Longitudinal tracts in macaque and human

We used connectivity-based parcellations of coronal regions of interest (ROIs) through the temporal white matter to identify the bodies of the main longitudinal tracts of the temporal lobe, which subsequently informed the position of the masks used to perform probabilistic tractography in order to reconstruct the course of the tracts.

In macaques, we were able to reconstruct tracts similar to earlier studies [13,20,22] (Fig 1A, S1 Fig). The superior temporal gyrus contained a single cluster, from which we reconstructed a tract that can be identified as the middle longitudinal fascicle (MdLF) because of its course through the superior temporal gyrus and termination points mainly in the inferior parietal lobe but also branching toward occipital regions. The inferior temporal gyrus contained a single cluster, from which we reconstructed a tract similar to the ILF, terminating in inferior occipital and inferior parietal regions. Medial to these tracts, we identified a tract that is reminiscent of the inferior fronto-occipital fascicle (IFOF), running from the occipital cortex medially through the temporal cortex at the level of the inferior temporal gyrus. Among the tracts we identified, it was the only one to have very clear projections to the frontal lobe. The IFOF as identified here is likely to be a multisynaptic pathway because we are using the method of tractography and not tract tracing. Indeed, tract tracing identifies a monosynaptic section of the IFOF termed the extreme capsule [22]. Importantly, a recent dissection study identified an IFOF in the macaque similar to our tractography results, demonstrating a biological basis of our finding [23]. We provide a more in-depth comparison of these tracts as obtained using different methodologies in the Supporting information section (S1 Fig, S1 Text).

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Fig 1.

A 3D representation and coronal sections of longitudinal temporal tracts in macaques (A) and humans (B). Tractograms were log transformed and normalized for display. The coronal sections from left to right are taken from posterior to anterior. Blue, MdLF; green, IFOF; yellow, ILF (macaque) or ILFmed (human); red, ILFlat. Thresholds for the tracts are as follows: 0.7 for MDLF; 0.75 for IFOF; and 0.7 for ILF, ILFmed, and ILFlat. Human data are available from the Human Connectome Project (www.humanconnectome.org). Macaque postmortem data are available from the PRIME-DE repository (http://fcon_1000.projects.nitrc.org/indi/PRIME/oxford2.html) [24]. IFOF, inferior fronto-occipital fascicle; ILF, inferior longitudinal fascicle; ILFlat, inferior longitudinal fascicle lateral; ILFmed, inferior longitudinal fascicle medial; MdLF, middle longitudinal fascicle; PRIME-DE, Primate Data Exchange.

https://doi.org/10.1371/journal.pbio.3000810.g001

The same procedure in humans identified a tract through the superior temporal gyrus similar to the MdLF (Fig 1B). In the inferior temporal gyrus, the clustering could reliably identify two clusters with differential connectivity to the rest of the brain where we would expect the territory of the ILF to be. We suggest that the higher number of clusters found in humans reflects a higher complexity in the temporal lobe white matter organization of this species. We interpret this in the light of previous studies supporting a subdivision of the ILF into two subcomponents [19,25]. Accordingly, the tractography identified different tractograms for the two subcomponents. One of them runs more laterally in the inferior temporal gyrus and slightly invades the middle temporal gyrus, whereas the other runs more medially and ventrally, mainly in the fusiform gyrus, and reaches more inferior occipital regions. We refer to these tracts as the ILF lateral (ILFlat) and ILF medial (ILFmed) subcomponents of the ILF. We identified a tract very similar to the IFOF that runs predominantly through the middle temporal gyrus, although it is located more medially than the other two tracts. Indeed, from the surface projections (Fig 2A and 2B, S2 Fig), it can be seen that the middle temporal gyrus in humans is mainly reached by the ILFlat and the IFOF.

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Fig 2.

Surface projection of longitudinal temporal tracts in macaques (A) and humans (B). Shown are the group averages of the normalized, log-transformed, and thresholded tracts in the right hemisphere only (left hemisphere results can be found in S2 Fig). Blue, MdLF; green, IFOF; yellow, ILF (macaque) or ILFmed (humans); red, ILFlat. Thresholds for the tracts are as follows: 0.7 for MDLF; 0.75 for IFOF; and 0.7 for ILF, ILFmed, and ILFlat. Human data are available from the Human Connectome Project (www.humanconnectome.org). Macaque postmortem data are available from the PRIME-DE repository (http://fcon_1000.projects.nitrc.org/indi/PRIME/oxford2.html). IFOF, inferior fronto-occipital fascicle; ILF, inferior longitudinal fascicle; ILFlat, inferior longitudinal fascicle lateral; ILFmed, inferior longitudinal fascicle medial; MdLF, middle longitudinal fascicle; PRIME-DE, Primate Data Exchange.

https://doi.org/10.1371/journal.pbio.3000810.g002

In summary, compared with the macaque, the human IFOF is located more lateral and runs predominantly along the middle temporal gyrus (which is not present in the macaque). Also, unlike macaques, human ILF can be subdivided into two subcomponents. The MdLF has a similar path in both species, which indicates that its main connectivity pattern across species should not be fundamentally different.

Longitudinal tracts in great apes

Using the same procedure of connectivity-based clustering followed by tractography as for humans and macaques, we identified longitudinal temporal tracts in the chimpanzee and gorilla. We used both in vivo and ex vivo data in the chimpanzee and obtained comparable results (Fig 3A and 3B), indicating that our results generalize across the different imaging protocols. However, because the left hemisphere in the postmortem sample was slightly flattened in the extraction protocols, it is hard to interpret the results obtained in that hemisphere.

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Fig 3.

A 3D representation of longitudinal temporal tracts in chimpanzees in vivo (A) postmortem (B) and gorilla (C). Tractograms were log transformed and normalized for display. The coronal sections from left to right are taken from posterior to anterior. Blue, MdLF; green, IFOF; yellow, ILFmed; red, ILFlat. Thresholds for the tracts are as follows: 0.7 for MDLF; 0.75 for IFOF; and 0.7 for ILFmed and ILFlat. In vivo chimpanzee data are available from the National Chimpanzee Brain Resource (www.chimpanzeebrain.org). Gorilla and chimpanzee postmortem data are available from https://doi.org/10.5281/zenodo.3901205. IFOF, inferior fronto-occipital fascicle; ILF, inferior longitudinal fascicle; ILFlat, inferior longitudinal fascicle lateral; ILFmed, inferior longitudinal fascicle medial; MdLF, middle longitudinal fascicle.

https://doi.org/10.1371/journal.pbio.3000810.g003

We identified the MdLF running through the superior temporal lobe following a similar course as human and macaque MdLF, reinforcing the argument for a conserved tract across all studied anthropoid primates (Fig 3A, 3B and 3C). In the inferior temporal lobe, the territory occupied by the ILF was similar to that in humans and could similarly be divided into two subcomponents. We therefore will refer to them as ILFlat and ILFmed as well. Although the differentiation is strong in the chimpanzee, specifically in the right hemisphere, the separation of the ILF into two subcomponents in gorilla, although present, is less pronounced. Different methods to assess the number of clusters in the temporal white matter did not reach a consistent conclusion in the gorilla (S2 Text, S8 Fig), suggesting that the gorilla temporal lobe white matter might not be as complex as in chimpanzees and humans. We suggest that this is related to the anatomy of its temporal lobe, with a less prominent fusiform gyrus and a less distinct difference between the inferior and middle temporal gyrus in the gorilla, which is associated with a shallower inferior temporal gyrus, compared with the chimpanzee. The IFOF was also present in great apes, and its course was similar to the human IFOF, running more medially than other tracts as well as in the middle temporal gyrus. In chimpanzee in vivo, it seems that the IFOF does not reach as lateral as in the other great ape data. In gorilla, it can be observed that the IFOF is most prominent in its posterior section, whereas it is quite limited in its frontal projections. However, having only one sample for the gorilla because of the presence of a frontal lesion on the left hemisphere, we cannot draw too-strong conclusions from this observation.

To further illustrate these tracts in the great apes, we represented their tractograms on the cortical surface (Fig 4, S3 Fig). In other words, we examined where the tractogram approached the gray/white matter border. Although one should be cautious in interpreting such results because of the presence of superficial white matter systems [26] and possible gyral bias of tractography toward the surface, it provides an illustration of these fiber tracts in the great ape, in which they are currently underexplored. It allows one to represent not only the differences in tract organization with other species but also the sulcal and gyral patterns of these brains, which are not well known. This showed that for both species, the MdLF reaches mostly superior temporal gyrus areas as it does in the macaque and human. The ILF subcomponents are similar to those in the human, with the majority of the lateral part of the inferior temporal cortex reached predominantly by the ILFlat and the medial part reached predominantly by the ILFmed. The IFOF is the only one of these tracts that has projections to the frontal cortex.

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Fig 4.

Surface representation of longitudinal temporal tracts in chimpanzees (A) and gorilla (B). Shown are the group average (chimpanzee in vivo) or individual results (chimpanzee postmortem and gorilla) of the normalized, log-transformed, smoothed, and thresholded right tracts (left hemisphere results can be found in S3 Fig for chimpanzee in vivo). Blue, MdLF; green, IFOF; yellow, ILFmed; red, ILFlat. Thresholds for the tracts are as follows: 0.7 for MDLF; 0.75 for IFOF; and 0.7 for ILFmed and ILFlat. In vivo chimpanzee data are available from the National Chimpanzee Brain Resource (www.chimpanzeebrain.org). Gorilla and chimpanzee postmortem data are available from https://doi.org/10.5281/zenodo.3901205. IFOF, inferior fronto-occipital fascicle; ILFlat, inferior longitudinal fascicle lateral; ILFmed, inferior longitudinal fascicle medial; MdLF, middle longitudinal fascicle.

https://doi.org/10.1371/journal.pbio.3000810.g004

Thus, great apes (including humans) seem to display a similar organization regarding the subcomponents of the ILF running in inferior and fusiform gyri, respectively. The ILFmed reaches an area reminiscent of the fusiform gyrus in all species, which is absent in the macaque, in which we did not detect the subdivision of the ILF. In line with this argument, we notice that these ILF subcomponents are less evident in the gorilla, in which the fusiform gyrus is less prominent than in chimpanzees and humans.

Dorsal longitudinal tract

We have concentrated thus far on the major ventral longitudinal tracts along the temporal cortex. However, a major dorsal longitudinal tract, the AF, runs between the frontal and temporal cortex. Previous studies have developed tractography protocols for this tract in the human, chimpanzee, and macaque, although the details differ between studies and groups [15,27]. Here, we used a consistent protocol to track the AF in all four species, creating a comparable result for the three species in which the AF has previously been defined and in the gorilla (Fig 5, S4 Fig).

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Fig 5. AF tractography protocols and results.

Top panel: AF tractography masks example for one individual macaque, human, chimpanzee (in vivo and postmortem), and gorilla, represented on the principal eigenvector (V1) map weighted by the fractional anisotropy map. The light-pink mask represents the seed, and the white masks represent the anterior and posterior waypoints. Bottom panel: 3D and surface representation of the right tractogram obtained for AF. (Left hemisphere results can be found in S4 Fig) Threshold of 0.75. R denotes right hemisphere. Human data are available from the Human Connectome Project (www.humanconnectome.org). Macaque postmortem data are available from the PRIME-DE repository (http://fcon_1000.projects.nitrc.org/indi/PRIME/oxford2.html). In vivo chimpanzee data are available from the National Chimpanzee Brain Resource (www.chimpanzeebrain.org). Gorilla and chimpanzee postmortem data are available from https://doi.org/10.5281/zenodo.3901205. AF, arcuate fascicle; PRIME-DE, Primate Data Exchange.

https://doi.org/10.1371/journal.pbio.3000810.g005

In macaques, the AF consisted of only a dorsal component, whereas in humans, its course exhibited a sharp curve to end up in the temporal lobe running mainly in the middle temporal gyrus, although quite medially. In chimpanzees, we observed a small curve as well, with AF reaching the superior temporal gyrus but not the middle temporal gyrus, as in humans. An arcuate path similar to that of the chimpanzee could be observed in gorilla. Thus, the AF expansion to the temporal lobe seems to be unique to humans and not present in other great apes studied here.

Divergence in macaque and human temporal cortex connectivity profiles driven by longitudinal tracts

The goal of the present work is to establish similarities and differences between the long-range connections of the temporal lobe across primate species, with the macaque and the human as the two most studied samples. If differences in these long-range connections exist, they should result in different connectivity profiles of temporal cortex gray matter between the two species. To establish whether this is the case, we compared the connectivity profiles of each gray matter vertex of the temporal lobe—in other words, the temporal lobe connectivity blueprint—across species. The logic of the approach is that for each vertex in each species, we quantify how well its connectivity fingerprint with the temporal cortex white matter tracts resembles that of any vertex in the other species. Thus, vertices with a low divergence score have a connectivity fingerprint also present in the other species, whereas vertices with a high divergence score are more likely to have a connectivity that is unique to that species. The connectivity fingerprint profile of a given vertex in this analysis is represented by the probability that the vertex is reached by each tract.

We first restricted the blueprint to four temporal tracts that have been well established in both humans and macaques—namely, the temporal part of the cingulum bundle, the fornix, the optic radiation, and the uncinate fascicle [27–30]. These tracts are known to be conserved across species, and hence we expect all vertices to have low divergence scores. Indeed, in this blueprint, we can observe that both human and macaque brain organization are predictable from one another (Fig 6A, S6A Fig). With those same tracts, but also adding the MdLF, the combined ILF, and the IFOF in the blueprint, we notice a small increase of the divergence score in some areas of the temporal lobe in both species (Fig 6B, S6B Fig). The example connectivity fingerprints in Fig 6A and 6B illustrate how the increase in divergence scores (computed as Kullback–Liebler [KL] divergence) is due to differences in the connections of these parts of the cortical surface with the underlying white matter. When only using the nonlongitudinal tracts (Fig 6A), all vertices have a low value because the pattern of connections of any part of the surface can be found in the other species. When adding the ventral longitudinal fibers, some differences are noticeable, and indeed a vertex with a high divergence score has a connectivity profile that differs even from its best matches in the other species (Fig 6B). Finally, when adding the AF, the profiles of connections of parts of the cortex are very different even from their best matches in the other species, indicating that this tract contributes most to the distinctive organization of the human temporal lobe (Fig 6C, S6C Fig).

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Fig 6. Results of the blueprint analysis.

The figure reports results on the right hemisphere (left hemisphere results can be found in S6 Fig) and shows the tracts used (top row), the resulting KL divergence (middle row) for human predicting macaque (left) and macaque predicting human (right), and the connectivity fingerprints (bottom row). The greener the vertices of the KL divergence map in one brain, the more their connectivity profile is similar to that of vertices in the other brain. The connectivity fingerprints show how likely it is for the vertex, highlighted by a white sphere in the brain above, to be reached by each tract (dotted line). The solid line represents how likely each tract is to reach the average of the 10 best-matching vertices in the other species. (A) Blueprints established using the cbt, the fx, the or, and the unc. (B) Blueprints established with the tracts in (A) and adding the MdLF, ILF (both subcomponents combined in humans), and IFOF. (C) Blueprint established with the tracts in (B) and adding the AF. Blue, MdLF; green, IFOF; yellow, ILF (macaque) or ILFmed (human); red, ILFlat; pink, AF. (D) Distribution of minimum KL values obtained for each blueprint. From left to right for the macaque, the human, and masked with the human AF. Light gray, nonlongitudinal tracts; gray, adding MdLF, ILFs, and IFOF; black, adding AF. Human data are available from the Human Connectome Project (www.humanconnectome.org). Macaque postmortem data are available from the PRIME-DE repository (http://fcon_1000.projects.nitrc.org/indi/PRIME/oxford2.html). AF, arcuate fascicle; cbt, temporal part of the cingulum bundle; fx, fornix; IFOF, inferior fronto-occipital fascicle; ILF, inferior longitudinal fascicle; ILFlat, inferior longitudinal fascicle lateral; ILFmed, inferior longitudinal fascicle medial; KL, Kullback–Liebler; MdLF, middle longitudinal fascicle; or, optic radiation; unc, uncinate fascicle; PRIME-DE, Primate Data Exchange.

https://doi.org/10.1371/journal.pbio.3000810.g006

These results are confirmed by the distribution of the divergence scores obtained with each blueprint (Fig 6D, S6D Fig). We can observe a shift toward higher minimum divergence scores in the distributions when adding the AF, which is confirmed by the distributions being statistically different (as assessed by the two-sample Kolmogorov–Smirnov test with p < 0.01). This shift is even more dramatic when focusing the human blueprint on the AF territory, confirming its influence on the increase of KL divergence.

One difference we found between the macaques and all the great apes was the dissociability of the two components of the ILF. Using the blueprint method, we next investigated the different impact of the two subcomponents of the ILF on similarity of macaque and human temporal lobe organization. When we ran the same analysis comparing macaque temporal lobe with a human temporal lobe in which the ILF either consists solely of the ILFlat or consists solely of the ILFmed, we observed that macaque temporal lobe contains substantially more vertices with a high KL divergence in the territory of the ILF when we use ILFlat. In other words, macaque and human temporal lobes are more similar if we assume equivalence of macaque ILF and human ILFmed than if we assume equivalence of macaque ILF and human ILFlat (Fig 7A and 7B, S7A Fig, S7B Fig). Computing the difference between the two divergence maps analyzed with the two different subcomponents thus allows one to investigate the putative homology (Fig 7C, S7C Fig). On this difference map, we can also notice that when using the ILFmed, the human brain better predicts the areas where the macaque ILF runs, but the macaque brain does not predict the part where the ILFlat runs anymore. When using the ILFlat in humans, it better predicts an area situated more in the domain of the IFOF in macaques. The distribution of the divergence scores in the territory of the macaque ILF, obtained when using one or the other human ILF, also shows a shift toward higher value when using the human ILFlat compared with the human ILFmed (Fig 7D, S7D Fig). The two distributions are statistically different (as assessed by the two-sample Kolmogorov–Smirnov test with p < 0.01).

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Fig 7. Blueprint method to determine differences between the human ILFs.

(A) Blueprint established as in Fig 6C but with the ILFlat for humans (no ILFmed). (B) Blueprint established as in (A) but with the ILFmed for humans (no ILFlat). (C) The different ILF tractograms are represented on the top row. On the bottom row is shown the KL difference between the two maps established in (A) and (B). More yellow vertices means that using ILFmed in humans as macaques’ ILF homologous results in lower KL divergence between the two species at these vertices, whereas more red applies to ILFlat. Blue, MdLF; green, IFOF; yellow, ILF (macaque) or ILFmed (human); red, ILFlat; pink, AF. (D) Distribution of minimum KL values in the macaque’s ILF territory obtained for the blueprints with the different human ILF subcomponents. Red, with human ILFlat; yellow, with human ILFmed. Human data are available from the Human Connectome Project (www.humanconnectome.org). Macaque postmortem data are available from the PRIME-DE repository (http://fcon_1000.projects.nitrc.org/indi/PRIME/oxford2.html). IFOF, inferior fronto-occipital fascicle; ILF, inferior longitudinal fascicle; ILFlat, inferior longitudinal fascicle lateral; ILFmed, inferior longitudinal fascicle medial; KL, Kullback–Liebler; MdLF, middle longitudinal fascicle; PRIME-DE, Primate Data Exchange.

https://doi.org/10.1371/journal.pbio.3000810.g007

Ethics statement

All in vivo data (human and chimpanzee) are from publicly available databases. The human data were obtained from the Human Connectome Project (HCP), and the approved consent procedure is fully described in the core HCP literature referenced here (www.humanconnectome.org). The chimpanzee data were obtained from chimpanzeebrain.org and collected at the Yerkes Primate Center under procedures that were carried out in accordance with protocols approved by YNPRC and the Emory University Institutional Animal Care and Use Committee (approval no. YER-2001206). All in vivo chimpanzee scans were done prior to 2012 and thus prior to the 2015 implementation of United States Fish and Wildlife Service and National Institutes of Health regulations governing research with chimpanzees. The ex vivo data were acquired from deceased animals that died of causes unrelated to this research project. As such, the research did not require a Home Office License as defined by the Animals (Scientific Procedures) Act 1986 of the United Kingdom.

Data acquisition and preprocessing

The acquisition and preprocessing of the diffusion MRI, structural, and surface data are described in detail in the Supporting information (S2 Text). Key parameters and information on the diffusion data are summarized in Table 1.

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Table 1. Overview of diffusion data.

https://doi.org/10.1371/journal.pbio.3000810.t001

Reconstruction of ILF, MdLF, and IFOF

Our first aim was to reconstruct the three major longitudinal tracts running through the temporal cortex in all four species: the ILF, MdLF, and IFOF (sometimes referred to as extreme capsule fiber complex [13]). Given that these tracts have not been previously described in chimpanzee and gorilla, we developed an unbiased connectivity-based clustering approach to define tractography seed and waypoint masks in the temporal lobe white matter of all species. We established and validated the approach using human and macaque data because the tracts have been previously identified in these two species [19,22,63]. Briefly, our approach was divided into two steps. We first used a connectivity-based parcellation of three coronal ROIs in the temporal lobe white matter of each individual to identify the bodies of each of the longitudinal tracts based on their connectivity profile to the rest of the brain (Fig 9A). The clusters obtained, belonging to our tracts of interests, were subsequently used in the second step of tract reconstruction as seed and waypoint masks (Fig 9B).

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Fig 9. Methods overview.

The macaque is shown as the example; the same methods were used for the other species, except that postmortem analyses were fully performed in subject space. (A) Connectivity-based clustering: (a) In each hemisphere, we defined three coronal ROIs in the white matter of the temporal lobe (excluding the area of the cingulum bundle). We transformed these ROIs to the diffusion space of each individual. (b) We then performed tractography from all the voxels in the ROI to the rest of the voxels in the whole brain. (c) We then computed the similarity matrix, representing the similarity in whole-brain connectivity of all voxels’ in each ROI. (d) We clustered the similarity matrix using the k-means algorithm, which resulted in clusters in the white matter ROI showing which voxels have similar connectivity to the rest of the brain. (e) These clusters were back projected onto each individual brain and transformed to standard space to give what we refer to as the “clustering results” (detailed in Figs 10 and 11). (B) Probabilistic tractography: from the clusters obtained at the clustering step, we established the masks for the tract reconstruction (detailed in Figs 10 and 11). Probabilistic tractography was performed as follows: streamlines were sent from the seed and were kept only if they went through one of the waypoints. The resulting tractograms were averaged across individuals. IFOF, inferior fronto-occipital fascicle; ILF, inferior longitudinal fascicle; MdLF, middle longitudinal fascicle; ROI, region of interest.

https://doi.org/10.1371/journal.pbio.3000810.g009

The clustering step was first applied in the macaque and human to validate the approach and was performed as follows. For both species, we defined three ROI masks (anterior, middle, and posterior) in standard space (MNI152 1 mm for humans and F99 0.5 mm for macaques), each composed of three consecutive coronal sections and containing only the temporal lobe white matter (Fig 9Aa). The area corresponding to the body of the cingulum bundle was excluded. These ROI masks were transformed to each subject’s diffusion space prior to performing probabilistic tractography using FSL’s probtrackx from all the voxels in the ROI to all the voxels in the brain. This completely unconstrained tractography is part of the clustering step and is not intended as a method to define the whole tracts but to set up constraints for the actual tractography performed in the later tract reconstruction step. We thus obtained for each ROI a matrix of dimensions “number of voxels in ROI” × “number of voxels in the brain,” representing the connectivity of each voxel in the white matter ROI to the rest of the brain, and transformed it back to standard space (Fig 9Ab). From this matrix, we derived a similarity matrix of dimensions “number of voxels in ROI” × “number of voxels in ROI,” representing the similarity between each white matter ROI voxels regarding their connectivity to the rest of the brain (Fig 9Ac). The three similarity matrices (one for each ROI: anterior, middle, and posterior) were each submitted to k-means clustering to group together those voxels that have a similar profile of connections to the rest of the brain (Fig 9Ad) [64,65]. The logic of this approach is that all voxels belonging to the same fiber tract will show a similar connectivity profile, which enables us to infer each tract’s most likely location in the temporal white matter ROIs.

From the current knowledge of tract anatomy in the temporal lobe [19,22,27], we could then determine which cluster belonged to each tract for each subject. In macaques, we set three clusters in the middle and posterior ROI, which was presumed to reconstruct MdLF, ILF, and IFOF, except for the most anterior slice, in which we set it to four clusters to account for the presence of the uncinate fascicle. These assumptions are based on previous tract tracing literature [66]. In humans, we set one more cluster in each ROI because it has been shown that the ILF could be subdivided [19,25]. We justify a lower number of clusters in macaques by hypothesizing a more complex organization of temporal lobe white matter in humans than in macaque. This hypothesis has been verified by various measures that we detail in S2 Text and illustrate in S8 Fig.

After identifying which cluster belonged to each tract for all individuals in one species, we then added the clusters belonging to the same tract in each coronal ROI to obtain a probability map of cluster position for each tract and ROI. Thus, we obtained probability maps for anterior, middle, and posterior positions of each tract. We refer to these probability maps as “clustering results” (Figs 9Ae and 10, top row). Based on these maps, we defined the seed (from middle position) and waypoint masks (from anterior and posterior positions) for the subsequent tract reconstruction (Fig 10, bottom row). All the masks for a given species were made of the same number of voxels across tracts, and masks from different tracts did not overlap.

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Fig 10.

Tractography masks (bottom row) derived from clustering results (top row) in macaques and humans. (A) In macaques, the clustering results are thresholded to show the overlap between at least two subjects out of four. (B) In humans, the clustering results are thresholded to show the overlap between at least four subjects out of 10. Blue, MdLF; green, IFOF; yellow, ILF (macaque) or ILFmed (humans); red, ILFlat. R denotes right hemisphere. Human data are available from the Human Connectome Project (www.humanconnectome.org). Macaque postmortem data are available from the PRIME-DE repository (http://fcon_1000.projects.nitrc.org/indi/PRIME/oxford2.html). IFOF, inferior fronto-occipital fascicle; ILF, inferior longitudinal fascicle; ILFlat, inferior longitudinal fascicle lateral; ILFmed, inferior longitudinal fascicle medial; MdLF, middle longitudinal fascicle; PRIME-DE, Primate Data Exchange.

https://doi.org/10.1371/journal.pbio.3000810.g010

Following the clustering step to identify the bodies of all tracts in three different positions along the anteroposterior axis, we reconstructed, in a second step, each tract by performing probabilistic tractography informed by the clustering step. For each tract, the tractography started from the middle seed mask defined in the previous step and was constrained by two waypoints that were anterior and posterior to the seed and also defined in the clustering step. Probabilistic tractography, as implemented in FSL’s probtrackx algorithm, started streamlines in the seed, and each streamline then followed local orientations sampled from the posterior distribution given by bedpostX [67]; only streamlines that passed through one or the other waypoint were retained. To further constrain the tractography, we also defined exclusion masks based on the anatomical description of the tracts in previous literature. Streamlines hitting these exclusion masks were deleted (Fig 9B). The exclusion masks were drawn as follows: for all tracts, we used sagittal midline sections to avoid tract crossing to the other hemisphere; a dorsal exclusion mask to avoid self–back projection from the longitudinal tracts; and exclusion masks for the fornix, cingulum bundle, and AF seed (defined as described below), as well as the medial section of the optic radiation and the temporal section of the uncinate fascicle. For each tract, waypoints and seeds of the other tracts were also used as exclusion masks. Subcomponents of ILF, however, were not used as exclusion masks for each other. To avoid tracts erroneously crossing gray matter, we also used a termination mask consisting of the entire gray matter defined in subject space (using FAST from FSL [68] for macaques and using the individual surface for humans). When reaching these termination masks, the streamlines were terminated but not deleted. Because the macaques’ gray matter masks were less precise, we added an exclusion mask in insular cortex and putamen. The resulting tractogram was normalized for each subject by dividing its path distribution by the total number of generated streamlines not rejected by waypoint or exclusion mask criteria. We then averaged the tract distribution over all subjects and down-sampled the average tractogram to 1-mm resolution (isotropic voxels) in F99 space for macaques and 2 mm in MNI152 space for humans. For visualization, 3D renderings of the log-transformed and thresholded average tractograms were generated using in-house Matlab code.

Having performed these analyses for the human and the macaque, we then applied the same procedure to the chimpanzee and gorilla data. The in vivo chimpanzee data were treated exactly as the human and macaque data. We performed a clustering procedure by setting the number of clusters to four because of the similar anatomy of the temporal lobe gyri and sulci between humans, gorillas, and chimpanzees. Indeed, although previous studies have shown differences in the gyral pattern between hemisphere, including notably an asymmetry of the Sylvian fissure and occipital bending between chimpanzees and humans [69], their overall sulci pattern remains more similar than compared with the macaque, which lacks defined middle temporal and fusiform gyri [1]. We performed a similar analysis as in humans and macaques to confirm that this was an optimal number of clusters (S2 Text, S8 Fig). For the chimpanzees, the measures used pointed toward the solution using a high number of clusters. However, for the gorilla, the different measures do not uniformly point toward solutions using a high or low number of clusters, and having only one hemisphere available for this species, we cannot strongly conclude on this issue (S2 Text, S8 Fig).

In this manner, we were able to define four tracts in the temporal lobe: the IFOF, MdLF, ILFlat, and ILFmed. From the clustering results (Fig 11, top rows), we defined the masks used in the tract reconstruction (Fig 11, bottom rows). The probabilistic tractography algorithm used the same exclusion masks as described for the macaques as well as the gray matter termination masks. The tract distributions obtained were averaged over three subjects and down-sampled to 1-mm resolution in Yerkes29 space. For the two postmortem ape samples (gorilla and chimpanzee), we used the same procedure as described above, except that all the analyses were only performed in the diffusion space of each subject and not transformed to a standard space because only one sample per species was available. Again, for visualization, 3D renderings of the log-transformed and thresholded average tractograms were generated using Matlab code made in-house. We also generated a surface representation of the tracts in great apes using tools from Connectome Workbench [70]. For the in vivo chimpanzees, we projected the averaged tracts to the Yerkes29 template (previously created from 29 chimpanzee scans) and we used the respective individual surfaces in T1 space for the postmortem gorilla and chimpanzee.

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Fig 11.

Tractography masks (bottom row) derived from clustering results (top row) in great apes. In chimpanzee in vivo, the clustering results show the three subjects overlapped (no thresholding is applied). One subject was available for each of the chimpanzee and gorilla postmortem samples. Blue, MdLF; green, IFOF; yellow, ILFmed; red, ILFlat. R denotes right hemisphere. In vivo chimpanzee data are available from the National Chimpanzee Brain Resource (www.chimpanzeebrain.org). Gorilla and chimpanzee postmortem data are available from https://doi.org/10.5281/zenodo.3901205. IFOF, inferior fronto-occipital fascicle; ILF, inferior longitudinal fascicle; ILFlat, inferior longitudinal fascicle lateral; ILFmed, inferior longitudinal fascicle medial; MdLF, middle longitudinal fascicle.

https://doi.org/10.1371/journal.pbio.3000810.g011

For the tract reconstruction, we used the same tractography settings in all species (10,000 samples, 2,000 steps, curvature threshold 0.2), except for the step length to account for differences in brain size and relative resolution (0.5 mm in humans and chimpanzees in vivo, 0.3 in gorilla and chimpanzee postmortem, and 0.25 in macaque postmortem).

Reconstruction of AF

The AF is a dorsal longitudinal tract that runs from the inferior frontal cortex in a posterior direction and, in the human brain, curves ventrally to terminate in the temporal cortex. Because of its variability between species [15], we could not use the temporal cortex coronal ROI sections to identify it in all species with the clustering approach outlined above. We therefore adapted the method used previously by Eichert and colleagues [14] to reconstruct the tract from a manually defined seed outside the temporal cortex, just posterior to the level of the central sulcus (Fig 5 top rows). We also placed an axial mask as a posterior waypoint in the parietal–temporal white matter posterior to the caudal end of the Sylvian fissure. The mask was placed in a comparable location in all species to not bias the tract’s posterior course (Fig 5, top rows). To make the tractography comparable to that of the other longitudinal tracts outlined above, we added an anterior waypoint to the protocol of Eichert and colleagues [14], allowing us to track from a middle seed to anterior and posterior waypoints as well. We used the same exclusion masks for all species as in Eichert and colleagues [14]: a midline mask, two subcortical white matter and extreme/external capsule masks, and one mask through the superior parietal cortex.

In the great apes, we examined where the tractogram approached the gray/white matter border to represent their tractograms on the cortical surface (Fig 4, S3 Fig). We normalized, log transformed, smoothed with a kernel of 2 mm full-width half maximum (FWHM), and thresholded these surface tractograms.

The clustering and tract reconstruction steps have also been performed with alternative tractography techniques to validate our approach (S2 Text, S10–S14 Figs).

Comparison of macaque and human temporal lobe connectivity blueprints

To establish the effects of any differences in the longitudinal temporal tracts across species on the connectivity profiles of temporal cortex gray matter, we established the white matter connectivity of each part of the temporal cortex gray matter, the so-called “connectivity blueprint,” using the method established by Mars and colleagues (2018) [20]. The connectivity blueprint is in effect a matrix that describes how well each vertex of the temporal lobe gray/white matter border, as defined in the surface, is reached by each of the white matter tracts of the temporal lobe. Each row of this matrix describes the “connectivity fingerprint” of each vertex of the temporal lobe with each white matter tract (Fig 12A). Therefore, the connectivity fingerprint is a subset of the connectivity blueprint focusing on a particular vertex instead of the whole brain. Each column of the blueprint matrix corresponds to one tract. Because the tracts are defined in a similar way in the different species, they present a common space that allows us to compare connectivity fingerprints across the two brains, testing whether there are unique connectivity fingerprints in one brain compared with the other.

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Fig 12. Blueprint method to assess connectivity divergence between macaque and human temporal cortex.

(A) To compute the blueprint, we multiplied a matrix representing the connectivity of each cortical vertex (surface space) to each brain voxel (volume space) with a matrix representing how each brain voxel (volume space) is reached by each tract. Because we reconstructed the same tracts in both macaques and humans, the columns of the blueprint define a common space to compare the connectivity of the temporal cortex. The rows of the blueprint correspond to the connectivity fingerprints of each vertex. The columns of the matrix correspond to the surface tractograms. (B) Using the KL divergence measure to compare every vertex in one brain to every vertex in the other brain, we can identify vertices in the two brains in which the smallest divergence is found. If this smallest divergence is very low (green on the scale), it means that the same connectivity profile can be found at these vertices for both brains. If the smallest divergence is very high (pink on the scale), it means that the connectivity profile at this vertex is very different to any connectivity profile of the other brain vertices. (C) Illustration of the KL divergence computation for two macaque vertices. For each of the vertices, a connectivity fingerprint representing how the vertex is connected to each tract was computed. Using a KL divergence measure, this connectivity fingerprint was compared with connectivity fingerprints of all human vertices. The vertex with the connectivity fingerprint most similar to the macaque one, therefore with the smallest KL divergence, is picked, and this KL divergence value is assigned to the monkey vertex. AF, arcuate fascicle; cbt, temporal part of the cingulum bundle; fx, fornix; IFOF, inferior fronto-occipital fascicle; ILF, inferior longitudinal fascicle; KL, Kullback–Liebler; MdLF, middle longitudinal fascicle; or, optic radiation; unc, uncinate fascicle.

https://doi.org/10.1371/journal.pbio.3000810.g012

We directly compared these blueprints between the macaque and the human brain (Fig 12). We focused on these two species because they are the most studied species among the ones presented in the current study. Furthermore, the course and connectivity of other nonlongitudinal tracts reaching the temporal lobe, such as the uncinate fascicle, the cingulum bundle, and the fornix, have been well established in these species [20,22].

We established the connectivity blueprint using the tractograms of ILF, MdLF, IFOF, and AF as established above and supplemented them with tractograms of the other major white matter tracts in the temporal lobe—namely, the temporal part of the cingulum bundle, the fornix, the optic radiation, and uncinate fascicle—using established tractography protocols [20]. A tractogram can be represented as a column vector describing how well each voxel is reached by the tract (Fig 12A). By concatenating the vectors corresponding to each tract, we obtained a matrix representing how each voxel (row) is reached by each tract (column). Second, we also calculated whole-hemisphere vertexwise connectivity matrices obtained by tracking from all vertices in the gray/white matter border of the surface file to all voxels in the brain in volumetric space (Fig 12A). These matrices were computed for both hemispheres and for each subject individually before creating an average for each hemisphere and species. Then, we multiplied the tractogram’s matrix with this averaged matrix in both species, obtaining the blueprint matrix (Fig 12A). We also individually extracted each column of this matrix to obtain a surface representation of the different tracts, which we will refer to as “surface projections.”

The connectivity blueprints of different species can be compared by calculating the KL divergence between the connectivity profiles of each vertex in one brain with that of each vertex in the other brain (Fig 12B). The KL divergence measures how the probability distribution of likelihoods of one vertex to be reached by each of the temporal lobe tracts is different in one brain from the probability distribution of each vertex in the other brain. For each vertex in one brain, we could then determine another vertex in the other brain that had the minimum KL divergence—in other words, the most similar connectivity profile. If a vertex in one species has a very similar connectivity profile to that of a vertex in the other brain, meaning that they are reached by a similar combination of tracts, the minimum KL divergence will be low. If, on the other hand, a vertex’s fingerprint connectivity profile is unique in one brain, its minimum KL divergence will be high (Fig 12C).

We first defined the blueprints based on the well-established tracts: the cingulum bundle, the fornix, the optical radiation, and the uncinate fascicle. Because we did not expect these tracts to be massively different between the two species, we expected the KL to be very low and, therefore, this comparison to serve as a reference point for the following blueprint comparisons. Then, we added the tracts of interest: MdLF, ILF, IFOF, and AF. To assess the reorganization of the human ILF subcomponents, we generated blueprints with both ILF subcomponents combined or either one or the other while keeping the macaque ILF constant. This manipulation enabled us to compare the ILF subcomponents’ distinct prediction of the macaque brain organization. We compared these maps visually by subtracting the KL divergence scores spatial map established with human ILFmed from the one established with human ILFlat. Furthermore, we masked the blueprint to only focus on the temporal, occipital, and inferior parietal lobes. To assess the divergence between species more quantitively, we obtained the distribution of the minimum KL divergence scores obtained with each blueprint: with nonlongitudinal tracts; adding MdLF, ILF, and IFOF; adding the AF; with the human ILFlat; and finally, with the human ILFmed (Fig 6D). We included only nonzero values, which are the vertices with differences in their connectivity fingerprints. Each distribution of minimum KL divergence scores indicates how similar the connectivity fingerprints of this brain are with that of another brain. All distributions were then compared using the two-sample Kolmogorov–Smirnov test (with p < 0.01).

For illustration, but not for establishing the blueprint, we smoothed the surface tracts with a kernel of 2 mm FWHM for macaques and 4 mm for humans. We then log transformed and normalized the surface tract distributions to facilitate the threshold determination.