Anatomy and Embryology

, Volume 210, Issue 5–6, pp 373–386

Quantitative architectural analysis: a new approach to cortical mapping

  • A. Schleicher
  • N. Palomero-Gallagher
  • P. Morosan
  • S. B. Eickhoff
  • T. Kowalski
  • K. de Vos
  • K. Amunts
  • K. Zilles
Original Article


Recent progress in anatomical and functional MRI has revived the demand for a reliable, topographic map of the human cerebral cortex. Till date, interpretations of specific activations found in functional imaging studies and their topographical analysis in a spatial reference system are, often, still based on classical architectonic maps. The most commonly used reference atlas is that of Brodmann and his successors, despite its severe inherent drawbacks. One obvious weakness in traditional, architectural mapping is the subjective nature of localising borders between cortical areas, by means of a purely visual, microscopical examination of histological specimens. To overcome this limitation, more objective, quantitative mapping procedures have been established in the past years. The quantification of the neocortical, laminar pattern by defining intensity line profiles across the cortical layers, has a long tradition. During the last years, this method has been extended to enable a reliable, reproducible mapping of the cortex based on image analysis and multivariate statistics. Methodological approaches to such algorithm-based, cortical mapping were published for various architectural modalities. In our contribution, principles of algorithm-based mapping are described for cyto- and receptorarchitecture. In a cytoarchitectural parcellation of the human auditory cortex, using a sliding window procedure, the classical areal pattern of the human superior temporal gyrus was modified by a replacing of Brodmann’s areas 41, 42, 22 and parts of area 21, with a novel, more detailed map. An extension and optimisation of the sliding window procedure to the specific requirements of receptorarchitectonic mapping, is also described using the macaque central sulcus and adjacent superior parietal lobule as a second, biologically independent example. Algorithm-based mapping procedures, however, are not limited to these two architectural modalities, but can be applied to all images in which a laminar cortical pattern can be detected and quantified, e.g. myeloarchitectonic and in vivo high resolution MR imaging. Defining cortical borders, based on changes in cortical lamination in high resolution, in vivo structural MR images will result in a rapid increase of our knowledge on the structural parcellation of the human cerebral cortex.


Cytoarchitecture Receptorarchitecture Auditory cortex Central sulcus Multivariate statistics 









Brodmann’s area


cluster analysis


central sulcus


cortical depth


Grey level index


Heschl’s gyrus


Heschl’s sulcus


intraparietal sulcus


length of feature vector


Mahalanobis distance


magnet resonance imaging


middle temporal gyrus


number of profiles in a cortical sector


level of significance


region of interest


superior temporal gyrus


superior temporal sulcus


sliding window


temporal plane


width of a cortical layer


  1. Amunts K, Zilles K (2001) Advances in cytoarchitectonic mapping of the human cerebral cortex. Anat Basis Funct Magn Reson Imaging 11:151–169Google Scholar
  2. Amunts K, Schleicher A, Bürgel U, Mohlberg H, Uylings HBM, Zilles K (1999) Broca’s region revisited: cytoarchitecture and intersubject variability. J Comp Neurol 412:319–341PubMedCrossRefGoogle Scholar
  3. Amunts K, Malicovic A, Mohlberg H, Schormann T, Zilles K (2000) Brodmann’s areas 17 and 18 brought into stereotactic space—where and how variable? Neuroimage 11:66–84PubMedCrossRefGoogle Scholar
  4. Amunts K, Schleicher A, Zilles K (2002) Architectonic mapping of the human cerebral cortex. In: Schüz A, Miller R (eds) Cortical areas: unity and diversity. Taylor & Francis, New York, NY, pp 29–52Google Scholar
  5. Annese J, Pitiota A, Dinova ID, Toga AW (2004) A myelo-architectonic method for the structural classification of cortical areas. Neuroimage 21:15–26PubMedCrossRefGoogle Scholar
  6. Artacho-Perula E, Arbizu J, Arroyo-Jimenez M del M, Marcos P, Martinez-Marcos A, Blaizot X, Insausti R (2004) Quantitative estimation of the primary auditory cortex in human brains. Brain Res 1008:20–28PubMedCrossRefGoogle Scholar
  7. Bok ST, van Kip MJE (1939) The size of the body and the size and the number of the nerve cells in the cerebral cortex. Acta Ned Morphol 3:1–22Google Scholar
  8. Bortz J (1999) Statistik für Sozialwissenschaftler. Springer, Berlin Heidelberg New YorkGoogle Scholar
  9. Brodmann K (1909) Vergleichende Lokalisationslehre der Großhirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues. J.A. Barth, LeipzigGoogle Scholar
  10. Burwell RD (2001) Borders and cytoarchitecture of the perirhinal and postrhinal cortices in the rat. J Comp Neurol 437:17–41PubMedCrossRefGoogle Scholar
  11. Crum WR, Griffin LD, Hill DL, Hawkes DJ (2003) Zen and the art of medical image registration: correspondence, homology, and quality. Neuroimage 20:1425–1437PubMedCrossRefGoogle Scholar
  12. de Vos K, Pool CW, Sanz-Arigita EJ, Uylings HBM (2004) Curvature effects in observer independent cytoarchitectonic mapping of the human cerebral cortex. Proceedings of the Second Vogt–Brodmann Symposium, Research Center Jülich, Germany, p 44Google Scholar
  13. Dixon WJ, Brown MB, Engelman L, Hill MA, Jennrich RI (1988) BMDP Statistical Software Manual. University of California Press, Berkley, CAGoogle Scholar
  14. Eickhoff S, Geyer S, Amunts K, Mohlberg H, Zilles K (2002) Cytoarchitectonic analysis and stereotaxic map of the human secondary somatosensory cortex region. Neuroimage 16(S1):1780Google Scholar
  15. Eickhoff S, Schleicher A, Zilles K, Amunts K (2003) Automated exploratory delineation and analysis of cortical areas. Program No. 863.4. Society for Neuroscience, Washington, DC (Online)Google Scholar
  16. Eickhoff S, Walters N, Schleicher A, Egan G, Watson J, Zilles K, Amunts K (2004) High resolution MR imaging reveals microstructural features of the cerebral cortex. Hum Brain Mapp 24:206–215CrossRefGoogle Scholar
  17. Fatterpekar GM, Naidich TP, Delman BN, Aguinaldo JG, Gultekin H, Sherwood CC, Hof R, Drayer BP, Fayad ZA (2002) Cytoarchitecture of the human cerebral cortex: MR microscopy of excised specimens at 9.4 Tesla. AJNR Am J Neuroradiol 23:1313–1321PubMedGoogle Scholar
  18. Geyer S, Ledberg A, Schleicher A, Kinomura S, Schormann T, Bürgel U, Larsson J, Zilles K, Roland PE (1996) Two different areas within the primary motor cortex of man. Nature 382:805–807PubMedCrossRefGoogle Scholar
  19. Geyer S, Schleicher A, Zilles K (1999) Areas 3a, 3b, and 1 of human primary somatosensory cortex. 1. Microstructural organization and interindividual variability. Neuroimage 10:63–83PubMedCrossRefGoogle Scholar
  20. Gower JC (1985) Measures of similarity, dissimilarity, and distance. In: Kotz S, Johnson NL (eds) Encyclopaedia of statistical sciences, vol 5. Wiley, New YorkGoogle Scholar
  21. Grefkes C, Geyer S, Schormann T, Roland P, Zilles K (2001) Human somatosensory area 2: observer-independent cytoarchitectonic mapping, interindividual variability, and population map. Neuroimage 14:617–631PubMedCrossRefGoogle Scholar
  22. Hackett TA, Preuss TM, Kaas JH (2001) Architectonic identification of the core region in auditory cortex of macaques, chimpanzees, and humans. J Comp Neurol 441:197–222PubMedCrossRefGoogle Scholar
  23. Hopf A (1966) Über eine Methode zur objektiven Registrierung der Myeloarchitektonik der Hirnrinde. J Hirnforsch 8:302–313Google Scholar
  24. Hopf A (1968a) Registration of the myeloarchitecture of the human frontal lobe with an extinction method. J Hirnforsch 10:259–269Google Scholar
  25. Hopf A (1968b) Photometric studies on the myeloarchitecture of the human temporal lobe. J Hirnforsch 10:285–297Google Scholar
  26. Hudspeth AJ, Ruark JE, Kelly JP (1976) Cytoarchitectonic mapping by microdensitometry. Proc Natl Acad Sci USA 73:2928–2931PubMedCrossRefGoogle Scholar
  27. Jones SE, Buchbinder BR, Aharon I (2000) Three-dimensional mapping of cortical thickness using Laplace’s equation. Hum Brain Mapp 11:12–32PubMedCrossRefGoogle Scholar
  28. Kruggel F, Bruckner MK, Arendt T, Wiggins CJ, von Cramon DY (2003) Analyzing the neocortical fine-structure. Med Image Anal 7:251–264PubMedCrossRefGoogle Scholar
  29. Lidow MS, Goldman-Rakic PS, Rakic P, Gallager DW (1988) Differential quenching and limits of resolution in autoradiograms of brain tissue labelled with 3H-, 125I- and 14C-compounds. Brain Res 459:105–119PubMedCrossRefGoogle Scholar
  30. Merker B (1983) Silver staining of cell bodies by means of physical development. J Neurosci Methods 9:235–241PubMedCrossRefGoogle Scholar
  31. Morecraft RJ, Cipolloni PB, Stilwell-Morecraft KS, Gedney MT, Pandya DN (2004) Cytoarchitecture and cortical connections of the posterior cingulate and adjacent somatosensory fields in the rhesus monkey. J Comp Neurol 469:37–69PubMedCrossRefGoogle Scholar
  32. Morosan P, Rademacher J, Schleicher A, Amunts K, Schormann T, Zilles K (2001) Human primary auditory cortex: cytoarchitectonic subdivisions and mapping into a spatial reference system. Neuroimage 13:684–701PubMedCrossRefGoogle Scholar
  33. Morosan P, Palomero-Gallagher N, Rademacher J, Schleicher A, Mohlberg H, Amunts K, Zilles K (2004a) Cyto- and receptor architecture of human auditory cortex. Proceedings of the Second Vogt–Brodmann Symposium, the converge of structure and function, Jülich, p 31Google Scholar
  34. Morosan P, Schleicher A, Amunts K, Zilles K (2004b) Multimodal architectonic mapping of human superior temporal gyrus. Anat Embryol (this issue)Google Scholar
  35. Mountcastle VB (1978) An organizing principle for cerebral function: the unit module and the distributed system. In: Edelmann GM, Mountcastle VB (eds) The mindful brain: cortical organization and the group-selective theory of higher brain function. MIT Press, Cambridge, pp 7–51Google Scholar
  36. Ramm P, Kulick JH, Stryker MP, Frost BJ (1984) Video and scanning microdensitometer-based imaging systems in autoradiographic video densitometry. J Neurosci Methods 11:89–100PubMedCrossRefGoogle Scholar
  37. Roland PE, Zilles K (1994) Brain atlases—a new research tool. TINS 17:458–467PubMedGoogle Scholar
  38. Sanz-Arigita EJ, de Vos K, Pool CW, Uylings HBM (2002) Multivariate quantitative cytoarchitectonics. Laminar characterization of cortical microstructure by cell-type selection. Neuroimage 16(2 Suppl 1)Google Scholar
  39. Sanz-Arigita EJ, de Vos K, Pool CW, Uylings HBM (2004) Multivariate quantitative analysis of the microstructure of the cingulate cortex—areas 24 of Brodmann. Abstracts of the Second Vogt–Brodmann Symposium, the converge of structure and function, Jülich, p 44Google Scholar
  40. Schleicher A, Zilles K (1990) A quantitative approach to cytoarchitectonics: analysis of structural inhomogeneities in nervous tissue using an image analyser. J Microsc 157:367–381PubMedGoogle Scholar
  41. Schleicher A, Ritzdorf H, Zilles K (1987) Erster Ansatz zur objektiven Lokalisation von Arealgrenzen im Cortex cerebri. Verh Anat Ges 81:867–868Google Scholar
  42. Schleicher A, Amunts K, Geyer S, Morosan P, Zilles K (1999) Observer-independent method for microstructural parcellation of cerebral cortex: a quantitative approach to cytoarchitectonics. Neuroimage 9:165–177PubMedCrossRefGoogle Scholar
  43. Schleicher A, Amunts K, Geyer S, Kowalski T, Schormann T, Palomero-Gallagher N, Zilles K (2000) A stereological approach to human cortical architecture: identification and delineation of cortical areas. J Chem Neuroanat 20:31–47PubMedCrossRefGoogle Scholar
  44. Schmitt O, Böhme M (2002) A robust transcortical profile scanner for generating 2-d traverses in histological sections of richly curved cortical courses. Neuroimage 16:1103–1119PubMedCrossRefGoogle Scholar
  45. Schmitt O, Hömke L, Dümbgen L (2003) Detection of cortical transition regions utilizing statistical analyses of excess masses. Neuroimage 19:42–63PubMedGoogle Scholar
  46. Schmitt O, Pakura M, Aach T, Hömke L, Böhme M, Bock S, Preusse S (2004) Analysis of nerve fibres and their distribution in histological sections of the human brain. Microsc Res Tech 63:220–243PubMedCrossRefGoogle Scholar
  47. Sherwood CC, Broadfield DC, Holloway RL, Gannon PJ, Hof PR (2003) Variability of Broca’s area homologue in African great apes: implications for language evolution. Anat Rec 271A:276–285CrossRefGoogle Scholar
  48. Talairach J, Tournoux P (1988) Co-planar stereotactic atlas of the human brain. 3-dimensional proportional system: an approach to the cerebral imaging. Thieme, StuttgartGoogle Scholar
  49. Timm NH (2002) Applied multivariate analysis. Springer, Berlin Heidelberg New YorkGoogle Scholar
  50. Vogt C, Vogt O (1919) Allgemeinere Ergebnisse unserer Hirnforschung. J Psychol Neurol 25:279–461Google Scholar
  51. von Economo K, Koscinas G (1925) Die Cytoarchitektonic der Hirnrinde des erwachsenen Menschen. Springer, WienGoogle Scholar
  52. Walters NB, Egan GF, Kril JJ, Kean M, Waley P, Jenkinson M, Watson JD (2003) In vivo identification of human cortical areas using high-resolution MRI: an approach to cerebral structure–function correlation. Proc Natl Acad Sci USA 100:2981–2986 (Epub 2003 Feb 24)Google Scholar
  53. Walters B, Eickhoff S, Schleicher A, Zilles K, Egan GF, Amunts K, Watson JDG (submitted) Observer independent analysis of high-resolution MR images of the human cerebral cortex: in vivo delineation of cortical areasGoogle Scholar
  54. Wree A, Schleicher A, Zilles K (1982) Estimation of volume fractions in nervous tissue with an image analyzer. J Neurosci Methods 6:29–43PubMedCrossRefGoogle Scholar
  55. Zilles K, Palomero-Gallagher N (2001) Cyto-, myelo-, and receptor architectonics of the human parietal cortex. Neuroimage 14:8–20CrossRefGoogle Scholar
  56. Zilles K, Schlaug G, Matelli M, Luppino G, Schleicher A, Qü M, Dabringhaus A, Seitz R, Roland PE (1995) Mapping of human and macaque sensorimotor areas by integrating architectonic, transmitter receptor, MRI and PET data. J Anat 187:515–537PubMedGoogle Scholar
  57. Zilles K, Schleicher A, Palomero-Gallagher N, Amunts K (2002a) Quantitative analysis of cyto- and receptor architecture of the Human brain. In: Toga AW, Maziotta JC (eds) Brain mapping: the methods, 2nd edn. Academic, Amsterdam, pp 573–602Google Scholar
  58. Zilles K, Palomero-Gallagher N, Grefkes C, Scheperjans F, Boy C, Amunts K, Schleicher A (2002b) Architectonics of the human cerebral cortex and transmitter receptor fingerprints: reconciling functional neuroanatomy and neurochemistry. Eur Neuropsychopharmacol 12:587–599CrossRefGoogle Scholar
  59. Zilles K, Eickhoff S, Palomero-Gallagher N (2003) The human parietal cortex: a novel approach to its architectonic mapping. Adv Neurol 93:1–21PubMedGoogle Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • A. Schleicher
    • 1
  • N. Palomero-Gallagher
    • 2
  • P. Morosan
    • 2
  • S. B. Eickhoff
    • 2
  • T. Kowalski
    • 2
  • K. de Vos
    • 4
  • K. Amunts
    • 2
  • K. Zilles
    • 1
    • 2
    • 3
  1. 1.C. and O. Vogt Brain Research InstituteUniversity of DüsseldorfDüsseldorfGermany
  2. 2.Research Center JülichJülichGermany
  3. 3.Brain Imaging Center WestJülichGermany
  4. 4.Netherlands Institute for Brain ResearchAmsterdamThe Netherlands

Personalised recommendations