Flexible annotation atlas of the mouse brain: combining and dividing brain structures of the Allen Brain Atlas while maintaining anatomical hierarchy

A brain atlas is necessary for analyzing structure and function in neuroimaging research. Although various annotation volumes (AVs) for the mouse brain have been proposed, it is common in magnetic resonance imaging (MRI) of the mouse brain that regions-of-interest (ROIs) for brain structures (nodes) are created arbitrarily according to each researcher’s necessity, leading to inconsistent ROIs among studies. One reason for such a situation is the fact that earlier AVs were fixed, i.e. combination and division of nodes were not implemented. This report presents a pipeline for constructing a flexible annotation atlas (FAA) of the mouse brain by leveraging public resources of the Allen Institute for Brain Science on brain structure, gene expression, and axonal projection. A mere two-step procedure with user-specified, text-based information and Python codes constructs FAA with nodes which can be combined or divided objectively while maintaining anatomical hierarchy of brain structures. Four FAAs with total node count of 4, 101, 866, and 1,381 were demonstrated. Unique characteristics of FAA realized analysis of resting-state functional connectivity (FC) across the anatomical hierarchy and among cortical layers, which were thin but large brain structures. FAA can improve the consistency of whole brain ROI definition among laboratories by fulfilling various requests from researchers with its flexibility and reproducibility. Highlights – A flexible annotation atlas (FAA) for the mouse brain is proposed. – FAA is expected to improve whole brain ROI-definition consistency among laboratories. – The ROI can be combined or divided objectively while maintaining anatomical hierarchy. – FAA realizes functional connectivity analysis across the anatomical hierarchy. – Codes for FAA reconstruction is available at https://github.com/ntakata/flexible-annotation-atlas – Datasets for resting-state fMRI in awake mice are available at https://openneuro.org/datasets/ds002551

[1]  Thomas E. Nichols,et al.  Best practices in data analysis and sharing in neuroimaging using MRI , 2017, Nature Neuroscience.

[2]  Hideyuki Okano,et al.  Physiological effects of a habituation procedure for functional MRI in awake mice using a cryogenic radiofrequency probe , 2016, Journal of Neuroscience Methods.

[3]  N. Logothetis,et al.  High-Resolution fMRI Reveals Laminar Differences in Neurovascular Coupling between Positive and Negative BOLD Responses , 2012, Neuron.

[4]  Hideyuki Okano,et al.  In vivo microscopic voxel-based morphometry with a brain template to characterize strain-specific structures in the mouse brain , 2017, Scientific Reports.

[5]  P. Hof,et al.  A three-dimensional digital atlas database of the adult C57BL/6J mouse brain by magnetic resonance microscopy , 2005, Neuroscience.

[6]  Susumu Mori,et al.  Magnetic resonance imaging-based mouse brain atlas and its applications. , 2011, Methods in molecular biology.

[7]  Ann K. Shinn,et al.  Abnormal Medial Prefrontal Cortex Resting-State Connectivity in Bipolar Disorder and Schizophrenia , 2011, Neuropsychopharmacology.

[8]  Data sharing and the future of science , 2018, Nature Communications.

[9]  Takeharu Nagai,et al.  High-Speed and Scalable Whole-Brain Imaging in Rodents and Primates , 2017, Neuron.

[10]  Hongkui Zeng,et al.  Neuroinformatics of the Allen Mouse Brain Connectivity Atlas. , 2015, Methods.

[11]  K. Svoboda,et al.  A large field of view two-photon mesoscope with subcellular resolution for in vivo imaging , 2016, bioRxiv.

[12]  Kenji F. Tanaka,et al.  Optogenetic Activation of CA1 Pyramidal Neurons at the Dorsal and Ventral Hippocampus Evokes Distinct Brain-Wide Responses Revealed by Mouse fMRI , 2015, PloS one.

[13]  M. Mintun,et al.  Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus , 2010, Proceedings of the National Academy of Sciences.

[14]  D. Hasselquist,et al.  No evidence that carotenoid pigments boost either immune or antioxidant defenses in a songbird , 2018, Nature Communications.

[15]  Alan C. Evans,et al.  A three-dimensional MRI atlas of the mouse brain with estimates of the average and variability. , 2005, Cerebral cortex.

[16]  H. Groenewegen,et al.  Topographical organization and relationship with ventral striatal compartments of prefrontal corticostriatal projections in the rat , 1992, The Journal of comparative neurology.

[17]  Jonathan R. Polimeni,et al.  Laminar (f)MRI: A short history and future prospects , 2019, NeuroImage.

[18]  K. Harris,et al.  Laminar Structure of Spontaneous and Sensory-Evoked Population Activity in Auditory Cortex , 2009, Neuron.

[19]  Scott Hamilton,et al.  In Vivo 3D Digital Atlas Database of the Adult C57BL/6J Mouse Brain by Magnetic Resonance Microscopy , 2008, Frontiers in neuroanatomy.

[20]  Sridevi Polavaram,et al.  Win–win data sharing in neuroscience , 2017, Nature Methods.

[21]  Markus Aswendt,et al.  Processing Pipeline for Atlas-Based Imaging Data Analysis of Structural and Functional Mouse Brain MRI (AIDAmri) , 2019, Front. Neuroinform..

[22]  K. Ohki,et al.  Transient neuronal coactivations embedded in globally propagating waves underlie resting-state functional connectivity , 2016, Proceedings of the National Academy of Sciences.

[23]  Qionghai Dai,et al.  Video-rate imaging of biological dynamics at centimetre scale and micrometre resolution , 2019, Nature Photonics.

[24]  Stephen J. Gotts,et al.  Brain networks, dimensionality, and global signal averaging in resting-state fMRI: Hierarchical network structure results in low-dimensional spatiotemporal dynamics , 2017, NeuroImage.

[25]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[26]  Dimitri Van De Ville,et al.  Dynamic reorganization of intrinsic functional networks in the mouse brain , 2017, NeuroImage.

[27]  J. Gore,et al.  Functional connectivity with cortical depth assessed by resting state fMRI of subregions of S1 in squirrel monkeys , 2018, Human brain mapping.

[28]  Fritjof Helmchen,et al.  Layer-specific integration of locomotion and sensory information in mouse barrel cortex , 2019, Nature Communications.

[29]  H. Hirase,et al.  Astrocyte Calcium Signaling Transforms Cholinergic Modulation to Cortical Plasticity In Vivo , 2011, The Journal of Neuroscience.

[30]  Yun Wang,et al.  Hierarchical organization of cortical and thalamic connectivity , 2019, Nature.

[31]  M. Mallar Chakravarty,et al.  Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis , 2019, NeuroImage.

[32]  Xilin Shen,et al.  Simultaneous mesoscopic and two-photon imaging of neuronal activity in cortical circuits , 2018, Nature Methods.

[33]  N. Wenderoth,et al.  Structural Basis of Large-Scale Functional Connectivity in the Mouse , 2017, The Journal of Neuroscience.

[34]  Marc Joliot,et al.  Brain activity at rest: a multiscale hierarchical functional organization. , 2011, Journal of neurophysiology.

[35]  Hideyuki Okano,et al.  Optogenetic astrocyte activation evokes BOLD fMRI response with oxygen consumption without neuronal activity modulation , 2018, Glia.

[36]  Mark S. Cembrowski,et al.  Heterogeneity within classical cell types is the rule: lessons from hippocampal pyramidal neurons , 2019, Nature Reviews Neuroscience.

[37]  T. A. Carpenter,et al.  Voxel-based morphometry in the R6/2 transgenic mouse reveals differences between genotypes not seen with manual 2D morphometry , 2009, Neurobiology of Disease.

[38]  Allan R. Jones,et al.  A mesoscale connectome of the mouse brain , 2014, Nature.

[39]  Lydia Ng,et al.  Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system , 2012, Nucleic Acids Res..

[40]  Trygve E Bakken,et al.  Identification of genetic markers for cortical areas using a Random Forest classification routine and the Allen Mouse Brain Atlas , 2019, bioRxiv.

[41]  Jeff W. Lichtman,et al.  Clarifying Tissue Clearing , 2015, Cell.

[42]  Hideyuki Okano,et al.  Assessing cortical plasticity after spinal cord injury by using resting-state functional magnetic resonance imaging in awake adult mice , 2018, Scientific Reports.

[43]  Binbin Nie,et al.  A stereotaxic MRI template set of mouse brain with fine sub-anatomical delineations: Application to MEMRI studies of 5XFAD mice. , 2019, Magnetic resonance imaging.

[44]  Pascal Fries,et al.  Cortical layers, rhythms and BOLD signals , 2017, NeuroImage.

[45]  A. Koretsky,et al.  Deciphering laminar-specific neural inputs with line-scanning fMRI , 2013, Nature Methods.

[46]  D. V. van Essen,et al.  Windows on the brain: the emerging role of atlases and databases in neuroscience , 2002, Current Opinion in Neurobiology.

[47]  E. Bullmore,et al.  Wiring cost and topological participation of the mouse brain connectome , 2015, Proceedings of the National Academy of Sciences.

[48]  Kenji F. Tanaka,et al.  Identification of the extent of cortical spreading depression propagation by Npas4 mRNA expression , 2015, Neuroscience Research.

[49]  Seong-Gi Kim,et al.  Foundations of layer-specific fMRI and investigations of neurophysiological activity in the laminarized neocortex and olfactory bulb of animal models , 2017, NeuroImage.

[50]  G. Allan Johnson,et al.  Waxholm Space: An image-based reference for coordinating mouse brain research , 2010, NeuroImage.

[51]  Zhifeng Liang,et al.  Time to wake up: Studying neurovascular coupling and brain-wide circuit function in the un-anesthetized animal , 2016, NeuroImage.

[52]  Matthew E. Larkum,et al.  Cortical dendritic activity correlates with spindle-rich oscillations during sleep in rodents , 2017, Nature Communications.

[53]  Alessandro Gozzi,et al.  Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis , 2020, NeuroImage.

[54]  E. Susaki,et al.  Whole-Brain Imaging with Single-Cell Resolution Using Chemical Cocktails and Computational Analysis , 2014, Cell.

[55]  Allan MacKenzie-Graham,et al.  In vivo magnetic resonance images reveal neuroanatomical sex differences through the application of voxel-based morphometry in C57BL/6 mice , 2017, NeuroImage.

[56]  Georgios A Keliris,et al.  On the Usage of Brain Atlases in Neuroimaging Research , 2018, Molecular Imaging and Biology.

[57]  H. Steiner,et al.  Addiction-related gene regulation: Risks of exposure to cognitive enhancers vs. other psychostimulants , 2013, Progress in Neurobiology.

[58]  R. Mark Henkelman,et al.  High resolution three-dimensional brain atlas using an average magnetic resonance image of 40 adult C57Bl/6J mice , 2008, NeuroImage.

[59]  Andrew L. Janke,et al.  A segmentation protocol and MRI atlas of the C57BL/6J mouse neocortex , 2013, NeuroImage.

[60]  Karl Deisseroth,et al.  An interactive framework for whole-brain maps at cellular resolution , 2017, Nature Neuroscience.

[61]  Andrew L. Janke,et al.  An ontologically consistent MRI-based atlas of the mouse diencephalon , 2017, NeuroImage.