Scandinavian Journal of Psychology, 2014, 55, 202–211

DOI: 10.1111/sjop.12098

Where numbers meet words: A common ventral network for semantic classification KLAUS WILLMES,1 KORBINIAN MOELLER2 and ELISE KLEIN1,2 1 2

Department of Neurology, Section Neuropsychology, University Hospital, RWTH Aachen University, Aachen, Germany KMRC – Knowledge Media Research Center, T€ubingen, Germany

Willmes, K., Moeller, K. & Klein, E. (2014). Where numbers meet words: A common ventral network for semantic classification. Scandinavian Journal of Psychology 55, 202–211. Recent research has shown that both language and number processing are clear examples of distributed and connected processing in the human brain, emphasizing the importance of white matter connections between the associated cortex sites. Against this background we hypothesized joint cognitive processes and functions in a cross-domain manner to be reflected by the involvement of specific white matter tracts. Therefore, we evaluated white matter connectivity for the specific cognitive process of semantic classification, which is an integral part of tasks commonly employed to investigate the neural correlates of language and number processing. In line with our expectations, fiber tracking results clearly indicated a common ventral network for semantic classification for the domains of language and number processing. Thereby, the present data are hard to reconcile with a localizationalist view on processing characteristics of the human brain, but strongly suggest that white matter connectivity should be considered when investigating the neural underpinnings of human cognition. Key words: language, number processing, cognitive function, ventral stream, fiber tracking. Klaus Willmes, Section Neuropsychology, Department of Neurology, University Hospital, RWTH Aachen University, Aachen, Germany. Tel: +49-241-80-89970; fax: +49-241-80-82598; e-mail: [email protected]

INTRODUCTION In the history of science, the invention of new measurement techniques often allowed for asking scientific questions with more rigour and precision. Diffusion derived virtual in vivo tractography of the white matter fibers of the brain (cf. Jones, 2011) has paved the way for a new hodological approach to the study of brain function. In the history of attempts to explain disorders of cognitive functions after acquired brain damage more attention has been paid to localizing those functions in circumscribed brain areas and attribute disorders to lesions of these areas. Nevertheless, there was often a move to also hold disconnections of commissural fibers between the two hemispheres, corticosubcortical projection fibers, and cortico-cortical association fibers responsible for disorders of higher brain functions (Catani & Ffytche, 2005). Such a disconnection view – strongly advocated by Geschwind (1965a, 1965b) in his highly influential publication on “disconnection syndromes in animals and man” particularly in the context of language disorders – is well in line with the notion of the brain being composed of localized but connected specialized areas (Catani, 2011) working together both in a parallel as well as in a hierarchically ordered, sequential fashion. Employing advanced diffusion spectrum imaging, Hagmann and coworkers (2008) revealed in individual participants, how these structurally segregated and functionally specialized regions of human cerebral cortex are densely connected by a network of cortico-cortical association fiber tracts. Ivar Reinvang in his book entitled “aphasia and brain organization” (Reinvang, 1985) had already made a strong move towards a systems level perspective on acquired language disorders. He suggested “systemic localization” as the overarching principle of brain organization, in which the function of a brain area is not determined by its anatomical structure alone but by “its relationship to other areas within the bounds determined by © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

its anatomical structure” (Reinvang, 1985, p. 18). This view allows for developmental changes in the pattern of function-tostructure relationships as well as change and recovery of function after acquired brain damage with white matter pathways mediating such systemic adjustments. Reinvang took aphasia as his test case when providing both empirical data and theoretical arguments for a network perspective on higher brain functions. Starting from well-known perisylvian language areas of the (left) hemisphere dominant for language (Broca’s area, Wernicke’s area, supramarginal and angular gyrus), Reinvang not only advocated considering the interconnections (prominently the arcuate fasciculus) between areas, all to be potentially contributing to language impairments when lesioned, but he also suggested including neighboring areas, which – when lesioned in isolation – would mostly not lead to aphasia but contribute to “symptom formation.” The new “candidates for status as language areas” in the latter sense were several structures of the lenticular zone, comprising insular cortex, capsula extrema, claustrum, capsula externa, and basal ganglia nuclei, as well as structures of the limbic system, comprising among others cingulate and parahippocampal gyri, the hippocampal formation, and thalamic nuclei. It may be more than a coincidence that several of the “neighboring areas” considered by Reinvang are integral parts of the so called ventral pathway for language, initially implicitly described by Wernicke (1874) and other contemporaries and brought back to full light by Weiller and colleagues (2011). These latter authors argue “that it is not single brain areas or a single tract system, but the context dependent and bidirectional interaction of anterior and posterior language zones and non-linguistic conceptual processing along two major and equivalent streams which seem to be necessary for language” (Weiller, Bormann, Saur, Musso & Rijntjes, 2011, p. 36). The authors thus demonstrate that human language processing also fits into the general modern neurobiological as well as cognitive and

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neuropsychological conceptualization of a basic dual – ventral and dorsal – pathway system of overall brain organization (cf. Rijntjes, Weiller, Bormann & Musso, 2012). The study of numerical cognition and acalculia, a collection of impairments in the processing of numbers and mental calculation, has also witnessed a history of localizationist and connectionist views, although starting later in history and being less well researched than the domain of language processing. Henschen (1920) first coined the term “Akalkulie” (acalculia) (and dismissed the alternative terms “Anarithmetik” and “Amathesie”) to designate acquired impairments in his impressively comprehensive work on clinical-anatomical correlations in aphasia, amusia, and acalculia written in German. He considered calculation mechanisms (“Rechenmechanismen”) to be comparable in complexity to language- and music-mechanisms forming an anatomofunctional system, which he tentatively termed a “psychic formation” (“psychischer Verband”), alluding to a military formation, which is also composed of different units (divisions, brigades, batallions) working together in military operations (Henschen, 1919). Each functional formation is subserved by distinct cortical centers and their related association fibers, the cortical centers showing a certain degree of independence but also interdependencies mediated by association fibers, even if topologically distant. Against this background of distributed and connected processing in the human brain, observed for both the domains of language and number processing, we were interested in whether it may be possible to reduce the notion of such distributed and connected processing to the idea of underlying joint cognitive processes or functions applied in a cross-domain rather than a domain-specific manner. In line with the importance of white matter connections of cortex sites associated with specific domains described above, we hypothesized that these common underlying cognitive processes or functions should be indicated by the involvement of specific white matter tracts connecting core regions subserving domain-specific aspects of these processes. Therefore, we aimed at evaluating white matter connectivity for the example of the specific cognitive process of semantic classification, which is an integral part of tasks commonly employed to investigate the neural correlates of language (e.g., classification of letter strings as words or non-words) and number processing (e.g., odd/even classification of digits or classifying number magnitude as smaller/larger than a standard). In the following we will first describe a tentative model of lefthemispheric networks for language processing, before discussing possible complements in numerical cognition to identify possible common key areas associated with semantic classification in both domains that can serve as seed points for our intended fiber tracking analyses to investigate common white matter tracts. Only recently, Vigneau, Beaucousin, Herve et al. (2006) performed a large-scale meta-analysis on 730 activation peak coordinates reported in 129 language studies with the aim of evaluating the composition of the left-hemispheric network for language processing in frontal, temporal, and inferior parietal regions. By submitting activation peaks issued from relevant component-specific contrasts of the respective individual studies to a spatial clustering algorithm, mean activation peaks in MNI space were estimated for each of these language components. In this way 30 activation clusters were isolated, “defining the © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

A common ventral network for semantic classification 203 functional fields constituting three distributed networks of frontal and temporal areas and revealing the functional organization of the left hemisphere for language” (Vigneau et al., 2006, p. 1414). Based on the constructional properties of tasks performed, the authors differentiated between distributed networks for phonological, semantic, and sentence processing and interpreted their results to be hard to reconcile with the view of a modular organization of language in the left hemisphere. Instead, they suggested a model of large-scale architecture networks (from primary motor areas to superior temporal and frontal gyrus) to be involved in human language processing at a fine-scale functional level (e.g., the inferior frontal gyrus (IFG) activated for semantic classification). Importantly, however, to fully account for the functional network idea advocated in the model by Vigneau et al. (2006), it is assumed that in semantic classification a cascade of processing steps takes place starting in the angular gyrus (AG), then connecting with the superior temporal gyrus (STG) and consecutively further temporal cortical areas, before processes are first submitted to the triangular part of the IFG (IFGtri, BA 45) and then from IFGtri to the opercular part of the IFG (IFGoper, BA 44; see Fig. 1 for an illustration with the red line indicating the postulated (polysynaptic) parieto-frontal connection via the temporal cortex). As indicated by the black dots, key areas for semantic classification as identified by Vigneau et al. (2006) were the AG, the STG, and the IFGtri. Moreover, dark grey dots represent further areas postulated to contribute to semantic classification in language such as the IFGoper, the IFGorb, the middle temporal gyrus (MTG), and the superior temporal sulcus (STS, cf. Vigneau et al., 2006). Finally, even though not included in the final model for semantic classification by Vigneau et al. (2006) the supramarginal gyrus (SMG) is considered here because both, Fig. 3 of the meta-analysis by Vigneau et al. (2006) as well as the original studies (e.g., Binder, McKiernan, Parsons et al., 2003) reported SMG activation. Compared to language processing, knowledge about the connectivity of brain regions associated with numerical cognition is only at its beginning. The currently most influential model of numerical cognition is the so-called Triple Code Model (TCM,

Fig. 1. Semantic classification network in language (following Vigneau et al., 2006). Black dots indicate key areas for semantic classification, dark grey dots further areas postulated to contribute to semantic classification in language. The red line indicates the postulated (polysynaptic) parieto-frontal connection via the temporal cortex. The SMG is depicted in light grey since both, Fig. 3 of the meta-analysis by Vigneau et al. (2006) as well as the original studies (e.g., Binder et al., 2003) reported SMG activation as well, which, however, was not considered for the final model for semantic classification.

204 K. Willmes et al. Dehaene & Cohen, 1995; 1997, Dehaene, Piazza, Pinel & Cohen, 2003). As an anatomo-functional model the TCM proposes three different representations to underlie numerical cognition with their assumed cortical localizations: (i) a visual number form representation associated with bilateral occipital perception areas; (ii) a verbal numerical representation associated with left-hemispheric peri-sylvian areas and the AG; and (iii) a representation of number magnitude associated with the bilateral intraparietal sulcus (IPS). Additionally, the TCM proposes the involvement of (pre)frontal areas associated with, for instance, working memory and executive functions with increasing task difficulty but without further specifications of these areas (e.g., Dehaene & Cohen, 1997). More recent neuro-imaging studies point to a critical involvement of the inferior frontal gyrus (e.g., BA 44 and 45; cf. Arsalidou & Taylor, 2011 for a recent metaanalysis of the available neuro-imaging data). Additionally, a number of other cortex areas has repeatedly been observed to be associated with number processing, such as the supra-marginal gyrus (e.g., Arsalidou & Taylor, 2011; Klein, Moeller, Nuerk & Willmes, 2010; Qin, Carter, Silk et al., 2004) or the superior temporal gyrus (e.g., Arsalidou & Taylor, 2011; Klein et al., 2010). Nevertheless, unlike the fact that numerical cognition is thus a clear case of distributed processing and different from language processing, there is currently no comprehensive model describing the connectivity of the respective cortex sites associated with number processing (but see Klein, Moeller, Glauche, Weiller & Willmes, 2013a; Cantlon, Davis, Libertus, Kahane, Brannon & Pelphrey, 2011 for first attempts). Thus, it was not possible to derive the seed regions for our fiber tracking analysis from a model on the connectivity of cortex sites for numerical cognition. Instead, we had to take one step back. Similar to the original procedure by Vigneau et al. (2006) to identify activation peaks of relevant component-specific contrasts of semantic classification from the respective primary studies, we derived activation peaks from a re-analysis of a recent fMRI study involving both odd/even and smaller/larger classifications on numbers (Klein et al., 2010). Thereby, we aimed at accounting for any number specific aspects of the respective classification process (as also reflected in the Vigneau et al., 2006, model for language processing) and thus avoiding any kind of truncating of our fiber tract model for numerical classifications. Pursuing our idea of a common classification process subserved by specific white matter structures, we expected to observe common ventral fronto-parietal white matter connections for semantic classification in both the domains of language (e.g., classifying letter strings as words or non-words; nouns as denoting living/non-living entities, etc.) and number processing (odd/ even and smaller/larger classifications of numbers) involving the AG, SMG, STG at the temporo-parietal and the IFG (BA 44 and 45) at the frontal end.

MATERIAL AND METHODS DTI Data acquisition Participants. The DTI data were collected from a sample of 20 righthanded healthy volunteers (15 females, mean age: 22 years, range

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Scand J Psychol 55 (2014) 18–25 years). Participants were scanned with the approval of the local ethics committee of the Medical Faculty of the Eberhard Karls University of T€ ubingen. All participants gave their written informed consent. All participants had no neurological or psychiatric history and were not taking any psychoactive medication. Data acquisition. The MRI and DTI data were acquired on a 3T Siemens TIM Trio scanner (Siemens, Erlangen). For DTI a total of 70 scans with 69 slices was acquired using a diffusion sensitive spin-echo EPI sequence with CSF-suppression (61 diffusion encoding gradient directions (b-factor = 1000 s/mm2) and 9 scans without diffusion weighting (b-factor = 0 s/mm2), voxel size = 2 9 2 9 2 mm3, matrix size = 104 9 104 pixel2, TR = 11.8 s, TE = 96 ms, TI = 2.3 s). Furthermore, an additional high-resolution T1 anatomical scan was obtained (160 slices, voxel size = 1 9 1 9 1 mm3, TR = 2.2 s, TE = 2.6 ms, FOV = 240 9 240 9 160 mm3). For each slice, raw diffusion-weighted data was registered and corrected simultaneously for subject motion and eddy current induced geometrical distortions using ExploreDTI (http://www.exploredti.com; see Leemans & Jones, 2009). Standard diffusion tensor tractography does not allow reconstructing the three branches of the superior longitudinal fasciculus (SLF; Thiebaut de Schotten, Ffytche, Bizzi, 2011) because of the crossing of the dorsal association fibers with commissural and projection fibers. Therefore, constraint spherical deconvolution (CSD) was performed to estimate multiple orientations in voxels containing different populations of crossing fibers (Alexander, 2006). Afterwards whole-brain deterministic tractography was employed using an interpolated streamline algorithm that propagates from voxel to voxel following a step length of 1 mm and a maximum angle threshold of 35°. Fractional anisotropy (FA), a scalar value that describes the degree of diffusion anisotropy, was computed from the eigenvalues of the diffusion tensor along the defined segments (Basser & Pierpaoli, 1996). Voxels showing FA values below 0.2 were excluded from tractography (Jones, 2003, 2004; Jones et al., 2002). The motion-corrected whole-brain tractography was then imported to TrackVis (http://www.trackvis.org; Wedeen, Wang, Schmahmann et al., 2008). This fiber-tracking software allows, among other features, for the identification of the tracts and their visualization in 3-dimensional space.

Definition of seed regions The anatomical regions included in the deterministic fiber tracking for semantic categorization in language processing were taken from the meta-analysis by Vigneau et al. (2006). Because only regions but no coordinates were provided in that study (see Fig. 1), we first calculated anatomical ROIs for the areas of interest (AG, STG, BA 45, and BA 45) using the SPM Anatomy Toolbox, available with all published cytoarchitectonic maps from www.fz-juelich.de/ime/spm_anatomy_toolbox. Second, we verified whether all peak voxels for the areas reported in the original studies, which had been entered into the meta-analysis of Vigneau et al. (2006) for fronto-parietal activation in semantic classification (e.g., Binder, Frost, Hammeke, Rao & Cox, 1996; Binder et al., 2003; Scott, Leff & Wise, 2003; Thompson-Schill, D’Esposito, Aguirre & Farah, 1997), were indeed part of the respective ROIs. Since this was the case, we calculated mean coordinates from the original studies for each ROI using a procedure similar to the one used by Dehaene et al. (2003) for his meta-analysis. For numerical data the seed regions were extracted from the t-maps of the fMRI analysis in the Klein et al. (2010) study in a more fine-grained way, taking into account also secondary peak voxels and providing MNI coordinates instead of Talairach coordinates, as used in the original study (FWE cluster-threshold corrected p < 0.05, cluster size of k = 10 voxels). Whenever the seed regions were reported as core regions for semantic classification, often even overlapping in language processing (Vigneau et al., 2006) and numerical cognition (Klein et al., 2010), they were considered for the present study. In particular, overlapping regions were BA 45, BA 44, and SMG, while STG, AG, and IPS were reported as critical

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Scand J Psychol 55 (2014) regions in the original studies. Interestingly, BA 45 (pars triangularis of Broca’s area) was found to be critical for both semantic classification in language (Vigneau et al., 2006, Fig. 3) as well as numerical cognition (Klein et al., 2010). Moreover, the peak activations from Klein et al. (2010) for BA 45, BA 44, and SMG were situated within the masks created for the language task. In a next step, each of these seed regions was transferred to the native space of each participant′s DTI data and enlarged to a sphere with a radius of 4 mm, each containing 33 seed voxels (e.g., Kreher, Schnell, Mader et al., 2008; Saur, Kreher, Schnell et al., 2008; Suchan, Umarova, Schnell et al., 2013 for a similar procedure). These spheres defined the seed regions for the fiber tracking procedure. After having acquired the whole-brain tractography, the 20 data sets were spatially normalized and averaged using a method similar to the one previously described by Jones et al. (2002) and Catani & Thiebaut de Schotten (2008). Based on previous tractography work (Klein et al., 2013a), the regions-of-interest (ROIs) were delineated manually as spheres around the coordinates of the seed regions (Table 1) by one of the authors (EK, experienced in neuro-anatomy) on the axial, coronal, and sagittal FA images of each participant and were used as seed regions for tracking. The two main fiber pathways described in the results section (below) were identified in each of the 20 participants. Similar to Catani and Thiebaut de Schotten (2008) one representative data set was used to perform virtual dissections and create the 3D images of the white matter tracts (Figs. 3–6).

RESULTS fMRI re-analysis (Klein et al., 2010) For the contrast parity vs. control, the re-analysis using the Anatomy Toolbox revealed not only activation in the triangular part of the IFG (BA 45) and the IPS. The large parietal cluster consisting of 156 voxels clearly also extended to the supramarginal gyrus (SMG). These respective secondary peak coordinates were not presented separately in the original study. In particular, Fig. 2, panel A in the present study depicts the contrast parity vs. control (cf. Fig. 1, panel C in Klein et al., 2010). Furthermore, for the contrast number comparison vs. control, activation was found in the IPS and the SMG, but also in the opercular part of the IFG (BA 44). Therefore, we briefly provide the results of the mean coordinates for language processing as well as the additional IPS coordinate for numerical processing obtained by this more finegrained re-analysis in Table 1.

Fiber tracking First, separate trackings were run between the regions suggested to be crucial parts of the semantic classification network in language. These regions included the left AG and SMG in the parietal cortex, the STG in the temporal lobe, and the triangular part of the IFG (BA 45) in the frontal cortex. From all respective parieto-temporal regions (AG, STG, SMG) the fibers were traveling ventrally via the external and extreme capsule (EC/EmC system) between claustrum and putamen to the triangular part of the IFG (BA 45, see Fig. 3A). A tracking between the IPS, which is crucial for number comparison and IFGtri (BA 45) did not reveal a connection in any of the 20 participants. Second, separate trackings were run between the regions mentioned above (AG, STG, SMG, IPS) and the opercular part of the IFG (BA 44). While fibers connecting the AG with BA 44 were traveling again ventrally via the EC/EmC system, STG and SMG were connected ventrally via the EC/EmC system as well as dorsally via the SLF II. Finally, BA 44 was also connected dorsally via the SLF II to the IPS, the latter region having been suggested to be critical for the number comparison task (Fig. 3B). Interestingly, connectivity of the AG and the STG with the triangular part of the IFG seemed to be identical: the same fiber tracts seemed to run ventrally via the EC/EmC system. Based on the data presented here it cannot be decided whether the connection between AG and IFGtri (or between STG and IFGtri) is poly- or monosynaptic (i.e., running via the STG as a mediator area or directly). Most probably, there might be both mono- as well as polysynaptic connections (Fig. 4A and B). According to the model proposed by Vigneau et al. (2006), it is assumed that semantic classification comprises a cascade of processing steps starting in the AG, then connecting with STG and consecutively with further temporal cortical areas, before processes are first submitted to IFGtri (BA 45) and then from IFGtri to IFGoper (BA 44). Yet, while it cannot be excluded that BA 45 and BA 44 might be connected via U-fibers, it becomes obvious that the temporo-parietal cortex areas are connected with BA 45 (Fig. 5A) directly via the same (ventral) fiber pathways (see Fig. 5C for a tracking of all regions), while these areas are connected to BA 44 (Fig. 5B) both with these ventral fiber pathways as well as with a dorsal pathway (probably the superior

Table 1. Overview about the seed regions used in the present study. Seed regions were primarily retrieved from language studies; additional seed regions, obtained from numerical cognition studies, are given in the right half of the table Language seed regions

Additional numerical seed regions

MNI coordinates

MNI coordinates

Seed region

Angular gyrus Superior temporal gyrus Supramarginal gyrus IFGtri (BA 45) IFGoper (BA 44) Intraparietal sulcus

x

y

z

-44 -53 -51 -51 -48

-69 -43 -52 30 15

33 17 48 8 17

x

y

z

-42

-46

49

Abbreviations: BA – Brodmann Area; IFG – inferior frontal gyrus; oper – pars opercularis; tri – pars triangularis; MNI – Montreal Neurological Institute © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

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Fig. 2. Panel A depicts larger activation for parity decisions vs. control (pink noise) at an FWE cluster-threshold corrected p-value of p < 0.05 and k = 10 voxels. The intraparietal activation extends into the SMG, and the peak activation in the IFG is situated in the triangular part of Broca’s area (BA 45). Panel B depicts stronger activation for number comparison vs. control (pink noise; FWE cluster-threshold corrected p < 0.05, k = 10 voxels). The intraparietal activation is located deep in the fundus of the IPS (indicated by less strong red colour) and the frontal activation is situated in the opercular part of the IFG (BA 44). Panel C shows the proposed pattern of dorsal and ventral connections for the two numerical tasks.

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revealing a stable ventral network, since this connection was found in all participants. To identify the core of this network, which is involved both in semantic classification in language processing as well as in numerical processing, we tracked only fibers, which encompassed all three areas AG, SMG, and STG together, revealing a common core network for semantic classification in language processing as well as numerical cognition (Fig. 6B).

DISCUSSION

Fig. 3. Panel A depicts connections from the respective region (AG, STG, SMG) to the triangular part of the IFG (BA 45). All fibers are traveling ventrally via the EC/EmC system between claustrum and putamen. Panel B shows connections from the respective regions to the opercular part of the IFG (BA 44). While fibers connecting the AG with BA 44 are traveling ventrally via the EC/EmC system as well, STG as well as SMG are connected both ventrally via the EC/EmC system as well as dorsally via the SLF II. Finally, BA 44 is also connected dorsally via the SLF II to the IPS, which is especially critical for the number comparison task.

longitudinal fasciculus (SLF) II and III). On the one hand, this means that a purely ventral connection for semantic classification via temporal areas as proposed by Vigneau et al. (2006) is unlikely. On the other hand, it becomes evident that all areas crucial for semantic classification either in language or in numerical cognition seem to be connected via the ventral EC/EmC system. Therefore, this system may most probably be a crucial component in semantic classification. Based on these considerations, we tracked all fibers connecting the areas AG, SMG, and STG with IFGtri (BA 45, Fig. 6A), © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

The current study set off to evaluate the idea of joint crossdomain cognitive processes and functions underlying the notion of distributed and connected processing in the human brain, as reflected by common white matter tracts connecting the respective cortex sites involved. In particular, we were interested whether a joint white matter tract can be observed for the specific cognitive process of semantic classification in language (e.g., word/non-word) and number processing (e.g., odd/even and smaller/larger). Considering a recent model for the connectivity of the human language system and a re-analysis of neuroimaging results on number processing, we expected a common ventral tract along the EC/EmC system. Our data corroborated this hypothesis. The fiber tracking results clearly indicated a common ventral network for semantic classification for the domains of language and number processing running along the EC/EmC system and connecting AG, SMG, and STG to the IFGtri (BA 45), which could be identified in each participant. Interestingly, the IPS, a structure critically involved in the processing of number magnitude, was not connected to the IFGtri. Nevertheless, when evaluating fronto-parietal connections to the IFGoper (BA 44), we observed exclusively ventral (for AG), ventral and dorsal (for STG and SMG), as well as exclusively dorsal (for IPS) connections, of which the ventral ones again ran along the EC/EmC system, whereas the dorsal ones followed the SLF II / III system. Thus, this network – considering all specifications for both language and number specific classifications (in particular, the number specific IPS and IFGoper seed points) – seems to involve ventral as well as dorsal connections (cf. Fig. 7A). However, when focusing on those seed points associated with semantic classification in either language processing

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Fig. 4. Panel A depicts the connectivity of the AG with the triangular part of the IFG (BA 45), while Panel B shows the connectivity of the STG with the triangular part of the IFG (BA 45). While it cannot be decided based on the data presented here whether the connection between AG and IFGtri is mono- or polysynaptic (i.e., running via the STG as mediator area), it becomes obvious that from both, AG and STG largely identical fiber tracts seem to run ventrally via the EC/EmC system.

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Fig. 5. Panel A depicts the connectivity of the temporo-parietal areas AG, STG and SMG with the triangular part of the IFG (BA 45). According to the meta-analysis of Vigneau et al. (2006), AG and STG are part of a network for semantic classification in language. Fibers, which also cross the SMG, which was also observed in the original studies but not mentioned in the meta-analysis, are marked with blue. It should be noted that these fibers also connect to AG and STG as well (see Fig. 4A and B). In numerical tasks, only SMG is found for semantic classification. Therefore, the blue fibers in Panel A depict as well the ventral pathways which are connecting SMG with IFGtri (BA 45) in numerical tasks (parity decisions, number comparison). Put differently, the blue fibers in Panel A show a ventral „core network for semantic classification”, which is independent of the input. Panel B depicts the connectivity of AG, STG, SMG, and IPS with the opercular part of the IFG (BA 44). In addition to the ventral connection via the EC/EmC system a dorsal connection following the SLF II is observed. Note that the IPS is crucial for numerical tasks such as number comparison. Panel C shows both the connectivity of both, BA 45 and 44 with all temporo-parietal areas. It should be noted that the ventral connection is identical for both areas and thus involved in all tasks needing semantic classification.

(i.e., STG, AG) or jointly in language processing and number processing, a joint ventral fronto-parietal connection between the AG, SMG, STG, and the IFGtri, encompassing the ventral EC/ EmC system, emerges revealing a common network (cf. Fig. 7B). Please note that fiber tracts connecting the AG and the STG with the triangular part of the IFG seemed to be identical and a considerable number of these fiber tracts also encompassed the SMG, further corroborating the notion of constituting a common ventral © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

network for semantic classification in language processing and numerical cognition. Thereby, the present data are hard to reconcile with a localizationist view on processing characteristics of the human brain. Instead of corroborating the notion of specific cortex sites subserving specific cognitive functions, these data strongly suggest that white matter connectivity needs to be considered, when investigating the neural underpinnings of human cognition (see

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Fig. 6. Panel A shows a tracking of all fibers connecting the areas (AG, SMG, STG) with IFGtri (BA 45, Fig. 6A) revealing a stable ventral network. Panel B depicts only fibers, which compassed all three areas (AG, SMG, STG), revealing a common core (sub)network for semantic classification in language processing as well as numerical cognition.

ffytche & Catani, 2005; Mesulam, 2012, for a more detailed discussion of this point). In this vein, our fiber tracking results follow up on, but also, extend the study by Rusconi and colleagues (2009) on the origin of the Gerstmann syndrome. On the one hand, we also found that white matter structures seem to be vitally involved in the processing of information from different domains. However, on the other hand, we went one step further than Rusconi and colleagues (2009). These authors concluded that a lesion of separate but spatially convergent fiber tracts involved in the four domains seems to cause the selective association of acalculia, finger agnosia, left-right disorientation, and agraphia in Gerstmann’s syndrome. Here, we suggest that it is not only physical proximity of white matter tracts that may link specific white matter structures to different cognitive domains. Instead, we propose that the link may be functional - a common cross-domain cognitive process or function, which is associated with some specific white matter connection. In the present case we were able to identify a common ventral core network for semantic classification in language and number processing, which encompasses the ventral EC/EmC system. This EC/EmC system is a ventral pathway traveling between insula and putamen and connecting the AG to the inferior frontal gyrus, pars triangularis (BA 45, Broca’s area; see Klein et al., 2013a). The EC/EmC system corresponds to rostral/anterior parts of the bundle termed inferior occipitofrontal fasciculus (IFOF/IOF) in the DTI-based JuBrain atlas (B€urgel, Amunts, Hoemke, Mohlberg, Gilsbach & Zilles, 2006) implemented in the Anatomy Toolbox of the Juelich Research Center for white matter fiber tracts, as already pointed out by other authors (Catani, Howard, Pajevic, Jones, 2002; Suchan et al., 2013). Interestingly, for the case of language processing this ventral pathway has already been described by Wernicke (1874) to be mediated through the converging fibrae propriae in the insula and has also been described to be vitally involved in language processing by Reinvang © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

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Fig. 7. Schematic illustrations of the networks involved. Panel A depicts schematically both the connectivity involved in language processing and numerical cognition including all critical areas mentioned by Vigneau et al. (2006) as well as Klein et al. (2010). Importantly, this network considering thus nearly all functions associated with semantic classification includes both dorsal as well as ventral connections. In contrast, Panel B depicts schematically only those areas closely involved in semantic classification either in language processing (STG, AG) or in both, language processing and numerical processing (SMG). Importantly, all fibers not only encompass the ventral EC/EmC system; a considerable number of fibers connecting IFGtri (BA 45) with STG and AG encompasses the SMG as well, implying a common ventral network for semantic classification.

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(1985). Very recently, this ventral fiber tract gained increasing research interest again (see Weiller et al., 2011 for a review), as a pathway involved in “mapping sensory or phonological representations onto lexical conceptual representations” (Weiller et al., 2011, p. 33). This fits nicely with our proposition that there seems to be a ventral network for semantic classification, because any kind of classification requires a lexical concept such as an entry in the mental lexicon for words, number parity or magnitude to operate on. On a more general account, this overarching property of the ventral network suggests that the neural substrates of cognitive processing may be organized only partially in a domain-specific way, but may also reflect functional domain-general processing principles, upon which domain-specific processes project. This is in line with the fact that there is currently renewed interest in different types of neural disconnection syndromes (see Kleinschmidt & Vuilleumier, 2013 for a review), and only recently even the collection of symptoms associated with hemineglect has been proposed to be caused by neural disconnection of a frontoparietal network subserving the cognitive function of spatial awareness (Thiebaut de Schotten, Urbanski, Duffau et al., 2005). We are well aware that this is a highly speculative account, awaiting further empirical support and theoretical elaboration. Nevertheless, as outlined in the introduction, the ventral and dorsal tracts observed in the present study relate nicely to the “dual loop system” proposed for language processing (Weiller et al. 2011): within the dual loop system, the ventral system is assumed to be dedicated to categorical decisions dependent on item structure, whereas the dorsal system is supposed to subserve processing of sequences of mental objects/items instead. This account has recently been extended to different modalities by Rijntjes and colleagues (2012; e.g. vision, attention, see Table 2, p. 9). The present study suggests that the dual loop framework may be extended to numerical cognition as well. In particular, within this account the dorsal stream has been associated with a general (time dependent) capacity to analyze a sequence of segments, either in time or in space, as well as fast on-line integration between sensory event information and “internal models or emulators” independent of modality. Interestingly, this notion may be transferred easily to numerical cognition. For instance, whenever the magnitude of a number has to be compared to a fixed standard, the sequence and relations of numbers as represented on the mental number line need to be evaluated, a process probably involving the SLF II. With decreasing distance between the numbers-to-be-compared (i.e., when the larger/smaller decision gets more difficult, e.g., Moyer & Landauer, 1967), the SLF III may be additionally recruited as an even stronger connection between the intraparietal cortex with BA 44 (also associated with working memory, e.g., Saur et al., 2008). On the other hand, the ventral route has been suggested to be involved in the identification of structural relations independent of modality and of the sequence of occurrence of elements (Weiller et al., 2011). Thus, the ventral route in numerical cognition may not only be devoted to structural relations but also, more generally, to the categorical identification of the numbers’ semantic properties, irrespective of their sequence (both, on the number line or in the task). In summary, general principles associated with the ventral processing pathway, as identified in studies on language processing (Saur © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd

A common ventral network for semantic classification 209 et al., 2008; Weiller et al., 2011) and already adapted to further modalities/domains (Rijntjes et al., 2012) can easily be transferred to numerical cognition as well (see also Klein et al., 2013a). Thereby, our data point to more basic cross-domain cognitive processes to be subserved by the dual loop system. Importantly, such a view has viable consequences for the way neuropsychological impairments should be assessed and reported. So far, the vast majority of neuropsychological case studies focus on impairments within a specific domain rather than also considering cross-domain functional deficits to underlie domain-specific impairments. On the one hand, in single case studies on language processing numerical skills are usually not reported at all. On the other hand, in numerical cognition research language processing is typically assessed (e.g., in aphasic patients, Dehaene & Cohen, 1997) but not as systematically as would be desirable. In particular, language processing tasks are usually only considered to specify a circumscribed number processing deficit, but they are not examined systematically with the view of evaluating possible cross-domain functional deficits. Consequently, the focus in neuropsychological case studies is on detecting (double) dissociations within domains to draw conclusions on the “functional lesions” of those patients with respect to a model of the cognitive architecture of the respective domain. However, as argued, for instance, by Caramazza (1986, p. 65) associations of (functional) deficits may be as informative as dissociations – considering in the present case the possibility of cross-domain processes/functions to be involved in human cognition – when concluding that “there are situations where our models do require that we predict the co-occurrence of symptoms given some hypothesis of the nature of the functional lesion.” For the present case of semantic classification, one might speculate that a disconnection of the ventral network encompassing the EC/EmC system described before should result in a more widespread cross-domain deficit in semantic classification. Accordingly, those patients should exhibit problems not only in language processing (e.g., deciding whether a given letter string is a word or not) but also in number processing (e.g., parity judgment and magnitude comparison). Yet, while the proposition of such cross-domain functional deficits for the case of language and number processing (e.g., for semantic classification) still awaits thorough empirical evaluation, it has recently been proposed that a disconnection of the ventral EC/EmC system might underlie specific impairments of rote multiplication performance. Klein et al. (2013b) argued that the disconnection of this ventral pathway may account for reported deficits in the retrieval of multiplication facts (such as for patients described by Cohen & Dehaene, 2000; Dehaene & Cohen, 1997; Van Harskamp & Cipolotti, 2001). Interestingly, multiplication facts are assumed to be at least partially represented verbally in a mental lexicon (e.g., Dehaene & Cohen, 1997). So, comparable to the case of deciding whether a given letter string is a word or not, rote multiplication requires the classification of an arithmetic problem to correspond to a memory entry or not. Additionally, the fact that a disconnection of the ventral EC/EmC system led to such a specific deficit for numerical knowledge, assumed to be represented in a verbal mode, provides first (tentative) evidence for our proposition of a cross-domain functional cognitive

210 K. Willmes et al. architecture for language and number processing. Importantly, however, such a cross-domain functional deficit may only be detected when the respective assessment tools are routinely administered together. A close link between the domains of language and number processing may not be surprising in the end, as there are several theoretical accounts emphasizing the importance of language for the phylogenetic and ontogenetic development of the human faculty of number processing (e.g., Carey, 1998; Fuson, 1988; and even Henschen, 1920). For instance, Wiese (2007, p. 758) concludes that “it is language that opened the way for numerical cognition, suggesting that it is no accident that the same species that possesses the language faculty as a unique trait should also be the one that developed a systematic concept of number.” Therefore, the domains of language and number processing seem to be a good starting point for a more systematic evaluation of cross-domain functional links. Another domain possibly complementing these two may be spatial processing. On the one hand, there is accumulating evidence that there is a spatially oriented mental number line upon which number magnitude is represented (see G€obel, Shaki & Fischer, 2011; de Hevia, Vallar & Girelli, 2008; Bueti & Walsh, 2009 for reviews). On the other hand, there is a growing body of literature on the association of language with space (e.g., Dudschig, Lachmair, de la Vega, De Filippis & Kaup, 2012a, 2012b; 2013; Kaup, De Filippis, Lachmair, de la Vega & Dudschnig, 2012; Lachmair, Dudschnig, De Filippis, de la Vega & Kaup, 2011) suggesting that spatial location information is activated automatically when nouns or adjectives are processed. Finally, there is a recent study bringing together the representations of language, number, and space. Matthews and Dylman (2013) observed that across multiple tasks English speakers preferentially used a “larger” comparative (e.g., more, taller, higher) to indicate the magnitude relation of two objects (as compared to a “smaller” comparative, e.g., less, shorter, lower) and that this effect was more pronounced when the larger item was presented either on the left, when displayed simultaneously, or second, when displayed sequentially. Thus, a systematic evaluation of specific cross-domain functional relationships seems to be a promising enterprise. The current research was supported by the Leibniz-Competition Fund (SAW) providing funding to Elise Klein in the funding line “Women in Academic Leadership Positions”. We are indebted to Gianluca Mingoia and Georg Eder from the “Brain Imaging Facility of the Interdisciplinary Centre for Clinical Research within the Faculty of Medicine at the RWTH Aachen University” for their precious help in acquiring the MRI/ fMRI data, and to Johannes Rennig for his precious help in acquiring the DTI data.

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Where numbers meet words: a common ventral network for semantic classification.

Recent research has shown that both language and number processing are clear examples of distributed and connected processing in the human brain, emph...
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