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.

  • Review Article
  • Published:

The anatomical basis of functional localization in the cortex

Key Points

  • The functions of a cortical area are determined by its extrinsic connections and intrinsic properties. Each cortical area has a unique pattern of cortico-cortical connections — a 'connectional fingerprint'.

  • No two areas share identical connection patterns. Instead, there are families of areas that share a resemblance in their connections.

  • Cortical areas also have their own 'functional fingerprints': the proportions of cells that fire in association with different tasks or task events differ between areas. The connectional fingerprint seems to underlie such cell-firing differences.

  • In addition to electrophysiological approaches, imaging will be a useful tool for detecting functional fingerprints, because it allows comparisons of activations across many cortical areas and across a wide range of tasks.

Abstract

The functions of a cortical area are determined by its extrinsic connections and intrinsic properties. Using the database CoCoMac, we show that each cortical area has a unique pattern of cortico-cortical connections — a 'connectional fingerprint'. We present examples of such fingerprints and use statistical analysis to show that no two areas share identical patterns. We suggest that the connectional fingerprint underlies the observed cell-firing differences between areas during different tasks. We refer to this pattern as a 'functional fingerprint' and present examples of such fingerprints. In addition to electrophysiological analysis, functional fingerprints can be determined by functional brain imaging. We argue that imaging provides a useful way to define such fingerprints because it is possible to compare activations across many cortical areas and across a wide range of tasks.

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

Access options

Buy this article

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

Figure 1: Diagram of anatomical fingerprints for two prefrontal areas — Walker's areas 9 and 14.
Figure 2: Analysis of prefrontal connectivity.
Figure 3: Diagram of anatomical fingerprints for two premotor areas — F3 and F5.
Figure 4: Analysis of premotor connectivity.
Figure 5: Cell firing associated with a pair of visually and memory-guided tasks.
Figure 6: Functional fingerprints for five motor areas.

Similar content being viewed by others

References

  1. Franz, S. I. & Lashley, K. S. The retention of habits by the rat after destruction of the frontal parts of the cerebrum. Psychobiology 1, 3–18 (1917).

    Article  Google Scholar 

  2. Lashley, K. S. The Neuropsychology of Lashley(eds Beach, F. A., Hebb, D. O., Morgan, C. T. & Nissen, H. W.) (McGraw–Hill, New York, 1960).

    Google Scholar 

  3. Pribram, K. H. & Mishkin, M. Simultaneous and successive visual discrimination by monkeys with inferotemporal lesions. J. Comp. Physiol. Psychol. 48, 198–202 (1955).

    Article  CAS  PubMed  Google Scholar 

  4. Mishkin, M. Effects of small frontal lesions on delayed alternation in monkeys. J. Neurophysiol. 20, 615–622 (1957).

    Article  CAS  PubMed  Google Scholar 

  5. Mishkin, M. in The Brain and Human Behavior (ed. Karczmar, A. G.) 187–208 (Springer, Berlin, 1972).

    Book  Google Scholar 

  6. Halsband, U. & Passingham, R. E. Premotor cortex and the conditions for movement in monkeys (Macaca mulatta). Behav. Brain Res. 18, 269–276 (1985).

    Article  CAS  PubMed  Google Scholar 

  7. Petrides, M. in The Frontal Lobes Revisited (ed. Perecman, E.) 91–108 (IBRN, New York, 1987).

    Google Scholar 

  8. Goldman, P. S., Rosvold, H. E., Vest, B. & Galkin, T. W. Analysis of the delayed-alternation deficit produced by dorsolateral prefrontal lesions in the rhesus monkey. J. Comp. Physiol. Psychol. 77, 212–220 (1971).

    Article  CAS  PubMed  Google Scholar 

  9. Kertesz, A. Localization and Neuroimaging in Neuropsychology (Academic, New York, 1994).

    Google Scholar 

  10. Damasio, H. & Damasio, A. R. Lesion Analysis in Neuropsychology (Oxford Univ. Press, Oxford, UK, 1989).

    Google Scholar 

  11. Young, M. P., Hilgetag, C. C. & Scannell, J. W. On imputing function to structure from the behavioural effects of brain lesions. Philos Trans R Soc Lond B Biol Sci 355, 147–161 (2000).An analysis of the problems associated with interpreting lesion effects and double dissociations, showing that the interpretation is clarified when the position of the areas in the anatomical network are taken into account.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Petersen, S. E., Fox, P. T., Posner, M. I., Mintun, M. & Raichle, M. E. Positron emission tomographic studies of the cortical anatomy of single-word processing. Nature 331, 585–589 (1988).

    Article  CAS  PubMed  Google Scholar 

  13. Frackowiak, R. S. J., Friston, K. J., Frith, C. D., Dolan, R. J. & Mazziotta, J. C. Human Brain Function (Academic, San Diego, 1997).

    Google Scholar 

  14. Barbas, H. & Rempel-Clower, H. Cortical structure predicts the pattern of corticocortical connections. Cereb. Cortex 7, 635–646 (1997).

    Article  CAS  PubMed  Google Scholar 

  15. Rockel, A. J., Hiorns, R. W. & Powell, T. P. S. The basic uniformity in structure of the neocortex. Brain 103, 221–244 (1980).

    Article  CAS  PubMed  Google Scholar 

  16. Jones, E. G. Making brain connections: neuroanatomy and the work of TPS Powell, 1923–1996. Annu. Rev. Neurosci. 22, 49–103 (1999).

    Article  CAS  PubMed  Google Scholar 

  17. Lund, J. S., Yoskioka, T. & Levitt, J. B. Comparison of intrinsic connectivity in different areas of macaque monkey cerebral cortex. Cereb. Cortex 3, 148–162 (1993).

    Article  CAS  PubMed  Google Scholar 

  18. Hudspeth, A. K., Ruark, J. E. & Kelly, J. P. Cytoarchitectonic mapping by microdensitometry. Proc. Natl Acad. Sci. USA 73, 2928–2931 (1976).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Geyer, S. et al. Receptor autoradiographic mapping of the mesial and premotor cortex of the macaque monkey. J. Comp. Neurol. 397, 231–250 (1998).This study provides proof that areas differ in their receptor fingerprints — that is, in the degree of binding for a range of receptors.

    Article  CAS  PubMed  Google Scholar 

  20. Stephan, K. E. et al. CoCoMac: advanced database methodology for the collation of connectivity data on the macaque brain (CoCoMac). Philos Trans R Soc Lond B Biol Sci 356, 1159–1186 (2001).A paper on database methodology that includes the data on prefrontal connections on which figures 1 and 2 are based.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Walker, E. A. A cytoarchitectural study of the prefrontal area of macaque monkey. J. Comp. Neurol. 73, 59–86 (1940).

    Article  Google Scholar 

  22. Young, M. P. The organization of neural systems in the primate cerebral cortex. Proc. R. Soc. Lond. B 252, 13–18 (1993).This paper uses MDS to group cortical areas into different systems.

    Article  CAS  Google Scholar 

  23. Scannell, J. W., Blakemore, C. & Young, M. P. Analysis of connectivity in the cat cerebral cortex. J. Neurosci. 15, 1463–1483 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kötter, R., Hilgetag, C. C. & Stephan, K. E. Connectional characteristics of areas in Walker's map of primate prefrontal cortex. Neurocomputing 3840, 741–746 (2001).

    Article  Google Scholar 

  25. Barbas, H. & Pandya, D. N. Architecture and intrinsic connections of the prefrontal cortex in the rhesus monkey. J. Comp. Neurol. 286, 353–375 (1989).

    Article  CAS  PubMed  Google Scholar 

  26. Carmichael, S. T. & Price, J. L. Connectional networks within the orbital and medial prefrontal cortex of macaque monkeys. J. Comp. Neurol. 371, 179–207 (1996).

    Article  CAS  PubMed  Google Scholar 

  27. Kötter, R. et al. Multimodal characterisation of cortical areas by multivariate analyses of receptor binding and connectivity data. Anat. Embryol. 204, 333–350 (2001).This paper presents a method for combining the statistical analysis of data in different modalities. It formally compares the classification of areas on the basis of receptor binding and anatomical connectivity, and presents the table of connections that was used in our analysis of motor areas.

    Article  Google Scholar 

  28. Stephan, K. E., Zilles, K. & Kötter, R. Coordinate-independent mapping of structural and functional cortical data by objective relational transformation (ORT). Phil. Trans. R. Soc. Lond. B 355, 37–54 (2000).

    Article  CAS  Google Scholar 

  29. Young, M. P. et al. Non-metric multidimensional scaling in the analysis of neuroanatomical connection data and the organization of the primate cortical visual system. Philos Trans R Soc Lond B Biol Sci 348, 281–308 (1995).

    Article  CAS  PubMed  Google Scholar 

  30. Brodmann, K. Vergleichende Lokalisationlehre der Grosshirnrinde (Barth, Leipzig, 1909).

    Google Scholar 

  31. Matelli, M., Luppino, G. & Rizzolatti, G. Pattern of cytochrome oxidase activity in frontal agranular cortex of the macaque monkey. Behav. Brain Res. 18, 125–136 (1985).

    Article  CAS  PubMed  Google Scholar 

  32. Matelli, M., Luppino, G. & Rizzolatti, G. Architecture of superior and mesial area 6 and the adjacent cingulate cortex in the macaque monkey. J. Comp. Neurol. 311, 445–462 (1991).

    Article  CAS  PubMed  Google Scholar 

  33. Dombrowski, S. M., Hilgetag, C. C. & Barbas, H. Quantitative architecture distinguishes prefrontal cortex systems in the rhesus monkey. Cereb. Cortex 11, 975–989 (2001).

    Article  CAS  PubMed  Google Scholar 

  34. Selemon, L. D. & Goldman-Rakic, P. S. Common cortical and subcortical targets of the dorsolateral prefrontal and parietal cortices in the rhesus monkey: evidence for a distributed neural network subserving spatially guided behavior. J. Neurosci. 8, 4049–4068 (1988).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Friedman, H. R. & Goldman-Rakic, P. S. Coactivation of prefrontal cortex and inferior parietal cortex in working memory tasks revealed by 2DG functional mapping in the rhesus monkey. J. Neurosci. 14, 2775–2788 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Chafee, M. V. & Goldman-Rakic, P. S. Matching patterns of activity in primate prefrontal area 8a and parietal area 7ip during a spatial working memory task. J. Neurophysiol. 79, 2919–2940 (1998).

    Article  CAS  PubMed  Google Scholar 

  37. Owen, A. et al. Redefining the functional organization of working memory processes within human lateral prefrontal cortex. Eur. J. Neurosci. 11, 567–574 (1999).

    Article  CAS  PubMed  Google Scholar 

  38. Rowe, J. B., Toni, I., Josephs, O., Frackowiak, R. S. J. & Passingham, R. E. Separate fronto-parietal systems for selection versus maintenance within working memory. Science 288, 1656–1660 (2000).

    Article  CAS  PubMed  Google Scholar 

  39. Zilles, K. et al. Anatomy and transmitter receptors of the supplementary motor areas in the human and nonhuman primate brain. Adv. Neurol. 70, 29–43 (1996).

    CAS  PubMed  Google Scholar 

  40. Young, M. P. Objective analysis of the topological organization of the primate cortical visual system. Nature 358, 152–154 (1992).

    Article  CAS  PubMed  Google Scholar 

  41. Scannell, J. W., Burns, G. A. P. C., Hilgetag, C. C., O'Neil, M. A. & Young, M. P. The connectional organization of the cortico-thalamic system of the cat. Cereb. Cortex 9, 277–299 (1999).

    Article  CAS  PubMed  Google Scholar 

  42. Hilgetag, C. C., Burns, G. A. P. C., O'Neill, M. A., Scannell, J. W. & Young, M. P. Anatomical connectivity defines the organisation of clusters of cortical areas in macaque monkey and cat. Philos Trans R Soc Lond B Biol Sci 355, 91–110 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Stephan, K. E. et al. Computational analysis of functional connectivity between areas of primate cerebral cortex. Phil. Trans. R. Soc. Lond. B 355, 111–126 (2000).

    Article  CAS  Google Scholar 

  44. Teuber, H. L. Physiological psychology. Annu. Rev. Psychol. 6, 267–296 (1955).

    Article  CAS  PubMed  Google Scholar 

  45. Dewson, J. H., Pribram, K. H. & Lynch, J. C. Effects of ablations of temporal cortex upon speech sound discrimination in the monkey. Exp. Neurol. 24, 579–591 (1969).

    Article  PubMed  Google Scholar 

  46. Pohl, W. Dissociation of spatial discrimination deficits following frontal and parietal lesions in monkeys. J. Comp. Physiol. Psychol. 82, 227–239 (1973).

    Article  CAS  PubMed  Google Scholar 

  47. Mishkin, M., Ungerleider, L. G. & Macko, K. A. Object vision and spatial vision: two cortical pathways. Trends Neurosci. 6, 414–417 (1983).

    Article  Google Scholar 

  48. Goldman, P. S. & Rosvold, H. E. Localization of function within the dorsolateral prefrontal cortex of the rhesus monkey. Exp. Neurol. 27, 291–304 (1970).

    Article  CAS  PubMed  Google Scholar 

  49. Pribram, K. H. A further experimental analysis of the behavioral deficit that follows injury to the primate frontal cortex. Exp. Neurol. 3, 432–466 (1961).

    Article  CAS  PubMed  Google Scholar 

  50. Goldman-Rakic, P. S. in The Prefrontal Cortex (eds Roberts, A. C., Robbins, T. W. & Weiskrantz, L.) 117–130 (Oxford Univ. Press, Oxford, UK, 1998).

    Google Scholar 

  51. Buckley, M. J., Gaffan, D. & Murray, E. A. Functional double dissociation between two inferior temporal cortical areas: perirhinal versus middle temporal gyrus. J. Neurophysiol. 77, 587–598 (1997).

    Article  CAS  PubMed  Google Scholar 

  52. DeYoe, E. A. & Van Essen, D. C. Concurrent processing streams in monkey visual cortex. Trends Neurosci. 11, 219–226 (1988).

    Article  CAS  PubMed  Google Scholar 

  53. Van Essen, D. G. & DeYoe, E. A. in The Cognitive Neurosciences (ed. Gazzaniga, M. S.) 383–400 (MIT Press, Cambridge, Massachusetts, 1995).

    Google Scholar 

  54. Zeki, S. A Vision of the Brain (Blackwell, Oxford, UK, 1993).

    Google Scholar 

  55. Muakkassa, K. F. & Strick, P. L. Frontal lobe inputs to primate motor cortex: evidence for four somatotopically organized 'premotor areas'. Brain Res. 177, 176–182 (1979).

    Article  CAS  PubMed  Google Scholar 

  56. Barbas, H. & Pandya, D. N. Architecture and frontal cortical connections of the premotor cortex (area 6) in the rhesus monkey. J. Comp. Neurol. 256, 211–228 (1987).

    Article  CAS  PubMed  Google Scholar 

  57. Dum, R. P. & Strick, P. L. Cortical inputs to the digit representations in the primary motor cortex and the dorsal premotor area of the cebus monkey. Soc. Neurosci. Abstr. 23, 502.12 (1997).

    Google Scholar 

  58. Okano, K. & Tanji, J. Neuronal activity in the primate motor fields of the agranular frontal cortex preceding visually triggered and self-paced movements. Exp. Brain Res. 66, 155–166 (1987).

    Article  CAS  PubMed  Google Scholar 

  59. Alexander, G. E. & Crutcher, M. D. Preparation for movement: neural representation of intended direction in three motor areas of the monkey. J. Neurophysiol. 64, 133–150 (1990).

    Article  CAS  PubMed  Google Scholar 

  60. Crutcher, M. D. & Alexander, G. E. Movement-related neuronal activity selectively coding either direction or muscle pattern in three motor areas of the monkey. J. Neurophysiol. 64, 151–163 (1990).

    Article  CAS  PubMed  Google Scholar 

  61. Tanji, J. & Shima, K. Role for supplementary motor area cells in planning several moves ahead. Nature 371, 413–416 (1994).

    Article  CAS  PubMed  Google Scholar 

  62. Shen, L. & Alexander, G. E. Preferential representation of instructed target location versus limb trajectory in dorsal premotor cortex. J. Neurophysiol. 77, 1195–1212 (1997).

    Article  CAS  PubMed  Google Scholar 

  63. Shen, L. & Alexander, G. E. Neural correlates of a spatial sensory-to-motor transformation in the primary motor cortex. J. Neurophysiol. 77, 1171–1194 (1997).

    Article  CAS  PubMed  Google Scholar 

  64. Matsuzaka, Y. & Tanji, J. Changing directions of forthcoming arm movements: neuronal activity in the presupplementary and supplementary motor area of monkey cerebral cortex. J. Neurophysiol. 76, 2327–2342 (1996).

    Article  CAS  PubMed  Google Scholar 

  65. Shima, K., Mushiake, H., Saito, N. & Tanji, J. Role for cells in the presupplementary motor area in updating motor plans. Proc. Natl Acad. Sci. USA 93, 8694–8698 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Mushiake, H., Inase, M. & Tanji, J. Neuronal activity in the primate premotor, supplementary, and precentral motor cortex during visually guided and internally determined sequential movements. J. Neurophysiol. 66, 705–718 (1991).This paper presents data on the percentage of cells in three motor areas that fire in relation to two tasks — sequences guided by visual cues and sequences guided by memory. It shows that a comparison of the cell properties in related areas can indicate differences in the proportion of cells that fire in relation to such tasks.

    Article  CAS  PubMed  Google Scholar 

  67. Halsband, U., Matsuzaka, Y. & Tanji, J. Neuronal activity in the primate supplementary, pre-supplementary and premotor cortex during externally and internally instructed sequential movements. Neurosci. Res. 20, 149–155 (1994).

    Article  CAS  PubMed  Google Scholar 

  68. Rizzolatti, G., Luppino, G. & Matelli, M. The organization of the cortical motor system: new concepts. Electroencephalogr Clin Neurophysiol 106, 283–296 (1998).

    Article  CAS  PubMed  Google Scholar 

  69. Sakata, H., Taira, M., Murata, A. & Mine, S. Neural mechanisms of visual guidance of hand actions in the parietal cortex of the monkey. Cereb. Cortex 5, 429–438 (1995).

    Article  CAS  PubMed  Google Scholar 

  70. Sakata, H., Taira, M., Kusonoki, M., Murata, A. & Tanaka, V. The parietal association cortex in depth perception and visual control of hand action. Trends Neurosci. 20, 350–356 (1997).

    Article  CAS  PubMed  Google Scholar 

  71. Jeannerod, M., Arbib, M. A., Rizzolatti, G. & Sakata, H. Grasping objects: the cortical mechanisms of visuomotor transformation. Trends Neurosci. 18, 314–320 (1995).

    Article  CAS  PubMed  Google Scholar 

  72. Murata, A. et al. Object representation in the ventral premotor cortex (area F5) of the monkey. J. Neurophysiol. 78, 2226–2230 (1997).

    Article  CAS  PubMed  Google Scholar 

  73. Schell, G. R. & Strick, P. L. The origin of thalamic inputs to the arcuate premotor and supplementary motor areas. J. Neurosci. 4, 539–560 (1984).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Matelli, M. & Luppino, G. in Thalamic Networks for Relay and Modulation (eds Mietciacchi, D., Molinari, M., Miacchi, G. & Jones, E. G.) 165–174 (Pergamon, Oxford, UK, 1994).

    Google Scholar 

  75. Middleton, F. A. & Strick, P. L. in The Cerebellum and Cognition (ed. Schmahmann, J. D.) 61–83 (Academic, New York, 1997).

    Google Scholar 

  76. Van Donkelaar, P., Stein, J. F., Passingham, R. E. & Miall, R. C. Neuronal activity in the basal ganglia- and cerebellar-receiving areas of the thalamus during visually-triggered and internally-generated limb movements. J. Neurophysiol. 82, 934–945 (1999).

    Article  CAS  PubMed  Google Scholar 

  77. Johnson, P. B., Ferraina, S., Bianchi, L. & Caminiti, R. Cortical networks for visual reaching: physiological and anatomical organization of frontal and parietal lobe arm regions. Cereb. Cortex 6, 102–119 (1996).

    Article  CAS  PubMed  Google Scholar 

  78. Matelli, M., Govoni, P., Galletti, C., Kutz, D. F. & Luppino, G. Superior area 6 afferents from the superior parietal lobule in the macaque monkey. J. Comp. Neurol. 402, 327–352 (1998).

    Article  CAS  PubMed  Google Scholar 

  79. Colby, C. L. & Duhamel, J. R. Heterogeneity of extrastriate visual areas and multiple parietal areas in the macaque monkey. Neuropsychologia 29, 517–537 (1991).

    Article  CAS  PubMed  Google Scholar 

  80. Humphrey, D. R. & Tanji, J. in Motor Control: Concepts and Issues (eds Humphrey, D. R. & Freund, H.-J) 413–443 (Wiley, New York, 1991).A meta-analysis of studies on the features to which cells respond in three cortical motor areas and in parietal area 5. The data form the basis for the functional fingerprints that are presented in figure 6a of the present article.

    Google Scholar 

  81. Moffet, A., Ettlinger, G., Morton, H. B. & Piercy, M. F. Tactile discrimination performance in the monkey: the effect of ablation of various subdivisions of posterior parietal cortex. Cortex 3, 59–96 (1967).

    Article  Google Scholar 

  82. Heeger, D. J., Boynton, G. M., Demb, J. B., Seidemann, E. & Newsome, W. T. Motion opponency in visual cortex. J. Neurosci. 19, 7162–7174 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Heeger, D. J., Huk, A. C., Geisler, W. S. & Albrecht, D. G. Spikes versus BOLD: what does neuroimaging tell us about neuronal activity? Nature Neurosci. 3, 631–633 (2000).

    Article  CAS  PubMed  Google Scholar 

  84. Rees, G., Friston, K. & Koch, C. A direct quantitative relationship between the functional properties of human and macaque V5. Nature Neurosci. 3, 716–723 (2000).

    Article  CAS  PubMed  Google Scholar 

  85. Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001).In this study, electrophysiological activity and the BOLD signal were measured simultaneously in macaques. This paper provides the most direct evidence on the relationship between these two measures.

    Article  CAS  PubMed  Google Scholar 

  86. Bandettini, P. A. & Ungerleider, L. G. From neuron to BOLD: new connections. Nature Neurosci. 4, 864–866 (2001).

    Article  CAS  PubMed  Google Scholar 

  87. Chawla, D., Lumer, E. & Friston, K. J. The relationship between synchronisation among neuronal populations and their mean activity levels. Neural Comput. 11, 1389–1411 (1999).

    Article  CAS  PubMed  Google Scholar 

  88. Chawla, D., Lumer, E. D. & Friston, K. J. Relating macroscopic measures of brain activity to fast, dynamic neuronal interactions. Neural Comput. 12, 2805–2821 (2000).

    Article  CAS  PubMed  Google Scholar 

  89. Georgopoulos, A. P., Kalaska, J. F., Caminiti, R. & Massey, J. T. Spatial coding of movement: a hypothesis concerning the coding of movement direction by motor cortical populations. Exp. Brain Res. 7, 327–336 (1983).

    Article  Google Scholar 

  90. Georgopoulos, A. P., Schwartz, A. B. & Kettner, R. E. Neuronal population coding of movement direction. Science 233, 1416–1419 (1986).

    Article  CAS  PubMed  Google Scholar 

  91. Chen, L. L. & Wise, S. P. Conditional oculomotor learning: population vectors in the supplementary eye field. J. Neurophysiol. 78, 1166–1169 (1997).

    Article  CAS  PubMed  Google Scholar 

  92. Wise, S. P. & Murray, E. A. Arbitrary associations between antecedents and actions. Trends Neurosci. 23, 271–276 (2000).

    Article  CAS  PubMed  Google Scholar 

  93. Paus, T., Koski, L., Zografos, C. & Westbury, C. Regional differences in the effects of task difficulty and motor output on blood flow response in the human anterior cingulate cortex: a review of 107 PET activation studies. Neuroreport 9, 37–47 (1998).

    Article  Google Scholar 

  94. Koski, L. & Paus, T. Functional connectivity of the anterior cingulate cortex within the human frontal lobe: a brain-mapping meta-analysis. Exp. Brain Res. 133, 55–65 (2000).

    Article  CAS  PubMed  Google Scholar 

  95. Duncan, J. & Owen, A. M. Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends Neurosci. 23, 475–483 (2000).

    Article  CAS  PubMed  Google Scholar 

  96. Van Horn, J. D. et al. The Functional Magnetic Resonance Imaging Data Center (fMRIDC): the challenges and rewards of large-scale databases in imaging studies. Phil. Trans. R. Soc. Lond. B 356, 1323–1339 (2001).

    Article  CAS  Google Scholar 

  97. Friston, K. J. The labile brain. I. Neuronal transients and nonlinear coupling. Phil. Trans. R. Soc. Lond. B 355, 215–236 (2000).

    Article  CAS  Google Scholar 

  98. Friston, K. J. Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp. 2, 56–78 (1995).

    Article  Google Scholar 

  99. Scannell, J. W. et al. Visual motion processing in the anterior ectosylvian sulcus of the cat. J. Neurophysiol. 76, 895–907 (1996).Scannell et al . used multivariate analysis of a large database to predict the response properties of cells in a higher visual area in the cat. They confirmed the predictions by making direct electrophysiological recordings in this area.

    Article  CAS  PubMed  Google Scholar 

  100. Burns, G. A. P. C. & Young, M. P. Analysis of the connectional organization of neural systems associated with the hippocampus in rats. Philos Trans R Soc Lond B Biol Sci 355, 55–70 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Chatfield, C. & Collins, A. J. An Introduction to Multivariate Analysis (Chapman & Hall, New York, 1991).

    Google Scholar 

  102. Cover, T. M. & Thomas, J. A. Elements of Information Theory (John Wiley, New York, 1991).

    Book  Google Scholar 

  103. Logothetis, N. K., Guggenberger, H., Peled, S. & Pauls, J. Functional imaging of the monkey brain. Nature Neurosci. 2, 555–562 (1999).

    CAS  PubMed  Google Scholar 

  104. Di Virgilio, G. & Clarke, S. Direct interhemispheric visual inputs to human speech areas. Hum. Brain Mapp. 5, 347–354 (1997).

    Article  CAS  PubMed  Google Scholar 

  105. Crick, F. & Jones, E. Backwardness of human neuroanatomy. Nature 361, 109–110 (1993).

    Article  CAS  PubMed  Google Scholar 

  106. Conturo, T. E. et al. Tracking neuronal fiber pathways in the living human brain. Proc. Natl Acad. Sci. USA 96, 10422–10427 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Poupon, C. et al. Regularization of diffused-based direction maps for the tracking of brain white matter fascicles. Neuroimage 12, 184–195 (2000).

    Article  CAS  PubMed  Google Scholar 

  108. Parker, G. J. M. et al. In vivo tracing of anatomical fibre tracts in the macaque and human brain using diffusion tensor imaging and fast marching tractography. Neuroimage 15, 797–809 (2002).

    Article  PubMed  Google Scholar 

  109. Petrides, M. & Pandya, D. N. Dorsolateral prefrontal cortex: comparative cytoarchitectonic analysis in the human and the macaque brain and corticocortical connection patterns. Eur. J. Neurosci. 11, 1011–1036 (1999).

    Article  CAS  PubMed  Google Scholar 

  110. Roland, P. E. & Zilles, K. The developing European computerized human brain database for all imaging modalities. Neuroimage 4, S39–S47 (1996).

    Article  CAS  PubMed  Google Scholar 

  111. Mazziotta, J. C. et al. in Brain Mapping: the Systems (eds Toga, A. & Mazziotta, J. C.) 132–158 (Academic, New York, 2000).

    Google Scholar 

  112. Geyer, S., Schormann, T., Mohlberg, H. & Zilles, K. Areas 3a, 3b, and 1 of human primary somatosensory cortex. II. Spatial normalization to standard anatomical space. Neuroimage 11, 684–696 (2000).

    Article  CAS  PubMed  Google Scholar 

  113. Amunts, K. et al. Broca's region revisited: cytoarchitecture and intersubject variability. J. Comp. Neurol. 412, 319–341 (1999).

    Article  CAS  PubMed  Google Scholar 

  114. Watson, J. D. C., Frackowiak, R. S. J. & Zeki, S. in Functional Organization of the Human Visual Cortex (eds Gulyas, B., Ottoson, D. & Roland, P. E.) 317–328 (Pergamon, Oxford, UK, 1993).

    Book  Google Scholar 

  115. Shipp, S., Watson, J. D. G., Frackowiak, R. S. J. & Zeki, S. Retinotopic maps in human prestriate visual cortex: the demarcation of area V2 and V3. Neuroimage 2, 125–133 (1995).

    Article  CAS  PubMed  Google Scholar 

  116. Tootell, R. B. H. & Taylor, J. B. Anatomical evidence for MT and additional cortical visual areas in humans. Cereb. Cortex 5, 39–55 (1995).

    Article  CAS  PubMed  Google Scholar 

  117. Van Essen, D. C. et al. Mapping visual cortex in monkeys and humans using surface-based atlases. Vision Res. 41, 1359–1378 (2001).

    Article  CAS  PubMed  Google Scholar 

  118. Vanduffel, W. J. M. et al. Visual motion processing investigated using contrast agent-enhanced fMRI in awake behaving monkeys. Neuron 32, 565–577 (2001).

    Article  CAS  PubMed  Google Scholar 

  119. Felleman, D. J. & Van Essen, D. C. Distributed hierarchical processing in primate cerebral cortex. Cereb. Cortex 1, 1–47 (1991).

    Article  CAS  PubMed  Google Scholar 

  120. Grill-Spector, K. et al. A sequence of object-processing stages revealed by fMRI in the human occipital lobe. Hum. Brain Mapp. 6, 316–328 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Sereno, M. E., Trinath, T., Augath, M. & Logothetis, N. K. Three-dimensional shape representation in monkey cortex. Neuron 33, 635–652 (2002).

    Article  CAS  PubMed  Google Scholar 

  122. Halgren, E. et al. Location of human face-selective cortex with respect to retinotopic areas. Hum. Brain Mapp. 7, 29–27 (1999).

    Article  CAS  PubMed  Google Scholar 

  123. Tootell, R. B. H. et al. Visual motion aftereffect in human cortical area MT revealed by functional magnetic resonance imaging. Nature 375, 139–141 (1995).

    Article  CAS  PubMed  Google Scholar 

  124. Seghier, M. et al. Moving illusory contours activate primary visual cortex: an fMRI study. Cereb. Cortex 10, 663–670 (2000).

    Article  CAS  PubMed  Google Scholar 

  125. Orban, G. A., Sunaert, S., Todd, J. T., Van Hecke, P. & Marchal, G. Human cortical regions involved in extracting depth from motion. Neuron 24, 929–940 (1999).

    Article  CAS  PubMed  Google Scholar 

  126. Sunaert, S., Van Hecke, P., Marchal, G. & Orban, G. A. Attention to speed of motion, speed discrimination and task difficulty: an fMRI study. Neuroimage 11, 612–623 (2000).

    Article  CAS  PubMed  Google Scholar 

  127. Moore, C. & Engel, S. A. Neural response to perception of volume in the lateral occipital complex. Neuron 29, 277–286 (2001).References 125–127 show the feasibility of using fMRI to present a wide range of visual stimulus types. The results of such studies could be used to construct functional fingerprints of different areas.

    Article  CAS  PubMed  Google Scholar 

  128. Borg, I. & Groenen, P. Modern Multidimensional Scaling (Springer, New York, 1997).

    Book  Google Scholar 

Download references

Acknowledgements

This work was supported by the Wellcome Trust (R.E.P.), the Brain Research Trust (K.E.S.) and the Deutsche Forschungsgemeinschaft (R.K.). We are grateful to C. Hilgetag and K. Friston for their comments on the manuscript before submission, and to A. Duggins and W. Penny for helpful statistical discussions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Richard E. Passingham.

Related links

Related links

FURTHER INFORMATION

CoCoMac

Encyclopedia of Life Sciences

brain imaging: localization of brain functions

brain imaging: observing ongoing neural activity

computed tomography

magnetic resonance imaging

MIT Encyclopedia of Cognitive Sciences

cerebral cortex

cortical localization, history of

Lashley, Karl Spencer (1890–1958)

magnetic resonance imaging

positron emission tomography

Glossary

BETZ CELLS

Giant pyramidal neurons that are located in layer V of the primary motor cortex. Their axons project to the spinal cord, terminating directly on motor neurons.

MULTIDIMENSIONAL SCALING

A multivariate statistical method that provides a visual representation of the pattern of similarities between data sets. For example, given a matrix of similarities between various phenotypes, multidimensional scaling plots them on a map such that phenotypes that are perceived to be similar are placed near to each other, and those that are perceived to be different are placed far apart.

HIERARCHICAL CLUSTER ANALYSIS

A multivariate method for solving classification problems. The object is to sort items into groups such that the degree of association is strong between members of the same cluster and weak between members of different clusters. In addition, this technique visualizes the hierarchical structure of similarity between all identified clusters.

SPEARMAN CORRELATION MATRIX

A matrix of so-called Spearman correlation coefficients, each of which represents a measure of association between two sets of rank-ordered measurements.

STRYCHNINE NEURONOGRAPHY

A method in which (potentially polysynaptic) anatomical connections are identified by applying strychnine to one area and then recording spikes in other areas.

SET-RELATED ACTIVITY

Neuronal activity that reflects the behavioural 'set' of the animal, which can include information about a planned movement or about the state of readiness of the animal.

MULTIPLE CORRESPONDENCE ANALYSIS

A method that aims to explain the relationships between multiple variables that are identified on identical or different measurement scales, and may include categorical data.

GENERAL LINEAR MODEL

A general mathematical framework from which many commonly used statistical procedures (for example, analysis of variance) are derived.

INFORMATION THEORY

A scientific discipline that is concerned with mathematical laws underlying systems that transmit, store and process information. It also deals with the quantitative measurement of various types of information.

DIFFUSION-WEIGHTED IMAGING

A magnetic resonance imaging method that makes use of the variability in the random movement of water molecules in nervous tissue, which is restricted by cell bodies, blood vessels, axon bundles and other structures. Two opposite magnetic field gradients are applied. The magnetic spins will be de-phased by the first gradient and, because of water diffusion, the second gradient will not completely re-phase them. As the directionality of diffusion is highly ordered in white matter, the spatial orientation of the bundles can be reconstructed.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Passingham, R., Stephan, K. & Kötter, R. The anatomical basis of functional localization in the cortex. Nat Rev Neurosci 3, 606–616 (2002). https://doi.org/10.1038/nrn893

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrn893

This article is cited by

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