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Mapping Connectional Differences between Humans and Macaques in the Nucleus Accumbens Shell-Core Architecture

Two nucleus accumbens subregions, the shell and core, differ in the patterns whereby they integrate signals from prefrontal and limbic areas of the brain. In this study, we investigated whether the disproportionate volumetric differences of these brain areas, particularly the prefrontal cortex, between humans and macaques are accompanied by unique modifications of their macroscopic integrative connections with the shell and core. More specifically, we characterized the tractographic connectivity profiles of the human and macaque shell-core architecture and compared them between the two species. To make the cross-species comparisons more viable, we used the same whole-brain voxel-wise tractography-defined shell-like and core-like divisions in the two species as seeds and delineated pairs of interspecies connectionally comparable (ICC) target regions based on the similarity of the resting-state functional connectivity profiles for the two species, and finally used these seeds and ICC targets to establish a fingerprint-based common space for cross-species comparisons. Our results revealed that dissimilar structural connectivity profiles were found in the prefrontal but not the subcortical target group. We further localized this difference to specific targets to infer possible functional modifications between the two species.


Introduction
The nucleus accumbens (Acb) is believed to integrate mnemonic and emotional 2 signals from the prefrontal and limbic regions, helping organisms to achieve 3 motivationally relevant goals by facilitating action selection, e.g., procuring things 4 worth having or avoiding aversive consequences. Furthermore, in part because of 5 their distinct structural and functional connectivity profiles, the well-documented Acb 6 subregions, the shell and core, appear to promote distinct patterns of behavior during 7 action selection (Floresco, 2015). Specifically, the shell receives prominent 8 glutaminergic projections from the ventromedial prefrontal cortex (PFC), subiculum, 9 CA1 fields of the hippocampus (HIPP), and parvicellular basolateral amygdala 10 (AMYG) as well as small dopaminergic projections from the ventral tegmental area 11 and functionally plays a role in suppressing low-or non-rewards stimuli that may 12 interfere with the best available reward-predicting stimuli utilizing value-driven 13 decision making (Groenewegen et al., 1987;Wright and Groenewegen, 1996;14 Heidbreder and Groenewegen, 2003; Stopper and Floresco, 2011). The core, however, 15 receives prominent projections from the prelimbic cortex and magnocellular 16 basolateral AMYG as well as small dopaminergic projections from the substantia 17 nigra and functionally plays a role in selectively instigating an approach toward an 18 incentive stimulus associated with the best available reward after Pavlovian cue   2005) has not been established. In addition, researchers 10 found that the striatum, the parent structure of the Acb, presented species differences 11 in regional gene expression (Sousa et al., 2017) and exceptional volumetric changes 12 during primate evolution (Barger et al., 2014). All these seem to support the idea that 13 there may be unique modifications of the structural connectivity of the Acb shell-core 14 architecture, thereby leading to changes in the functions of these Acb subregions. 15 Quantitative interspecies comparison of structural and functional connectivity has 16 proven to be challenging (Jbabdi et al., 2013;Mars et al., 2014). Compared to 17 traditional comparative anatomy, comparative neuroimaging using magnetic 18 resonance imaging (MRI) allows us to make large-scale comparative analyses and is 19 one of the few techniques that can truly bridge the gap between species by identical 20 non-invasive data acquisition and data handling (Thiebaut de Schotten et al., 2018). 21 Comparative MRI is an effective tool for characterizing multimodal connectivity  Neubert et al., 2015). Especially for the latter, the 4 connectivity fingerprint (or its variant, e.g., the blueprint) framework has been used to 5 establish a common space in which a brain area's connectivity profiles characterized 6 in different brains can be aligned and compared between species (Mars et al., 2016, 7 2018a, 2018b). Such fingerprint-based common space approaches fully exploit the 8 possibilities offered by neuroimaging techniques but have a methodological 9 bottleneck, i.e., how to improve the commonness and representation resolution of the 10 common space to make the approaches more persuasive. For example, there are 11 currently no atlases that allow us to directly extract homologous cortical regions to 12 establish fingerprint-based common spaces for reasonable cross-species comparison.

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In this study, we explored the hypothesis that the structural connectivity profile of 14 the Acb shell-core architecture, if characterized by connections with the highly 15 developed PFC regions as well as with the subcortical structures, might differ 16 between humans and macaques because of differences in their evolutionary paths. 17 Compared with mixed comparative approaches (e.g., tracing vs. tractography), as was 18 done in previous studies (Jbabdi et al., 2013;Donahue et al., 2016), we decided that 1) 19 holistic connectivity features-defined brain regions of interest (ROIs) were the best 20 brain nodes for structural and functional connectivity analyses and that 2) using 21 homologous, or at least interspecies connectionally comparable (ICC), regions to 22 characterize the nodes' connectivity profiles for cross-species comparisons 1 represented a more reasonable approach. Therefore, we first generated the shell-like 2 and core-like regions based on whole-brain tractography in the two species as seeds 3 and delineated pairs of ICC targets based on the similarity between their resting-state 4 functional connectivity (rsFC) profiles. We used these ICC targets to characterize the 5 seeds' structural connectivity profiles in the two species and compared them using a 6 fingerprint-based common space approach. We also analyzed the possible differences 7 in single structural connectivities. (3.0 T MRI scanner; acquired using a multi-shell approach at a 1.25 mm isotropic 5 resolution) and resting-state functional MRI (rsfMRI; 2 mm isotropic resolution; 6 1,200 time points) data in an earlier study (Xia et al., 2017). In this study, we used 7 these diffusion images to generate the human seeds, i.e., the Acb shell-like and 8 core-like divisions, based on whole-brain voxel-wise tractography (Fig. 1A). Then, 9 we checked the data quality and availability of the rsfMRI data (see Supplemental 10 section 1) and used them to select targets and to delineate the ICC targets (Fig. 1B). 11 Finally, we used the diffusion images to characterize the structural connectivity 12 profiles of the human seeds for cross-species comparisons (Fig. 1C 1000 s/mm 2 and 4 non-diffusion-weighted images) to ensure that they could be used 21 9 to perform the tractographic analysis and used them to define the shell-like and 1 core-like regions based on whole-brain voxel-wise tractography (Xia et al., 2019b). In 2 this study, we extracted these Acb parcels as the macaque seeds (Fig 1A), while using to test the reproducibility of this dichotomous Acb subdivision. Similarity, we 6 checked the data quality and availability of the rsfMRI data in MMDS2 (240 volumes; 7 voxel sizes = 1.803×1.803×1.8 mm; see Supplemental section 1) and used them to 8 define targets and to delineate the ICC targets (Fig. 1B). Finally, we used the 9 diffusion images in MMDS1 to characterize the structural connectivity profiles of the 10 macaque seeds for cross-species comparisons (Fig. 1C). The preprocessing steps 11 (Supplemental section 2) for the macaque MRI data were identical to those used for 12 the human MRI data to mitigate the influence of different preprocessing steps on the 13 cross-species comparison. functional connectivity strength with the seeds, we pre-selected many targets from the existing atlases 1 and then redefined their boundaries using a fingerprint-based common space approach to generate the 2 ICC targets for the two species. (C) Cross-species comparisons. We used these ICC targets to 3 characterize the structural connectivity profiles of the seeds in the two primate brains for cross-species 4 comparison. Additional experiments and checks were run on some steps and are marked using 'a, b, c, 5 and d'. Tractography-defined Seeds of the Shell and Core 7 Although it is relatively new, tractography-based parcellation is a mature method section 3 for details) that has previously been used to generate human and macaque 10 Acb connection units (i.e., a region consisting of voxels with similar structural 11 connectivity features) by parcellating the striatum (Xia et al., 2019a). In this study, we 12 further parcellated these Acb connection units to define the shell and core connection 13 units in the two species. Specifically, we parcellated the human Acb connection unit, 14 rather than the previously used microanatomically delineated Acb region (Baliki et al., divisions. We had previously parcellated the macaque Acb connection unit into 17 shell-like and core-like divisions based on whole-brain voxel-wise tractography using 18 the high-resolution diffusion images in MMDS1 (Xia et al., 2019b). In this study, we 19 directly extracted these Acb parcels as seeds for the subsequent analysis and repeated 20 this procedure using the low-resolution diffusion images in MMDS2 to validate the 1 reproducibility of the parcellation results (Fig. 1a). 3 The fingerprint framework was constructed using a group of targets that met two 4 criteria: 1) have a strong structural connectivity and functional coupling pattern, i.e., 5 rsFC, with either the human or macaque seeds (see detailed criterion in Supplemental 6 section 5) and 2) have substantial evidence-based homologs in this area between the 7 two species. Additionally, the target group should be able to make a unique 8 connectivity characterization of these seeds, so as to distinguish the seeds from each 9 other and from the other neighboring brain regions. 10 For humans, the prefrontal areas 11, 11m, 13, 14m, 25, 32pl, and 47o extracted 11 from a tractography-defined brain atlas (Neubert et al., 2015) and the AMYG, HIPP, . In this study, we considered these prefrontal areas and subcortical 2 structures separately as candidates for targets to construct the fingerprint frameworks. 3 In addition, we repeated the above procedure using the histologically-defined 4 macaque shell and core (Paxinos et al., 2009;Calabrese et al., 2015) and validated the 5 reproducibility of the pre-selection of these targets (Fig. 1b). Then, we roughly 6 matched these human and macaque target regions based on macroscopic 7 morphological landmarks and added non-matching targets (i.e., corresponding areas 8 selected in only one species) to achieve the first criterion. Next, because some brain 9 areas may have no clear homolog between the two species (Bush and Allman, 2004), 10 we searched earlier studies to determine whether evidence-based non-homologous 11 areas and considerable interspecies differences exist for each pair of targets. If so, the 12 target was eliminated from the group to meet the second criterion. both the shell and the core could be distinguished from the rest of the striatum using 18 their structural connectivity profiles characterized by the prefrontal areas as well as by 19 whole-brain voxels (Supplemental section 6). If they did, we could use the prefrontal 20 areas to make unique structural connectivity characterizations of the shell and of the 21 core to perform reasonable cross-species comparisons. 22 Delineating ICC Targets in the PFC 1 The targets identified above for the two species were extracted from different types of 2 brain atlases and could only be roughly matched across the species based on their 3 general location. Specifically, the subcortical targets had relatively clear boundaries. 4 They are undoubtedly pairs of homologs and thus could be used directly to establish 5 the fingerprint-based common space for cross-species comparisons. In contrast, no 6 macroscopic morphological landmarks supported such a clear-cut definition of the 7 homologous target areas in the PFC. So, we did the next best thing: We redefined the 8 boundaries of the pre-selected prefrontal targets based on the similarity of the rsFC 9 profiles to ensure 1) they were connection units and 2) they were ICC brain regions.  distinctly different from the pre-selected histologically-defined area 25 (red-tinged cluster). 7

Constructing Fingerprint Frameworks
We began the delineation of the ICC targets in the human population, as follows: where, p and q are two vectors representing the functional connectivity fingerprints to 10 be compared; and n is the number of arms of the fingerprint. 11 Given a delineated ICC target in the human brain, we delineated the 12 corresponding ICC target in the macaque brain as follows: 6) We brought the 23 13 homologous areas (3 mm isotropic; cf. Neubert et al., 2015) from MNI monkey space 14 back into native space (24 macaques in MMDS2) like the first step. 7) For each 15 homologous area, in each subject's functional space, we first calculated the functional 16 couplings between this area and voxels in the PFC to generate the rsFC map. Then, 17 we transformed these rsFC maps into MNI monkey space to generate the 18 group-averaged rsFC map. The resulting 23 group-averaged rsFC maps were used to 19 extract the fingerprint for each voxel in the PFC in MNI monkey space. 8) For each 20 voxel in the given ICC target in the human brain, we first calculated its fingerprint 1 and considered it as the eigen fingerprint. Then, we calculated the cosine similarities 2 between this eigen fingerprint and the fingerprints of voxels in the macaque PFC to 3 generate a corresponding SDM for the macaque brain. 9) We normalized these SDMs 4 to their respective maximum and then averaged all the SDMs (e.g., 107 SDMs in Fig.   5 2D). 10) We thresholded the group-averaged SDM by extracting the top 5% most 6 similar voxels (discrete voxels were removed from the maximal cluster) to obtain the 7 final ICC target in the macaque brain (Fig. 2E). Additionally, we adjusted the size of 8 the 23 homologous areas (from 6 mm to 5 mm isotropic in the human brain and from 9 3 mm to 2 mm isotropic in the macaque brain) to validate the reproducibility of the  feature, the structural connectivity ratio, to build structural connectivity fingerprints to 20 detect specific structural connectivity differences in the Acb shell-core architecture 1 between the two species. whether the target tended to connect with the shell or the core, respectively. 8 Similar calculation steps and processing parameters were used to calculate the 9 structural connectivity fingerprints for the two species. More specifically, we brought between each target and the two seeds to generate a structural connectivity ratio. 18 Finally, using structural connectivity ratios calculated separately for the prefrontal and 19 subcortical target groups, we calculated fingerprints to characterize the structural 20 connectivity profiles of the Acb shell-core architecture for each individual. In addition, 21 for display purposes, we generated group-averaged structural connectivity fingerprints 1 for the prefrontal and subcortical target groups for the two species. 2 We used a permutation test to detect whether the structural connectivity profiles using an independent two-sample t test at the 5% significance level.

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Both the high-resolution ex vivo (Fig. 3A) and low-resolution in vivo (Fig. 3B) 3 macaque MRI datasets showed that the Acb can be subdivided into ventromedial and whole-brain voxel-wise tractography (Fig. 3D). The tractography-defined human and 15 macaque Acb shell-core architecture have similar topological distribution and were 16 considered to be the best brain nodes for subsequent structural connectivity analyses. based on whole-brain voxel-wise tractography to generate the human seeds, i.e., the shell-like and 8 core-like divisions. Acronyms: left hemisphere, LH; right hemisphere, RH. All the coordinates are 9 shown in MNI (humans) or MNI monkey (macaques) space. 10 Defined Fingerprint Framework 11 The prefrontal areas and subcortical structures enabled us to make unique structural 12 connectivity characterizations of the shell and core (Supplemental section 6). Then, 7 13 human subcortical structures, the AMYG, HIPP, THA, MidB, Ca, Pu, and Pa, were 14 pre-selected as candidates for the subcortical target group to construct a fingerprint 15 framework. Also, 7 human prefrontal areas, 11, 11m, 13, 14m, 25, 32pl, and 47o, 16 extracted from the tractography-defined atlas (Neubert et al., 2015), were pre-selected 17 as candidates for the prefrontal target group to construct another fingerprint 18 framework. After further investigation, we found that only the most medial part of 1 area 11 (a strip region located in the medial orbital gyrus) had a strong connectivity 2 with the human seeds. We divided this strip region into three and merged these 3 divisions into the neighboring areas 11m, 14m, and 13. Area 47o, located in the 4 posterior part of the lateral orbitofrontal cortex, was eliminated from the target group 5 due to its considerable functional connectivity difference between the two species 6 (Neubert et al., 2015). We confirmed this conclusion in a subsequent delineation of 7 the ICC brain area in area 47o. In the end, we pre-selected 5 prefrontal and 7 8 subcortical targets in the human brain that could be used to construct fingerprint 9 frameworks. Similarly, using the same analyses for the macaque brain, 7 subcortical 10 structures, the AMYG, HIPP, THA, MidB, Ca, Pu, and Pa, and 8 prefrontal areas, 11 10m, 11m, 13a, 14m, 14o, 25, 32, and periallocortex, extracted from the histological 12 atlas, were pre-selected separately as candidates for the prefrontal and subcortical 13 target groups to construct the fingerprint frameworks. In addition, we found that the 14 same prefrontal and subcortical targets could be identified when the histologically 15 defined macaque shell and core regions were used as seeds. 16 Coincidentally, but understandably, the pre-selected 7 human subcortical 17 structures completely overlapped with the pre-selected 7 macaque subcortical 18 structures, i.e., they were identified as being the homologs between the two species 19 and thus could be used directly to construct the final fingerprint framework. On the 20 other hand, the pre-selected human and macaque prefrontal target regions could only 21 be roughly matched based on macroscopic morphological landmarks (Supplemental 22 section 7; Fig. 4). For example, the histologically defined macaque area 32, located in 1 the perigenual anterior cingulate cortex, corresponded to the tractography-defined 2 human area 32pl. Finally, because the number of the pre-selected human prefrontal 3 targets was less than the number of pre-selected macaque prefrontal targets (5 < 8), 4 we began delineation of the ICC targets in the human population. . We roughly matched these brain areas across the species based on macroscopic morphological 11 landmarks and used thick and thin lines to reflect the strong and slight corresponding relationships, 12 respectively. 13 14 For each hemisphere, we delineated 5 pairs of ICC targets in the human and macaque 15 PFC. We began this procedure with the 5 human prefrontal targets and thus named 16 them h11m, h13, h14m, h25, and h32 ('h' is short for 'homologous'). Taking h25 as 1 an example (see the blue cluster in Fig. 5), in each human subject's functional space, a 2 SDM, summarizing the degree of correspondence between the rsFC patterns of each 3 voxel in the human PFC and the central region of the area 25, was calculated and then 4 transformed into MNI space. We calculated the group-averaged SDM and thresholded 5 it to generate the area h25 in the human brain. Next, for each voxel in the human area 6 h25, we calculated a corresponding SDM in the macaque PFC. Then, the 7 group-averaged SDM was generated and thresholded, with the resulting macaque area 8 h25 being located in histological area 25, with some extensions into the medial 9 periallocortex. Finally, we validated the reproducibility of the delineation of the area 10 h25 (Fig. S5). 11 Using this procedure, we delineated other pairs of ICC targets in the two primate  pairs of ICC targets, h11m, h13, h14m, h25, and h32, which are displayed in red, cyan, green, blue, and 3 yellow, respectively, using the color ranges: [t, 1], t is the threshold value shown next to the colorbar. 4

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Using the tractography-defined seeds (the shell-like and core-like divisions) and the 7 6 pre-selected subcortical structures, we built the structural connectivity fingerprint for 7 each individual in the two species and displayed their group-averaged fingerprints in 8 Fig. 6 (top panel). Permutation tests indicated good consistency between the two 9 groups of fingerprints for the two species, as can be seen from the observed cosine 10 similarity, which was less than the calculated test criterion at the 5% significance 11 level in the right tail of the histogram. More specifically, the tractography results 12 indicated that the AMYG and HIPP clearly tended to connect with the shell-like  group-averaged human and macaque structural connectivity fingerprints are shown using radar maps. 16 The convergence between the two groups of fingerprints for the two species was tested using a 1 permutation test, and the results are shown in the histogram. The single structural connectivity ratio 2 was calculated for each subcortical target in the two species. An independent two-sample t test was 3 used to determine whether the two population means were equal, and the result is shown in the grouped 4 box chart. The results indicate that the observed cosine similarity between the two group-averaged 5 fingerprints of the two species was less than the calculated test criterion in the right tail of the 6 histogram, suggesting a similar distribution of the two groups of fingerprints. In addition, no 7 statistically significant single structural connectivity ratio difference was found between the two 8 species. Acronyms: putamen, Pu; caudate nucleus, Ca; pallidum, Pa; amygdala, AMYG; hippocampus, 9 HIPP; thalamus, THA; midbrain, MidB; Humans, Hum; Macaques, Mac. 10 Altered PFC-Acb Structural Connectivity 11 Using these seeds and 5 delineated ICC prefrontal targets, we built additional pairs of 12 structural connectivity fingerprints for each individual in the human and macaque 13 groups and displayed the two group-averaged fingerprints in Fig. 7 (top panel). Unlike 14 the above subcortical result, we found a rare cosine similarity in the permutation 15 distribution, as can be seen in the right tail of the histogram. The observed cosine 16 similarity was higher than the calculated test criterion at the 5% significance level. 17 Therefore, we rejected the null hypothesis and concluded that the prefrontal areas of 18 the two primate species evolved dissimilar gross structural connectivity trends to the 19 shell-core architecture. This result seems to align with evidences for heterochronic ). We concluded that one or more prefrontal targets had noticeably different 1 structural connectivity trends in the two species and that this could be further checked 2 by performing single structural connectivity analyses. 3 For each delineated prefrontal target, we calculated the structural connectivity 4 ratio between this region and the two seeds in each of the two species (Fig. 7). More 5 specifically, area h25 had a stronger structural connectivity to the shell-like area than 6 to the core-like (about 0.8 of the structural connectivity ratio) in both species, which 7 was consistent with previous findings that the rodent infralimbic area (rodent  detailed legend. The results indicate that the two groups of fingerprints could not be accepted as 8 convergent fingerprints because the observed cosine similarity was higher than the calculated criterion 9 in the right tail of the histogram. The gross structural connectivity ratio (i.e., fingerprint) differences 10 between the two species were localized to h13 (LH: humans: 0.61 ± 0.20 > macaques: 0.40 ± 0.12; p = 11 5.7e-03. RH: humans: 0.74 ± 0.17 > macaques: 0.39 ± 0.10; p = 1.65e-06) and h32 (LH: humans: 0.82 12 ± 0.16 > macaques: 0.55 ± 0.08; p = 3.62e-05. RH: humans: 0.79 ± 0.16 > macaques: 0.60 ± 0.15; p = 13 3.1e-03). Both of these showed significantly different structural connectivity ratios (* p < .05). 14 15 Discussion 1 We generated spatially corresponding shell-like and core-like divisions as seeds and 2 delineated ICC targets in human and macaque brains to build structural connectivity 3 fingerprints for viable cross-species comparisons. We revealed that the prefrontal but 4 not the subcortical target group had dissimilar structural connectivity with the 5 shell-core architecture between the two species. We localized this overall difference 6 to specific prefrontal targets in single structural connectivity analyses and analyzed 7 their possible influence on functions. 8 Microstructural and molecular features (e.g., cyto-and myelo-architecture) may 9 determine the local processing capabilities of a brain region, whereas connectivity  ICC targets, were generated to support the subsequent connectivity analyses. Second, 9 to understand whether the connections between homologous brain regions have 10 changed, it helps to be able to overlay homologous brain regions of the species of 11 interest and then compare their connectivity matrices (Thiebaut de Schotten et al., 12 2018). In view of this, we identified the homologous relationships of the pre-selected 13 subcortical targets in the two species and redefined the boundaries of pre-selected 14 prefrontal targets to make sure they were ICC regions. In addition, we validated the 15 reproducibility of the definition of the seeds, pre-selected the targets, delineated the 16 ICC prefrontal targets, and characterized the structural connectivity of the shell-core 17 architecture (Supplemental section 11). The verification results indicated that there 18 was no serious snowballing effect resulting from the chain analysis used in this study. 19 In any case, all these measures enabled us to establish a viable comparative 20 framework while holding many other irrelevant factors constant so that we could 21 obtain credible comparison results.  Stephan et al., 1987). In this study, by 11 showing that the fingerprints had a strong cosine similarity, we revealed that the  These distinct functions may be respectively supported by their distinctive preferred 11 structural connectivity with the human shell-like and the macaque core-like regions. 2) 12 Area h32 has been implicated as involved with working memory for  In conclusion, we improved the fingerprint-based common space approaches by 3 providing a reasonable comparative framework. Using this approach, we revealed 4 conserved structural connectivity profiles of the Acb shell-core architecture with 5 subcortical structures but dissimilar structural connectivity profiles with prefrontal 6 regions. These results are in keeping with the well-accepted conclusion that the 7 human PFC has undergone considerable expansion and become more developed than 8 that of other primates. However, all these conserved and changed structural 9 connectivities only reflect general neuroanatomical evolutionary trends. Whether or 10 not any task-specific-related function of the Acb subregions has been affected will, of 11 course, be partially dictated by which connections are involved and how the signals 12 are integrated and interact via these connections.

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We thank Shan Yu and George Paxinos for their insightful discussions and useful 14 suggestions. We also thank Rhoda E. and Edmund F. Perozzi for editing assistance. 15 Conflict of Interest: None declared. Results are available for download from Gitlab at 16 https://gitlab.com/xlxia/cross-species-comparison-of-the-Acb. 17 18