, author = {Rubel, Oliver and Tritt, Andrew and Ly, Ryan and Dichter, Benjamin K and Ghosh, Satrajit and Niu, Lawrence and Baker, Pamela and Soltesz, Ivan and Ng, Lydia and Svoboda, Karel and Frank, Loren and Bouchard, Kristofer E} } @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 {59912, title = {NWB:N 2.0: An accessible data standard for neurophysiology.}, journal = {bioRxiv}, year = {2019}, month = {2019 Jan 17}, abstract = {

Neurodata Without Borders: Neurophysiology (NWB:N) is a data standard for neurophysiology, providing neuroscientists with a common standard to share, archive, use, and build common analysis tools for neurophysiology data. With NWB:N version 2.0 (NWB:N 2.0) we made significant advances towards creating a usable standard, software ecosystem, and vibrant community for standardizing neurophysiology data. In this manuscript we focus in particular on the NWB:N data standard schema and present advances towards creating an accessible data standard for neurophysiology.

}, doi = {10.1101/523035}, author = {Rubel, Oliver and Tritt, Andrew and Dichter, Benjamin and Braun, Thomas and Cain, Nicholas and Clack, Nathan G and Davidson, Thomas J and Dougherty, Max and Fillion-Rubin, Jean-Christophe and Graddis, Nile and Grauer, Michael and Kiggins, Justin T and Niu, Lawrence and Ozturk, Doruk and Schroeder, William and Soltesz, Ivan and Sommer, Friedrich T and Svoboda, Karel and Ng, Lydia and et al} } @article {47895, title = {Canonical genetic signatures of the adult human brain.}, journal = {Nature neuroscience}, volume = {18}, year = {2015}, month = {2015 Dec}, pages = {1832-44}, abstract = {

The structure and function of the human brain are highly stereotyped, implying a conserved molecular program responsible for its development, cellular structure and function. We applied a correlation-based metric called differential stability to assess reproducibility of gene expression patterning across 132 structures in six individual brains, revealing mesoscale genetic organization. The genes with the highest differential stability are highly biologically relevant, with enrichment for brain-related annotations, disease associations, drug targets and literature citations. Using genes with high differential stability, we identified 32 anatomically diverse and reproducible gene expression signatures, which represent distinct cell types, intracellular components and/or associations with neurodevelopmental and neurodegenerative disorders. Genes in neuron-associated compared to non-neuronal networks showed higher preservation between human and mouse; however, many diversely patterned genes displayed marked shifts in regulation between species. Finally, highly consistent transcriptional architecture in neocortex is correlated with resting state functional connectivity, suggesting a link between conserved gene expression and functionally relevant circuitry.

}, keywords = {Adult, Animals, Brain, Gene Regulatory Networks, Humans, Mice, Nerve Net, Transcriptome}, issn = {1546-1726}, doi = {10.1038/nn.4171}, author = {Hawrylycz, Michael and Miller, Jeremy A and Menon, Vilas and Feng, David and Dolbeare, Tim and Guillozet-Bongaarts, Angela L and Jegga, Anil G and Aronow, Bruce J and Lee, Chang-Kyu and Bernard, Amy and Glasser, Matthew F and Dierker, Donna L and Menche, J{\"o}rg and Szafer, Aaron and Collman, Forrest and Grange, Pascal and Berman, Kenneth A and Mihalas, Stefan and Yao, Zizhen and Stewart, Lance and Barab{\'a}si, Albert-L{\'a}szl{\'o} and Schulkin, Jay and Phillips, John and Ng, Lydia and Dang, Chinh and Haynor, David R and Jones, Allan and Van Essen, David C and Koch, Christof and Lein, Ed} } @article {46326, title = {Mapping social behavior-induced brain activation at cellular resolution in the mouse.}, journal = {Cell Reports}, volume = {10}, year = {2015}, month = {2015 Jan 13}, pages = {292-305}, abstract = {

Understanding how brain activation mediates behaviors is a central goal of systems neuroscience. Here, we apply an automated method for mapping brain activation in the mouse in order to probe how sex-specific social behaviors are represented in the male brain. Our method uses the immediate-early-gene c-fos, a marker of neuronal activation, visualized by serial two-photon tomography: the c-fos-GFP+ neurons are computationally detected, their distribution is registered to a reference brain and a brain atlas, and their numbers are analyzed by statistical tests. Our results reveal distinct and shared female and male interaction-evoked patterns of male brain activation representing sex discrimination and social recognition. We also identify brain regions whose degree of activity correlates to specific features of social behaviors and estimate the total numbers and the densities of activated neurons per brain areas. Our study opens the door to automated screening of behavior-evoked brain activation in the mouse.

}, issn = {2211-1247}, doi = {10.1016/j.celrep.2014.12.014}, author = {Kim, Yongsoo and Venkataraju, Kannan Umadevi and Pradhan, Kith and Mende, Carolin and Taranda, Julian and Turaga, Srinivas C and Arganda-Carreras, Ignacio and Ng, Lydia and Hawrylycz, Michael J and Rockland, Kathleen S and Seung, H Sebastian and Osten, Pavel} } @article {6952, title = {Cell-type-based model explaining coexpression patterns of genes in the brain.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {111}, year = {2014}, month = {2014 Apr 8}, pages = {5397-402}, abstract = {

Spatial patterns of gene expression in the vertebrate brain are not independent, as pairs of genes can exhibit complex patterns of coexpression. Two genes may be similarly expressed in one region, but differentially expressed in other regions. These correlations have been studied quantitatively, particularly for the Allen Atlas of the adult mouse brain, but their biological meaning remains obscure. We propose a simple model of the coexpression patterns in terms of spatial distributions of underlying cell types and establish its plausibility using independently measured cell-type-specific transcriptomes. The model allows us to predict the spatial distribution of cell types in the mouse brain.

}, issn = {1091-6490}, doi = {10.1073/pnas.1312098111}, author = {Grange, Pascal and Bohland, Jason W and Okaty, Benjamin W and Sugino, Ken and Bokil, Hemant and Nelson, Sacha B and Ng, Lydia and Hawrylycz, Michael and Mitra, Partha P} } @article {47897, title = {A high-resolution spatiotemporal atlas of gene expression of the developing mouse brain.}, journal = {Neuron}, volume = {83}, year = {2014}, month = {2014 Jul 16}, pages = {309-23}, abstract = {

To provide a temporal framework for the genoarchitecture of brain development, we generated in situ hybridization data for embryonic and postnatal mouse brain at seven developmental stages for \~{}2,100 genes, which were processed with an automated informatics pipeline and manually annotated. This resource comprises 434,946 images, seven reference atlases, an ontogenetic ontology, and tools to explore coexpression of genes across neurodevelopment. Gene sets coinciding with developmental phenomena were identified. A temporal shift in the principles governing the molecular organization of the brain was detected, with transient neuromeric, plate-based organization of the brain present at E11.5 and E13.5. Finally, these data provided a transcription factor code that discriminates brain structures and identifies the developmental age of a tissue, providing a foundation for eventual genetic manipulation or tracking of specific brain structures over development. The resource is available as the Allen Developing Mouse Brain Atlas (http://developingmouse.brain-map.org).

}, keywords = {Animals, Brain, Brain Mapping, Gene Expression, Gene Expression Profiling, Gene Expression Regulation, Developmental, Mice}, issn = {1097-4199}, doi = {10.1016/j.neuron.2014.05.033}, author = {Thompson, Carol L and Ng, Lydia and Menon, Vilas and Martinez, Salvador and Lee, Chang-Kyu and Glattfelder, Katie and Sunkin, Susan M and Henry, Alex and Lau, Christopher and Dang, Chinh and Garcia-Lopez, Raquel and Martinez-Ferre, Almudena and Pombero, Ana and Rubenstein, John L R and Wakeman, Wayne B and Hohmann, John and Dee, Nick and Sodt, Andrew J and Young, Rob and Smith, Kimberly and Nguyen, Thuc-Nghi and Kidney, Jolene and Kuan, Leonard and Jeromin, Andreas and Kaykas, Ajamete and Miller, Jeremy and Page, Damon and Orta, Geri and Bernard, Amy and Riley, Zackery and Smith, Simon and Wohnoutka, Paul and Hawrylycz, Michael J and Puelles, Luis and Jones, Allan R} } @article {47904, title = {Multi-scale correlation structure of gene expression in the brain.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {24}, year = {2011}, month = {2011 Nov}, pages = {933-42}, abstract = {

The mammalian brain is best understood as a multi-scale hierarchical neural system, in the sense that connection and function occur on multiple scales from micro to macro. Modern genomic-scale expression profiling can provide insight into methodologies that elucidate this architecture. We present a methodology for understanding the relationship of gene expression and neuroanatomy based on correlation between gene expression profiles across tissue samples. A resulting tool, NeuroBlast, can identify networks of genes co-expressed within or across neuroanatomic structures. The method applies to any data modality that can be mapped with sufficient spatial resolution, and provides a computation technique to elucidate neuroanatomy via patterns of gene expression on spatial and temporal scales. In addition, from the perspective of spatial location, we discuss a complementary technique that identifies gene classes that contribute to defining anatomic patterns.

}, keywords = {Algorithms, Animals, Atlases as Topic, Brain, Brain Chemistry, Data Mining, Databases, Genetic, Gene Expression, Mice, Mice, Inbred C57BL, Nerve Tissue Proteins, Tryptophan Hydroxylase}, issn = {1879-2782}, doi = {10.1016/j.neunet.2011.06.012}, author = {Hawrylycz, Mike and Ng, Lydia and Page, Damon and Morris, John and Lau, Chris and Faber, Sky and Faber, Vance and Sunkin, Susan and Menon, Vilas and Lein, Ed and Jones, Allan} }