Comparison of functional and cytoarchitectonic maps of human visual areas V1, V2, V3d, V3v, and V4(v)

NeuroImage 49 (2010) 1171–1179

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Comparison of functional and cytoarchitectonic maps of human visual areas V1, V2, V3d, V3v, and V4(v) Marcus Wilms a,⁎, Simon B. Eickhoff a,c,e, Lars Hömke a, Claudia Rottschy a, Milenko Kujovic b, Katrin Amunts a,c,e, Gereon R. Fink a,d,f a

Institute for Neuroscience and Medicine (INM-1, INM-2, INM-3), Research Centre Jülich, Jülich, Germany C. and O. Vogt Institute for Brain Research, Heinrich Heine University, Düsseldorf, Germany Department of Psychiatry and Psychotherapy, RWTH Aachen University, Aachen, Germany d Brain Imaging Centre West, Research Centre Jülich, Jülich, Germany e Jülich–Aachen Research Alliance, JARA, Jülich, Germany f Department of Neurology, University Hospital Cologne, Cologne University, Cologne, Germany b c

a r t i c l e

i n f o

Article history: Received 19 June 2009 Revised 16 September 2009 Accepted 25 September 2009 Available online 1 October 2009

a b s t r a c t Cytoarchitectonic maps of human striate and extrastriate visual cortex based upon post-mortem brains can be correlated with functionally defined cortical areas using, for example, fMRI. We here assess the correspondence of anatomical maps of the visual cortex with functionally defined in vivo visual areas using retinotopic mapping. To this end, anatomical maximum probability maps (aMPM) derived from individual cytoarchitectonic maps of striate and extrastriate visual areas were compared with functional localisers for the early visual areas. Using fMRI, we delineated dorsal and ventral human retinotopic areas V1, V2, and V3, as well as a quarter-field visual field representation lateral to V3v, V4(v), in 24 healthy subjects. Based on these individual definitions, a functional maximum probability map (fMPM) was then computed in analogy to the aMPM. Functional and anatomical MPMs were highly correlated at group level: 78.5% of activated voxels in the fMPM were correctly assigned by the aMPM. The group aMPM was less effective in predicting functional retinotopic areas in the individual brain due to the large inter-individual variability in the location and extent of visual areas (mean overlap 32–69%). We conclude that cytoarchitectonic maps of striate and extrastriate visual areas may provide a valuable method for assigning functional group activations and thus add valuable a priori knowledge to the analysis of functional imaging data of the visual cortex. © 2009 Elsevier Inc. All rights reserved.

Introduction Retinotopic mapping has become the current gold standard for the in vivo delineation of early visual cortical areas in the human brain using non-invasive functional imaging. The principle behind this approach is to delineate the borders of retinotopically organised areas by finding the visual field meridian representations on cortical surface representations of individual brains (Sereno et al., 1995; DeYoe et al., 1996; Engel et al., 1997). The collection of retinotopic functional imaging data together with high-resolution structural data needed for cortical surface reconstruction is, however, time consuming and may thus limit the applicability of this approach, e.g., when examining patients who do not tolerate being scanned for a long time. Moreover excentric visual field localisations are technically difficult to access by retinotopic mapping due to limitations in the visual angle covered by ⁎ Corresponding author. Cognitive Neurology, Institute for Neuroscience and Medicine, Forschungszentrum Jülich, Leo-Brandt-Str. 5, 52425 Jülich, Germany. Fax: +49 2461 61 1518. E-mail address: [email protected] (M. Wilms). 1053-8119/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2009.09.063

standard stimulation devices. In practice, this approach is hence often restricted to the delineation of cortical regions representing the more central visual field. Finally, it is hardly feasible to obtain functional localisers for all areas involved in complex cortical networks, even in the visual cortex where higher areas become more and more tuned to particular stimulus features. Anatomical a priori information on the location and extent of cortical areas in the visual system may help to overcome such limitations by providing a complementary frame of reference. Such anatomical maps based on the probabilistic, observer-independent mapping of cortical areas (Schleicher et al., 2005) have already been applied as references for the localisation of functional activations in fMRI and MEG studies of visual processing (Larsson et al., 2002; Barnikol et al., 2006; Dammers et al., 2007; Wohlschläger et al., 2005). Given that the location and extent of visual areas can also be delineated in vivo by retinotopic mapping as described above, the question obviously arises, how closely the maps derived from in vivo functional imaging and post-mortem anatomical analysis correspond to each other. This question is addressed here directly by correlating architectonic and functional delineations of the early visual areas V1, V2, V3d,

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V3v, and V4(v). Retinotopic visual areas were delineated on surface reconstructions of the cortical grey–white matter boundary, which has emerged as the method of choice as it allows a definition of the meridian representation in spite of the convoluted cortical surface. Using this approach, the anatomical correlates of areas V1 (BA17) and V2 (BA18) (Amunts et al., 2000), which have already been compared to functional data (Wohlschläger et al., 2005) using a volume-based delineation, were re-analysed. We then quantitatively compared functionally defined dorsal V3 (V3d), ventral V3 (V3v), as well as a quarter-field representation lateral to V3v, V4(v), with their cytoarchitectonically defined putative anatomical correlate areas hOc3d (human occipital area 3, dorsal; Kujovic et al., 2007), hOc3v (human occipital area 3, ventral; Rottschy et al., 2007), and hOc4v (human occipital area 4, ventral; Rottschy et al., 2007). Methods Subjects and experimental paradigm Twenty-four healthy subjects aged 21–38 years (mean age 26.4 ± 4.5 years, 10 female) participated in the study. All subjects were healthy without any history of neurological or psychiatric diseases, and had normal visual acuity. Informed written consent was obtained from all subjects. The study was approved by the ethics committee of the University Hospital Aachen, Germany. Stimuli were presented via fMRI compatible goggles (subjects S1– S10: Silent Vision™, Avotec, FL; S11–S24: VisuaStim™, Resonance Technology, CA). Retinotopic mapping of the polar angle was performed by a rotating wedge of 10° polar width, which smoothly traversed a circular grey aperture of 9° (S1–S10) or 11.25° (S11–S24) radius once every 36.3 s. Stimuli reversed their check polarity at 7.5 Hz to achieve a maximum perceived brightness and contrast (Brücke, 1864; Grüsser and Creutzfeldt, 1957). Sizes and velocities of the stimuli were matched so that each point within the aperture was effectively stimulated for 1 s. One stimulation cycle comprised eight clockwise or counter-clockwise revolutions followed by a short pause of a uniformly grey background. This stimulation cycle was repeated twice for each stimulus direction in alternating order. Stimulus sequences were custom programmed using IDL software (version 6.0 © 2003, Research Systems, Inc., Boulder, CO) and presented using the Presentation™ software (version 9.7-9.9, http://www.neurobs.com). Subjects were asked to fixate a small (0.15°) fixation cross in the middle of their visual field. To ensure consistent fixation subjects had to report changes in the colour of the fixation cross by pressing a button with their hand, which randomly occurred on average two times per stimulus revolution. Eye movements were monitored throughout the measurements by means of an infra-red camera (S1–S10: Avotec Real Eye™, FL; S11–S24: VisuaStim™, Resonance Technology, CA). Image acquisition processing Ten subjects were scanned with a 1.5 T scanner (subjects S1–S10, Siemens Magnetom Sonata, Erlangen, Germany) with a standard bird-cage head coil. Four whole brain T1-weighted anatomical MRI (1 × 1 × 1 mm3) were acquired using a 3D MP-RAGE sequence [repetition time (TR) = 2200 ms, echo time (TE) = 3.93 ms, inversion time (TI) = 1200 ms, flip angle (FA) = 15°, field of view (FOV) = 256 mm]. Functional echoplanar images (EPI) were obtained from a gradient-echo sequence [TR = 865 ms, TE = 50 ms, FA = 70°, FOV = 200 × 200 mm2, slice thickness = 4 mm, gap = 0.4 mm, inplane resolution = 3.125 × 3.125 mm2, matrix = 64 × 64]. Ten fMRI slices were obtained orthogonal to the calcarine sulcus and covering the entire occipital pole. Fourteen subjects (subjects S11–S24) were scanned at 3 T (Siemens Trio, Erlangen, Germany) with a standard bird-cage head coil. We acquired two whole brain T1-weighted

anatomical MRI (1 × 1 × 1 mm3) using the 3D MP-RAGE sequence [TR = 2250 ms, TE = 3.93 ms, TI = 900 ms, FA = 9°, FOV = 256 mm] and functional EPI measurements [TR = 1100 ms, TE = 30 ms, FA = 66°, FOV = 192 × 192 mm2, slice thickness = 3 mm, gap = 0.3 mm, in-plane resolution = 3.0 × 3.0 mm2, matrix = 64 × 64, 18 slices in the same orientation as above]. Head movements of all subjects were minimised by the slight pressure of the protective headphones. The partial brain coverage substantially reduced the TR which allowed a higher temporal sampling of the BOLD responses. We acquired one additional whole brain EPI volume with the identical slice orientation as in the partial brain coverage volumes prior to each EPI registration. This facilitated the coregistration of the partial brain coverage functional to the whole brain structural volumes. Imaging data from both scanners allowed comparable delineations of retinotopic maps. Data were thus pooled in order to increase the statistical power of our subsequent analyses. The structural scans for each subject were realigned and averaged to enhance the signal-to-noise ratio. The first nine EPI volumes were discarded to avoid transient magnetic saturation effects. Using SPM2 software (http://www.fil.ion.ucl.ac.uk/spm) all EPI images were realigned to compensate for head movements during scanning. Slice acquisition timing was interpolated to the acquisition time of a middle slice. We then coregistered the whole brain EPI to the partial brain mean EPI volume and subsequently the structural volume to the (coregistered) whole brain EPI. After coregistration, the averaged structural volume was spatially normalised to the stereotaxic space defined by the single-subject MNI template. The normalisation parameters were then applied to the EPI volumes which were hereby transformed to standard stereotaxic space and re-sampled at 3 × 3 × 3 mm3 voxel size. Drifts in the BOLD signal were removed by a band-pass filter between 1/6 Hz and 1/72 Hz cut-off. Delineation of retinotopic maps Retinotopic areas V1, V2, V3v, V3d, and V4(v) were delineated in the functional datasets using the standard phase mapping techniques which probe for each voxels' optimal stimulus condition with respect to polar angle (Sereno et al., 1995; DeYoe et al., 1996; Engel et al., 1997). This could be achieved via Fourier transforms of the BOLD signal timecourses in the frequency domain or – as done here – by performing crosscorrelations in the temporal domain, both actually being equivalent techniques (Engel et al., 1997). Each voxel's BOLD response time course was first interpolated to yield a temporal resolution of 100 ms. Time courses for the clockwise and counter-clockwise rotating wedge, respectively, were then averaged. As the wedge stimulus effectively stimulated each point within the visual field for 1 s, we modelled a hemodynamic response function by convolving a boxcar function between zero and 1 s with the canonical hemodynamic response function. The cross-correlation function (CCF) between the averaged BOLD signal and the model hemodynamic response was then obtained for each and every voxel. For any given voxel, the CCF peak estimated the temporal offset corresponding to the optimal stimulus polar angle. This temporal offset, however, depended on the actual hemodynamic delay at a given voxel, which was approximated by the canonical hemodynamic response function. Since the hemodynamic delay could be expected to be independent of the clockwise vs. counter-clockwise orientation of the rotating wedge stimulus, the average of the estimated CCF peak times for both stimulus directions could be assumed to cancel any residual hemodynamic response latencies and thus yielded the voxel's polar angle preference (Warnking et al., 2002). Voxels with CCF peak values below 0.5 were not included in further analyses as they apparently were insufficiently modulated by the stimulus. Cortical surface reconstructions were computed from the averaged, normalised anatomical volumes of each subject using the public domain software SurfRelax (Larsson, 2001; available from http://

M. Wilms et al. / NeuroImage 49 (2010) 1171–1179

www.cns.nyu.edu/~jonas). Retinotopic regions of interests for ventral and dorsal V1, V2, and V3 as well as a quarter-field representation lateral of ventral V3, V4(v), were delineated based on the projections of the obtained polar angle maps onto the corresponding flattened representations of the occipital cortex of the respective subject (Fig. 1). This methodology yielded hierarchically organised retinotopic maps with plausible relative positions that mimic those presented in numerous other studies (for a review see, for example, Wandell et al., 2007). Delineation of cytoarchitectonic areas and anatomical probabilistic maps Cortical areas were defined using an observer-independent cytoarchitectonic analysis (Schleicher et al., 2005). Hereby, borders between areas were identified by significant changes in the laminar cell density patterns in a sample of 10 post-mortem brains in striate (area V1/BA17) and extrastriate cortex (area V2/BA18, Amunts et al., 2000; hOc3d, Kujovic et al., 2007; areas hOc3v and hOc4v, Rottschy et al., 2007). Cytoarchitectonically, the areas can be described as follows: Area 17 showed a prominent layer IV which could be further subdivided into three sublayers. Layer IV became considerably thinner when moving from area 17 to area 18 and its subdivision into three sublayers stopped abruptly (Amunts et al., 2000). Areas hOc3d and hOc3v differed from each other by the number of large pyramidal cells in lower layer III as well as the cell density in layer IV and V (Rottschy et al., 2007). In addition, area hOc4v was characterised by a more cell dense layer II, a more pronounced increase of pyramidal cell density and size throughout layer III and a much more cell dense internal granular layer (IV) as compared to hOc3v. These cytoarchitectonic borders are closely matched by changes in cortical receptor architecture (Zilles and Amunts, 2009; Eickhoff et al., 2007). The cytoarchitectonically defined areas of the 10 brains were 3D reconstructed, and spatially normalised to the MNI single-subject template (Evans et al., 1992) using a nonlinear elastic registration algorithm (Amunts et al., 2004; Hömke, 2006). Following spatial normalisation, the corresponding areas of the different subjects were superimposed and a probabilistic map was generated for each area. It describes, for each voxel of the reference brain, how many individual brains overlapped with respect to the respective cytoarchitectonic

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area in a certain voxel. A maximum probability map (MPM) of all these areas, i.e., a summary map of the cytoarchitectonic organisation of the early human visual cortex, was computed by comparing the probabilities of all areas in each voxel and assigning each voxel to the most likely anatomical area (Eickhoff et al., 2005, 2006). Comparison of functional and anatomical MPMs After the delineation of retinotopic surface maps, volumes of interest were sampled at the vertex locations of the folded cortical surface using nearest neighbour interpolation, yielding separate volume data sets for dorsal and ventral V1, V2, V3, as well as V4(v) in each hemisphere of each of the 24 subjects. The superimposition of individual volumes separately for each visual area yielded functional probability maps for each retinotopically defined visual area. Like cytoarchitectonic probabilistic maps, these functional probability maps hence denote how many brains had a retinotopically defined representation of a particular area at any given voxel. In analogy to the algorithm outlined for the calculation of anatomical maximum probability maps (aMPM; Eickhoff et al., 2005, 2006), these retinotopic probability maps were combined into a functional maximum probability map (fMPM). As described for anatomical data, this map represents at each voxel the most likely (functionally defined) visual area, by comparing the probabilities of the different areas, i.e., their probabilistic maps, against each other. We compared MPMs based on volume-based normalisation. Alternative approaches for normalisation include, for example, surface-based inter-subject averaging, which may result in greater statistical power (and lower inter-subject variability) compared to volume-based normalisation in some regions of interest (Argall et al., 2006; Fischl et al., 1999; Hinds et al., 2009). Previous studies have shown that the inter-subject variability of different cortical regions, however, is different and the degree of the relationship of cortical areas to the folding pattern varies between brain regions (Fischl et al., 2008). These and other factors may significantly influence the results of spatial normalisation. Currently, there is no “gold standard” for spatial normalisation. We applied a volume-based normalisation based on an elastic model, since it enabled a robust and reproducible alignment of both functional and cytoarchitectonic maps into a common reference space.

Fig. 1. Flattened cortex projections of retinotopic phase maps. Example for the delineation of visual areas V1, V2, V3, and V4(v) on the flattened reconstruction of the grey/white matter border of the left and right occipital cortex in a single subject. Borders of visual areas were drawn where the visual field sign reversed, i.e., where the horizontal or vertical meridians were represented.

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The normalisation of anatomical and functional maps to the same stereotaxic space (MNI single-subject template) enabled the comparison of both data modalities on a voxel by voxel basis. More specifically we calculated the fraction of functional voxels that were assigned to each of the available anatomical maps. For example, we tested how many of the voxels labelled as V1 in the fMPM were assigned to its putative anatomical correlate BA17 or other anatomical areas as defined by the aMPM. Such approach yielded a measure for the anatomical maps' ability to predict functionally defined voxels. Results Using retinotopic mapping of polar angle and subsequent delineation of visual areas based on the representation of the vertical meridians, the following areas were defined separately for the left and right hemispheres of all 24 subjects: The dorsal and ventral parts of visual areas V1, V2, and V3, as well as a quarter-field visual field representation lateral of ventral V3, i.e., area V4(v). Similar to previous descriptions of anatomical data, also the functional probability maps for these areas overlapped considerably with each other in particular in those regions where their probabilities were rather low. This overlap can be attributed to the inter-subject variability in the location and size of visual areas. Nonetheless, the hierarchical progression of the areas V1 to V4(v) in both the ventral and dorsal part of the visual system can clearly be observed in both hemispheres by juxtaposition of the cortical surface covered by the individual functional probability maps (Fig. 2). Maximum probability maps were then computed for both the cytoarchitectonic (aMPM) and the functional probability maps (fMPM) by assigning each location of the occipital cortex to the most likely area at that position. Fig. 3 illustrates both the aMPM (upper row) and the fMPM (lower row) projected on the folded, inflated, and flattened surface reconstructions of the template brain's left and right hemispheres. From this comparison it becomes evident that the map volumes

assigned to the individual areas in the fMPM are considerably smaller as compared to those obtained from the anatomical mapping (aMPM). A quantitative comparison of the respective volumes then confirmed this smaller size of the functional MPM representations (Table 1). It should be noted, however, that the mean size of the individual retinotopic map volumes (that is, the volume of a retinotopically defined area, e.g., area V3v, averaged across all subjects) was considerably larger than the volume assigned to that area in the fMPM. Nevertheless, also these mean individual functional volumes were still on average about 30% smaller than the anatomical MPM representations (Table 1). The topographical correspondence between both MPMs is illustrated in Fig. 4 showing the aMPM and the fMPM in a series of horizontal sections. This comparison already demonstrates, that the overall pattern of aMPM and fMPM assignments is very much alike. Moreover, this qualitative comparison also shows a good correspondence of the borders of the striate cortex and the adjacent extrastriate visual areas. Functional and anatomical MPMs were then compared quantitatively in order to assess how well the post-mortem data corresponded with the in vivo results. This comparison was performed by calculating the overlap between the delineated retinotopic areas as given by their fMPM representations with each of the anatomically defined areas as given by the aMPM. Table 2 summarises the results of this evaluation by showing the percentage of each retinotopic area assigned to each of the individual anatomical areas. It can be seen that the large majority of each functionally defined area was assigned to the corresponding anatomical area, that is, was correctly predicted by the cytoarchitectonic maps. For example, 88.5% of the voxels corresponding to the functionally defined dorsal part of left area V1 were assigned to cytoarchitectonic BA17. The remaining 11.5% of the voxels making up dorsal V1 on the left side overlapped with the anatomically defined areas adjacent to BA17. Averaging over both hemispheres, 85.4% of V1, 74.9% of V2, 71.4% of V3d, 65.6% of V3v, and 78.7% of V4(v) were correctly predicted

Fig. 2. Hierarchical progression of the functionally defined visual areas V1 to V4(v) in both hemispheres. For ease of comparison, functional probability map overlays on surface reconstructions of the MNI template brain were homogenously coloured, thus showing all voxel assignments for each area.

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Fig. 3. Anatomical (upper row) and functional (lower row) maximum probability map projected on the folded, inflated, and flattened surface reconstructions of the MNI template brain's left and right hemispheres.

by the aMPM (Table 3). In summary, when considering all retinotopically assigned voxels in our group analysis, a mean proportion of 78.5% of them were correctly predicted by the anatomical probability maps.

Table 1 Comparison of MPMs and mean retinotopic map (RM) volumes for left (L), right (R), and merged left and right (L +R) visual cortical areas. Area BA17/V1

BA18/V2

hOc3d/V3d

hOc3v/V3v

hOC4v/V4v

Σ

L R L+R L R L+R L R L+R L R L+R L R L+R L R L+R

aMPM (mm3)

Mean RM size (mm3)

fMPM (mm3)

21,493 21,471 42,964 15,657 15,699 31,356 8612 7934 16,546 6145 6189 12,334 10,001 7799 17,800 61,908 59,092 121,000

13,489 12,142 25,631 10,033 9390 19,423 4339 3392 7731 4600 4988 9588 4292 3998 8290 36,753 33,910 70,663

5665 7256 12,921 4172 4278 8450 540 1294 1834 2258 1346 3604 1208 1017 2225 13,843 15,191 29,034

The aMPM is based on cytoarchitectonic delineations in 10 human post-mortem brains; data in the middle and right columns are based on the present retinotopic mapping study using fMRI. Volumes are provided in the reference space of the single-subject template of the Montreal Neurological Institute (MNI). As a consequence, the volumes of cytoarchitectonic areas described here are larger than those provided in the original publications of the probabilistic maps (Amunts et al., 2000; Rottschy et al., 2007).

In order to furthermore evaluate the applicability of the cytoarchitectonic aMPMs for the assignment of functional data obtained in single subjects, we also calculated the overlap of the individual retinotopic maps for each subject with the anatomical maps. The average fraction assigned to each area across subjects are shown in Table 4. It becomes evident that the overlaps of the retinotopic maps with the corresponding architectonic areas were considerably smaller as compared to the abovementioned group analysis. In particular, the proportions that were correctly assigned to the corresponding anatomical areas ranged from 32% to 69%. There was, however, also a considerable “spill-over” of voxel assignments into neighbouring areas, which is most probably due to the large interindividual variability in the location and extent of visual cortical areas. Discussion The cortical parcellation defined by quantitative cytoarchitectonic analysis and summarised by the maximum probability map is not only supported by other histological modalities such as receptor autoradiography (for a review see Zilles and Amunts, 2009) but also by in vivo approaches. Most of the work comparing histological borders and functional data has been performed by comparing the location of significant fMRI activations to cytoarchitectonic areas. Using such an approach, it has been shown that different cytoarchitectonic areas in the human parietal operculum contain separate somatotopic maps in the same manner as has been demonstrated for the homologues in non-human primates using invasive techniques (Eickhoff et al., 2007a). Likewise, cortical activations to visual motion stimuli revealed the functionally defined human area V5/MT+ that corresponded to the cytoarchitectonic area hOc5a (Wilms et al.,

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Fig. 4. Multislice transversal views of the MNI template brain with anatomical (A) and functional (B) MPM overlays (using MRIcron, http://www.sph.sc.edu/comd/rorden/ mricron).

2005). In addition to the steadily growing body of work showing a convergence between cytoarchitectonically defined cortical areas and the functional specialisation of the human brain, a convergence between histological features and regional variations of connectivity features has been shown (e.g., Johansen-Berg et al., 2004). It has been reported, for example, that the borders between BA 44 and BA 45 in Broca's region (Amunts et al., 2004) coincide closely with changes in cortical connectivity profiles (Anwander et al., 2007; Klein et al., 2007). Similar correspondences were shown in the SMA/ pre-SMA region and the thalamus (Johansen-Berg et al., 2004, 2005). In the light of these findings, we expected to find a correlation of cytoarchitectonic maximum probability maps of the human visual cortex with the corresponding maps based on retinotopic mapping. The purpose of this study was whether and to which extent the anatomical data would provide a reliable prediction of retinotopically defined areas. Our data show that the attribution of a particular voxel in the occipital lobe to a particular functional area based on fMRI matched the corresponding anatomical allocation in close to 80% of the cases. The data suggest a good correlation between post-mortem histological mapping and in vivo functional delineation of retinotopic Table 2 Percent overlap of functional (rows) with anatomical (columns) areas as defined by the respective MPMs, separately for left (L) and right (R) hemispheres. Area

BA17 L

V1d V1v V2d V2v V3d V3v V4v

L R L R L R L R L R L R L R

BA18 R

88.5

L

hOc3d R

9.6 81.0

77.6

17.8

92.4

24.5 0.2 2.9 1.7 13.7 0.0 9.3

0.0

0.0

0.0 24.1

61.4 0.0

0.0

0.0 0.0

68.1

0.0 3.9

3.4

71.7

15.0

0.0 0.3

0.0

0.0

0.0

0.1

0.0

15.9

0.0 0.0

18.1

70.6

6.1

0.2

12.2

72.1

0.0 0.0

0.0

0.0

28.7

0.2

0.0

83.2 67.6

R 0.0

1.1

2.8

L

9.9 87.4

7.4

V1 and V2 The calcarine sulcus is one of the primary fissures of the human visual brain. It is present in every individual brain and located at similar positions across subjects. It does, however, show a marked inter-individual variability in terms of its course and pattern (Ono et al., 1990). In spite of this macro-anatomical variability, a very stable relationship between the location of the calcarine fissure and the retinotopic organisation within V1 has been demonstrated (Rajimehr and Tootell, 2009). In particular, it has been shown that the horizontal meridian representation within V1 tends to be located closely to the fundus of the calcarine sulcus. One explanation for this phenomenon is provided by the tension-based model of cortical folding (Van Essen, 1997). It postulates that neighbouring areas with strong topographic connectivity such as matching retinotopic representations on either side of a mirror-symmetric boundary tend to produce an outward fold, i.e., a gyrus. Correspondingly, the horizontal meridian, i.e., the cortex furthest away from the areal boundary, would predominantly be located within the sulci due to topological

Σ

hOc4v R

1.0

7.4

3.9

L 0.3

0.0

77.7

14.0

hOc3v R

1.6

21.3

19.5

L

areas in the early visual cortex. This suggests the feasibility of anatomical maps as an alternative to retinotopic mapping in circumstances when retinotopic mapping is too time consuming or cannot be achieved for other reasons.

68.4

100.0 100.0 100.0 100.0 100.0 99.4 100.0 100.0 99.5 90.5 100.0 100.0 87.4 89.0

Table 3 Percent overlap of functional (rows) with anatomical (columns) areas as defined by the respective MPMs (merged left and right hemispheres). Area

BA17

V1d V1v V1 V2d V2v V2 V3d V3v V3 V4v

84.5 86.3 85.4

BA18

hOc3d

hOc3v

hOc4v

80.7 69.7 74.9 71.4 65.6 67.5 78.7

M. Wilms et al. / NeuroImage 49 (2010) 1171–1179 Table 4 Mean percent overlap of individual retinotopic maps (rows) with areas defined by the aMPM (columns), separately for left (L) and right (R) hemispheres. Area

BA17 L

V1d V1v V2d V2v V3d V3v V4v

L R L R L R L R L R L R L R

BA18 R

65.8

L

hOc3d R

17.1 57.4

65.7

19.9

68.6

47.1 9.4

35.8 28.5

7.7 11.0 23.4 9.3 15.1

2.3

1.0

0.8 32.1

32.1 8.5

1.5

0.9 1.4

35.0

2.5 5.5

10.2

35.5

21.7

0.2 5.4

3.5

0.9

1.8

0.8

0.5

17.5

0.3 0.9

17.4

38.6

12.7

3.4

18.8 0.3

0.3 1.4

1.6

14.9 55.1

18.0

data for both dorsal and ventral V3 was close to that noted for the less variable earlier visual areas. V4(v)

R

1.0

2.0

35.3 34.9

L 1.1

5.5

12.7

Σ

hOc4v R

7.7

19.0

20.0

L 4.2

0.4

43.3

34.0

hOc3v R

4.2

20.0

28.3

L

1177

35.6

92.4 86.3 93.0 93.3 86.8 75.1 92.0 94.5 81.4 63.8 91.7 93.1 77.2 75.7

constraints. This idea assumes that the topographic layout of retinotopic areas precedes the development of cortical folds during cortical development, which is in line with current models of topographically organised thalamic input driving the differentiation of sensory cortices (Kaas, 1997). Comparing cytoarchitectonic with functional mappings, we found that in contrast to the retinotopic definitions of V1 and V2, their anatomical counterparts (BA17 and BA18, respectively) were not separated into a ventral and a dorsal part due to the homogenous cytoarchitecture of these areas (Amunts et al., 2000). Consequently, both upper and lower field representations of V1 and V2 were compared to these anatomical maps. Our data showed that functionally defined areas V1 and V2 largely overlap with BA17 and BA18, respectively, replicating the results of a previous, volume-based analysis not using the currently prevailing surface-based definition of retinotopic areas (Wohlschläger et al., 2005). Besides these differences in the definition of retinotopic areas, both studies also differed with respect to the applied analysis. Wohlschläger and coworkers compared functionally defined probability maps for V1 and V2 with cytoarchitectonically defined ones for BA17 and BA18. In the present study, we compared anatomical and functional maximum probability maps (MPMs), which accommodate the problem of overlapping representations by denoting the most likely area at each position, hereby defining a summary map of probabilistic information. The present results extend the previous comparisons of functional and anatomical mapping of the visual cortex (Wohlschläger et al., 2005) by an enhanced definition of retinotopic areas, the analysis of additional regions, and the evaluation of the correspondence between summary maps for either modality. V3d and V3v In contrast to BA17 and BA18, the putative dorsal and ventral correlates of V3, areas hOc3d and hOc3v differ in their cytoarchitecture (Rottschy et al., 2007; Kujovic et al., 2007). Jointly, about 70% of all functionally defined “V3 voxels” were correctly assigned by the aMPM representations of these two areas (Table 3). This finding indicates that anatomical post-mortem data are in good agreement with functional definitions based on retinotopy beyond V2 as well. We found only a small number of voxels with high probabilities for retinotopically defined V3d across subjects. Consequently, the size of V3d as defined by the fMPM was relatively small (Table 1), and well below the average sizes of individual retinotopic V3d maps. Thus, the data show that the small size of the fMPM representation for (left) V3d results from the inter-individual variability in the functionally defined borders of this area rather than a small volume of V3d per se. Nevertheless, the correspondence between anatomical and functional

For many years, macaque area V4 has also been referred to as the V4 complex (Felleman and Van Essen, 1991; Gattass et al., 1988), which acknowledges potentially redundant visual field representations within this region. Moreover, while there is a general agreement on the definition and retinotopic organisation of the first two visual areas, i.e., V1 and V2, and a wide consensus on most features of the third visual area V3 (though the relationship between V3v and V3d is still disputed) multiple concepts and terminologies exist for the V4 region. In non-human primates and the initial human neuroimaging studies V4 was defined as a colour-sensitive area containing a hemifield representation on each hemisphere (Zeki et al., 1991; Zeki, 1978), while the cortex anterior and dorsally to it received less attention. With the advent of in vivo retinotopic imaging in humans using fMRI, the cortex anterior to V3v was delineated as a quarterfield representation (V4v) in both field sign-defined and meridiandefined retinotopic mapping studies (DeYoe et al., 1996; Sereno et al., 1995). Moreover, neither map provided a more detailed concept of the cortex anterior to this quarter-field representation, as it apparently contained a more complex organisation than the representation of a single quarter- or hemi-field. A later map followed this definition of V4v but proposed the existence of a colour-sensitive area containing a complete hemi-field representation anterior to it (Hadjikhani et al., 1998; Tootell and Hadjikhani, 2001). Evidently, this area (V8) closely matches the original definition of V4 as the human colour centre holding a hemi-field representation but differs from it by the existence of V4v interposed between V3v and V4/V8. This V8 view is opposed by later retinotopic mapping studies supporting the earlier definition of V4 as a hemi-field representation anterior to V3v that is sensitive to colour (Brewer et al., 2005; Wade et al., 2002). This area V4 is then followed by two adjacent quarter-field representations forming areas VO-1 and VO-2. That is, these maps hypothesise the existence of a hemi-field representation, formed by two complementing quarter-field representations, anterior to classically defined V4. A minor modification of this view defined these two quarter-field representations as separate areas termed VOF (VO-1) and VMO (VO-2) while keeping the definition of V4 unchanged (Tyler et al., 2005). More recently, Hansen and colleagues (2007) proposed a concept comprising a quarter-field representation in area V4v that is matched by a lower quarter-field representation anterior to V3d, which is termed V4d. Ventral and dorsal V4 are then followed by the lateral occipital complex. The latter, finally, seems to correspond roughly to the two areas defined as LO1 and LO2 (Larsson and Heeger, 2006). This overview illustrates the yet unresolved dispute on the organisation of the visual cortex anterior to V3v. In our data, the anterior border of V3v was clearly demarcated in all subjects by a representation of the upper vertical meridian. However, the anterior border of the subsequent area was considerably less distinct. In particular, we observed both a hemi-field representation that was located immediately anterior to V3v (corresponding to hV4 as described by Brewer et al., 2005) as well as situations reminiscent of a hemi-field representation that was separated from V3v by an additional quarter-field representation (similar to the V4v/ V8 model reported by Hadjikhani et al., 1998). No final statement could be derived from the current data regarding the organisation of the cortex anterior to V3v due to the lack of colour stimuli, which represent an important driving factor for V4 activation. We therefore only considered a quarter-field representation of the contralateral visual field directly adjacent to V3v. The area termed V4(v) in the present study is hence either corresponding to V4v in the V8 model (Hadjikhani et al., 1998) or is part of hV4 as defined by Brewer and coworkers (2005). Area V4(v) was compared to the anatomical map

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of hOc4v, its putative cytoarchitectonic correlate (Rottschy et al., 2007; Eickhoff et al., 2008). Table 2 shows a high overlap between these two maps with only a few misassigned voxels. Surprisingly at first sight, most misassigned V4(v) voxels were allocated to cytoarchitectonic BA17, i.e., an area to which it does not have a direct anatomical or functional border. An explanation for this misassignment is that the anterior end of V4(v) in the collateral sulcus closely approaches the depth of the calcarine sulcus which hosts ventral V1 (BA17). With respect to the aforementioned controversy regarding the organisation of the retinotopic cortex anterior to V3v, it is noteworthy that retinotopically defined V4(v) is located predominantly in the posterior part of hOc4v. It hence seems to correspond to only a part of this anatomical area rather than occupying it completely. This arrangement may therefore suggest that another retinotopic entity (most likely the complementary hemi-field) could be represented in hOc4v as well. Whether this hypothesis holds and a hemi-field representation within hOc4v can be established has to be the subject of further research. Comparison across areas It can be noted (Table 2) that the correspondence between functional and anatomical MPMs was maximal in V1 and decreased when moving anteriorly towards V4(v). This decrease may have methodical reasons: During spatial normalisation to the stereotaxic space of the MNI single-subject template, macroscopically dominant features such as the calcarine sulcus are mapped more reliably as compared to smaller, more variable landmarks. Consequently, V1 which is mostly buried within the calcarine sulcus is less prone to normalisation artefacts than V2–V4. On the other hand, however, it is also well established that primary areas such as V1 are usually most stable in terms of size and location across subjects, while higher areas show increasing variability. Such increased variability in both anatomical and functional data may have contributed to the somewhat lower correspondence between them. Throughout all areas it could be noted that their delineations based on functional, i.e., retinotopic, criteria led to smaller volumes as compared to the respective anatomical counterparts. This finding is likely to be based on a variety of reasons: First, the constrained visual angle (up to 11.25° radius) of the visual stimulus excluded probing the visual periphery in the fMRI setting. Second, due to the limited spatial resolution of our stimulation device, central stimuli (eccentricity b∼0.5° visual angle) were hardly perceived as moving and flickering. Consequently, cortex with visual receptive fields close to the fovea was only inefficiently modulated by the periodic stimulus and thus not reliably accessible by the retinotopic mapping. In summary, the presented retinotopic maps therefore cover only those parts of the visual cortex that were effectively modulated by our stimulation, roughly corresponding to eccentricities between 0.5° and 11.25°. In contrast, anatomical mapping of visual areas by cytoarchitectonic criteria was not constrained to any visual field locations. Consequently, histological delineations resulted in a complete coverage of the individual areas and hence larger volumes as compared to retinotopic mapping. Finally, functional measurements were carried out at a lower spatial resolution compared to the 1 mm3 resolution of the anatomical maps. Partial volume effects may thus have reduced the functional signal in voxels fractionally containing subcortical white matter or CSF. Such voxels with lower retinotopic signal-tonoise ratios were less likely to yield a reliable delineation of functional areas and to contribute to the retinotopic maps, a problem not faced in anatomical mapping. However, as most fMRI studies use a setup very comparable to the one described here, the obtained results of good agreement between retinotopy and histology should be generalisable to other neuroimaging studies of the human visual system.

Comparison of the aMPM with single-subject fMRI data Compared to the good reliability achieved by cytoarchitectonic maps in assigning functional group data, the assignment of singlesubject retinotopic data was less precise, matching previous observations on this issue (Wohlschläger et al., 2005; Wilms et al., 2005). This poorer correlation may, however, be explained by the considerable inter-individual variability in both the location of visual cortical areas (Dougherty et al., 2003) as well as their surface areas, in particular reported also for early visual areas (Dougherty et al., 2003; Amunts et al., 2000; Andrews et al., 1997; reviewed by Stensaas et al., 1974). Such inter-individual variability must lead to reduced alignment between a group-mean anatomical probability map and the fMPM of a different subject's (individual) brain. Obviously one would expect the overlap to be much higher, if the aMPM and the fMPM were obtained from the same subject (if this were somehow possible). It is furthermore noteworthy that the mean sizes of individual retinotopic maps were intermediate between the corresponding areas as defined by the fMPM and those yielded by the aMPM (Table 1). This situation may be explained by the notion that the mean (across individuals) size of an area is not influenced by the between subject variability in the location of a particular area. With the spatial superimposition of these maps needed for the computation of a fMPM, however, the inter-individual variability in the location of visual areas influences the ensuing between-subject overlap. Those voxels, where a particular area was only inconsistently observed, may turn out not to meet the criteria for inclusion into the MPM any more. The smaller size of the MPMs relative to the mean size of the individual areas is hence a direct reflection of their interindividual variability in location, which is also contributing to the lower reliability of anatomical maps for assigning single-subject data. Conclusion Our data's most important implication for fMRI studies will pertain to situations where retinotopic maps have not been acquired for the individual subject, but detailed information about the activated visual areas is nevertheless useful. While retinotopic mapping is now routinely possible, this situation may nevertheless arise in experiments where scanning time is very limited (for example in the investigation of patients) or where the main focus rested on other functional systems. In such cases where individual retinotopic maps cannot serve as a reference, the most prevalent approach so far has been the comparison to macro-anatomical landmarks. These, however, only feature a tentative relationship with the anatomical and functional organisation of the cerebral cortex and hence do not allow inference on the activated visual areas. In contrast, the comparison of retinotopic maps for areas V1, V2, V3d, V3v, and V4(v) with cytoarchitectonic data of BA17, BA18, hOC3d, hOC3v, and hOC4v based on maximum probability maps clearly showed good correspondence between anatomical and functional summary maps of the early human visual cortex. Cytoarchitectonically defined maps of the striate and extrastriate visual areas may thus supplement retinotopic mapping for assigning functional group activations. In contrast to the latter, they do not require dedicated and time-intense localiser scans and they add valuable a priori knowledge to the analysis of functional imaging data of the visual cortex. Acknowledgments We are grateful to our colleagues from the MR group of the Institute for Neurosciences and Medicine for their assistance in acquiring the fMRI data. G.R.F. was supported by the Deutsche

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