1932

Abstract

Cognitive neuroscience has highlighted the cerebral cortex while often overlooking subcortical structures. This cortical proclivity is found in basic and translational research on many aspects of cognition, especially higher cognitive domains such as language, reading, music, and math. We suggest that, for both anatomical and evolutionary reasons, multiple subcortical structures play substantial roles across higher and lower cognition. We present a comprehensive review of existing evidence, which indeed reveals extensive subcortical contributions in multiple cognitive domains. We argue that the findings are overall both real and important. Next, we advance a theoretical framework to capture the nature of (sub)cortical contributions to cognition. Finally, we propose how new subcortical cognitive roles can be identified by leveraging anatomical and evolutionary principles, and we describe specific methods that can be used to reveal subcortical cognition. Altogether, this review aims to advance cognitive neuroscience by highlighting subcortical cognition and facilitating its future investigation.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-neuro-110920-013544
2022-07-08
2024-11-08
Loading full text...

Full text loading...

/deliver/fulltext/neuro/45/1/annurev-neuro-110920-013544.html?itemId=/content/journals/10.1146/annurev-neuro-110920-013544&mimeType=html&fmt=ahah

Literature Cited

  1. Amaral DG, Schumann CM, Nordahl CW. 2008.. Neuroanatomy of autism. . Trends Neurosci. 31::13745
    [Google Scholar]
  2. Attal Y, Bhattacharjee M, Yelnik J, Cottereau B, Lefèvre J, et al. 2007.. Modeling and detecting deep brain activity with MEG & EEG. . In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 493740. Piscataway, NJ:: IEEE
    [Google Scholar]
  3. Azevedo FA, Carvalho LR, Grinberg LT, Farfel JM, Ferretti RE, et al. 2009.. Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. . J. Comp. Neurol. 513::53241
    [Google Scholar]
  4. Bassett DS, Sporns O. 2017.. Network neuroscience. . Nat. Neurosci. 20::35364
    [Google Scholar]
  5. Bertolero MA, Yeo BT, D'Esposito M. 2015.. The modular and integrative functional architecture of the human brain. . PNAS 112::E6798807
    [Google Scholar]
  6. Bewernick BH, Kayser S, Sturm V, Schlaepfer TE. 2012.. Long-term effects of nucleus accumbens deep brain stimulation in treatment-resistant depression: evidence for sustained efficacy. . Neuropsychopharmacology 37::197585
    [Google Scholar]
  7. Brandt J, Rogerson M, Al-Joudi H, Reckess G, Shpritz B, et al. 2015.. Betting on DBS: effects of subthalamic nucleus deep brain stimulation on risk taking and decision making in patients with Parkinson's disease. . Neuropsychology 29::62231
    [Google Scholar]
  8. Caballero-Gaudes C, Reynolds RC. 2017.. Methods for cleaning the BOLD fMRI signal. . NeuroImage 154::12849
    [Google Scholar]
  9. Caligiore D, Pezzulo G, Baldassarre G, Bostan AC, Strick PL, et al. 2017.. Towards a systems-level view of cerebellar function: the interplay between cerebellum, basal ganglia, and cortex. . Cerebellum 16::20329
    [Google Scholar]
  10. Carrera E, Tononi G. 2014.. Diaschisis: past, present, future. . Brain 137::240822
    [Google Scholar]
  11. Chib VS, Yun K, Takahashi H, Shimojo S. 2013.. Noninvasive remote activation of the ventral midbrain by transcranial direct current stimulation of prefrontal cortex. . Transl. Psychiatry 3::e268
    [Google Scholar]
  12. Collins E, Park J, Behrmann M. 2017.. Numerosity representation is encoded in human subcortex. . PNAS 114::E280615
    [Google Scholar]
  13. Crosson B, McGregor K, Gopinath KS, Conway TW, Benjamin M, et al. 2007.. Functional MRI of language in aphasia: a review of the literature and the methodological challenges. . Neuropsychol. Rev. 17::15777
    [Google Scholar]
  14. DaSilva AF, Truong DQ, DosSantos MF, Toback RL, Datta A, Bikson M. 2015.. State-of-art neuroanatomical target analysis of high-definition and conventional tDCS montages used for migraine and pain control. . Front. Neuroanat. 9::89
    [Google Scholar]
  15. De Smet HJ, Paquier P, Verhoeven J, Mariën P. 2013.. The cerebellum: its role in language and related cognitive and affective functions. . Brain Lang. 127::33442
    [Google Scholar]
  16. Dehaene S, Cohen L. 2007.. Cultural recycling of cortical maps. . Neuron 56::38498
    [Google Scholar]
  17. Dehaene S, Molko N, Cohen L, Wilson AJ. 2004.. Arithmetic and the brain. . Curr. Opin. Neurobiol. 14::21824
    [Google Scholar]
  18. Delazer M, Domahs F, Lochy A, Karner E, Benke T, Poewe W. 2004.. Number processing and basal ganglia dysfunction: a single case study. . Neuropsychologia 42::105062
    [Google Scholar]
  19. Dickstein SG, Bannon K, Castellanos FX, Milham MP. 2006.. The neural correlates of attention deficit hyperactivity disorder: an ALE meta-analysis. . J. Child Psychol. Psychiatry 47::105162
    [Google Scholar]
  20. Diedrichsen J. 2006.. A spatially unbiased atlas template of the human cerebellum. . NeuroImage 33::12738
    [Google Scholar]
  21. Draganski B, Kherif F, Klöppel S, Cook PA, Alexander DC, et al. 2008.. Evidence for segregated and integrative connectivity patterns in the human basal ganglia. . J. Neurosci. 28::714352
    [Google Scholar]
  22. Earle FS, Del Tufo SN, Evans TM, Lum JA, Cutting LE, Ullman MT. 2020.. Domain-general learning and memory substrates of reading acquisition. . Mind Brain Educ. 14::17686
    [Google Scholar]
  23. Eltahawy HA, Saint-Cyr J, Giladi N, Lang AE, Lozano AM. 2004.. Primary dystonia is more responsive than secondary dystonia to pallidal interventions: outcome after pallidotomy or pallidal deep brain stimulation. . Neurosurgery 54::61321
    [Google Scholar]
  24. Engel AK, Moll CK, Fried I, Ojemann GA. 2005.. Invasive recordings from the human brain: clinical insights and beyond. . Nat. Rev. Neurosci. 6::3547
    [Google Scholar]
  25. Evans TM, Ullman MT. 2016.. An extension of the procedural deficit hypothesis from developmental language disorders to mathematical disability. . Front. Psychol. 7::1318
    [Google Scholar]
  26. Fedorenko E, Thompson-Schill SL. 2014.. Reworking the language network. . Trends Cogn. Sci. 18::12026
    [Google Scholar]
  27. Feng X, Altarelli I, Monzalvo K, Ding G, Ramus F, et al. 2020.. A universal reading network and its modulation by writing system and reading ability in French and Chinese children. . eLife 9::e54591
    [Google Scholar]
  28. Feng X, Deistung A, Dwyer MG, Hagemeier J, Polak P, et al. 2017.. An improved FSL-FIRST pipeline for subcortical gray matter segmentation to study abnormal brain anatomy using quantitative susceptibility mapping (QSM). . J. Magn. Reson. Imaging 39::11022
    [Google Scholar]
  29. Fitch WT. 2000.. The evolution of speech: a comparative review. . Trends Cogn. Sci. 4::25867
    [Google Scholar]
  30. Fitch WT. 2005.. The evolution of language: a comparative review. . Biol. Philos. 20::193203
    [Google Scholar]
  31. Friederici AD, Gierhan SM. 2013.. The language network. . Curr. Opin. Neurobiol. 23::25054
    [Google Scholar]
  32. Friend DM, Kravitz AV. 2014.. Working together: basal ganglia pathways in action selection. . Trends Neurosci. 37:(6):3013
    [Google Scholar]
  33. Gathercole SE, Baddeley AD. 2014.. Working Memory and Language. New York:: Psychology
    [Google Scholar]
  34. Genon S, Reid A, Langner R, Amunts K, Eickhoff SB. 2018.. How to characterize the function of a brain region. . Trends Cogn. Sci. 22::35064
    [Google Scholar]
  35. Givens BS, Olton DS. 1990.. Cholinergic and GABAergic modulation of medial septal area: effect on working memory. . Behav. Neurosci. 104::84955
    [Google Scholar]
  36. Gould SJ, Vrba ES. 1982.. Exaptation—a missing term in the science of form. . Paleobiology 8::415
    [Google Scholar]
  37. Hage SR, Nieder A. 2016.. Dual neural network model for the evolution of speech and language. . Trends Neurosci. 39::81329
    [Google Scholar]
  38. Haines DE. 2004.. Neuroanatomy: An Atlas of Structures, Sections, and Systems. New York:: Lippincott, Williams & Wilkins
    [Google Scholar]
  39. Hansel C, Linden DJ, D'Angelo E. 2001.. Beyond parallel fiber LTD: the diversity of synaptic and non-synaptic plasticity in the cerebellum. . Nat. Neurosci. 4::46775
    [Google Scholar]
  40. Hariz MI, Robertson MM. 2010.. Gilles de la Tourette syndrome and deep brain stimulation. . Eur. J. Neurosci. 32::112834
    [Google Scholar]
  41. Heinze H-J, Heldmann M, Voges J, Hinrichs H, Marco-Pallares J, et al. 2009.. Counteracting incentive sensitization in severe alcohol dependence using deep brain stimulation of the nucleus accumbens: clinical and basic science aspects. . Front. Hum. Neurosci. 3::22
    [Google Scholar]
  42. Hunt S, Low J, Burns K. 2008.. Adaptive numerical competency in a food-hoarding songbird. . Proc. R. Soc. B 275::237379
    [Google Scholar]
  43. Janata P. 2005.. Brain networks that track musical structure. . Ann. N. Y. Acad. Sci. 1060::11124
    [Google Scholar]
  44. Jankovic J. 2008.. Parkinson's disease: clinical features and diagnosis. . J. Neurol. Neurosurg. Psychiatry 79::36876
    [Google Scholar]
  45. Ji JL, Spronk M, Kulkarni K, Repovš G, Anticevic A, Cole MW. 2019.. Mapping the human brain's cortical–subcortical functional network organization. . NeuroImage 185::3557
    [Google Scholar]
  46. Johari K, Walenski M, Reifegersete J, Ashrafi F, Behroozmand R, et al. 2019.. A dissociation between syntactic and lexical processing in Parkinson's disease. . J. Neurolinguistics 51::22135
    [Google Scholar]
  47. Keeser D, Meindl T, Bor J, Palm U, Pogarell O, et al. 2011.. Prefrontal transcranial direct current stimulation changes connectivity of resting-state networks during fMRI. . J. Neurosci. 31::1528493
    [Google Scholar]
  48. Kotz SA, Schwartze M. 2010.. Cortical speech processing unplugged: a timely subcortico-cortical framework. . Trends Cogn. Sci. 14::39299
    [Google Scholar]
  49. Koziol LF, Budding DE. 2009.. Subcortical Structures and Cognition: Implications for Neuropsychological Assessment. Berlin:: Springer
    [Google Scholar]
  50. Kreitzer AC, Malenka RC. 2008.. Striatal plasticity and basal ganglia circuit function. . Neuron 60::54354
    [Google Scholar]
  51. Krishnaswamy P, Obregon-Henao G, Ahveninen J, Khan S, Babadi B, et al. 2017.. Sparsity enables estimation of both subcortical and cortical activity from MEG and EEG. . PNAS 114::E1046574
    [Google Scholar]
  52. Krolak-Salmon P, Hénaff MA, Tallon-Baudry C, Yvert B, Guénot M, et al. 2003.. Human lateral geniculate nucleus and visual cortex respond to screen flicker. . Ann. Neurol. 53::7380
    [Google Scholar]
  53. Lachaux J-P, Axmacher N, Mormann F, Halgren E, Crone NE. 2012.. High-frequency neural activity and human cognition: past, present and possible future of intracranial EEG research. . Prog. Neurobiol. 98::279301
    [Google Scholar]
  54. Lee DJ, Gurkoff GG, Izadi A, Berman RF, Ekstrom AD, et al. 2013.. Medial septal nucleus theta frequency deep brain stimulation improves spatial working memory after traumatic brain injury. . J. Neurotrauma 30::13139
    [Google Scholar]
  55. Leszczyński M, Staudigl T. 2016.. Memory-guided attention in the anterior thalamus. . Neurosci. Biobehav. Rev. 66::16365
    [Google Scholar]
  56. Li X, Nahas Z, Kozel FA, Anderson B, Bohning DE, George MS. 2004.. Acute left prefrontal transcranial magnetic stimulation in depressed patients is associated with immediately increased activity in prefrontal cortical as well as subcortical regions. . Biol. Psychiatry 55::88290
    [Google Scholar]
  57. Logie RH, Gilhooly KJ, Wynn V. 1994.. Counting on working memory in arithmetic problem solving. . Mem. Cogn. 22::395410
    [Google Scholar]
  58. Lum JA, Ullman MT, Conti-Ramsden G. 2013.. Procedural learning is impaired in dyslexia: evidence from a meta-analysis of serial reaction time studies. . Res. Dev. Dis. 34::346076
    [Google Scholar]
  59. MacLean PD. 1988.. Triune brain. . In Comparative Neuroscience and Neurobiology, ed. LN Irwin , pp. 12628. Berlin:: Springer
    [Google Scholar]
  60. Mah Y-H, Husain M, Rees G, Nachev P. 2014.. Human brain lesion–deficit inference remapped. . Brain 137::252231
    [Google Scholar]
  61. Mariën P, Ackermann H, Adamaszek M, Barwood CH, Beaton A, et al. 2014.. Consensus paper: language and the cerebellum: an ongoing enigma. . Cerebellum 13::386410
    [Google Scholar]
  62. Mariën P, Baillieux H, De Smet HJ, Engelborghs S, Wilssens I, et al. 2009.. Cognitive, linguistic and affective disturbances following a right superior cerebellar artery infarction: a case study. . Cortex 45::52736
    [Google Scholar]
  63. Martinez-Gonzalez C, Bolam JP, Mena-Segovia J. 2011.. Topographical organization of the pedunculopontine nucleus. . Front. Neuroanat. 5::22
    [Google Scholar]
  64. Miranda PC, Lomarev M, Hallett M. 2006.. Modeling the current distribution during transcranial direct current stimulation. . Clin. Neurophysiol. 117::162329
    [Google Scholar]
  65. Münte TF, Heldmann M, Hinrichs H, Marco-Pallares J, Krämer UM, et al. 2008.. Nucleus accumbens is involved in human action monitoring: evidence from invasive electrophysiological recordings. . Front. Hum. Neurosci. 2::11
    [Google Scholar]
  66. Murphy E, Hoshi K, Benítez-Burraco A. 2021.. Subcortical syntax: reconsidering the neural dynamics of language. . PsyArXiv. https://psyarxiv.com/29cjw/
  67. Nicolson RI, Fawcett AJ. 2007.. Procedural learning difficulties: reuniting the developmental disorders?. Trends Neurosci. 30::13541
    [Google Scholar]
  68. Nioche C, Cabanis E, Habas C. 2009.. Functional connectivity of the human red nucleus in the brain resting state at 3T. . Am. J. Neuroradiol. 30::396403
    [Google Scholar]
  69. Noback CR, Strominger NL, Demarest RJ, Ruggiero DA. 2005.. The Human Nervous System: Structure and Function. Berlin:: Springer
    [Google Scholar]
  70. Papavassiliou E, Rau G, Heath S, Abosch A, Barbaro NM, et al. 2004.. Thalamic deep brain stimulation for essential tremor: relation of lead location to outcome. . Neurosurgery 54::112030
    [Google Scholar]
  71. Parazzini M, Fiocchi S, Rossi E, Paglialonga A, Ravazzani P. 2011.. Transcranial direct current stimulation: estimation of the electric field and of the current density in an anatomical human head model. . IEEE Trans. Biomed. Eng. 58::177380
    [Google Scholar]
  72. Parvizi J. 2009.. Corticocentric myopia: old bias in new cognitive sciences. . Trends Cogn. Sci. 13::35459
    [Google Scholar]
  73. Patel AD. 2003.. Language, music, syntax and the brain. . Nat. Neurosci. 6::67481
    [Google Scholar]
  74. Pessoa L. 2014.. Understanding brain networks and brain organization. . Phys. Rev. 11::40035
    [Google Scholar]
  75. Poldrack RA. 2007.. Region of interest analysis for fMRI. . Soc. Cogn. Affect. Neurosci. 2::6770
    [Google Scholar]
  76. Postuma RB, Dagher A. 2006.. Basal ganglia functional connectivity based on a meta-analysis of 126 positron emission tomography and functional magnetic resonance imaging publications. . Cereb. Cortex 16::150821
    [Google Scholar]
  77. Quaresima V, Bisconti S, Ferrari M. 2012.. A brief review on the use of functional near-infrared spectroscopy (fNIRS) for language imaging studies in human newborns and adults. . Brain Lang. 121::7989
    [Google Scholar]
  78. Rorden C, Karnath H-O. 2004.. Using human brain lesions to infer function: a relic from a past era in the fMRI age?. Nat. Rev. Neurosci. 5::81219
    [Google Scholar]
  79. Roşca EC. 2009.. Arithmetic procedural knowledge: a cortico-subcortical circuit. . Brain Res. 1302::14856
    [Google Scholar]
  80. Roth Y, Amir A, Levkovitz Y, Zangen A. 2007.. Three-dimensional distribution of the electric field induced in the brain by transcranial magnetic stimulation using figure-8 and deep H-coils. . J. Clin. Neurophysiol. 24::3138
    [Google Scholar]
  81. Santarnecchi E, Brem A-K, Levenbaum E, Thompson T, Kadosh RC, Pascual-Leone A. 2015.. Enhancing cognition using transcranial electrical stimulation. . Curr. Opin. Behav. Sci. 4::17178
    [Google Scholar]
  82. Sara SJ. 2009.. The locus coeruleus and noradrenergic modulation of cognition. . Nat. Rev. Neurosci. 10:(3):21123
    [Google Scholar]
  83. Saunders A, Oldenburg IA, Berezovskii VK, Johnson CA, Kingery ND, et al. 2015.. A direct GABAergic output from the basal ganglia to frontal cortex. . Nature 521::8589
    [Google Scholar]
  84. Savjani RR, Katyal S, Halfen E, Kim JH, Ress D. 2018.. Polar-angle representation of saccadic eye movements in human superior colliculus. . NeuroImage 171::199208
    [Google Scholar]
  85. Schmahmann JD, Guell X, Stoodley CJ, Halko MA. 2019.. The theory and neuroscience of cerebellar cognition. . Annu. Rev. Neurosci. 42::33764
    [Google Scholar]
  86. Sihvonen AJ, Ripollés P, Leo V, Rodríguez-Fornells A, Soinila S, Särkämö T. 2016.. Neural basis of acquired amusia and its recovery after stroke. . J. Neurosci. 36::887281
    [Google Scholar]
  87. Stephenson-Jones M, Samuelsson E, Ericsson J, Robertson B, Grillner S. 2011.. Evolutionary conservation of the basal ganglia as a common vertebrate mechanism for action selection. . Curr. Biol. 21:(13):108191
    [Google Scholar]
  88. Strafella AP, Paus T, Barrett J, Dagher A. 2001.. Repetitive transcranial magnetic stimulation of the human prefrontal cortex induces dopamine release in the caudate nucleus. . J. Neurosci. 21::RC157
    [Google Scholar]
  89. Tian F, Liu H. 2014.. Depth-compensated diffuse optical tomography enhanced by general linear model analysis and an anatomical atlas of human head. . NeuroImage 85::16680
    [Google Scholar]
  90. Tsivilis D, Vann SD, Denby C, Roberts N, Mayes AR, et al. 2008.. A disproportionate role for the fornix and mammillary bodies in recall versus recognition memory. . Nat. Neurosci. 11::83442
    [Google Scholar]
  91. Uddin LQ, Yeo BT, Spreng RN. 2019.. Towards a universal taxonomy of macro-scale functional human brain networks. . Brain Topogr. 32::92642
    [Google Scholar]
  92. Ullman MT. 2004.. Contributions of memory circuits to language: the declarative/procedural model. . Cognition 92::23170
    [Google Scholar]
  93. Ullman MT. 2006.. Is Broca's area part of a basal ganglia thalamocortical circuit?. Cortex 42::48085
    [Google Scholar]
  94. Ullman MT. 2007.. The biocognition of the mental lexicon. . In The Oxford Handbook of Psycholinguistics, ed. MG Gaskell , pp. 26786. Oxford, UK:: Oxford Univ. Press
    [Google Scholar]
  95. Ullman MT. 2016.. The declarative/procedural model: a neurobiological model of language learning, knowledge, and use. . In Neurobiology of Language, ed. G Hickok, SL Small , pp. 95368. Amsterdam:: Elsevier
    [Google Scholar]
  96. Ullman MT. 2020.. The declarative/procedural model: a neurobiologically motivated theory of first and second language. . In Theories in Second Language Acquisition, ed. B VanPatten, GD Keating, S Wulff , pp. 12861. New York:: Routledge
    [Google Scholar]
  97. Ullman MT, Earle FS, Walenski M, Janacsek K. 2020.. The neurocognition of developmental disorders of language. . Annu. Rev. Psychol. 71::389417
    [Google Scholar]
  98. Ulrich M, Lorenz S, Spitzer MW, Steigleder L, Kammer T, Grön G. 2018.. Theta-burst modulation of mid-ventrolateral prefrontal cortex affects salience coding in the human ventral tegmental area. . Appetite 123::91100
    [Google Scholar]
  99. Vann SD, Aggleton JP. 2004.. The mammillary bodies: two memory systems in one?. Nat. Rev. Neurosci. 5::3544
    [Google Scholar]
  100. Vargha-Khadem F, Gadian DG, Copp A, Mishkin M. 2005.. FOXP2 and the neuroanatomy of speech and language. . Nat. Rev. Neurosci. 6::13138
    [Google Scholar]
  101. Whaley NR, Fujioka S, Wszolek ZK. 2011.. Autosomal dominant cerebellar ataxia type I: a review of the phenotypic and genotypic characteristics. . Orphanet J. Rare Dis. 6::33
    [Google Scholar]
  102. Wright NF, Vann SD, Aggleton JP, Nelson AJ. 2015.. A critical role for the anterior thalamus in directing attention to task-relevant stimuli. . J. Neurosci. 35::548088
    [Google Scholar]
  103. Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, et al. 2011.. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. . J. Neurophysiol. 106::112565
    [Google Scholar]
/content/journals/10.1146/annurev-neuro-110920-013544
Loading
/content/journals/10.1146/annurev-neuro-110920-013544
Loading

Data & Media loading...

Supplementary Data

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error