Assignment of functional activations to probabilistic cytoarchitectonic areas revisited

www.elsevier.com/locate/ynimg NeuroImage 36 (2007) 511 – 521

Assignment of functional activations to probabilistic cytoarchitectonic areas revisited Simon B. Eickhoff, a,b,⁎ Tomas Paus, c,d,e Svenja Caspers, b Marie-Helene Grosbras, e,f Alan C. Evans, d,g Karl Zilles, a,b,d,h and Katrin Amunts a,d,h,i a

Institut für Medizin, Forschungszentrum Jülich GmbH, D-52425 Jülich, Germany C. & O. Vogt Institut für Hirnforschung, Düsseldorf, Germany c Cognitive Neuroscience Unit, Montreal Neurological Institute, McGill University, Montreal, Canada d International Consortium for Human Brain Mapping (ICBM), USA e Brain and Body Centre, University of Nottingham, Nottingham, UK f Department of Psychology and Centre for Cognitive NeuroImaging, University of Glasgow, UK g Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada h Brain Imaging Center West (BICW), Jülich, Germany i Klinik für Psychiatrie und Psychotherapie, RWTH Aachen University, Germany b

Received 27 November 2006; revised 19 February 2007; accepted 19 March 2007 Available online 10 April 2007

Probabilistic cytoarchitectonic maps in standard reference space provide a powerful tool for the analysis of structure–function relationships in the human brain. While these microstructurally defined maps have already been successfully used in the analysis of somatosensory, motor or language functions, several conceptual issues in the analysis of structure–function relationships still demand further clarification. In this paper, we demonstrate the principle approaches for anatomical localisation of functional activations based on probabilistic cytoarchitectonic maps by exemplary analysis of an anterior parietal activation evoked by visual presentation of hand gestures. After consideration of the conceptual basis and implementation of volume or local maxima labelling, we comment on some potential interpretational difficulties, limitations and caveats that could be encountered. Extending and supplementing these methods, we then propose a supplementary approach for quantification of structure–function correspondences based on distribution analysis. This approach relates the cytoarchitectonic probabilities observed at a particular functionally defined location to the areal specific null distribution of probabilities across the whole brain (i.e., the full probability map). Importantly, this method avoids the need for a unique classification of voxels to a single cortical area and may increase the comparability between results obtained for different areas. Moreover, as distribution-based labelling quantifies the “central tendency” of an activation with respect to anatomical areas, it will, in combination with the established methods, allow an advanced characterisation of the anatomical substrates of functional activations. Finally, the advantages and disadvantages of the various

⁎ Corresponding author. Institut für Medizin, Forschungszentrum Jülich GmbH, D-52425 Jülich, Germany. Fax: +49 2461 61 2820. E-mail address: [email protected] (S.B. Eickhoff). Available online on ScienceDirect (www.sciencedirect.com). 1053-8119/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2007.03.060

methods are discussed, focussing on the question of which approach is most appropriate for a particular situation. © 2007 Elsevier Inc. All rights reserved. Keywords: fMRI; Structure–function relationship; Mapping; Atlases

Introduction Studies in humans and non-human primates have shown that regionally specific differences in cortical function are underlined by concurrent differences in cyto-, myelo- and connectional architecture (Coq et al., 2004; Eickhoff et al., 2005b, 2006a; Luppino et al., 1991; Matelli et al., 1991; Nelissen et al., 2005; Wilms et al., 2005; Wu and Kaas, 2003). Consequently, there is an emerging consensus that cortical areas, defined by their microstructure and/or connectivity, can be regarded as the functional modules of the cerebral cortex (Eickhoff et al., 2005b; Felleman and Van Essen, 1991; Passingham et al., 2002; Zilles et al., 2002). Integration of functional observations with a detailed knowledge of microstructural architecture is therefore crucial for understanding the principles underlying cortical organisation. This integration has a longstanding tradition in experimental research in non-human primates, where the electrode tracks and thus regionally specific functional properties can directly be related to cyto- or myeloarchitectonic preparations of the same individual (Luppino et al., 1991). For humans, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) can provide information about the functional organisation of the cerebral cortex with a spatial resolution of few millimetres. There is, however, currently no

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method to analyse function and microstructure in one and the same human brain, although recent studies using high-resolution MR imaging with a spatial resolution at almost microscopic level provide some future hope in that respect (Augustinack et al., 2005; Eickhoff et al., 2005a; Fatterpekar et al., 2002; Walters et al., 2007). Therefore, the contemporary approach for analysing the correspondence between structure and function in the human brain is to perform both analyses separately (in two groups of subjects) and integrate the obtained data by brain atlases. The development of probabilistic cytoarchitectonic maps (Amunts and Zilles, 2001; Eickhoff et al., 2005b; Zilles et al., 2002) as an anatomical reference for functional neuroimaging studies promoted such integration, as in contrast to classical anatomical brain atlases (e.g., Brodmann, 1909), these maps (Table 1) are based on observer-independent cytoarchitectonic analysis of 10 post-mortem brains (Schleicher et al., 2005) and provide stereotaxic information on the location and variability of cortical areas in the MNI reference space (Eickhoff et al., 2005b). More recently, add-ons to the analysis software packages SPM (http://www.fil.ion.ucl.ac.uk/spm), FSL (http://www.fmrib.ox.ac. uk/fsl) and AFNI (http://afni.nimh.nih.gov/afni/) have been introduced, which enable a routine, standardised application of these architectonic maps as an anatomical reference for functional activations (Eickhoff et al., 2005b). This incorporation into widely used environments for the analysis of functional imaging data assisted the use of probabilistic cytoarchitectonic maps in the context of neuroimaging studies and may establish a common anatomical reference framework for neuroimaging experiments. In this context, anatomical information may take two important roles, (i) to supply a-priori information which can be used to investigate hypothesised regionally specific effects (Eickhoff et al., 2006c), (ii) to enable objective inference on the cortical areas forming the structural substrate of a functional activation (Eickhoff et al., 2005b). This report focuses on the second aspect, illustrated by an fMRI activation in the anterior parietal lobe evoked by viewing videos of emotionally neutral hand gestures. Using this example data, we will first review the basic approaches for anatomical localisation of functional activations using probabilistic cytoarchitectonic maps and comment on some of their potential difficulties. We will then propose an additional quantification of the correspondence between functional imaging activations and probabilistic anatomical maps based on distribution analysis, and close by discussing the advantages and disadvantages of the various methods in different scenarios, focussing on the question which approach is most appropriate for a particular situation.

Table 1 Primary auditory cortex (TE 1.0, 1.1, 1.2) Superior temporal gyrus (TE 3) Broca's region (BA 44, 45) Primary motor cortex (areas 4a, 4p) Premotor cortex (BA 6) Primary somatosensory cortex (BA 3a, 3b, 1) Somatosensory cortex (Area 2) Parietal operculum/SII (OP 1–4) Anterior intraparietal sulcus (hIp1, hIp2) Inferior parietal lobe (7 areas) Visual cortex (BA 17, 18) Visual cortex V3v (hOC3v), V4 (hOCdv) Visual cortex V5/MT+ (hOC5) Amygdala and hippocampus

Morosan et al., 2001 Morosan et al., 2005 Amunts et al., 1999 Geyer et al., 1996 Geyer, 2003 Geyer et al., 2000 Grefkes et al., 2001 Eickhoff et al., 2006d; Eickhoff et al., 2006a Choi et al., 2006 Caspers et al., 2006 Amunts et al., 2000 Rottschy et al., in press Malikovic et al., 2007 Amunts et al., 2005

List of probabilistic cytoarchitectonic maps published in journal articles or monographs. Further cortical and subcortical regions are currently under investigation.

dependent (BOLD) T2*-weighted echo-planar images (matrix size 64 × 64, 32 slices; TE = 50 ms; TR = 3 s; 180 frames collected after gradients reached steady-state, voxel size 4 × 4 × 4 mm3) were acquired for each subject on a 1.5 T Siemens Sonata scanner. The images were realigned to the first frame using AFNI (http://afni. nimh.nih.gov/afni) and spatially smoothed using a 6 mm full-width half-maximum Gaussian filter. The statistical analysis was performed using the MATLAB (Mathworks Inc) toolbox FMRISTAT (www.math.mcgill.ca/keith/fmristat). After fitting of a general linear model based on the stimulus time courses convolved with a canonical haemodynamic response function, contrast images for the comparison of neutral hands vs. control were computed for all subjects. Individual maps were then transformed into the MNI reference space (Collins et al., 1994; Evans et al., 1992; Holmes et al., 1998) and combined using a random-effects model using the MULTISTAT function from the FMRISTAT toolbox. The resulting t-statistic images were thresholded at P b 0.05 using Gaussian random-field theory to correct for multiple comparisons. From this analysis, the significant activation in the left parietal cortex associated with viewing neutral hand movements, as compared with viewing the control stimuli, is used to demonstrate the anatomical allocation of fMRI data (Fig. 1). Volume based labelling

Example fMRI data Existing approaches All methods are illustrated using fMRI data imaging the perception of dynamic hand movements and facial expressions (Grosbras and Paus, 2005). In particular, we examined the anatomical substrates of changes in fMRI signal, henceforth “activation”, that were observed on the left postcentral gyrus and anterior parietal cortex following the observation of emotionally neutral hand movements. Experimental details have been described elsewhere (Grosbras and Paus, 2005). In short, 20 healthy volunteers (10 females, age 19–46 years, mean = 28.6 years) were presented with short (2–5 s) black-and-white video clips depicting emotionally neutral hand actions or control stimuli (expanding and contracting circles). Video clips were arranged into 18-s blocks, containing 4–7 individual actions. A high-resolution T1-weighted 3D structural image (1 mm3 voxel size) as well as a series of blood oxygen level

Volume-based labelling of functional activations is based on calculating the intersection between a cluster of activation and anatomically defined areas. When the intersecting volume is related to the size of the respective cluster, the resulting “cluster labelling” partitions the functional activation based on the underlying anatomical structures (e.g., 50% of this activation is allocated to area X, 30% to area Y and 20% to area Z). Conversely, the intersection volume can also be expressed as percentage of the volumes of the anatomical structures, describing an “extent of activation labelling”. One of the main prerequisites for such volume-based labelling is a discrete classification of the brain volume into anatomical labels. That is, each voxel must be uniquely assigned to a specific anatomical class, e.g., a particular cytoarch-

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Fig. 1. Activation of the left postcentral gyrus associated with viewing emotionally neutral hands as opposed to control stimuli in 20 healthy subjects (random effects analysis, P b 0.05 corrected). The significant activation of this contrast is shown on a surface rendering of the MNI single subject reference brain, viewed from dorsal and left lateral (Collins et al., 1994; Evans et al., 1992; Holmes et al., 1998).

itectonic area or macroanatomical structure. Probabilistic labels, however, assign voxels to a particular label with variable degree of confidence, as in some voxels an area may always be observed (100% probability), in others only in half of the cases (i.e., 50% probability). Moreover, probabilistic labels are also non-exclusive, such that at a particular position area X might be found with a probability of 60%, whereas areas Y and Z are found with a probability of 20% each (cf. Fig. 2). Any set of probabilistic labels must therefore be collapsed into a discrete classification, summarising its probabilistic information, before volume-based labelling can be performed. A straightforward way for this categorisation is to assign each voxel to the most likely area at this position (Eickhoff et al., 2005b). The resulting

cytoarchitectonic maximum probability maps (MPM) represent a continuous non-overlapping parcellation of the cerebral cortex and provide discrete volumes of interest for each cytoarchitectonic area which can be used to compute intersection volumes in volume-based labelling (cf. Fig. 3). Example analysis Applying volume-based cluster labelling to the left parietal activation associated with the observation of emotionally neutral hand movements yielded the following results (Fig. 3): The majority of the activated voxels (36%) were assigned to Area 2 (Grefkes et al., 2001) and the inferior parietal area PFt (Caspers et al., 2006) (21.6%). Smaller parts of the cluster were allocated into the adjacent areas PF (Caspers et al., 2006) (3.5%), PFop (Caspers et al., 2006) (2.2%), hIP2 (Choi et al., 2006) (1.2%) as well as hIP1 (Choi et al., 2006) and Area 1 (Geyer et al., 2000) (less than 0.5% each). Relative to the size of the MPM representations of these cytoarchitectonic areas, the intersecting volumes translate into a relative extent of activation of 20.4% for Area 2, indicating that about a fifth of the voxels assigned to Area 2 showed significantly increased activity. 28.7% of area PFt, 1.9% of area PF, 4.1% of area PFop and 2.7% of area hIP2 also intersected with the examined cluster of activation. Potential confounds and caveats

Fig. 2. Coronal section through the T1-weighted MNI single-subject template at y = − 27 in anatomical MNI space (i.e., y = − 23 in the original MNI space). On this section the probabilistic maps of Area 1 (green) and Area 2 (red) are superimposed on each other to illustrate the considerable overlap between the probabilistic maps of these (neighbouring) cortical areas.

While volume-based labelling is a straightforward, robust approach to the quantification of structure–function correspondence, two potentially confounds have to be considered. The intersection between anatomical and functionally defined volumes is always expressed relative to their absolute volumes, which may vary considerably. For example, the right hippocampal– amygdala transition region (HATA) has a volume of 356 mm3 (Amunts et al., 2005) whereas right Brodmann area 6 has a volume of 29.382 mm3 (Geyer, 2003). Functional activations in turn vary from single supra-threshold voxels to extended activations comprising whole brain lobes. Consequently, the relation between structural and functional volumes can be highly variable, which in turn influences the volume-based labelling. If a large activation is (partially) located in a small area, the overlap will most likely be low with respect to the functional volume (Cluster labelling) and high relative to the anatomical one (Extent of activation). These relations

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Fig. 3. Anatomical labelling by the SPM anatomy toolbox showing the conventional description of the previously mentioned functional cluster (Fig. 1). The functional activation is overlaid on a coronal section through the grey level coded MPM (y = − 32) for visual inspection. The quantitative information on the correspondence between structure and function is displayed next to the section.

are reversed if a small activation is located in a large area. “Cluster” and “Extend of activation” labelling thus should always be considered jointly to account for the range of possible volume relations between anatomical labels and functional activations. Although it has been shown that the MPM provides a reliable classification of cytoarchitectonically mapped voxels into distinct areas (Eickhoff et al., 2006c) any discrete classification inevitably ignores the overlap between different probabilistic maps (Fig. 2). MPM-based labelling thus cannot account for the fact that several architectonic areas may be found at a given activated voxel (cf. Fig. 2). Summarising these probabilities over all activated voxels thus translates into a probability distribution for each area underpinning an activated cluster (Fig. 4A, green curve), which is not considered in MPM-based labelling. This leads to a tempting simplification offered by volume labelling if information like “75% of the activated volume is located in the MPM volume of area X” is not interpreted in a probabilistic context. The only valid conclusion from this information, however, would be that for 75% of the activated voxels area X was the most likely underlying anatomical area. In this case, the conclusion will remain strictly probabilistic. A conclusion in the line of “75% of the activation is located in area X”, on the other hand, is not valid since it implies an absolute confidence that those voxels allocated to area X represent activation from this area. For most cases of functional activations, however, this is not the case (Fig. 2). Distribution-based cluster assignment Here we propose a supplementary approach for volume-based labelling, whose key concept is compare the probability distribution for each area, at those voxels, where its (un-thresholded) probability map and the functional activation intersect to the

overall (i.e., brain-wide) probability distributions for this area (Fig. 4A). This approach allows assessing the “central tendency” of functional activations, i.e., whether an activation is located more in the central respectively peripheral parts of an anatomical probability map. First, the relative over/underrepresentation of the 10 probability classes (10–100%) is calculated. Note that as the anatomical probability for a particular area at a given voxel reflects the fraction of individual cases (out of ten examined post-mortem brains) where this area was present, anatomical probabilities will always be confined to steps of full 10%. As only non-zero probabilities are included, ten classes (10%, 20%, … 100%) hence complete describe the respective distribution. After reslicing to a 1-mm isotropic grid matching the anatomical data, the examined parietal activation consisted of 4194 voxels. 2960 of these overlapped with the un-thresholded probability map of Area 2. Out of those 2960 voxels, 546 corresponded to locations where Area 2 was found with 50% probability. The relative frequency of “50% voxels” for Area 2 in the activated volume was thus 18.4% (546/2960). However, amongst the entire probability map for Area 2, the relative frequency of “50% voxels” was only 9.4%. Therefore, the 50% probability class was overrepresented by + 96.9% [(18.4 − 9.4) / 9.4 ] (Fig. 4B). Likewise, the 60% probability class was overrepresented by 115% as the relative frequency of “60% voxels“ for Area 2 in the activated volume was 11.4%, whilst only 5.3% of the entire Area 2 probability map showed probabilities of 60%. In contrast, the 20% probability class was underrepresented by − 45.7% [11.4% in the activated Area 2 voxels vs. 5.3% of all Area 2 voxels]. The relative over/underrepresentations for all areas intersecting with the functional activation are shown as a colour coded table (Fig. 5), which enables a good orientation, which probability classes were found more or less often in each area as expected from

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Fig. 4. (A) Schematic illustration of the comparison between the overall probability distribution for a cytoarchitectonic area (red) and the probability distribution for that area at the location of a functional activation (green). In both curves, voxels where that area was found with a probability of 0%, i.e., where this area was absent, were not included. (B) Comparison of the overall probability distribution for Area 2 (red) to the probability distribution for Area 2 at the location of the functional activation (green). Voxels where Area 2 absent were not included. (B) Relative difference between the observed probability class representations (green curve in panel A) and those predicted from the brain-wise null distribution (red curve in panel A). In this graph, a value of, e.g., +50% indicates that this probability class was found 1.5 times as often as expected.

its overall probability distribution. To summarise which area most notably exceeded the expected probability distribution, the mean probability (cases where this area was present out of 10 examined brains; averaged across all individual voxel) for each area at the location of a functional activation is divided by the overall mean probability of the respective area (Fig. 5). Pexcess ¼

mean probability for area X at the location of the functional activation overall mean probability for area X in all voxels where it was observed

This quotient (Pexcess) indicates how much more (or less) likely an area was observed in the functionally defined volume than could be expected if the probabilities at that location would follow their overall distribution. A Pexcess N 1 consequently indicates a rather central location of the activation with respect to this area, a Pexcess b 1 a more peripheral one. For the exemplary activation, the most overrepresented area, i.e., the area for which the observed probabilities most exceeded those predicted from its overall distribution, was Area 2 (Fig. 5). For area PFt, the overrepresentation (red in Fig. 5) shifted towards lower probability classes. However, as PFt showed a generally more leftskewed probability distribution (note the absence of voxels where

PFt was found with 90 or 100% probability), the quotient between the mean probability at the activated voxels and that for the entire probability map (Pexcess) was just slightly lower as compared to Area 2 (1.35 vs. 1.4). The Pexcess values for hIP1, hIP2, PF and PFop ranged from 0.7 to 0.86, demonstrating the advantage of accounting for the overall likelihood of high probabilities. Although the mean probability for area hIP2 in the activated voxels is only about half of that for area PF (16.2% vs. 30.0%), its Pexcess was nevertheless higher since high probability voxels are observed only rarely for hIP2 but frequently for Area PF. Areas 1 and 3b finally were found with about half of their average probability. Distribution-based analysis hence revealed that the activation is located more towards the centre of Area 2 and area PFt, and rather peripherally with respect to, e.g., Areas 1 and 3b. In combination with the results from the MPM-based labelling (Fig. 3), it may thus be concluded that Area 2 and area PFt seem to represent the most likely structural substrates of the examined activation. Furthermore, processing of these stimuli seems to engage (although on a lower level) intraparietal areas hIP1 and hIP2 as well as inferior parietal areas PF and PFop. In comparison to the results from classical volume-based labelling,

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Fig. 5. Distribution-based cluster labelling for the examined activation. Areas, which overlap with the activation, are displayed in rows, different probability classes in columns. The relative over/underrepresentation of a class (cf. Fig. 4B) is colour coded from dark blue (− 100%, indicating a probability class not found at the location of the functional activation) to dark red (indicating the maximum overrepresentation). That is, red boxes indicate probability probabilities, which were overrepresented at the location of the functional activation, blue ones those which were underrepresented. Black boxes finally denote probability classes, which were not observed for the respective area. To the right of the colour table, the mean probability for each area at the location of a functional activation is divided by the overall mean probability of the respective area. This quotient indicates how much more likely this area was observed in the functionally defined volume as could be expected if the probabilities at that location would follow their overall distribution.

the higher relevance of the hIP1 and hIP2 becomes evident and demonstrates the advantage of accounting for the overall distribution. Both of these small regions show low overlap in the MPM-based labelling. Relative to the generally low likelihoods for these areas, however, the observed probabilities render them also a likely part of the anatomical substrate of the examined activation. Local maxima labelling Existing approaches

single one of these locations — that one whose coordinates correspond to the transformed coordinates from the low-resolution grid. The other – volumetrically corresponding – voxels may, however, have different probability values for the respective cytoarchitectonic area. To take into account this discrepancy and hence to increase the reliability of anatomical allocation, the probability at the directly corresponding voxel is thus augmented by the probability range for the surrounding voxels, corresponding to the remaining volume of the low-resolution voxel of interest. Example analysis

Whereas volume-based approaches treat all super-threshold voxels as equivalent, local maxima labelling focuses only on the most significant voxels, reflecting the location of the strongest (functional) effects. Furthermore, and in contrast to the total cluster volume, these centres of activation are not affected by the threshold applied for statistical inference. The labelling of a local maximum can be performed by comparing its location to the MPM, testing whether this position is assigned to a specific cytoarchitectonic area. Furthermore, by reading the individual probabilities for all cytoarchitectonic areas at the respective position, more detailed information on its anatomical location can be obtained. In this context, it has to be considered that the spatial resolution of the probabilistic maps (1 mm voxel size) is considerably higher than the resolution of the functional images (usually 2–4 mm voxel size after normalisation). The probabilities for the directly corresponding voxels may therefore over- or underestimate the probabilities. This misestimation is due to the fact that, when transforming a specific voxel coordinate from the low-resolution functional to the highresolution anatomical grid, the larger voxel (corresponding to, e.g., 8 or 27 of the smaller ones) is related to the probability of only a

For our example data, local maxima labelling shows that the most significant maximum [(01) at x = − 52 y = − 32 z = 45, Fig. 3] was assigned to Area 2 with a probability of 40%. However, at the same position, area PFt was also found with 40% probability. The assignment to Area 2 is thus based on its higher average probability in the immediately adjacent voxels (Eickhoff et al., 2005b). The second maximum [(02) at x = − 36 y = − 48 z = 57] was not assigned to any area as the highest probability for a cytoarchitectonically defined area was only 30% (observed for Area 2). Minor local maxima [(03) and (04)] were assigned to Area 2 (50% probability) and area PFt (40%). Potential confounds and caveats When interpreting areal probabilities in local maxima labelling, it must be considered that the overall (brain-wide) distributions of cytoarchitectonic probabilities may differ greatly between areas. For example, the distribution of TE 1.2 (Morosan et al., 2001) is strongly left-skewed indicating many voxels where TE 1.2 was found with low probabilities, whereas that for BA 17

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Fig. 6. Overall (i.e., brain wide) distribution of probability values for primary visual Area 17 (Amunts et al., 2000) shown in light grey and primary auditory area TE 1.2 (Morosan et al., 2001) shown in dark grey. The diagram illustrates that the number of high probability voxels is substantially larger for Area 17, i.e., the probability distribution of this area is much stronger right-skewed. Area TE 1.2, on the other hand, is much more variable and is found in the majority of voxels with only a relatively low probability, i.e., has a strongly left-skewed distribution.

(Amunts et al., 1999) is more right-skewed, containing several voxels with high anatomical probabilities. Consequently, the mean probability for TE 1.2 was only 20%, whilst the mean probability for BA 17 was 43%. Therefore, the same absolute probability location in a particular cytoarchitectonic area may not have the same relevance for structure–function correspondence across areas. To illustrate this predicament, let us assume a voxel where areas X and Y are both found with a probability of 50%, which would indicate the same likelihood for both areas to represent the structural substrate of this activation. If in this case, however, area X is an area where many voxels had probabilities of 90 or 100% (e.g., BA 17), the observed probability of 50% indicates that this voxel was located at the periphery of this area as the observed probability is low relative to the overall probability distribution. On the other hand,

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area Y, whose probability is also 50%, may represent an area where only few voxels showed larger probabilities than 50% (e.g., TE 1.2). In this case, the same probability (50%) would indicate that the activation was located right in the centre of the respective probability map since it is very unlikely to observe voxels where this area had a higher probability. The relevance of a particular probability in the interpretation of the results is hence conditioned on the likelihood of observing these values for a particular area (Fig. 6). Note that theoretically it would also be possible to construct confidence intervals for the provided cytoarchitectonic probability values as these anatomical probabilities reflect, at any given voxel, the fraction of cases (out of ten brains) where this area was present. Consequently, they could also be regarded as maximum likelihood estimators of a binomial distribution from n = 10 observations for which confidence intervals could be derived. Including these intervals (which would be identical for a given probability at all locations) in the anatomical description of a particular location, however, would not advance inference on its putative anatomical substrate. Rather this interpretation relies on the following information: (a) which area is found most likely at a certain position, addressed by a comparison between concurring areas (i.e., MPMbased labelling as described above), and (b) how centrally is a particular voxel located with respect to this area, addressed by a comparison with the compete probability map for the respective area (i.e., the distribution-based approach described in the next paragraph). Distribution-based maxima assignment As discussed above, local maxima labelling should account for the fact that the same probability might indicate either the centre of the periphery of an area, depending on its overall distribution. More precisely, the indicated “central tendency” is contingent on the relative number of voxels that show higher anatomical probabilities. The distribution of the probability values for the respective area across the whole brain is thus regarded as null distribution against which the observed values are compared (Fig. 7). The relative

Fig. 7. Summary on the distribution-based local maxima labelling for the examined anterior parietal activation. In this display, the different cytoarchitectonic areas, which overlap with this cluster of activation, are displayed in rows, whereas the different probability classes form the columns. The relative representations of the individual classes (in the complete probability map, cf. red curve in Fig. 4A) are colour coded in a red to yellow-white sequence. The probabilities of the local maxima (numbered identical to Fig. 3) are indicated on this grid, as is the relative percentage of voxels in the probability map showing a higher probability.

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frequencies of voxels where higher probabilities are observed can then be compared across areas in spite of diverging probability distributions. At the location of the highest maximum [(01), cf. Fig. 3] in our example data, Area 2 and area PFt were both found with 40% probability. These areas, however, differed considerably in their probability distribution as PFt was not found with 90 or 100% probability at any left hemispheric voxel. Consequently, only 17% of area PFtTs probability map showed probabilities higher than 40% (the probability at the local maxima), whereas for Area 2 this was true for 20% of all voxels. Finally, the probability of 30% observed for area PF was exceeded by 46% of its probability map. Similar to MPM labelling, the distribution-based labelling thus indicates that both Area 2 and PFt have similar likelihoods to be the structural substrate of this activation. Slight discrepancies between the two approaches were nevertheless noted. Whereas this maximum was assigned to Area 2 in the MPM-based labelling due to its higher probability in the neighboring voxels, the distribution-based labelling indicated that for area PFt the observed probability of 40% might be more central with respect to its overall distribution and therefore more relevant. In summary, however, it seems that this local maximum is located in the border region between both areas. This allocation in turn can be interpreted as either an activation of neuronal populations located at a cytoarchitectonic border, which is a common feature in sensory cortex (cf. Eickhoff et al., 2006b), or as a reflection of more distributed activation in both areas, which finds its maximum representation at the anatomical border due to spatial smoothing and intersubject averaging. The second maximum [(02) in Fig. 3], not assigned to any area by local maxima labelling, showed a probability of 30% for Area 2, which was exceeded by 31% of this areas probability map. Since the probability for Area hIP2 was only 10% (which was exceeded by 43% of its whole probability map), an origin of this activation in an already defined cytoarchitectonic area seems rather unlikely. At the location of the third maximum, the probability for Area 2 was 50%, which was exceeded by only 10% of its probability map. Areas 1 and hIP2, on the other hand, were found with only lower probabilities, which were exceeded by more than a third of the respective probability maps. Here the information from distribution-based labelling thus matches the MPM-based assignment and puts further faith in the attribution of this maximum to Area 2. Likewise, the attribution of the fourth maximum to area PFt was also confirmed (17% exceeding voxels for area PFt, 65% for Area 2). Discussion Correlating the activation identified in functional imaging studies of the human brain with structural (e.g., cytoarchitectonic) information on the activated areas is a major methodological challenge for neuroscience research. Probabilistic cytoarchitectonic maps have provided a promising approach to these challenges in several studies of somatosensory (Eickhoff et al., 2006e,f; Grefkes et al., 2006; Naito et al., 2005; Young et al., 2004), motor (Binkofski et al., 2000, 2002; Naito et al., 1999), visual (Larsson et al., 1999, 2002; Wilms et al., 2005; Wunderlich et al., 2002) or language (Amunts et al., 2004; Heim et al., 2005, 2006; Horwitz et al., 2003; Nishitani et al., 2005) functions. In the light of these successful applications we have reviewed the two major strategies for anatomical labelling of functional imaging results, pointed to some of the potential difficulties that may arise in their interpretation and introduced supplementary characterisations for both cluster and

local maxima strategies based on distribution analysis. In the following paragraphs, we will focus on the advantages and disadvantages of the various methods in particular situations and particularly the question which approach to use to which goal. Probability vs. distribution-based labelling One of the fundamental principles of existing approaches to the anatomical location of functional activations is the distinct anatomical classification of the reference space by means of a maximum probability map (Eickhoff et al., 2005b). This classification is highly useful for defining volumes of interest (VOIs) comprising those locations, where a specific area is found more likely than any other one. One important application for these VOIs is the testing of anatomically specified hypothesis in functional imaging experiments (Eickhoff et al., 2006c). If such hypothesis is formulated, the search volume for the statistical inference on functional data can be reduced, which in turn ameliorates the severity of the multiple comparison problem (small volume correction; Worsley et al., 1996). A further interesting aspect for the application of distinctly classified anatomical regions of interest is the analysis of PET/ SPECT data (Hurlemann et al., 2005). In this context, the computations of receptor densities or changes in cerebral blood flow within these regions may replace hand-drawn measurement regions. This may in turn improve the comparability of results from different studies as the algorithmic approach for the MPM definition is completely objective and does not require interactions and decisions by the investigator. Distribution-based labelling, on the other hand, does not require any a-priori classification of voxels to cytoarchitectonic areas. Rather it is based on all probabilities observed for any area at the positions of the activated voxels, respectively, their local maxima. That is, there is no suggestion that a voxel necessarily belongs to a specific area. This approach can therefore circumvent the interpretational difficulty that most voxels assigned to a particular area in the MPM show probabilities well below 100%. Instead, distribution-based labelling reflects the entirety of the underlying cytoarchitectonic data since its not thresholded or classified. This more detailed information may in turn provide additional guidance for the interpretation of the obtained anatomical labels. Particularly, distribution-based labelling addresses the most important potential drawback of MPM-based assignment, namely the omission of the fact that voxels attributed to a particular area may show highly different probabilities. Since the proposed distribution-based labelling relates the probabilities observed at a functionally defined location to all probability values observed for that area, it allows the additional judgement whether the examined activation is located more towards the central respectively peripheral parts of the anatomical probability map. That is, it provides a quantitative measure of its “central tendency”, which in combination with the MPM-based information on the total overlap facilitates a more detailed assessment of the relationship between structure and function. This complement of information, and hence the advanced anatomical description, is further illustrated in Fig. 8, showing a frontal activation from the same dataset. Here distribution-based labelling showed that although ∼ 20% of the activated volume is allocated to Area 6, any interpretation of this brain activity arising from this area should be seen with precaution as it is located at its very periphery of this area, particularly when compared to the situation in Area 44.

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Fig. 8. Classical and distribution-based labelling of a second exemplary activation, showing frontal brain activity from the same dataset. Note that although ~20% of the activated volume in this cluster is allocated to Area 6, distribution-based labelling indicates that it may be located rather peripherally with respect to this area (Pexcess = 1.37), whereas the location with respect to Area 44 is substantially more central (Pexcess = 0.65). The combined information from MPM and distribution-based labelling hence allows attributing this activation with high confidence to Area 44.

A further advantage of the distribution-based approach is the increased comparability of the obtained results across clusters and brain regions as cytoarchitectonic probabilities observed at a particular, functionally defined, location are hereby “calibrated” using the areal specific null distribution of probabilities across the whole brain. Due to this calibration, factors that may influence the overall probability distribution of a cytoarchitectonic area such as its volume and shape as well as its microstructural and macroanatomical variability are largely removed. The resulting relative anatomical likelihoods are therefore independent of the examined cytoarchitectonic area and can hence be directly compared across clusters, even if these are located in different brain regions. Volume vs. local maxima labelling Volume and maxima based labelling convey different but complementary information about the cytoarchitectonic substrates of an activation. The main conceptual difference between both approaches pertains to how an activation is interpreted: either as a single entity (volume-based labelling) or part of a continuous statistical field and thus including meaningful internal variations (local maxima labelling). Conventionally, the analysis of neuroimaging data relies on a mass-univariate approach, yielding individual voxel-wise statistics (Kiebel and Holmes, 2003). However, due to the smoothness of fMRI data these voxel-wise statistics are highly correlated, resulting in a smooth field of statistical values (Worsley, 2003). Any thresholding will thus result in an “iceberg” problem since smooth data will be partitioned into “activated” (above threshold) and “not activated” (below threshold), although the difference between the least significant “activated” voxels and the most significant “not activated” voxels might be infinitesimally small.

These considerations provide important arguments for local maxima labelling. First, since the functional hypothesis is tested for each voxel separately, inference (which includes inference on the anatomical location) should be restricted to individual voxels. Moreover, due to the applied spatial smoothing, each voxel is a weighted mean of its local neighbourhood. Therefore, the local maximum represents already the region, where the assessed effect is most pronounced, eliminating the further need to examine other neighboring voxels. Finally, by using the local maximum as a representation of the underlying activation, one can circumvent the dilemma that different thresholds or thresholding strategies will result in differently sized activations: whereas the extent of activation is variable, its peak response is not. In other words, the size of the iceberg depends on the level it is viewed at, whereas its tip remains constant. The case for volume-based labelling, on the other hand, rests on the idea that a cluster as a whole, but not its surrounding voxels, is activated significantly enough to pass a required threshold. Consequently, the whole cluster can be considered an activated entity and be distinguished from its neighbouring voxels, which might be just slightly less significant—but nevertheless below threshold. In addition, it may be considered that volume-based labelling is less sensitive to smaller imperfections in image registrations. Because several (dozens to hundreds) of “activated” voxels are compared to the probabilistic cytoarchitectonic map, a shift of a few millimetres due to an imprecise spatial normalisation will usually only have a moderate effect on the results of volume-based labelling. The anatomical allocation of a single voxel, however, can be fundamentally affected by a slight displacement, particularly when it is close to the border between two areas. In summary, local maxima labelling focuses on the location of most pronounced activation and is more robust with respect to

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statistical thresholding. Cluster labelling treats the whole cluster as one “activated” entity and is more robust to misalignments in the registration of functional and anatomical data. Conclusions and perspective In this paper, we demonstrated the conceptual bases, implementations and potential difficulties of existing strategies for the probabilistic assignment of functional activations to anatomical areas. Extending these methods, we proposed a supplementary strategy based on distribution analysis, which relates the observed architectonic probabilities (at functionally defined locations) to the areal-specific probability distributions across the whole brain, hereby providing a quantitative measure for the “central tendency” of a particular activation with respect to anatomical areas. This approach therefore supplements complimentary descriptions to the previous approaches based on maximum probability maps. Whereas the MPM-based labelling deals with flat, binary masks for each area and describes to which extent anatomical and functional volumes intersect in general, distribution-based labelling adds the information of how “central” this overlap is located. Consequently, both measures jointly provide a comprehensive assessment of the anatomical location of a particular activation. Specific advantages and disadvantages of volume vs. local maximum and probability vs. distribution-based labelling have also been described in order to provide a guide on the most appropriate strategy for different situations for researchers, who consider interpreting their functional imaging results in relation to probabilistic cytoarchitectonic information. While anatomical labelling can provide answers to the question, which area – or areas – are the most likely substrate of an observed functional activation, it does not allow a formal assessment whether there has been an activation of area A in a particular task. The answer to such question would rather depend not only on the probability of area A being present at a given voxel, but also on the likelihood of activation at this location. That is, insight in the question whether there is activity in a particular area cannot be based on “activated voxels” derived from a thresholded SPM alone, but has to take into account uncertainties in the labelling of a voxel as being active. One of the most interesting perspectives for future work will therefore be a consideration of the continuous nature of both cytoarchitectonic and functional imaging data by joint functional–anatomical probability models, which combine for each voxel (or a constrained neighbourhood) the likelihood of a functional activation with those for different anatomical areas. Software implementation All procedures and methods described in this paper have been implemented as part of the SPM Anatomy toolbox, which is an open source software package freely available for download at www.fz-juelich.de/ime/spm_anatomy_toolbox. Acknowledgments This Human Brain Project/Neuroinformatics research was funded by the National Institute of Biomedical Imaging and Bioengineering, the National Institute of Neurological Disorders and Stroke and the National Institute of Mental Health. K.Z. acknowledges funding by the Deutsche Forschungsgemeinschaft (KFO-112) and the Volkswagenstiftung. We would like to thank Lars Hömke and Klaas Enno

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