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Repeated behavioural evolution is associated with convergence of gene expression in cavity-nesting songbirds

Nature Ecology and Evolution 2025年第5期9卷 845-856页

作者： Lipshutz, Sara E. Hibbins, Mark S. Bentz, Alexandra B. Buechlein, Aaron M. Empson, Tara A. George, Elizabeth M. Hauber, Mark E. Rusch, Douglas B. Schelsky, Wendy M. Thomas, Quinn K. Torneo, Samuel J. Turner, Abbigail M. Wolf, Sarah E. Woodruff, Mary J. Hahn, Matthew W. Rosvall, Kimberly A. Department of Biology Indiana University Bloomington IN United States Department of Biology Loyola University Chicago Chicago IL United States Department of Biology Duke University Durham NC United States Department of Ecology and Evolutionary Biology University of Toronto Toronto ON Canada School of Biological Sciences University of Oklahoma Norman OK United States Center for Genomics and Bioinformatics Indiana University Bloomington IN United States Department of Ecology and Evolutionary Biology University of Tennessee Knoxville TN United States Department of Evolution Ecology and Organismal Biology The Ohio State University Columbus OH United States Department of Evolution Ecology and Behavior University of Illinois Urbana-Champaign Urbana IL United States Illinois Natural History Survey Prairie Research Institute University of Illinois Urbana-Champaign Champaign IL United States Advanced Science Research Center and Programs in Biology and in Psychology Graduate Center of the City University of New York New York NY United States The Grainger College of Engineering University of Illinois Urbana-Champaign Urbana IL United States School of Biological Sciences University of Edinburgh Scotland Edinburgh United Kingdom United States Department of Agriculture Animal and Plant Health Inspection Services Veterinary Services Center for Epidemiology and Animal Health Fort Collins CO United States Department of Computer Science Indiana University Bloomington IN United States

Uncovering the genomic bases of phenotypic adaptation is a major goal in biology, but this has been hard to achieve for complex behavioural traits. Here we leverage the repeated, independent evolution of obligate cavity nesting in birds to test the hypothesis that pressure to compete for a limited breeding resource has facilitated convergent evolution in behaviour, hormones and gene expression. We used an integrative approach, combining aggression assays in the field, testosterone measures and transcriptome-wide analyses of the brain in wild-captured females and males. Our experimental design compared species pairs across five avian families, each including one obligate cavity-nesting species and a related species with a more flexible nest strategy. We find behavioural convergence, with higher levels of territorial aggression in obligate cavity nesters, particularly among females. Across species, levels of testosterone in circulation were not associated with nest strategy nor aggression. Phylogenetic analyses of individual genes and co-regulated gene networks revealed more shared patterns of brain gene expression than expected by drift, although the scope of convergent gene expression evolution was limited to a small percentage of the genome. When comparing our results to other studies that did not use phylogenetic methods, we suggest that accounting for shared evolutionary history may reduce the number of genes inferred as convergently evolving. Altogether, we find that behavioural convergence in response to shared ecological pressures is associated with largely independent evolution of gene expression across different avian families, punctuated by a narrow set of convergently evolving genes. © The Author(s), under exclusive licence to Springer Nature Limited 2025.

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Variations in kinase and effector signaling logic in a bacterial two component signaling network

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Journal of Biological Chemistry 2025年第6期301卷 108534页

作者： Swingle, Danielle Epstein, Leah Aymon, Ramisha Isiorho, Eta A. Abzalimov, Rinat R. Favaro, Denize C. Gardner, Kevin H. Structural Biology Initiative CUNY Advanced Science Research Center New YorkNY United States PhD. Program in Biochemistry The Graduate Center – City University of New York New YorkNY United States Department of Chemistry and Biochemistry City College of New York New YorkNY United States PhD. Programs in Biochemistry Biology and Chemistry The Graduate Center – City University of New York New YorkNY United States

The general stress response (GSR) protects bacteria from a wide range of stressors. In Alphaproteobacteria, GSR activation is coordinated by HWE/HisKA2 family histidine kinases (HKs), which can exhibit noncanonical structure and function. For example, while most light-oxygen-voltage sensor-containing HKs are light-activated dimers, the Rubellimicrobium thermophilum RT-HK has inverted "dark on, light off" signaling logic with a tunable monomer/dimer equilibrium. Here, we further investigate these atypical behaviors of RT-HK and characterize its downstream signaling network. Using hydrogen-deuterium exchange mass spectrometry, we find that RT-HK uses a signal transduction mechanism similar to light-activated systems, despite its inverted logic. Mutagenesis reveals that RT-HK autophosphorylates in trans, with changes to the Jα helix linking sensor and kinase domains affecting autophosphorylation levels. Exploring downstream effects of RT-HK, we identified two GSR genetic regions, each encoding a copy of the central regulator PhyR. In vitro measurements of phosphotransfer from RT-HK to the two putative PhyRs revealed that RT-HK signals only to one and does so at an increased intensity in the dark, consistent with its reversed logic. X-ray crystal structures of both PhyRs revealed a substantial shift within the receiver domain of one, suggesting a basis for RT-HK specificity. We probed further down the pathway using nuclear magnetic resonance to determine that the single NepR homolog interacts with both unphosphorylated PhyRs, and this interaction is decoupled from activation in one PhyR. This work expands our understanding of HWE/HisKA2 family signal transduction, revealing marked variations from signaling mechanisms previously identified in other GSR networks. © 2025 The Authors

关键词： Signaling

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Visual Information Processing Through the Interplay between Fine and Coarse Signal Pathways

SSRN

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SSRN 2023年

作者： Zou, Xiaolong Ji, Zilong Zhang, Tianqiu Huang, Tiejun Wu, Si School of Psychology and Cognitive Sciences IDG/McGovern Institute for Brain Research Peking-Tsinghua Center for Life Sciences Center of Quantitative Biology Academy for Advanced Interdisciplinary Studies Peking University Beijing China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Beijing Academy of Artificial Intelligence Beijing China School of Computer Science Peking University Beijing China

Object recognition is often viewed as a feedforward, bottom-up process in ma- chine learning, but in real neural systems, object recognition is a complicated process which involves the interplay between two signal pathways. One is the parvocellular pathway (P-pathway), which is slow and extracts fine features of objects;the other is the magnocellular pathway (M-pathway), which is fast and extracts coarse features of objects. It has been suggested that the inter- play between the two pathways endows the neural system with the capacity of processing visual information rapidly, adaptively, and robustly. However, the underlying computational mechanism remains largely unknown. In this study, we build a two-pathway model to elucidate the computational properties associ- ated with the interactions between two visual pathways. Specifically, we model two visual pathways using two convolution neural networks: one mimics the P-pathway, referred to as FineNet, which is deep, has small-size kernels, and receives detailed visual inputs;the other mimics the M-pathway, referred to as CoarseNet, which is shallow, has large-size kernels, and receives blurred visual inputs. We show that CoarseNet can learn from FineNet through imitation to improve its performance, FineNet can benefit from the feedback of CoarseNet to improve its robustness to noise;and the two pathways interact with each other to achieve rough-to-fine information processing. Using visual backward mask- ing as an example, we further demonstrate that our model can explain visual cognitive behaviors that involve the interplay between two pathways. We hope that this study gives us insight into understanding the interaction principles between two visual pathways. © 2023, The Authors. All rights reserved.

关键词： Object recognition

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Adaptation accelerating sampling-based Bayesian inference in attractor neural networksb> 22

Adaptation accelerating sampling-based Bayesian inference in...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Xingsi Dong Zilong Ji Tianhao Chu Tiejun Huang Wen-Hao Zhang Si Wu School of Psychology and Cognitive Sciences IDG/McGovern Institute for Brain Research PKU-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies Center of Quantitative Biology Peking University School of Psychology and Cognitive Sciences IDG/McGovern Institute for Brain Research PKU-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies Center of Quantitative Biology Peking University and Institute of Cognitive Neuroscience University College London School of Computer Science Peking University Lyda Hill Department of Bioinformatics O'Donnell Brain Institute UT Southwestern Medical Center

ISBN: (纸本)9781713871088

The brain performs probabilistic Bayesian inference to interpret the external world. The sampling-based view assumes that the brain represents the stimulus posterior distribution via samples of stochastic neuronal responses. Although the idea of sampling-based inference is appealing, it faces a critical challenge of whether stochastic sampling is fast enough to match the rapid computation of the brain. In this study, we explore how latent feature sampling can be accelerated in neural circuits. Specifically, we consider a canonical neural circuit model called continuous attractor neural networks (CANNs) and investigate how sampling-based inference of latent continuous variables is accelerated in CANNs. Intriguingly, we find that by including noisy adaptation in the neuronal dynamics, the CANN is able to speed up the sampling process significantly. We theoretically derive that the CANN with noisy adaptation implements the efficient sampling method called Hamiltonian dynamics with friction, where noisy adaption effectively plays the role of momentum. We theoretically analyze the sampling performances of the network and derive the condition when the acceleration has the maximum effect. Simulation results validate our theoretical analyses. We further extend the model to coupled CANNs and demonstrate that noisy adaptation accelerates the sampling of the posterior distribution of multivariate stimuli. We hope that this study enhances our understanding of how Bayesian inference is realized in the brain.

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Oscillatory tracking of continuous attractor neural networks account for phase precession and procession of hippocampal place cells 22

Oscillatory tracking of continuous attractor neural networks...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Tianhao Chu Zilong Ji Junfeng Zuo Wen-Hao Zhang Tiejun Huang Yuanyuan Mi Si Wu School of Psychology and Cognitive Sciences IDG/McGovern Institute for Brain Research Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies Center of Quantitative Biology Peking University School of Psychology and Cognitive Sciences IDG/McGovern Institute for Brain Research Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies Center of Quantitative Biology Peking University and Institute of Cognitive Neuroscience University College London Lyda Hill Department of Bioinformatics O'Donnell Brain Institute UT Southwestern Medical Center School of Computer Science Peking University Center for Neurointelligence School of Medicine Chongqing University

ISBN: (纸本)9781713871088

Hippocampal place cells of freely moving rodents display an intriguing temporal organization in their responses known as 'theta phase precession', in which individual neurons fire at progressively earlier phases in successive theta cycles as the animal traverses the place fields. Recent experimental studies found that in addition to phase precession, many place cells also exhibit accompanied phase procession, but the underlying neural mechanism remains unclear. Here, we propose a neural circuit model to elucidate the generation of both kinds of phase shift in place cells' firing. Specifically, we consider a continuous attractor neural network (CANN) with feedback inhibition, which is inspired by the reciprocal interaction between the hippocampus and the medial septum. The feedback inhibition induces intrinsic mobility of the CANN which competes with the extrinsic mobility arising from the external drive. Their interplay generates an oscillatory tracking state, that is, the network bump state (resembling the decoded virtual position of the animal) sweeps back and forth around the external moving input (resembling the physical position of the animal). We show that this oscillatory tracking naturally explains the forward and backward sweeps of the decoded position during the animal's locomotion. At the single neuron level, the forward and backward sweeps account for, respectively, theta phase precession and procession. Furthermore, by tuning the feedback inhibition strength, we also explain the emergence of bimodal cells and unimodal cells, with the former having co-existed phase precession and procession, and the latter having only significant phase precession. We hope that this study facilitates our understanding of hippocampal temporal coding and lays foundation for unveiling their computational functions.

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Slow and weak attractor computation embedded in fast and strong E-I balanced neural dynamics 23

Slow and weak attractor computation embedded in fast and str...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xiaohan Lin Liyuan Li Boxin Shi Tiejun Huang Yuanyuan Mi Si Wu Nat'l Key Lab for Multimedia Information Processing Nat'l Eng. Lab of Visual Technology School of Computer Science Peking University and Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies School of Psychological and Cognitive Sciences Beijing Key Laboratory of Behavior and Mental Health IDG/McGovern Institute for Brain Research Center of Quantitative Biology Peking University Department of Mathematics Peking University and Institute of Neuroinformatics University of Zurich Nat'l Key Lab for Multimedia Information Processing Nat'l Eng. Lab of Visual Technology School of Computer Science Peking University and Institute for Artificial Intelligence Peking University Department of Psychology Tsinghua University Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies School of Psychological and Cognitive Sciences Beijing Key Laboratory of Behavior and Mental Health IDG/McGovern Institute for Brain Research Center of Quantitative Biology Peking University

Attractor networks require neuronal connections to be highly structured in order to maintain attractor states that represent information, while excitation and inhibition balanced networks (E-INNs) require neuronal connections to be random and sparse to generate irregular neuronal firing. Despite being regarded as canonical models of neural circuits, both types of networks are usually studied independently, and it remains unclear how they coexist in the brain, given their very different structural demands. In this study, we investigate the compatibility of continuous attractor neural networks (CANNs) and E-INNs. In line with recent experimental data, we find that a neural circuit can exhibit both the traits of CANNs and E-INNs when the neuronal synapses consist of two sets: one set is strong and fast for irregular firing, and the other set is weak and slow for attractor dynamics. In addition, both simulations and theoretical analysis reveal that the network exhibits enhanced performance compared to the case of using only one set of synapses, with accelerated convergence of attractor states and retained E-I balanced condition for localized input. We hope that this study provides insight into how structured neural computations are realized by irregular firings of neurons.

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Multiple Neuronal Specializations Elicited By Socially Driven Recognition Of Food Odors

arXiv

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arXiv 2024年

作者： Plusnin, Viktor Khoroshavkina, Nadezhda Abonakour, Ali Onuchin, Arsenii Manyukhina, Viktoriya Titova, Nadezhda Kirsanov, Artem Solodovnikov, Vasiliy Pospelov, Nikita Sotskov, Vladimir Institute for Advanced Brain Studies Lomonosov Moscow State University Moscow Russia Laboratory of Neuronal Intelligence Lomonosov Moscow State University Moscow Russia International Laboratory of Cluster Geometry National Research University Higher School of Economics Moscow Russia Institute of Higher Nervous Activity and Neurophysiology RAS Moscow Russia Moscow Institute of Physics and Technology Dolgoprudny Russia Skolkovo Institute of Science and Technology Moscow Russia Laboratory of Complex Networks Center for Neurophysics and Neuromorphic Technologies Moscow Russia Moscow State University of Psychology and Education Moscow Russia Institute of Cognitive Neuroscience National Research University Higher School of Economics Moscow Russia Laboratory for Neurobiology of Memory P.K.Anokhin Institute of Normal Physiology Moscow Russia Center of Interdisciplinary Research in Biology Collège de France Paris France Institute of Biology École Normale Supérieure Paris France

This study investigates the dynamics of non-spatial specializations in hippocampal place cells during exposure to novel environments. Hippocampal place cells, known for their role in spatial mapping, exhibit multi-modal responses to sensory cues. The research focuses on understanding how these cells adapt their specialization in response to novel stimuli, specifically examining non-spatial determinants such as odors and social interactions. Using a social-driven food odor recognition model in mice, the study records CA1 hippocampal neuron activity through miniscope imaging. The experimental design involves demonstrations of novel odors to mice, followed by observation sessions with food options. The analysis employs deep neural network tools for behavior tracking and the custom-developed INTENS software package for identifying neural specializations. Results indicate multiple specializations, particularly those related to odor, with differences observed between training and testing sessions. The findings suggest a temporal aspect to the formation of these specializations in novel conditions, necessitating further investigation for precise tracking. © 2024, CC BY.

关键词： Odors

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Dopamine and acetylcholine correlations in the nucleus accumbens depend on behavioral task states

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Current biology 2025年第6期35卷 1400-1407.e3页

作者： Costa, Kauê Machado Zhang, Zhewei Deutsch, Douglas Zhuo, Yizhou Li, Guochuan Li, Yulong Schoenbaum, Geoffrey National Institute on Drug Abuse Intramural Research Program National Institutes of Health Baltimore 21224 MD United States Department of Psychology University of Alabama at Birmingham Birmingham 35223 AL United States State Key Laboratory of Membrane Biology School of Life Sciences Peking University Beijing 100871 China PKU-IDG/McGovern Institute for Brain Research Beijing 100871 China Peking-Tsinghua Center for Life Sciences New Cornerstone Science Laboratory Academy for Advanced Interdisciplinary Studies Peking University Beijing 100871 China Chinese Institute for Brain Research Beijing 102206 China Institute of Molecular Physiology Shenzhen Bay Laboratory Guangdong Shenzhen 518055 China National Biomedical Imaging Center Peking University Beijing 100871 China

Dopamine release in the nucleus accumbens (NAcc) changes quickly in response to errors in predicting events like reward delivery1,2,3 but also slowly ramps up when animals are moving toward a goal.4,5,6,7,8,9,10 This ... 详细信息

Dopamine release in the nucleus accumbens (NAcc) changes quickly in response to errors in predicting events like reward delivery¹^,²^,³ but also slowly ramps up when animals are moving toward a goal.⁴^,⁵^,⁶^,⁷^,⁸^,⁹^,¹⁰ This ramping has attracted much recent attention, as there is controversy regarding its computational role⁵^,⁷^,⁹^,¹¹ and whether they are driven by dopamine neuron firing⁷^,⁸^,⁹ or local circuit mechanisms.⁵^,⁶ If the latter is true, cholinergic transmission would be a prime candidate mechanism,¹²^,¹³^,¹⁴ and acetylcholine and dopamine signals should be positively correlated during behavior, particularly during motivated approach. However, in the dorsal striatum, striatal cholinergic interneurons typically “dip” their activity when reward or associated cues are presented, in opposition to dopamine,¹⁵^,¹⁶^,¹⁷^,¹⁸ and acetylcholine and dopamine release is generally anti-correlated in vivo.¹⁹^,²⁰ Furthermore, acetylcholine and dopamine have opposing effects on downstream striatal projection neurons (SPNs),²¹^,²² which suggests that cholinergic dips create a permissive window for dopamine to drive plasticity.²³ These studies therefore suggest that dopamine and acetylcholine should be anti-correlated during behavior. We tested between these hypotheses by simultaneously recording accumbal dopamine and acetylcholine signals in rats executing a task involving motivated approach. We found that dopamine ramps were not

关键词： acetylcholine approach dopamine fiber photometry instrumental motivation ramps rat reward prediction error salience

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Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries (vol 55, pg 89, 2023)

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NATURE GENETICS 2023年第3期55卷 519-520页

作者： Fernandez-Rozadilla, Ceres Timofeeva, Maria Chen, Zhishan Law, Philip Thomas, Minta Schmit, Stephanie Diez-Obrero, Virginia Hsu, Li Fernandez-Tajes, Juan Palles, Claire Sherwood, Kitty Briggs, Sarah Svinti, Victoria Donnelly, Kevin Farrington, Susan Blackmur, James Vaughan-Shaw, Peter Shu, Xiao-ou Long, Jirong Cai, Qiuyin Guo, Xingyi Lu, Yingchang Broderick, Peter Studd, James Huyghe, Jeroen Harrison, Tabitha Conti, David Dampier, Christopher Devall, Mathew Schumacher, Fredrick Melas, Marilena Rennert, Gad Obon-Santacana, Mireia Martin-Sanchez, Vicente Moratalla-Navarro, Ferran Oh, Jae Hwan Kim, Jeongseon Jee, Sun Ha Jung, Keum Ji Kweon, Sun-Seog Shin, Min-Ho Shin, Aesun Ahn, Yoon-Ok Kim, Dong-Hyun Oze, Isao Wen, Wanqing Matsuo, Keitaro Matsuda, Koichi Tanikawa, Chizu Ren, Zefang Gao, Yu-Tang Jia, Wei-Hua Hopper, John Jenkins, Mark Win, Aung Ko Pai, Rish Figueiredo, Jane Haile, Robert Gallinger, Steven Woods, Michael Newcomb, Polly Duggan, David Cheadle, Jeremy Kaplan, Richard Maughan, Timothy Kerr, Rachel Kerr, David Kirac, Iva Bohm, Jan Mecklin, Lukka-Pekka Jousilahti, Pekka Knekt, Paul Aaltonen, Lauri Rissanen, Harri Pukkala, Eero Eriksson, Johan Cajuso, Tatiana Hanninen, Ulrika Kondelin, Johanna Palin, Kimmo Tanskanen, Tomas Renkonen-Sinisalo, Laura Zanke, Brent Mannisto, Satu Albanes, Demetrius Weinstein, Stephanie Ruiz-Narvaez, Edward Palmer, Julie Buchanan, Daniel Platz, Elizabeth Visvanathan, Kala Ulrich, Cornelia Siegel, Erin Brezina, Stefanie Gsur, Andrea Campbell, Peter Chang-Claude, Jenny Hoffmeister, Michael Brenner, Hermann Slattery, Martha Potter, John Tsilidis, Konstantinos Schulze, Matthias Gunter, Marc Murphy, Neil Castells, Antoni Castellvi-Bel, Sergi Moreira, Leticia Arndt, Volker Shcherbina, Anna Stern, Mariana Pardamean, Bens Bishop, Timothy Giles, Graham Southey, Melissa Idos, Gregory McDonnell, Kevin Abu-Ful, Zomoroda Greenson, Joel Shulman, Katerina Lejbkowicz, Flavio Offit, Kenneth Su, Yu-Ru Steinfelder, Robert Keku, Temitope van Guelpen, Bethany Hudson, Thomas Hampel, Heather Pearlman, Edinburgh Cancer Research Centre Institute of Genomics and Cancer University of Edinburgh Edinburgh UK Genomic Medicine Group Instituto de Investigacion Sanitaria de Santiago Santiago de Compostela Spain Colon Cancer Genetics Group Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK Danish Institute for Advanced Study Department of Public Health University of Southern Denmark Odense Denmark Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University Medical Center Nashville TN USA Division of Genetics and Epidemiology Institute of Cancer Research London UK Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA USA Genomic Medicine Institute Cleveland Clinic Cleveland OH USA Population and Cancer Prevention Program Case Comprehensive Cancer Center Cleveland OH USA Colorectal Cancer Group ONCOBELL Program Bellvitge Biomedical Research Institute Barcelona Spain Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain Consortium for Biomedical Research in Epidemiology and Public Health Madrid Madrid Spain Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain Department of Biostatistics School of Public Health University of Washington Seattle WA USA Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham UK Department of Public Health Richard Doll Building University of Oxford Oxford UK Department of Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA Department of Preventive Medicine USC Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA USA Center for Public Health Genomics Department of Public Health Sciences University of Virginia Charlottesville VA USA Department of Populat

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

关键词： Colorectal cancer Epigenomics Genome-wide association studies Transcriptomics

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A consensus protocol for functional connectivity analysis in the rat brain (vol 26, pg 673, 2023)

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NATURE NEUROscience 2023年第6期26卷 1127-1128页

作者： Grandjean, Joanes Desrosiers-Gregoire, Gabriel Anckaerts, Cynthia Angeles-Valdez, Diego Ayad, Fadi Barriere, David A. Blockx, Ines Bortel, Aleksandra Broadwater, Margaret Cardoso, Beatriz M. Celestine, Marina Chavez-Negrete, Jorge E. Choi, Sangcheon Christiaen, Emma Clavijo, Perrin Colon-Perez, Luis Cramer, Samuel Daniele, Tolomeo Dempsey, Elaine Diao, Yujian Doelemeyer, Arno Dopfel, David Dvorakova, Lenka Falfan-Melgoza, Claudia Fernandes, Francisca F. Fowler, Caitlin F. Fuentes-Ibanez, Antonio Garin, Clement M. Gelderman, Eveline Golden, Carla E. M. Guo, Chao C. G. Henckens, Marloes J. A. G. Hennessy, Lauren A. Herman, Peter Hofwijks, Nita Horien, Corey Ionescu, Tudor M. Jones, Jolyon Kaesser, Johannes Kim, Eugene Lambers, Henriette Lazari, Alberto Lee, Sung-Ho Lillywhite, Amanda Liu, Yikang Liu, Yanyan Y. Lopez-Castro, Alejandra Lopez-Gil, Xavier Ma, Zilu MacNicol, Eilidh Madularu, Dan Mandino, Francesca Marciano, Sabina McAuslan, Matthew J. McCunn, Patrick McIntosh, Alison Meng, Xianzong Meyer-Baese, Lisa Missault, Stephan Moro, Federico Naessens, Daphne M. P. Nava-Gomez, Laura J. Nonaka, Hiroi Ortiz, Juan J. Paasonen, Jaakko Peeters, Lore M. Pereira, Mickael Perez, Pablo D. Pompilus, Marjory Prior, Malcolm Rakhmatullin, Rustam Reimann, Henning M. Reinwald, Jonathan Del Rio, Rodrigo Triana Rivera-Olvera, Alejandro Ruiz-Perez, Daniel Russo, Gabriele Rutten, Tobias J. Ryoke, Rie Sack, Markus Salvan, Piergiorgio Sanganahalli, Basavaraju G. Schroeter, Aileen Seewoo, Bhedita J. Selingue, Erwan Seuwen, Aline Shi, Bowen Sirmpilatze, Nikoloz Smith, Joanna A. B. Smith, Corrie Sobczak, Filip Stenroos, Petteri J. Straathof, Milou Strobelt, Sandra Sumiyoshi, Akira Takahashi, Kengo Torres-Garcia, Maria E. Tudela, Raul van den Berg, Monica van der Marel, Kajo van Hout, Aran T. B. Vertullo, Roberta Vidal, Benjamin Vrooman, Roel M. Wang, Victora X. Wank, Isabel Watson, David J. G. Yin, Ting Zhang, Yongzhi Zurbruegg, Stefan Achard, Sophie Alcauter, Sarael Auer, Dorothee P. Barbier, Emmanuel L. Baudewig, Juergen Beckmann, Chri Donders Institute for Brain Behaviour and Cognition Radboud University Nijmegen The Netherlands Department for Medical Imaging Radboud University Medical Center Nijmegen The Netherlands Cerebral Imaging Centre Douglas Mental Health University Institute Verdun QC Canada Integrated Program in Neuroscience McGill University Montreal QC Canada Bio-imaging Lab University of Antwerp Antwerp Belgium µNEURO Research Centre of Excellence University of Antwerp Antwerp Belgium Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro Mexico Biological and Biomedical Engineering McGill University Montreal QC Canada McConnell Brain Imaging Centre McGill University Montreal QC Canada Montreal Neurological Institute McGill University Montreal QC Canada UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction Physiologie de la reproduction et des comportements Centre de recherche INRAE de Nouzilly Tours France Department of Neurology and Neurosurgery McGill University Montreal QC Canada Center for Animal MRI University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Neurology University of North Carolina at Chapel Hill Chapel Hill NC USA Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill NC USA Preclinical MRI Champalimaud Research Champalimaud Centre for the Unknown Lisbon Portugal Laboratoire des Maladies Neurodégénératives Molecular Imaging Research Center (MIRCen) Université Paris-Saclay Commissariat à l’Énergie Atomique et aux Énergies Alternatives (CEA) CNRS Fontenay-aux-Roses France Departamento de Neurobiología Conductual y Cognitiva Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro México Translational Neuroimaging and Neural Control Group High-Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tuebingen Germany Graduate Training Centre of Neuroscience International Max

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

关键词： Databases Neural circuits

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