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Towards a mechanistic understanding of the human subcortex

Abstract

The human subcortex is a densely populated part of the brain, of which only 7% of the individual structures are depicted in standard MRI atlases. In vivo MRI of the subcortex is challenging owing to its anatomical complexity and its deep location in the brain. The technical advances that are needed to reliably uncover this 'terra incognita' call for an interdisciplinary human neuroanatomical approach. We discuss the emerging methods that could be used in such an approach and the incorporation of the data that are generated from these methods into model-based cognitive neuroscience frameworks.

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Figure 1: Visualization of the human subcortex.
Figure 2: A functional theory on cortico–basal ganglia–thalamo–cortical network.
Figure 3: Multilevel data acquisition pipeline.

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References

  1. Federative Committee on Anatomical Terminology. Terminologia Anatomica: International Anatomical Terminology 1st edn (Thieme Stuttgart, 1998).

  2. Dunbar, R. I. M. Neocortex size as a constraint on group size in primates. J. Hum. Evol. 22, 469–493 (1992).

    Article  Google Scholar 

  3. Alkemade, A., Keuken, M. C. & Forstmann, B. U. A perspective on terra incognita: uncovering the neuroanatomy of the human subcortex. Front. Neuroanat. http://dx.doi.org/10.3389/fnana.2013.00040 (2013).

  4. de Hollander, G., Keuken, M. C. & Forstmann, B. U. The subcortical cocktail problem; mixed signals from the subthalamic nucleus and substantia nigra. PLoS ONE 10, e0120572 (2015).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  5. Amunts, K. & Zilles, K. Architectonic mapping of the human brain beyond Brodmann. Neuron 88, 1086–1107 (2015).

    Article  CAS  PubMed  Google Scholar 

  6. van Veen, V., Krug, M. K. & Carter, C. S. The neural and computational basis of controlled speed–accuracy tradeoff during task performance. J. Cogn. Neurosci. 20, 1952–1965 (2008).

    Article  PubMed  Google Scholar 

  7. Forstmann, B. U. et al. Striatum and pre-SMA facilitate decision-making under time pressure. Proc. Natl Acad. Sci. USA 105, 17538–17542 (2008).

    Article  CAS  PubMed  Google Scholar 

  8. Forstmann, B. U. et al. Cortico-striatal connections predict control over speed and accuracy in perceptual decision making. Proc. Natl Acad. Sci. USA 107, 15916–15920 (2010).

    Article  CAS  PubMed  Google Scholar 

  9. Bogacz, R. & Gurney, K. The basal ganglia and cortex implement optimal decision making between alternative actions. Neural Comput. 19, 442–477 (2007).

    Article  PubMed  Google Scholar 

  10. Bogacz, R., Wagenmakers, E.-J., Forstmann, B. U. & Nieuwenhuis, S. The neural basis of the speed–accuracy tradeoff. Trends Neurosci. 33, 10–16 (2010).

    Article  CAS  PubMed  Google Scholar 

  11. Ding, L. & Gold, J. I. The basal ganglia's contributions to perceptual decision making. Neuron 79, 640–649 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Redgrave, P., Prescott, T. J. & Gurney, K. The basal ganglia: a vertebrate solution to the selection problem? Neuroscience 89, 1009–1023 (1999).

    Article  CAS  PubMed  Google Scholar 

  13. Johansen-Berg, H. Human connectomics — what will the future demand? Neuroimage 80, 541–544 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Keuken, M. C. et al. Quantifying inter-individual anatomical variability in the subcortex using 7 T structural MRI. Neuroimage 94, 40–46 (2014).

    Article  CAS  PubMed  Google Scholar 

  15. Baecke, S. et al. A proof-of-principle study of multi-site real-time functional imaging at 3 T and 7 T: implementation and validation. Sci. Rep. 5, 8413–8418 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hahn, A., Kranz, G. S., Seidel, E. M., Sladky, R. & Kraus, C. Comparing neural response to painful electrical stimulation with functional MRI at 3 and 7 T. Neuroimage 82, 336–343 (2013).

    Article  PubMed  Google Scholar 

  17. Hale, J. R. et al. Comparison of functional connectivity in default mode and sensorimotor networks at 3 and 7 T. MAGMA 23, 339–349 (2010).

    Article  PubMed  Google Scholar 

  18. Cho, Z. H. et al. New brain atlas — mapping the human brain in vivo with 7.0 T MRI and comparison with postmortem histology: will these images change modern medicine? Int. J. Imaging Syst. Technol. 18, 2–8 (2008).

    Article  Google Scholar 

  19. Cho, Z. H. et al. Direct visualization of deep brain stimulation targets in Parkinson disease with the use of 7-tesla magnetic resonance imaging. J. Neurosurg. 113, 639–647 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Kerl, H. U. et al. The subthalamic nucleus at 7.0 Tesla: evaluation of sequence and orientation for deep-brain stimulation. Acta Neurochir. 154, 2051–2062 (2012).

    Article  PubMed  Google Scholar 

  21. Kerl, H. U. Imaging for deep brain stimulation: the zona incerta at 7 Tesla. World J. Radiol. 5, 5–16 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Kerl, H. U. et al. The subthalamic nucleus at 3.0 Tesla: choice of optimal sequence and orientation for deep brain stimulation using a standard installation protocol. J. Neurosurg. 117, 1155–1165 (2012).

    Article  PubMed  Google Scholar 

  23. Yao, B. et al. Susceptibility contrast in high field MRI of human brain as a function of tissue iron content. Neuroimage 44, 1259–1266 (2009).

    Article  PubMed  Google Scholar 

  24. Kerl, H. U., Gerigk, L., Huck, S., Al-Zghloul, M. & Groden, C. Visualisation of the zona incerta for deep brain stimulation at 3.0 Tesla. Clin. Neuroradiol. 22, 55–68 (2012).

    Article  CAS  PubMed  Google Scholar 

  25. Edelstein, W. A., Glover, G. H., Hardy, C. J. & Redington, R. W. The intrinsic signal-to-noise ratio in NMR imaging. Magn. Reson. Med. 3, 604–618 (1986).

    Article  CAS  PubMed  Google Scholar 

  26. Wiggins, G. C. et al. 96-channel receive-only head coil for 3 Tesla: design optimization and evaluation. Magn. Reson. Med. 62, 754–762 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Federau, C. & Gallichan, D. Motion-correction enabled ultra-high resolution in-vivo 7 T-MRI of the brain. PLoS ONE 11, e0154974 (2016).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  28. Morelli, J. N. et al. An image-based approach to understanding the physics of MR artifacts. Radiographics 31, 849–866 (2011).

    Article  PubMed  Google Scholar 

  29. Kanowski, M. et al. Direct visualization of anatomic subfields within the superior aspect of the human lateral thalamus by MRI at 7 T. AJNR Am. J. Neuroradiol. 35, 1721–1727 (2014).

    Article  CAS  PubMed  Google Scholar 

  30. Tourdias, T., Saranathan, M., Levesque, I. R., Su, J. & Rutt, B. K. Visualization of intra-thalamic nuclei with optimized white-matter-nulled MPRAGE at 7 T. Neuroimage 84, 534–545 (2014).

    Article  PubMed  Google Scholar 

  31. Saranathan, M., Tourdias, T., Bayram, E., Ghanouni, P. & Rutt, B. K. Optimization of white-matter-nulled magnetization prepared rapid gradient echo (MP-RAGE) imaging. Magn. Reson. Med. 73, 1786–1794 (2015).

    Article  PubMed  Google Scholar 

  32. van der Zwaag, W., Schäfer, A., Marques, J. P., Turner, R. & Trampel, R. Recent applications of UHF-MRI in the study of human brain function and structure: a review. NMR Biomed. 29, 1274–1288 (2015).

    Article  PubMed  Google Scholar 

  33. Langkammer, C. et al. Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study. Neuroimage 62, 1593–1599 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Schweser, F., Deistung, A. & Reichenbach, J. R. Foundations of MRI phase imaging and processing for Quantitative Susceptibility Mapping (QSM). Z. Med. Phys. 26, 6–34 (2016).

    Article  PubMed  Google Scholar 

  35. Schweser, F., Deistung, A., Lehr, B. W. & Reichenbach, J. R. Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: an approach to in vivo brain iron metabolism? Neuroimage 54, 2789–2807 (2011).

    Article  PubMed  Google Scholar 

  36. Zecca, L., Youdim, M., Riederer, P., Connor, J. & Crichton, R. Iron, brain ageing and neurodegenerative disorders. Nat. Rev. Neurosci. 5, 863–873 (2004).

    Article  CAS  Google Scholar 

  37. Aquino, D. et al. Age-related iron deposition in the basal ganglia: quantitative analysis in healthy subjects. Radiology 252, 165–172 (2009).

    Article  PubMed  Google Scholar 

  38. Haacke, E. M. et al. Quantitative susceptibility mapping: current status and future directions. Magn. Reson. Med. 33, 1–25 (2015).

    Google Scholar 

  39. Hollander, G. et al. A gradual increase of iron toward the medial-inferior tip of the subthalamic nucleus. Hum. Brain. Mapp. 35, 4440–4449 (2014).

    Article  PubMed  Google Scholar 

  40. Visser, E. et al. Automatic segmentation of the striatum and globus pallidus using MIST: multimodal image segmentation tool. Neuroimage 125, 479–497 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Visser, E., Keuken, M. C., Forstmann, B. U. & Jenkinson, M. Automated segmentation of the substantia nigra, subthalamic nucleus and red nucleus in 7 T data at young and old age. Neuroimage 139, 324–336 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Strotmann, B. et al. High-resolution MRI and diffusion-weighted imaging of the human habenula at 7 tesla. J. Magn. Reson. Imaging 39, 1018–1026 (2013).

    Article  PubMed  Google Scholar 

  43. Wargo, C. J. & Gore, J. C. Localized high-resolution DTI of the human midbrain using single-shot EPI, parallel imaging, and outer-volume suppression at 7 T. Magn. Reson. Imaging 31, 810–819 (2013).

    Article  PubMed  Google Scholar 

  44. Dyvorne, H., O'Halloran, R. & Balchandani, P. Ultrahigh field single-refocused diffusion weighted imaging using a matched-phase adiabatic spin echo (MASE). Magn. Reson. Med. 75, 1949–1957 (2015).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  45. Keuken, M. C. et al. The subthalamic nucleus during decision-making with multiple alternatives. Hum. Brain. Mapp. 36, 4041–4052 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Polders, D. L. et al. Signal to noise ratio and uncertainty in diffusion tensor imaging at 5, 3.0, and 7.0 Tesla. J. Magn. Reson. Imaging 33, 1456–1463 (2011).

    Article  PubMed  Google Scholar 

  47. Heidemann, R. M., Anwander, A., Feiweier, T., Knösche, T. R. & Turner, R. k-Space and q-space: combining ultra-high spatial and angular resolution in diffusion imaging using ZOOPPA at 7 T. Neuroimage 60, 967–978 (2012).

    Article  PubMed  Google Scholar 

  48. Calamante, F. et al. Super-resolution track-density imaging of thalamic substructures: comparison with high-resolution anatomical magnetic resonance imaging at 7.0 T. Hum. Brain Mapp. 34, 2538–2548 (2012).

    Article  PubMed  Google Scholar 

  49. Michalareas, G. et al. Alpha-beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas. Neuron 89, 384–397 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. DeSimone, K., Viviano, J. D. & Schneider, K. A. Population receptive field estimation reveals new retinotopic maps in human subcortex. J. Neurosci. 35, 9836–9847 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. De Martino, F. et al. Spatial organization of frequency preference and selectivity in the human inferior colliculus. Nat. Commun. 4, 1386–1388 (2015).

    Article  CAS  Google Scholar 

  52. Engel, S. A., Glover, G. H. & Wandell, B. A. Retinotopic organization in human visual cortex and the spatial precision of functional MRI. Cereb. Cortex 7, 181–192 (1997).

    Article  CAS  PubMed  Google Scholar 

  53. Shmuel, A., Yacoub, E., Chaimow, D., Logothetis, N. K. & Ugurbil, K. Spatio-temporal point-spread function of fMRI signal in human gray matter at 7 Tesla. Neuroimage 35, 539–552 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Yacoub, E. et al. Imaging brain function in humans at 7 Tesla. Magn. Reson. Med. 45, 588–594 (2001).

    Article  CAS  PubMed  Google Scholar 

  55. Uludag, K., Müller-Bierl, B. & Ugurbil, K. An integrative model for neuronal activity-induced signal changes for gradient and spin echo functional imaging. Neuroimage 48, 150–165 (2009).

    Article  PubMed  Google Scholar 

  56. Robitaille, P.-M. & Berliner, L. Ultra High Field Magnetic Resonance Imaging (Springer, 2007).

    Google Scholar 

  57. Barry, R. L. et al. On the origins of signal variance in fMRI of the human midbrain at high field. PLoS ONE 8, e62708 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Peters, A. M. et al. T 2* measurements in human brain at 1.5, 3 and 7 T. Magn. Reson. Imaging 25, 748–753 (2007).

    Article  PubMed  Google Scholar 

  59. de Zwart, J. A., van Gelderen, P., Kellman, P. & Duyn, J. H. Application of sensitivity-encoded echo-planar imaging for blood oxygen level-dependent functional brain imaging. Magn. Reson. Imaging 48, 1011–1020 (2002).

    Google Scholar 

  60. Pruessmann, K. P., Weiger, M., Scheidegger, M. B. & Boesiger, P. SENSE: sensitivity encoding for fast MRI. Magn. Reson. Med. 42, 952–962 (1999).

    Article  CAS  PubMed  Google Scholar 

  61. Shapiro, E. M., Sharer, K., Skrtic, S. & Koretsky, A. P. In vivo detection of single cells by MRI. Magn. Reson. Med. 55, 242–249 (2006).

    Article  PubMed  Google Scholar 

  62. Makris, N. et al. Volumetric parcellation methodology of the human hypothalamus in neuroimaging: normative data and sex differences. Neuroimage 69, 1–10 (2013).

    Article  PubMed  Google Scholar 

  63. Stucht, D. et al. Highest resolution in vivo human brain MRI using prospective motion correction. PLoS ONE 10, e0133921 (2015).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  64. Frost, R. et al. Prospective motion correction and selective reacquisition using volumetric navigators for vessel-encoded arterial spin labeling dynamic angiography. Magn. Reson. Med. 76, 1420–1430 (2015).

    Article  PubMed  CAS  Google Scholar 

  65. Cabezas, M., Oliver, A., Lladó, X., Freixenet, J. & Cuadra, M. B. A review of atlas-based segmentation for magnetic resonance brain images. Comput. Methods Programs Biomed. 104, e158–e177 (2011).

    Article  PubMed  Google Scholar 

  66. Heimann, T. & Meinzer, H.-P. Statistical shape models for 3D medical image segmentation: a review. Med. Image Anal. 13, 543–563 (2009).

    Article  PubMed  Google Scholar 

  67. Patenaude, B., Smith, S. M., Kennedy, D. N. & Jenkinson, M. A. Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage 56, 907–922 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Fischl, B. et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).

    Article  CAS  PubMed  Google Scholar 

  69. Makris, N. et al. Decreased volume of left and total anterior insular lobule in schizophrenia. Schizophr. Res. 83, 155–171 (2006).

    Article  PubMed  Google Scholar 

  70. Shattuck, D. W. et al. Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage 39, 1064–1080 (2008).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  72. Forstmann, B. U. et al. Cortico-subthalamic white matter tract strength predict interindividual efficacy in stopping a motor response. Neuroimage 60, 370–375 (2012).

    Article  PubMed  Google Scholar 

  73. Keuken, M. C. et al. Ultra-high 7 T MRI of structural age-related changes of the subthalamic nucleus. J. Neurosci. 33, 4896–4900 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Kim, J., Lenglet, C., Duchin, Y., Sapiro, G. & Harel, N. Semiautomatic segmentation of brainsubcortical structures from high-field MRI. IEEE J. Biomed. Health Inform. 18, 1678–1695 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Daugherty, A. M., Haacke, E. M. & Raz, N. Striatal iron content predicts its shrinkage and changes in verbal working memory after two years in healthy adults. J. Neurosci. 35, 6731–6743 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Stelzer, J., Lohmann, G., Mueller, K., Buschmann, T. & Turner, R. Deficient approaches to human neuroimaging. Front. Hum. Neurosci. 8, 462 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Turner, R. in High-Field MR Imaging (Springer, 2011).

    Google Scholar 

  78. Turner, R. & Geyer, S. Comparing like with like: the power of knowing where you are. Brain Connect. 4, 547–557 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Turner, R. in Microstructural Parcellation of the Human Cerebral Cortex ( eds Geyer, S. & Turner, R. ) (Springer, 2013).

    Google Scholar 

  80. Weiss, M. et al. Spatial normalization of ultrahigh resolution 7 T magnetic resonance imaging data of the postmortem human subthalamic nucleus: a multistage approach. Brain Struct. Funct. 220, 1695–1703 (2015).

    Article  PubMed  Google Scholar 

  81. Amunts, K. et al. BigBrain: an ultrahigh-resolution 3D human brain model. Science 340, 1472–1475 (2013).

    Article  CAS  PubMed  Google Scholar 

  82. Shi, S. R., Key, M. E. & Kalra, K. L. Antigen retrieval in formalin-fixed, paraffin-embedded tissues: an enhancement method for immunohistochemical staining based on microwave oven heating of tissue sections. J. Histochem. Cytochem. 39, 741–748 (1991).

    Article  CAS  PubMed  Google Scholar 

  83. Chu, W.-S. et al. Ultrasound-accelerated formalin fixation of tissue improves morphology, antigen and mRNA preservation. Mod. Pathol. 18, 850–863 (2004).

    Article  CAS  Google Scholar 

  84. van Duijn, S. et al. MRI artifacts in human brain tissue after prolonged formalin storage. Magn. Reson. Med. 65, 1750–1758 (2011).

    Article  PubMed  Google Scholar 

  85. Hauptmann, G., Lauter, G. & Söll, I. Detection and signal amplification in zebrafish RNA FISH. Methods 98, 50–59 (2016).

    Article  CAS  PubMed  Google Scholar 

  86. Ravid, R., Van Zwieten, E. J. & Swaab, D. F. Brain banking and the human hypothalamus — factors to match for, pitfalls and potentials. Prog. Brain Res. 93, 83–95 (1992).

    Article  CAS  PubMed  Google Scholar 

  87. Alkemade, A. et al. AgRP and NPY expression in the human hypothalamic infundibular nucleus correlate with body mass index, whereas changes in αMSH are related to type 2 diabetes. J. Clin. Endocrinol. Metab. 97, E925–E933 (2012).

    Article  CAS  PubMed  Google Scholar 

  88. Kretzschmar, H. Brain banking: opportunities, challenges and meaning for the future. Nat. Rev. Neurosci. 10, 70–78 (2009).

    Article  CAS  PubMed  Google Scholar 

  89. Weiskopf, N., Mohammadi, S., Lutti, A. & Callaghan, M. F. Advances in MRI-based computational neuroanatomy. Curr. Opin. Neurol. 28, 313–322 (2015).

    Article  CAS  PubMed  Google Scholar 

  90. Stüber, C. et al. Myelin and iron concentration in the human brain: a quantitative study of MRI contrast. Neuroimage 93, 95–106 (2014).

    Article  PubMed  CAS  Google Scholar 

  91. Schmierer, K. et al. Quantitative magnetic resonance of postmortem multiple sclerosis brain before and after fixation. Magn. Reson. Med. 59, 268–277 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  92. Dawe, R. J., Bennett, D. A., Schneider, J. A., Vasireddi, S. K. & Arfanakis, K. Postmortem MRI of human brain hemispheres: T 2 relaxation times during formaldehyde fixation. Magn. Reson. Med. 61, 810–818 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  93. Keren, N. I. et al. Histologic validation of locus coeruleus MRI contrast in post-mortem tissue. Neuroimage 113, 235–245 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Mottershead, J. P. et al. High field MRI correlates of myelin content and axonal density in multiple sclerosis. J. Neurol. 250, 1293–1301 (2003).

    Article  CAS  PubMed  Google Scholar 

  95. Plantinga, B. R. et al. Ultra-high field magnetic resonance imaging of the basal ganglia and related structures. Front. Hum. Neurosci. 8, 1–22 (2014).

    Article  Google Scholar 

  96. Gazzaniga, M., Ivry, R. & Mangun, G. Cognitive Neuroscience (MIT Press, 2007).

    Google Scholar 

  97. Forstmann, B. U. & Wagenmakers, E.-J. (eds) An Introduction to Model-Based Cognitive Neuroscience (Springer, 2015).

    Book  Google Scholar 

  98. Ratcliff, R. A theory of memory retrieval. Psychol. Rev. 85, 59–108 (1978).

    Article  Google Scholar 

  99. Forstmann, B. U., Ratcliff, R. & Wagenmakers, E.-J. Sequential sampling models in cognitive neuroscience: advantages, applications, and extensions. Annu. Rev. Psychol. 67, 641–666 (2016).

    Article  CAS  PubMed  Google Scholar 

  100. Marr, D. Vision: a Computational Investigation into the Human Representation and Processing of Visual Information (San Francisco, 1982).

    Google Scholar 

  101. Jonas, E. & Kording, K. Automatic discovery of cell types and microcircuitry from neural connectomics. eLife 4, e04250 (2015).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  102. O'Doherty, J. et al. Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science 304, 452–454 (2004).

    Article  CAS  PubMed  Google Scholar 

  103. Sutton, R. S. & Barto, A. G. Introduction to Reinforcement Learning (MIT Press, 1998).

    Book  Google Scholar 

  104. Cavanagh, J. F. et al. Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold. Nat. Neurosci. 14, 1462–1467 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Logan, G. D. & Cowan, W. B. On the ability to inhibit thought and action: a theory of an act of control. Psychol. Rev. 91, 295–327 (1984).

    Article  Google Scholar 

  106. Lo, C. C. & Wang, X. J. Cortico–basal ganglia circuit mechanism for a decision threshold in reaction time tasks. Nat. Neurosci. 9, 956–963 (2006).

    Article  CAS  PubMed  Google Scholar 

  107. Frank, M. J. Hold your horses: a dynamic computational role for the subthalamic nucleus in decision making. Neural Netw. 19, 1120–1136 (2006).

    Article  PubMed  Google Scholar 

  108. de Hollander, G., Forstmann, B. U. & Brown, S. D. Different ways of linking behavioral and neural data via computational cognitive models. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 1, 101–109 (2016).

    Article  PubMed  Google Scholar 

  109. Frank, M. J. et al. fMRI and EEG predictors of dynamic decision parameters during human reinforcement learning. J. Neurosci. 35, 485–494 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Mittner, M., Hawkins, G. E., Boekel, W. & Forstmann, B. U. A. A neural model of mind wandering. Trends Cogn. Sci. 20, 570–578 (2016).

    Article  PubMed  Google Scholar 

  111. Turner, B. M., Forstmann, B. U., Love, B. C., Palmeri, T. J. & Van Maanen, L. Approaches to analysis in model-based cognitive neuroscience. J. Math. Psychol. http://dx.doi.org/10.1016/j.jmp.2016.01.001 (2016).

  112. Bogacz, R., Usher, M., Zhang, J. & McClelland, J. Extending a biologically inspired model of choice: multi-alternatives, nonlinearity and value-based multidimensional choice. Phil. Trans R. Soc. B 362, 1655–1670 (2007).

    Article  PubMed  Google Scholar 

  113. Bogacz, R., Hu, P., Holmes, P. & Cohen, J. Do humans produce the speed–accuracy trade-off that maximizes reward rate? Q. J. Exp. Psychol. 63, 863–891 (2010).

    Article  Google Scholar 

  114. Wei, W., Rubin, J. E. & Wang, X. J. Role of the Indirect pathway of the basal ganglia in perceptual decision making. J. Neurosci. 35, 4052–4064 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Turner, B. M., Rodriguez, C. A., Norcia, T. M., McClure, S. M. & Steyvers, M. Why more is better: simultaneous modeling of EEG, fMRI, and behavioral data. Neuroimage 128, 96–115 (2016).

    Article  PubMed  Google Scholar 

  116. Turner, B. M. et al. A Bayesian framework for simultaneously modeling neural and behavioral data. Neuroimage 72, 193–206 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  117. Turner, B. M., van Maanen, L. & Forstmann, B. U. Informing cognitive abstractions through neuroimaging: the neural drift diffusion model. Psychol. Rev. 122, 312–336 (2015).

    Article  PubMed  Google Scholar 

  118. van Maanen, L. et al. Neural correlates of trial-to-trial fluctuations in response caution. J. Neurosci. 31, 17488–17495 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Purcell, B. A. et al. Neurally constrained modeling of perceptual decision making. Psychol. Rev. 117, 1113–1143 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  120. Candela, L., Castelli, D., Manghi, P. & Tani, A. Data journals: a survey. J. Assoc. Inf. Sci. Technol. 66, 1747–1762 (2015).

    Article  Google Scholar 

  121. Poldrack, R. A. & Gorgolewski, K. J. Making big data open: data sharing in neuroimaging. Nat. Neurosci. 17, 1510–1517 (2014).

    Article  CAS  PubMed  Google Scholar 

  122. Poline, J.-B. et al. Data sharing in neuroimaging research. Front. Neuroinform. 6, 9 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  123. Eickhoff, S., Nichols, T. E., Van Horn, J. D. & Turner, J. A. Sharing the wealth: neuroimaging data repositories. Neuroimage 124, 1065–1068 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  124. [No authors listed.] More bang for your byte. Sci. Data 1, 140010 (2014).

  125. Wang, H.-R. “Publish or perish”: should this still be true for your data? Data Brief 1, 85–86 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  126. Duyn, J. H. The future of ultra-high field MRI and fMRI for study of the human brain. Neuroimage 62, 1241–1248 (2012).

    Article  PubMed  Google Scholar 

  127. Bronstein, J. M. et al. Deep brain stimulation for Parkinson disease: an expert consensus and review of key issues. Arch. Neurol. 68, 165 (2011).

    Article  PubMed  Google Scholar 

  128. de Koning, P. P., Figee, M., van den Munckhof, P., Schuurman, P. R. & Denys, D. Current status of deep brain stimulation for obsessive–compulsive disorder: a clinical review of different targets. Curr. Psychiatry Rep. 13, 274–282 (2011).

    Article  PubMed  Google Scholar 

  129. Groiss, S. J., Wojtecki, L., Südmeyer, M. & Schnitzler, A. Deep brain stimulation in Parkinson's disease. Ther. Adv. Neurol. Disord. 2, 20–28 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Christen, M., Bittlinger, M., Walter, H., Brugger, P. & Müller, S. Dealing with side effects of deep brain stimulation: lessons learned from stimulating the STN. AJOB Neurosci. 3, 37–43 (2012).

    Article  Google Scholar 

  131. Sporns, O., Tononi, G. & Kötter, R. The human connectome: a structural description of the human brain. PLoS Comput. Biol. 1, e42 (2005).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  132. Van Essen, D. C. et al. The Human Connectome Project: a data acquisition perspective. Neuroimage 62, 2222–2231 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Chevrier, A. D., Noseworthy, M. D. & Schachar, R. Dissociation of response inhibition and performance monitoring in the stop signal task using event-related fMRI. Hum. Brain Mapp. 28, 1347–1358 (2007).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  135. Forstmann, B. U. et al. Multi-modal ultra-high resolution structural 7-Tesla MRI data repository. Sci. Data 1, 140050–140058 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  136. Gorgolewski, K. J. et al. A high resolution 7-Tesla resting-state fMRI test-retest dataset with cognitive and physiological measures. Sci. Data 2, 140054 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  137. Tardif, C. L. et al. Open Science CBS Neuroimaging Repository: sharing ultra-high-field MR images of the brain. Neuroimage 124, 1143–1148 (2016).

    Article  PubMed  Google Scholar 

  138. Keuken, M. C. & Forstmann, B. U. A probabilistic atlas of the basal ganglia using 7 T MRI. Data Brief 4, 577–582 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  139. Hanke, M. et al. A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie. Sci. Data 1, 140003 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors thank A. Schäfer, R. Trampel and W. van der Zwaag for helpful discussion about this manuscript and R. Mulray who assisted in proofreading of the manuscript. The authors' research was supported by an ERC grant from the European Research Council (B.U.F.), a Vidi grant from the Dutch Organization for Scientific Research (B.U.F.), a grant by the dutch Hersenstichting (B.U.F. and A.A.), and the Dutch Parkinson Funds (B.U.F. and A.A.).

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The time constant that describes the decay of the transverse component of the net magnetization due to the accumulated phase differences that are caused by spin–spin interactions and local magnetic field inhomogeneities.

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Forstmann, B., de Hollander, G., van Maanen, L. et al. Towards a mechanistic understanding of the human subcortex. Nat Rev Neurosci 18, 57–65 (2017). https://doi.org/10.1038/nrn.2016.163

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