Skip to content
naulon

No retraction, correction or concern found when last checked on Oct 31, 2026.

neuromaps: structural and functional interpretation of brain maps

Ross D. Markello, Justine Y. Hansen, Zhen-Qi Liu and 10 more

Nature Methods · 2022 · CC BY-NC-SA

doi:10.21203/rs.3.rs-1296849/v1

Abstract

Imaging technologies are increasingly used to generate high-resolution reference maps of brain structure and function. Comparing experimentally generated maps to these reference maps facilitates cross-disciplinary scientific discovery. Although recent data sharing initiatives increase the accessibility of brain maps, data are often shared in disparate coordinate systems, precluding systematic and accurate comparisons. Here we introduce neuromaps, a toolbox for accessing, transforming and analyzing structural and functional brain annotations. We implement functionalities for generating high-quality transformations between four standard coordinate systems. The toolbox includes curated reference maps and biological ontologies of the human brain, such as molecular, microstructural, electrophysiological, developmental and functional ontologies. Robust quantitative assessment of map-to-map similarity is enabled via a suite of spatial autocorrelation-preserving null models. neuromaps combines open-access data with transparent functionality for standardizing and comparing brain maps, providing a systematic workflow for comprehensive structural and functional annotation enrichment analysis of the human brain.

References 106

  1. 1.
  2. 2.

    Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis: I. Segmentation and surface reconstruction. Neuroimage. 1999;9:179-194.

    Not in our library10.1006/nimg.1998.0395

  3. 3.

    Glasser MF, Van Essen DC. Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI. J. Neurosci. 2011;31:11597-11616.

    Not in our library10.1523/jneurosci.2180-11.2011

  4. 4.

    Burt JB, et al. Hierarchy of transcriptomic specialization across human cortex captured by structural neuroimaging topography. Nat. Neurosci. 2018;21:1251-1259.

    Not in our library10.1038/s41593-018-0195-0

  5. 5.

    Whitaker KJ, et al. Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome. Proc. Natl Acad. Sci. USA. 2016;113:9105-9110.

    Not in our library10.1073/pnas.1601745113

  6. 6.

    Huntenburg JM, et al. A systematic relationship between functional connectivity and intracortical myelin in the human cerebral cortex. Cereb. Cortex. 2017;27:981-997.

    Not in our library10.1093/cercor/bhx030

  7. 7.

    Hawrylycz M, et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 2012;489:391-399.

    Not in our library10.1038/nature11405

  8. 8.

    Akbarian S, et al. The PsychENCODE project. Nat. Neurosci. 2015;18:1707-1712.

    Not in our library10.1038/nn.4156

  9. 9.

    Scholtens LH, de Reus MA, de Lange SC, Schmidt R, van den Heuvel MP. An MRI Von Economo–Koskinas atlas. Neuroimage. 2018;170:249-256.

    Not in our library10.1016/j.neuroimage.2016.12.069

  10. 10.

    Vaishnavi SN, et al. Regional aerobic glycolysis in the human brain. Proc. Natl Acad. Sci. USA. 2010;107:17757-17762.

    Not in our library10.1073/pnas.1010459107

  11. 11.
  12. 12.

    Beliveau V, et al. A high-resolution in vivo atlas of the human brain’s serotonin system. J. Neurosci. 2017;37:120-128.

    Not in our library10.1523/jneurosci.2830-16.2016

  13. 13.

    Nørgaard M, et al. A high-resolution in vivo atlas of the human brain’s benzodiazepine binding site of GABAA receptors. Neuroimage. 2021;232:117878.

    Not in our library10.1016/j.neuroimage.2021.117878

  14. 14.
  15. 15.
  16. 16.

    Murray JD, et al. A hierarchy of intrinsic timescales across primate cortex. Nat. Neurosci. 2014;17:1661-1663.

    Not in our library10.1038/nn.3862

  17. 17.
  18. 18.
  19. 19.

    Hill J, et al. Similar patterns of cortical expansion during human development and evolution. Proc. Natl Acad. Sci. USA. 2010;107:13135-13140.

    Not in our library10.1073/pnas.1001229107

  20. 20.
  21. 21.
  22. 22.

    Reardon P, et al. Normative brain size variation and brain shape diversity in humans. Science. 2018;360:1222-1227.

    Not in our library10.1126/science.aar2578

  23. 23.

    Gorgolewski KJ, et al. NeuroVault.org: a repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain. Neuroimage. 2016;124:1242-1244.

    Not in our library10.1016/j.neuroimage.2015.04.016

  24. 24.

    Van Essen DC, et al. The Brain Analysis Library of Spatial maps and Atlases (BALSA) database. Neuroimage. 2017;144:270-274.

    Not in our library10.1016/j.neuroimage.2016.04.002

  25. 25.

    Voytek JB, Voytek B. Automated cognome construction and semi-automated hypothesis generation. J. Neurosci. Methods. 2012;208:92-100.

    Not in our library10.1016/j.jneumeth.2012.04.019

  26. 26.

    Reimand J, et al. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat. Protoc. 2019;14:482-517.

    Not in our library10.1038/s41596-018-0103-9

  27. 27.

    Baryshnikova A. Systematic functional annotation and visualization of biological networks. Cell Syst. 2016;2:412-421.

    Not in our library10.1016/j.cels.2016.04.014

  28. 28.
  29. 29.

    Van Essen DC, et al. The WU-Minn Human Connectome Project: an overview. Neuroimage. 2013;80:62-79.

    Not in our library10.1016/j.neuroimage.2013.05.041

  30. 30.

    Glasser MF, et al. A multi-modal parcellation of human cerebral cortex. Nature. 2016;536:171-178.

    Not in our library10.1038/nature18933

  31. 31.
  32. 32.

    Margulies DS, et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc. Natl Acad. Sci. USA. 2016;113:12574-12579.

    Not in our library10.1073/pnas.1608282113

  33. 33.

    Mueller S, et al. Individual variability in functional connectivity architecture of the human brain. Neuron. 2013;77:586-595.

    Not in our library10.1016/j.neuron.2012.12.028

  34. 34.

    Yarkoni T, Poldrack RA, Nichols TE, Van Essen DC, Wager TD. Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods. 2011;8:665-670.

    Not in our library10.1038/nmeth.1635

  35. 35.
  36. 36.

    Robinson EC, et al. MSM: a new flexible framework for multimodal surface matching. Neuroimage. 2014;100:414-426.

    Not in our library10.1016/j.neuroimage.2014.05.069

  37. 37.

    Robinson EC, et al. Multimodal surface matching with higher-order smoothness constraints. Neuroimage. 2018;167:453-465.

    Not in our library10.1016/j.neuroimage.2017.10.037

  38. 38.

    Alexander-Bloch AF, et al. On testing for spatial correspondence between maps of human brain structure and function. Neuroimage. 2018;178:540-551.

    Not in our library10.1016/j.neuroimage.2018.05.070

  39. 39.
  40. 40.
  41. 41.

    Baum GL, et al. Development of structure–function coupling in human brain networks during youth. Proc. Natl Acad. Sci. USA. 2020;117:771-778.

    Not in our library10.1073/pnas.1912034117

  42. 42.
  43. 43.

    Burt JB, Helmer M, Shinn M, Anticevic A, Murray JD. Generative modeling of brain maps with spatial autocorrelation. Neuroimage. 2020;220:117038.

    Not in our library10.1016/j.neuroimage.2020.117038

  44. 44.
  45. 45.

    Markello RD, Misic B. Comparing spatial null models for brain maps. Neuroimage. 2021;236:118052.

    Not in our library10.1016/j.neuroimage.2021.118052

  46. 46.

    Kogan A, Alpert K, Ambite JL, Marcus DS, Wang L. Northwestern University schizophrenia data sharing for SchizConnect: a longitudinal dataset for large-scale integration. Neuroimage. 2016;124:1196-1201.

    Not in our library10.1016/j.neuroimage.2015.06.030

  47. 47.

    Shafiei G, et al. Spatial patterning of tissue volume loss in schizophrenia reflects brain network architecture. Biol. Psychiatry. 2020;87:727-735.

    Not in our library10.1016/j.biopsych.2019.09.031

  48. 48.

    Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B. 1995;57:289-300.

    Not in our library

  49. 49.

    Murray RM, Lewis SW. Is schizophrenia a neurodevelopmental disorder? Br. Med. J. (Clin. Res. Ed.). 1987;295:681-682.

    Not in our library10.1136/bmj.295.6600.681

  50. 50.

    Rapoport J, Giedd J, Gogtay N. Neurodevelopmental model of schizophrenia: update 2012. Mol. Psychiatry. 2012;17:1228-1238.

    Not in our library10.1038/mp.2012.23

  51. 51.

    Dong, D. et al. Compressed sensorimotor-to-transmodal hierarchical organization in schizophrenia. Psychol. Med., 10.1017/S0033291721002129 (2021).

    Not in our library10.1017/s0033291721002129

  52. 52.

    Dong D, et al. Compression of cerebellar functional gradients in schizophrenia. Schizophr. Bull. 2020;46:1282-1295.

    Not in our library10.1093/schbul/sbaa016

  53. 53.

    Sydnor VJ, et al. Neurodevelopment of the association cortices: patterns, mechanisms, and implications for psychopathology. Neuron. 2021;109:2820-2846.

    Not in our library10.1016/j.neuron.2021.06.016

  54. 54.

    Buckner RL, Krienen FM. The evolution of distributed association networks in the human brain. Trends Cogn. Sci. 2013;17:648-665.

    Not in our library10.1016/j.tics.2013.09.017

  55. 55.

    Huntenburg JM, Bazin P-L, Margulies DS. Large-scale gradients in human cortical organization. Trends Cogn. Sci. 2018;22:21-31.

    Not in our library10.1016/j.tics.2017.11.002

  56. 56.

    Hansen JY, et al. Mapping gene transcription and neurocognition across human neocortex. Nat. Hum. Behav. 2021;5:1240-1250.

    Not in our library10.1038/s41562-021-01082-z

  57. 57.

    Glasser MF, Goyal MS, Preuss TM, Raichle ME, Van Essen DC. Trends and properties of human cerebral cortex: correlations with cortical myelin content. Neuroimage. 2014;93:165-175.

    Not in our library10.1016/j.neuroimage.2013.03.060

  58. 58.
  59. 59.

    Demirtaş M, et al. Hierarchical heterogeneity across human cortex shapes large-scale neural dynamics. Neuron. 2019;101:1181-1194.

    Not in our library10.1016/j.neuron.2019.01.017

  60. 60.

    Hilgetag, C. C., Goulas, A. & Changeux, J.-P. A natural cortical axis connecting the outside and inside of the human brain. Network Neuroscience10.1162/netn_a_00256 (2022).

    Not in our library10.1162/netn_a_00256

  61. 61.
  62. 62.

    Naganawa M, et al. First-in-human assessment of 11C-LSN3172176, an M1 muscarinic acetylcholine receptor PET radiotracer. J. Nucl. Med. 2021;62:553-560.

    Not in our library10.2967/jnumed.120.246967

  63. 63.

    Savli M, et al. Normative database of the serotonergic system in healthy subjects using multi-tracer PET. Neuroimage. 2012;63:447-459.

    Not in our library10.1016/j.neuroimage.2012.07.001

  64. 64.

    Smart K, et al. Sex differences in [11C] ABP688 binding: a positron emission tomography study of mGlu5 receptors. Eur. J. Nucl. Med. Mol. Imaging. 2019;46:1179-1183.

    Not in our library10.1007/s00259-018-4252-4

  65. 65.

    Glasser MF, et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage. 2013;80:105-124.

    Not in our library10.1016/j.neuroimage.2013.04.127

  66. 66.

    Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis: II: inflation, flattening, and a surface-based coordinate system. Neuroimage. 1999;9:195-207.

    Not in our library10.1006/nimg.1998.0396

  67. 67.

    Ad-Dab’bagh, Y. et al. The CIVET image-processing environment: a fully automated comprehensive pipeline for anatomical neuroimaging research. In Proceedings of the 12th Annual Meeting of the Organization for Human Brain Mapping, Vol. 2266 (2006).

    Not in our library

  68. 68.

    Brett M, Johnsrude IS, Owen AM. The problem of functional localization in the human brain. Nat. Rev. Neurosci. 2002;3:243-249.

    Not in our library10.1038/nrn756

  69. 69.

    Li, X. et al. Moving beyond processing and analysis-related variation in neuroscience. Preprint at 10.1101/2021.12.01.470790 (2021).

    Not in our library

  70. 70.

    Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. Neuroimage. 2012;62:782-790.

    Not in our library10.1016/j.neuroimage.2011.09.015

  71. 71.

    Fischl B, Sereno MI, Tootell RB, Dale AM. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 1999;8:272-284.

    Not in our library10.1002/(sici)1097-0193(1999)8:4<272::aid-hbm10>3.0.co;2-4

  72. 72.

    Evans, A. C. et al. 3D statistical neuroanatomical models from 305 MRI volumes. In 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference 1813–1817 (IEEE, 1993).

    Not in our library

  73. 73.

    Evans AC, Janke AL, Collins DL, Baillet S. Brain templates and atlases. Neuroimage. 2012;62:911-922.

    Not in our library10.1016/j.neuroimage.2012.01.024

  74. 74.

    Van Essen DC, Glasser MF, Dierker DL, Harwell J, Coalson T. Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. Cereb. Cortex. 2012;22:2241-2262.

    Not in our library10.1093/cercor/bhr291

  75. 75.

    Lyttelton O, Boucher M, Robbins S, Evans A. An unbiased iterative group registration template for cortical surface analysis. Neuroimage. 2007;34:1535-1544.

    Not in our library10.1016/j.neuroimage.2006.10.041

  76. 76.

    Fonov VS, Evans AC, McKinstry RC, Almli C, Collins D. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. Neuroimage. 2009;47:S102.

    Not in our library

  77. 77.

    Van Essen DC. Windows on the brain: the emerging role of atlases and databases in neuroscience. Curr. Opin. Neurobiol. 2002;12:574-579.

    Not in our library10.1016/s0959-4388(02)00361-6

  78. 78.

    Fonov V, et al. Unbiased average age-appropriate atlases for pediatric studies. Neuroimage. 2011;54:313-327.

    Not in our library10.1016/j.neuroimage.2010.07.033

  79. 79.

    Mazziotta J, et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos. Trans. R. Soc. Lond. B Biol. Sci. 2001;356:1293-1322.

    Not in our library10.1098/rstb.2001.0915

  80. 80.

    Ashburner J, Friston KJ. Nonlinear spatial normalization using basis functions. Hum. Brain Mapp. 1999;7:254-266.

    Not in our library10.1002/(sici)1097-0193(1999)7:4<254::aid-hbm4>3.0.co;2-g

  81. 81.

    Collins DL, Neelin P, Peters TM, Evans AC. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J. Comput. Assist. Tomogr. 1994;18:192-205.

    Not in our library

  82. 82.

    Hamm J, Ye DH, Verma R, Davatzikos C. GRAM: a framework for geodesic registration on anatomical manifolds. Med. Image Anal. 2010;14:633-642.

    Not in our library10.1016/j.media.2010.06.001

  83. 83.

    Nenning K-H, et al. Diffeomorphic functional brain surface alignment: functional demons. Neuroimage. 2017;156:456-465.

    Not in our library10.1016/j.neuroimage.2017.04.028

  84. 84.

    Rueckert D, et al. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging. 1999;18:712-721.

    Not in our library10.1109/42.796284

  85. 85.

    Tong T, Aganj I, Ge T, Polimeni JR, Fischl B. Functional density and edge maps: characterizing functional architecture in individuals and improving cross-subject registration. Neuroimage. 2017;158:346-355.

    Not in our library10.1016/j.neuroimage.2017.07.019

  86. 86.

    Woods RP, Grafton ST, Holmes CJ, Cherry SR, Mazziotta JC. Automated image registration: I. General methods and intrasubject, intramodality validation. J. Comput. Assist. Tomogr. 1998;22:139-152.

    Not in our library10.1097/00004728-199801000-00027

  87. 87.

    Yushkevich, P. A., Wang, H., Pluta, J. & Avants, B. B. From label fusion to correspondence fusion: a new approach to unbiased groupwise registration. In 2012 IEEE Conference on Computer Vision and Pattern Recognition 956–963 (IEEE, 2012).

    Not in our library10.1109/cvpr.2012.6247771

  88. 88.

    Lancaster JL, et al. Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template. Hum. Brain Mapp. 2007;28:1194-1205.

    Not in our library10.1002/hbm.20345

  89. 89.

    Laird AR, et al. Comparison of the disparity between Talairach and MNI coordinates in functional neuroimaging data: validation of the Lancaster transform. Neuroimage. 2010;51:677-683.

    Not in our library10.1016/j.neuroimage.2010.02.048

  90. 90.

    Buckner RL, Krienen FM, Castellanos A, Diaz JC, Yeo BT. The organization of the human cerebellum estimated by intrinsic functional connectivity. J. Neurophysiol. 2011;106:2322-2345.

    Not in our library10.1152/jn.00339.2011

  91. 91.
  92. 92.
  93. 93.

    Van Essen DC. A Population-Average, Landmark- and Surface-based (PALS) atlas of human cerebral cortex. Neuroimage. 2005;28:635-662.

    Not in our library10.1016/j.neuroimage.2005.06.058

  94. 94.

    Pérez F, Granger BE. IPython: a system for interactive scientific computing. Computing in Science and Engineering. 2007;9:21-29.

    Not in our library

  95. 95.

    Kluyver, T. et al. Jupyter Notebooks: A publishing format for reproducible computational workflows. In Positioning and Power in Academic Publishing: Players, Agents and Agendas (eds Loizides, F. & Schmidt, B.) 87–90 (IOS Press, 2016).

    Not in our library

  96. 96.

    Hunter JD. Matplotlib: a 2D graphics environment. Computing in Science and Engineering. 2007;9:90-95.

    Not in our library

  97. 97.

    Brett, M. et al. nipy/nibabel. Zenodo10.5281/zenodo.591597 (2019).

    Not in our library

  98. 98.
  99. 99.

    Oliphant, T. E. A Guide to NumPy Vol. 1 (Trelgol Publishing USA, 2006).

    Not in our library

  100. 100.

    Van Der Walt S, Colbert SC, Varoquaux G. The NumPy array: a structure for efficient numerical computation. Computing in Science and Engineering. 2011;13:22-30.

    Not in our library

  101. 101.

    McKinney, W. et al. Data structures for statistical computing in Python. In Proceedings of the 9th Python in Science Conference (eds van der Walt, S. & Millman, J.) Vol. 445, 56–61 (2010).

    Not in our library

  102. 102.

    Waskom, M. et al. nipy/pysurfer. Zenodo10.5281/zenodo.592515 (2020).

    Not in our library

  103. 103.

    Pedregosa F, et al. Scikit-learn: machine learning in Python. Journal of Machine Learning Research. 2011;12:2825-2830.

    Not in our library

  104. 104.
  105. 105.

    Waskom, M. et al. mwaskom/seaborn. Zenodo10.5281/zenodo.592845 (2020).

    Not in our library

  106. 106.

    Gale, D. J., Vos de Wael, R., Benkarim, O. & Bernhardt, B. Surfplot: publication-ready brain surface figures (v0.1.0). Zenodo10.5281/zenodo.5567926 (2021).

    Not in our library

Cited by papers we hold 4

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.