Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Topographic organization of the human subcortex unveiled with functional connectivity gradients

Nature Neuroscience volume 23, pages 1421–1432 (2020)Cite this article

Subjects

Abstract

Brain atlases are fundamental to understanding the topographic organization of the human brain, yet many contemporary human atlases cover only the cerebral cortex, leaving the subcortex a terra incognita. We use functional MRI (fMRI) to map the complex topographic organization of the human subcortex, revealing large-scale connectivity gradients and new areal boundaries. We unveil four scales of subcortical organization that recapitulate well-known anatomical nuclei at the coarsest scale and delineate 27 new bilateral regions at the finest. Ultrahigh field strength fMRI corroborates and extends this organizational structure, enabling the delineation of finer subdivisions of the hippocampus and the amygdala, while task-evoked fMRI reveals a subtle subcortical reorganization in response to changing cognitive demands. A new subcortical atlas is delineated, personalized to represent individual differences and used to uncover reproducible brain–behavior relationships. Linking cortical networks to subcortical regions recapitulates a task-positive to task-negative axis. This new atlas enables holistic connectome mapping and characterization of cortico–subcortical connectivity.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Gradientography in the human subcortex.
Fig. 2: Model selection: which gradient magnitude peaks are sufficiently large to warrant boundary delineation?
Fig. 3: A multiscale group-consensus parcellation atlas derived from 3T-rfMRI acquired in 1,080 healthy adults.
Fig. 4: Organization of the human subcortex.
Fig. 5: Parcellation homogeneity.
Fig. 6: Replication of 3T parcellation using 7T-rfMRI.
Fig. 7: Personalization of the scale IV atlas.
Fig. 8: Associations between individual variation in behavioral dimensions and subcortical functional connectivity.

Data availability

Neuroimaging data analyses were undertaken using publicly available human neuroimaging datasets acquired and maintained by the HCP18. These datasets are available for download to anyone agreeing to the Open Access Data Use Terms (https://db.humanconnectome.org/). Access to family structure data and several behavioral measures requires acceptance of the HCP Restricted Data Use Terms (https://www.humanconnectome.org/study/hcp-young-adult/document/restricted-data-usage). The independent validation dataset used to assess the reproducibility of the parcellation homogeneity results is currently not publicly available. The new atlas is openly available in the form of NIFTI (Neuroimaging Informatics Technology Initiative) and CIFTI (Connectivity Informatics Technology Initiative) files. To facilitate mapping of whole-brain connectomes, we have also integrated the new atlas into several well-known cortex-only parcellation atlases and the combined cortex–subcortex atlases are made openly available. Supplementary Table 5 provides details about the atlas formats that can be downloaded from the GitHub repository (https://github.com/yetianmed/subcortex).

Code availability

The Matlab (R2018b) codes to compute Laplacian eigenmaps, gradient magnitudes, diversity curves and other computational analyses undertaken as part of this study are openly available at https://github.com/yetianmed/subcortex. The following additional software packages used for this study are freely and openly available: Diffusion Toolkit (v.0.6.4.1) and TrackVis (v.0.6.1): http://trackvis.org/; NBS (v.1.2): https://www.nitrc.org/projects/nbs/; Icasso (v.1.21): https://research.ics.aalto.fi/ica/icasso/; NeuroMArVL: https://immersive.erc.monash.edu/neuromarvl/; PALM (v.alpha116): https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/PALM/ExchangeabilityBlocks; and fMRIPrep (v.1.5.9): https://fmriprep.org/en/stable/index.html.

References

  1. Eickhoff, S. B., Yeo, B. T. T. & Genon, S. Imaging-based parcellations of the human brain. Nat. Rev. Neurosci. 19, 672–686 (2018).

    CAS  PubMed  Google Scholar 

  2. Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Yeo, B. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011).

    PubMed  Google Scholar 

  4. Shepherd, G. M. Corticostriatal connectivity and its role in disease. Nat. Rev. Neurosci. 14, 278–291 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Choi, E. Y., Yeo, B. T. & Buckner, R. L. The organization of the human striatum estimated by intrinsic functional connectivity. J. Neurophysiol. 108, 2242–2263 (2012).

    PubMed  PubMed Central  Google Scholar 

  6. Ji, J. L. et al. Mapping the human brain’s cortical–subcortical functional network organization. NeuroImage 185, 35–57 (2019).

    PubMed  Google Scholar 

  7. Cohen, A. L. et al. Defining functional areas in individual human brains using resting functional connectivity MRI. NeuroImage 41, 45–57 (2008).

    PubMed  Google Scholar 

  8. Gordon, E. M. et al. Generation and evaluation of a cortical area parcellation from resting-state correlations. Cereb. Cortex 26, 288–303 (2016).

    PubMed  Google Scholar 

  9. Schaefer, A. et al. Local–global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb. Cortex 28, 3095–3114 (2018).

    PubMed  Google Scholar 

  10. Huntenburg, J. M., Bazin, P. L. & Margulies, D. S. Large-scale gradients in human cortical organization. Trends Cogn. Sci. 22, 21–31 (2018).

    PubMed  Google Scholar 

  11. Haak, K. V., Marquand, A. F. & Beckmann, C. F. Connectopic mapping with resting-state fMRI. NeuroImage 170, 83–94 (2018).

    PubMed  Google Scholar 

  12. Margulies, D. S. et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc. Natl Acad. Sci. USA 113, 12574–12579 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Guell, X., Schmahmann, J. D., Gabrieli, J. & Ghosh, S. S. Functional gradients of the cerebellum. eLife 7, e36652 (2018).

    PubMed  PubMed Central  Google Scholar 

  14. King, M., Hernandez-Castillo, C. R., Poldrack, R. A., Ivry, R. B. & Diedrichsen, J. Functional boundaries in the human cerebellum revealed by a multi-domain task battery. Nat. Neurosci. 22, 1371–1378 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Tian, Y. & Zalesky, A. Characterizing the functional connectivity diversity of the insula cortex: subregions, diversity curves and behavior. NeuroImage 183, 716–733 (2018).

    CAS  PubMed  Google Scholar 

  16. Coalson, T. S., Van Essen, D. C. & Glasser, M. F. The impact of traditional neuroimaging methods on the spatial localization of cortical areas. Proc. Natl Acad. Sci. USA 115, E6356–E6365 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Fox, M. D. et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl Acad. Sci. USA 102, 9673–9678 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Van Essen, D. C. et al. The WU–Minn Human Connectome Project: an overview. NeuroImage 80, 62–79 (2013).

    PubMed  Google Scholar 

  19. Finn, E. S. et al. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18, 1664 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Müller, E. et al. Core and matrix thalamic sub-populations relate to spatio-temporal cortical connectivity gradients. NeuroImage 222, 117224 (2020).

    PubMed  Google Scholar 

  21. Poppenk, J., Evensmoen, H. R., Moscovitch, M. & Nadel, L. Long-axis specialization of the human hippocampus. Trends Cogn. Sci. 17, 230–240 (2013).

    PubMed  Google Scholar 

  22. Amunts, K. et al. Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anat. Embryol. 210, 343–352 (2005).

    CAS  Google Scholar 

  23. Marquand, A. F., Haak, K. V. & Beckmann, C. F. Functional corticostriatal connection topographies predict goal directed behaviour in humans. Nat. Hum. Behav. 1, 0146 (2017).

    PubMed  PubMed Central  Google Scholar 

  24. Vu, A. et al. Tradeoffs in pushing the spatial resolution of fMRI for the 7T Human Connectome Project. NeuroImage 154, 23–32 (2017).

    Google Scholar 

  25. Gordon, E. M. et al. Precision functional mapping of individual human brains. Neuron 95, 791–807.e7 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Kong, R. et al. Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion. Cereb. Cortex 29, 2533–2551 (2019).

    PubMed  Google Scholar 

  27. Salehi, M. et al. There is no single functional atlas even for a single individual: functional parcel definitions change with task. NeuroImage 208, 116366 (2020).

    PubMed  Google Scholar 

  28. Zalesky, A., Fornito, A. & Bullmore, E. T. Network-based statistic: identifying differences in brain networks. NeuroImage 53, 1197–1207 (2010).

    PubMed  Google Scholar 

  29. Maia, T. V. & Frank, M. J. From reinforcement learning models to psychiatric and neurological disorders. Nat. Neurosci. 14, 154–162 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Forstmann, B. U., de Hollander, G., van Maanen, L., Alkemade, A. & Keuken, M. C. Towards a mechanistic understanding of the human subcortex. Nat. Rev. Neurosci. 18, 57–65 (2016).

    PubMed  Google Scholar 

  31. Vos de Wael, R. et al. Anatomical and microstructural determinants of hippocampal subfield functional connectome embedding. Proc. Natl Acad. Sci. USA 115, 10154–10159 (2018).

    PubMed  PubMed Central  Google Scholar 

  32. Churchland, P. S. & Sejnowski, T. J. Perspectives on cognitive neuroscience. Science 242, 741–745 (1988).

    CAS  PubMed  Google Scholar 

  33. Fan, L. et al. The Human Brainnetome Atlas: a new brain atlas based on connectional architecture. Cereb. Cortex 26, 3508–3526 (2016).

    PubMed  PubMed Central  Google Scholar 

  34. Pauli, W. M., Nili, A. N. & Tyszka, J. M. A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei. Sci. Data 5, 180063 (2018).

    PubMed  PubMed Central  Google Scholar 

  35. Plachti, A. et al. Multimodal parcellations and extensive behavioral profiling tackling the hippocampus gradient. Cereb. Cortex 29, 4595–4612 (2019).

    PubMed  PubMed Central  Google Scholar 

  36. Janssen, R. J., Jylänki, P., Kessels, R. P. C. & van Gerven, M. A. J. Probabilistic model-based functional parcellation reveals a robust, fine-grained subdivision of the striatum. NeuroImage 119, 398–405 (2015).

    CAS  PubMed  Google Scholar 

  37. Behrens, T. E. et al. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat. Neurosci. 6, 750–757 (2003).

    CAS  PubMed  Google Scholar 

  38. Haber, S. N. & Knutson, B. The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology 35, 4–26 (2010).

    PubMed  Google Scholar 

  39. Morel, A., Magnin, M. & Jeanmonod, D. Multiarchitectonic and stereotactic atlas of the human thalamus. J. Comp. Neurol. 387, 588–630 (1997).

    CAS  PubMed  Google Scholar 

  40. Meredith, G. E., Pattiselanno, A., Groenewegen, H. J. & Haber, S. N. Shell and core in monkey and human nucleus accumbens identified with antibodies to calbindin-D28k. J. Comp. Neurol. 365, 628–639 (1996).

    CAS  PubMed  Google Scholar 

  41. Cocchi, L. & Zalesky, A. Personalized transcranial magnetic stimulation in psychiatry. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 3, 731–741 (2018).

    PubMed  Google Scholar 

  42. Castro, D. C. & Bruchas, M. R. A motivational and neuropeptidergic hub: anatomical and functional diversity within the nucleus accumbens shell. Neuron 102, 529–552 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Pauli, W. M., O’Reilly, R. C., Yarkoni, T. & Wager, T. D. Regional specialization within the human striatum for diverse psychological functions. Proc. Natl Acad. Sci. USA 113, 1907–1912 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. MacAskill, A. F., Little, J. P., Cassel, J. M. & Carter, A. G. Subcellular connectivity underlies pathway-specific signaling in the nucleus accumbens. Nat. Neurosci. 15, 1624–1626 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Perez, S. M. & Lodge, D. J. Convergent inputs from the hippocampus and thalamus to the nucleus accumbens regulate dopamine neuron activity. J. Neurosci. 38, 10607–10618 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Pontieri, F. E., Tanda, G., Orzi, F. & Di Chiara, G. Effects of nicotine on the nucleus accumbens and similarity to those of addictive drugs. Nature 382, 255–257 (1996).

    CAS  PubMed  Google Scholar 

  47. Gallinat, J. et al. Abnormal hippocampal neurochemistry in smokers: evidence from proton magnetic resonance spectroscopy at 3T. J. Clin. Psychopharmacol. 27, 80–84 (2007).

    CAS  PubMed  Google Scholar 

  48. Xu, Z., Seidler, F. J., Ali, S. F., Slikker, W. Jr. & Slotkin, T. A. Fetal and adolescent nicotine administration: effects on CNS serotonergic systems. Brain Res. 914, 166–178 (2001).

    CAS  PubMed  Google Scholar 

  49. de Leon, J. & Diaz, F. J. A meta-analysis of worldwide studies demonstrates an association between schizophrenia and tobacco smoking behaviors. Schizophrenia Res. 76, 135–157 (2005).

    Google Scholar 

  50. Koukouli, F. et al. Nicotine reverses hypofrontality in animal models of addiction and schizophrenia. Nat. Med. 23, 347–354 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M. & Nichols, T. E. Permutation inference for the general linear model. NeuroImage 92, 381–397 (2014).

    PubMed  Google Scholar 

  52. Winkler, A. M., Webster, M. A., Vidaurre, D., Nichols, T. E. & Smith, S. M. Multi-level block permutation. NeuroImage 123, 253–268 (2015).

    PubMed  Google Scholar 

  53. Smith, S. M. et al. Resting-state fMRI in the Human Connectome Project. NeuroImage 80, 144–168 (2013).

    PubMed  Google Scholar 

  54. Barch, D. M. et al. Function in the human connectome: task-fMRI and individual differences in behavior. NeuroImage 80, 169–189 (2013).

    PubMed  Google Scholar 

  55. Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage 80, 105–124 (2013).

    PubMed  Google Scholar 

  56. Salimi-Khorshidi, G. et al. Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. NeuroImage 90, 449–468 (2014).

    PubMed  Google Scholar 

  57. Griffanti, L. et al. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. NeuroImage 95, 232–247 (2014).

    PubMed  Google Scholar 

  58. Parkes, L., Fulcher, B., Yucel, M. & Fornito, A. An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. NeuroImage 171, 415–436 (2018).

    PubMed  Google Scholar 

  59. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59, 2142–2154 (2012).

    PubMed  Google Scholar 

  60. Satterthwaite, T. D. et al. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. NeuroImage 64, 240–256 (2013).

    PubMed  Google Scholar 

  61. Van Dijk, K. R., Sabuncu, M. R. & Buckner, R. L. The influence of head motion on intrinsic functional connectivity MRI. NeuroImage 59, 431–438 (2012).

    PubMed  Google Scholar 

  62. Puckett, A. M. et al. Using multi-echo simultaneous multi-slice (SMS) EPI to improve functional MRI of the subcortical nuclei of the basal ganglia at ultra-high field (7T). NeuroImage 172, 886–895 (2018).

    PubMed  Google Scholar 

  63. Glasser, M. F. et al. The Human Connectome Project’s neuroimaging approach. Nat. Neurosci. 19, 1175–1187 (2016).

    PubMed  PubMed Central  Google Scholar 

  64. Glasser, M. F. et al. Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data. NeuroImage 181, 692–717 (2018).

    PubMed  Google Scholar 

  65. Cole, M. W. et al. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat. Neurosci. 16, 1348–1355 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods 16, 111–116 (2019).

    CAS  PubMed  Google Scholar 

  67. Percheron, G., Yelnik, J. & Francois, C. A Golgi analysis of the primate globus pallidus. III. Spatial organization of the striato–pallidal complex. J. Comp. Neurol. 227, 214–227 (1984).

    CAS  PubMed  Google Scholar 

  68. Yushkevich, P. A. et al. Quantitative comparison of 21 protocols for labeling hippocampal subfields and parahippocampal subregions in in vivo MRI: towards a harmonized segmentation protocol. NeuroImage 111, 526–541 (2015).

    PubMed  Google Scholar 

  69. Guadalupe, T. et al. Human subcortical brain asymmetries in 15,847 people worldwide reveal effects of age and sex. Brain Imaging Behav. 11, 1497–1514 (2017).

    PubMed  Google Scholar 

  70. de Hollander, G., Keuken, M. C., van der Zwaag, W., Forstmann, B. U. & Trampel, R. Comparing functional MRI protocols for small, iron-rich basal ganglia nuclei such as the subthalamic nucleus at 7 T and 3 T. Hum. Brain Mapp. 38, 3226–3248 (2017).

    PubMed  PubMed Central  Google Scholar 

  71. Belkin, M. & Niyogi, P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15, 1373–1396 (2003).

    Google Scholar 

  72. Fornito, A., Zalesky, A. & Breakspear, M. Graph analysis of the human connectome: promise, progress, and pitfalls. NeuroImage 80, 426–444 (2013).

    PubMed  Google Scholar 

  73. Cerliani, L. et al. Probabilistic tractography recovers a rostrocaudal trajectory of connectivity variability in the human insular cortex. Hum. Brain Mapp. 33, 2005–2034 (2012).

    PubMed  Google Scholar 

  74. Jeurissen, B., Descoteaux, M., Mori, S. & Leemans, A. Diffusion MRI fiber tractography of the brain. NMR Biomed. 32, e3785 (2019).

    PubMed  Google Scholar 

  75. Alexander, D. C. in Visualization and Processing of Tensor Fields (eds Weickert J. & Hagen, H.) Ch. 5 (Springer, 2006).

  76. Friman, O., Farneback, G. & Westin, C. F. A Bayesian approach for stochastic white matter tractography. IEEE Trans. Med. Imaging 25, 965–978 (2006).

    PubMed  Google Scholar 

  77. Peled, S., Friman, O., Jolesz, F. & Westin, C. F. Geometrically constrained two-tensor model for crossing tracts in DWI. Magn. Reson. Imaging 24, 1263–1270 (2006).

    PubMed  PubMed Central  Google Scholar 

  78. Behrens, T. E., Berg, H. J., Jbabdi, S., Rushworth, M. F. & Woolrich, M. W. Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? NeuroImage 34, 144–155 (2007).

    CAS  PubMed  Google Scholar 

  79. Wang, R., Benner, T., Sorensen, A. & Wedeen, V. Diffusion Toolkit: a software package for diffusion imaging data processing and tractography. Proc. Int. Soc. Magn. Reson. Med. 15, 3720 (2007).

    Google Scholar 

  80. O’Donnell, L. J. & Westin, C. F. Automatic tractography segmentation using a high-dimensional white matter atlas. IEEE Trans. Med. Imaging 26, 1562–1575 (2007).

    PubMed  Google Scholar 

  81. Müller, M. in Information Retrieval for Music and Motion 69–84 (Springer, 2007).

  82. Benjamini, Y. Discovering the false discovery rate. J. R. Stat. Soc. Ser. B 72, 405–416 (2010).

    Google Scholar 

  83. Serrano, M. Á., Boguñá, M. & Vespignani, A. Extracting the multiscale backbone of complex weighted networks. Proc. Natl Acad. Sci. USA 106, 6483–6488 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Meyer, F. Topographic distance and watershed lines. Signal Process. 38, 113–125 (1994).

    Google Scholar 

  85. Eickhoff, S. B. et al. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. NeuroImage 25, 1325–1335 (2005).

    PubMed  Google Scholar 

  86. Lancichinetti, A., Fortunato, S. & Kertész, J. Detecting the overlapping and hierarchical community structure in complex networks. N. J. Phys. 11, 033015 (2009).

    Google Scholar 

  87. Greene, A. S., Gao, S., Scheinost, D. & Constable, R. T. Task-induced brain state manipulation improves prediction of individual traits. Nat. Commun. 9, 2807 (2018).

    PubMed  PubMed Central  Google Scholar 

  88. Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S. & Petersen, S. E. Intrinsic and task-evoked network architectures of the human brain. Neuron 83, 238–251 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Medaglia, J. D., Lynall, M. E. & Bassett, D. S. Cognitive network neuroscience. J. Cogn. Neurosci. 27, 1471–1491 (2015).

    PubMed  PubMed Central  Google Scholar 

  90. Smith, S. M. et al. A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat. Neurosci. 18, 1565–1567 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Fornito, A., Zalesky, A., Pantelis, C. & Bullmore, E. T. Schizophrenia, neuroimaging and connectomics. NeuroImage 62, 2296–2314 (2012).

    PubMed  Google Scholar 

  92. Xia, C. H. et al. Linked dimensions of psychopathology and connectivity in functional brain networks. Nat. Commun. 9, 3003 (2018).

    PubMed  PubMed Central  Google Scholar 

  93. Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008, P10008 (2008).

    Google Scholar 

  94. Van der Waerden, B. Oder tests for two-sample problem and their power. Proc. K. Nederlandse Akademie van Wetenschappen. Ser. A 55, 453–458 (1952).

    Google Scholar 

  95. Himberg, J., Hyvarinen, A. & Esposito, F. Validating the independent components of neuroimaging time series via clustering and visualization. NeuroImage 22, 1214–1222 (2004).

    PubMed  Google Scholar 

  96. Hyvarinen, A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10, 626–634 (1999).

    CAS  PubMed  Google Scholar 

  97. Kuhn, H. W. The Hungarian method for the assignment problem. Nav. Res. Logist. Q 2, 83–97 (1955).

    Google Scholar 

  98. Freedman, D. & Lane, D. A nonstochastic interpretation of reported significance levels. J. Bus. Economic Stat. 1, 292–298 (1983).

    Google Scholar 

  99. Phillips, J. W. et al. A repeated molecular architecture across thalamic pathways. Nat. Neurosci. 22, 1925–1935 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. Hwang, K., Bertolero, M. A., Liu, W. B. & D’Esposito, M. The human thalamus is an integrative hub for functional brain networks. J. Neurosci. 37, 5594–5607 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Shine, J. M. et al. The low-dimensional neural architecture of cognitive complexity is related to activity in medial thalamic nuclei. Neuron 104, 849–855.e3 (2019).

    CAS  PubMed  Google Scholar 

  102. Mink, J. W. The basal ganglia: focused selection and inhibition of competing motor programs. Prog. Neurobiol. 50, 381–425 (1996).

    CAS  PubMed  Google Scholar 

  103. Janak, P. H. & Tye, K. M. From circuits to behaviour in the amygdala. Nature 517, 284–292 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank M. Glasser (Washington University) for provision of the Wishart filter code. We thank L. Cocchi (QIMR Berghofer Medical Research Institute) for provision of the additional validation dataset. All other data were provided by the HCP, the WU–Minn Consortium (1U54MH091657; Principal Investigators: D. Van Essen and K. Ugurbil) funded by the 16 National Institutes of Health (NIH) institutes and centers that support the NIH Blueprint for Neuroscience Research, and by the McDonnell Center for Systems Neuroscience at Washington University. Y.T. was supported by a NHMRC Project Grant awarded to A.Z. (APP1142801). M.B. and A.Z. were each supported by research fellowships from the NHMRC (APP1136649 and APP1118153, respectively).

Author information

Authors and Affiliations

  1. Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia

    Ye Tian & Andrew Zalesky

  2. Centre National de la Recherche Scientifique (CNRS) UMR 8002, Integrative Neuroscience and Cognition Center, Université de Paris, Paris, France

    Daniel S. Margulies

  3. Discipline of Psychiatry, Faculty of Health and Medicine, University of Newcastle, Newcastle, New South Wales, Australia

    Michael Breakspear

  4. School of Psychology, Faculty of Science, University of Newcastle, Newcastle, New South Wales, Australia

    Michael Breakspear

  5. Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, Victoria, Australia

    Andrew Zalesky

Authors
  1. Ye Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel S. Margulies
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael Breakspear
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrew Zalesky
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.T. and A.Z. conceived the idea and designed the study. Y.T. compiled the data, performed the analyses and prepared the visualizations. Y.T. and A.Z. drafted the manuscript. D.S.M. and M.B. provided critical conceptual input. All authors provided critical feedback and editing of the final manuscript. A.Z. supervised the research.

Corresponding authors

Correspondence to Ye Tian or Andrew Zalesky.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Janine Bijsterbosch, Rodrigo Braga and Evan Gordon for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–24 and Supplementary Tables 1–5.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tian, Y., Margulies, D.S., Breakspear, M. et al. Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nat Neurosci 23, 1421–1432 (2020). https://doi.org/10.1038/s41593-020-00711-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-020-00711-6

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing