Cross-species standardised cortico-subcortical tractography

Function-specific brain activity involves the integration of information from multiple remote brain regions. This integration is enabled by white matter (WM) bundles interconnecting different brain regions (Passingham et al., 2002; Mars et al., 2018a; Thiebaut de Schotten and Forkel, 2022; Pessoa, 2023). Of particular interest and importance are bundles connecting cortical areas with deep brain structures. Subcortical structures have important roles in affective, cognitive, motor, and social functions (Utter and Basso, 2008; Bickart et al., 2011; Berridge and Kringelbach, 2015), which emerge through interactions with cortical areas that such connections enable (Haber, 2016; Chumin et al., 2022; Bullock et al., 2022). Hence, the variability of these connections between individuals has been linked to differences in behavioural traits (Cohen et al., 2009; Forstmann et al., 2010; Forkel et al., 2022). Furthermore, their disruption has been associated with abnormal function and pathology in neurodegenerative and mental health disorders (Haber and Behrens, 2014; Heller, 2016; Haber et al., 2023; Weerasekera et al., 2024). In the clinic, individual variability in cortico-subcortical connectivity has been used to assist presurgical planning and predict personalised targets for efficacious interventions (Akram et al., 2017).

Chemical tracer studies in the non-human primate (NHP) brain have provided, and continue to provide, invaluable insights into cortico-subcortical connections and neuroanatomy in general (Heilbronner and Chafee, 2019). Examples include tracing of cortico-striatal connections (Lehman et al., 2011; Heilbronner and Haber, 2014; Haber, 2016; Safadi et al., 2018), amygdalofugal (AMF) connections (Oler et al., 2017), and thalamo-cortical connections (Yoshida and Benevento, 1981; Lehman et al., 2011). Comparative neuroanatomy studies can subsequently explore and translate principles of WM organisation learnt from NHPs to humans (Jbabdi et al., 2013; Safadi et al., 2018; Folloni et al., 2019). Brain imaging and, in particular, diffusion magnetic resonance imaging (dMRI) tractography (Jbabdi et al., 2015) is a crucial component in these comparative studies and beyond (Sotiropoulos et al., 2013; Assimopoulos et al., 2024; Sotiropoulos et al., 2025), allowing non-invasive mapping of these connections in the living human.

Towards this direction, recent dMRI-based frameworks have been developed to map respective WM bundles (tracts) across NHPs and humans (Warrington et al., 2020; Roumazeilles et al., 2020; Bryant et al., 2020; Bryant et al., 2021; Assimopoulos et al., 2024). These rely on standardised dMRI tractography protocols, comprising functionally driven, rather than geometric, definitions, enabling automated and generalisable mapping of homologous major WM tracts across species (Warrington et al., 2020). These developments have allowed cross-species neuroanatomy studies (Mars et al., 2018b; Mars et al., 2021) and mapping of connections across humans to study links with brain development, function, and dysfunction (Thiebaut de Schotten et al., 2020; Warrington et al., 2022). A current limitation of these imaging-based approaches is that they have mainly focused so far on cortico-cortical bundles (with the exception of cortico-thalamic connections).

Tractography protocols for WM bundles that reach deeper subcortical regions, for instance the striatum or the amygdala, are more difficult to standardise. The relative size and proximity of these bundles, and the WM complexities and bottlenecks they go through, can make their mapping through dMRI particularly challenging. As a consequence, considerably fewer studies have proposed solutions for their reproducible reconstruction, both within and across primate species, compared to more major cortico-cortical bundles (Wassermann et al., 2016; Wasserthal et al., 2018; Warrington et al., 2020; Maffei et al., 2021). Some existing studies have focused on cortico-striatal bundles (Forkel et al., 2014; Schilling et al., 2020), uncinate and AMF fasciculi (Folloni et al., 2019), parts of the extreme capsule (Mars et al., 2016), and the anterior limb of the internal capsule (Jbabdi et al., 2013; Safadi et al., 2018). However, these either utilise labour-intensive single-subject protocols (Safadi et al., 2018; Folloni et al., 2019), are not designed to be generalisable across species (Forkel et al., 2014; Schilling et al., 2020), or are based mostly on geometrically driven parcellations that do not necessarily preserve topographical principles of connections (Wasserthal et al., 2018). We propose an approach that addresses these challenges and is automated, standardised, generalisable across two species and includes a larger set of cortico-subcortical bundles than considered before, yielding tractography reconstructions that are driven by neuroanatomical constraints.

Specifically, we build upon our previous work on FSL-XTRACT (Mars et al., 2018b; Warrington et al., 2020; Assimopoulos et al., 2024) to propose standardised protocols and an end-to-end framework for automated cortico-subcortical tractography in the macaque and human brain, considering connections between the cortex and the caudate, putamen, and amygdala. To this end, we use prior anatomical knowledge from NHP tracers to define new generalisable protocols, including the AMF tract, the Muratoff bundle (MB) and the striatal bundle (external capsule) with its frontal, sensorimotor, temporal, and parietal parts, augmenting our previous protocols for hippocampal and thalamic tracts (Warrington et al., 2020). Due to their close proximity, we also develop new protocols for the respective extreme capsule parts (frontal, temporal, and parietal) and revise previously released protocols (Warrington et al., 2020) for the uncinate fasciculus (UF), anterior commissure (AC), and fornix (FX).

We demonstrate the mapping of the respective bundles in the human and macaque brain and show that tractography reconstructions follow topographical principles obtained from tracers. We show that the proposed definitions are robust against dMRI data quality and preserve aspects of individual variability stemming from family structure in humans, as reflected by higher similarity of reconstructed tracts in the brains of monozygotic twins compared to non-twin siblings and unrelated subjects. We subsequently demonstrate how these tractography reconstructions can improve the identification of homologous grey matter (GM) regions across species, both in cortex and subcortex, on the basis of similarity of GM areal connection patterns to the set of proposed WM bundles (Passingham et al., 2002; Mars et al., 2018b).

Using prior anatomical knowledge from tracer studies in the macaque, we developed new tractography protocols for the macaque brain and subsequently translated them to the human brain. We considered 23 tracts in total (11 bilateral, 1 commissural), which included tracts connecting the cortex to the amygdala, caudate, and putamen. Specifically, we developed protocols for the $AMF$ pathway, the sensorimotor, frontal, temporal, and parietal parts of the striatal bundle/external capsule ($St{B}_m,St{B}_f,St{B}_t,St{B}_p$), and the $MB$. Due to their proximity, we also developed protocols for the frontal, temporal, and parietal parts of the extreme capsule ($Em{C}_f$, $Em{C}_t$, $Em{C}_p$) (neighbouring to the corresponding external capsule parts), and revised previous protocols for the $\mathit{UF}$ (neighbouring to the $\mathit{AMF}$), the $\mathit{FX}$ (output tract of the hippocampus next to the amygdala), as well as the $AC$ (Table 1, Appendix 1—table 1).

We used the XTRACT approach (Warrington et al., 2020) to define tractography protocols, governed by two principles: (1) protocols are comprised of seed/stop/target/exclusion regions of interest (ROIs) defined in template space, so that they are standardised and generalisable (compared to subject-specific protocols), and (2) ROIs are coarse enough and defined equivalently between macaques and humans to enable the tracking of corresponding bundles across species. Full tractography protocols, and modifications to existing protocols, are described in detail in Methods. Protocols were defined in MNI152 template space for human tractography and F99 space (Van Essen, 2002; Glasser and Van Essen, 2011) for macaque tractography. Additionally, we generalised the macaque protocols to the NIMH Macaque Template (NMT v2) (Seidlitz et al., 2018). For clarity, results shown in the main text use the F99 space protocols. Comparison between results in NMT and F99 space can be seen in Appendix 1—figure 3.

Subcortical tract reconstruction across species and comparisons with tracers

Using dMRI data from the macaque ($N=6$) and human brain ($N=50$) and the defined protocols, we performed tractography reconstructions for all the tracts of interest. Maximum intensity projections of the resultant group-averaged tract reconstructions for the macaque and human are shown colour-coded in Figure 1 (individual tracts can be seen in Appendix 1—figure 1). These reveal overall correspondence in the main bodies of tracts across species, while capturing differences in (sub)cortical projections.

Figure 1

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We explored whether WM organisation principles known from the tracer literature are captured in these tractography reconstructions (Bullock et al., 2022; Lehman et al., 2011; Schmahmann and Pandya, 2006; Makris and Pandya, 2009; Choi et al., 2017a; Liu et al., 2020). For instance, the striatal bundle ($\mathit{StB}$)/external capsule is always medial to the extreme capsule and the MB runs along the head of the caudate nucleus. Figure 2A shows the relative positioning for $\mathit{St}{B}_f$, $\mathit{Em}{C}_f$, and $\mathit{MB}$ bundles. Correspondence between tractography results and tract tracing reconstruction in the macaque can be observed, with their relative positions being preserved. This relative position was preserved in the human tractography results as well. Furthermore, the medio-lateral separation is also observed in the other parts of $\mathit{StB}$ and $\mathit{EmC}$ (i.e. parietal and temporal), as shown in Appendix 1—figure 2. Similarly, for the $\mathit{AMF}$ bundle (Figure 2B), this runs through the AC and ventral pallidum, as well as, in its lateral part, over the $\mathit{UF}$. We see agreement with respect to these relative positions in both the macaque and human.

Figure 2

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In addition to the main WM core of the reconstructed bundles, we also explored agreement of the relative connectivity patterns within the striatum between tracers and tractography. Cortical injections of anterograde tracers from different parts of the macaque brain reveal a dorsolateral to ventromedial organisation in the putamen, from parietal to temporal projections (Figure 3). Using the path distribution of the tractography reconstructed $\mathit{StB}$ parts within the putamen, we could obtain a similar pattern in the macaque brain. This also resembled the pattern found in the human brain, as shown in both coronal and axial views.

Figure 3

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Generalisability across data and individuals

We subsequently explored generalisability and robustness of the tractography protocols against NHP template spaces and dMRI data quality. Appendix 1—figure 3 shows tract reconstructions in the macaque brain when using the F99 (Van Essen, 2002) vs the NMT (Seidlitz et al., 2018) templates, similar tractography reconstructions for protocols defined in either of the two templates.

To explore performance against data quality, we compared tractography reconstruction in very high-quality high-resolution data from the Human Connectome Project (HCP) (Van Essen et al., 2013; Sotiropoulos et al., 2013), to tractography in more standard quality data from the UK Biobank dataset (Miller et al., 2016). Appendix 1—figure 4 demonstrates the ability to reconstruct all tracts across a range of data qualities, with good correspondence of the main bodies of the tracts in both datasets. We quantified this agreement by calculating the mean Pearson’s correlation across the set of new and revised tracts for each unique pair of subjects across and within each of the HCP and UK Biobank (UKB) datasets (Figure 4A). For reference, we performed similar correlations for the original set of XTRACT tracts (Warrington et al., 2020) (see Appendix 1—table 1 for a list of Original vs New + Revised tracts). Higher correlation was observed within each dataset, but also a sufficiently high correlation between the two datasets. We found similar patterns across datasets both for the original and the new tracts, showcasing that the new protocols behave similarly to the widely used original XTRACT protocols, across data qualities.

Figure 4

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The mean agreement between HCP and UKB reconstructions was lower compared to within-dataset agreements. The two cohorts correspond to different age ranges, with HCP having younger adults than the UKB, which could be contributing to these differences. In addition, this was due to occasionally reconstructing a sparser path distribution in the low-resolution data, particularly for some of the new tracts, as both their relative size and their proximity make them more challenging. This highlights the potential importance of having high-resolution data in tracking WM bundles in densely packed areas of higher complexity. It is interesting to note, however, that similar tracts were less/more reproducible between subjects across data qualities. For the lower quality data from the UKB, the tracts with lowest agreement across subjects (Pessoa, 2023) were the $\mathit{AC}$ and the temporal part of the extreme capsule ($\mathit{Em}{C}_t$), while the highest correlations were for the $\mathit{MB}$ and the temporal part of the striatal bundle ($\mathit{St}{B}_t$). For the higher quality HCP data, the temporal part of the extreme capsule ($\mathit{Em}{C}_t$) and the $MB$ were also the tracts with the lowest/highest correlations across subjects, respectively. Hence, certain tract reconstructions were consistently more variable than others across subjects, which may hint at also being more challenging to reconstruct. Taken together, despite differences, our results suggest all tracts could be reconstructed across both data qualities in a generalisable manner (Appendix 1—figure 4).

We subsequently explored whether the proposed protocols preserve aspects of individual variability. We used the family structure in the HCP data to explore whether tract reconstructions from monozygotic twin pairs are more similar compared to tracts obtained from other pairs of siblings or unrelated subjects. As shown in Figure 4B, we found a decrease in pairwise tract similarity going from monozygotic twins to dizygotic twins and non-twin siblings, and to pairs of unrelated subjects. For reference, we performed the same analysis for the original XTRACT tracts and the same pattern persisted for the new (and revised) tracts, in agreement with previous work (Bohlken et al., 2014; Shen et al., 2014; Warrington et al., 2020). In each analysis, all pairwise differences were significant (Bonferroni corrected $p<0.05$; following a Mann–Whitney U-test), with the exception of dizygotic twins compared to non-twin siblings.

Examples of tract reconstructions on individual subjects are shown in Appendix 1—figure 6, Appendix 1—figure 7. The figures demonstrate tractography results for subjects corresponding to 10th, 50th, and 90th percentiles of the distribution of tract correlations to the HCP group average. This ranking was also representative of high, medium, and low subject motion across the cohort, respectively. Results demonstrate that the expected patterns are preserved for all tracts ($\mathit{MB}$, $\mathit{AMF}$, $\mathit{UF}$, $\mathit{EmC}$, $\mathit{StB}$ (frontal and parietal parts)). Appendix 1—figure 6 shows that even the relative medial–lateral organisation of the $\mathit{StB}$ with respect to the $\mathit{EmC}$ is also maintained across the three individual examples, in agreement with the group-average pattern.

Identifying homologues in cortex and subcortex using tractography patterns

Based on our previous work (Mars et al., 2018b), we used the similarity of areal connectivity patterns with respect to equivalently defined WM tracts across the two species to identify homologous GM regions between humans and macaques. With the addition of the new subcortical tracts, we could perform this task for deep brain structures (subcortical nuclei and hippocampus) with considerably greater granularity than before. Figure 5 demonstrates such identification task for five structures in the left hemisphere (caudate, putamen, thalamus, amygdala, hippocampus) using cortico-cortical and cortico-subcortical tracts (sets of tracts defined in Appendix 1—table 1). On the left, the regions in the macaque brain with the lowest divergence (highest similarity) in their connectivity patterns to the connectivity patterns of the corresponding human regions are shown in blue. Using only connectivity pattern similarity, these five structures can be matched almost perfectly across the two species. For instance, human putamen (left hemisphere) has more similar connectivity (lower divergence) to macaque putamen (left hemisphere), human thalamus (left hemisphere) to macaque thalamus (left hemisphere), etc. Since we are mapping structures in the left hemisphere using left hemisphere tracts, we observe a low similarity in the contralateral (right) hemisphere, as expected. On the right of Figure 5, this identification is quantified even further, highlighting the value of considering the new tracts. For every human left-hemisphere region (specified on the vertical axis), the boxplot of divergence of connectivity patterns to each of the five macaque deep brain regions (left hemisphere) is plotted. The best match corresponds to the boxplot with the lowest values (green) and the dashed blue lines show the medians of these boxplots for each case. For reference, the medians of the divergence values when not considering the new subcortical tracts are shown with the red dashed lines, which are overall more flat (with the exception of the hippocampus which has a connectivity pattern strongly driven by the dorsal subsection of the cingulum bundle ($\mathit{CBD}$), a cortico-cortical tract). It is evident that considering the new tracts provides enhanced contrast between the subcortical structures’ connectivity patterns, enabling their correct identification. The improvement is thus not in the best match, but in the specificity of the match.

Figure 5

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Having shown increased contrast and specificity in the mapping of deep brain structures, we investigated whether we see a similar effect in the cortex. We selected a set of nearby frontal region pairs to map across the human and the macaque (Figure 6), since a number of the new tracts connect frontal regions to the subcortex. Specifically, we considered the dorsomedial prefrontal cortex ($dmPFC$), the ventromedial prefrontal cortex ($\mathit{vmPFC}$), the rostral orbitofrontal cortex ($\mathit{OF}{C}_r$), and the frontal operculum ($FOp$). These regions were also chosen as they are part of different functional networks (default mode, limbic, and frontoparietal networks), equivalently defined between the macaque and human (Thomas Yeo et al., 2011).

Figure 6

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The prediction from human to macaque is shown in Figure 6A, while the converse prediction from macaque to human is shown in Figure 6B. In each case, we compare the prediction using only cortico-cortical tracts (column 1), using only cortico-subcortical tracts (column 2), and using the full set (column 3) (sets of tracts defined in Appendix 1—table 1 – the middle cerebellar peduncle ($\mathit{MCP}$) was not used in these comparisons). The predicted areas with the highest similarity in connectivity patterns are depicted in blue, while the a priori expected homologue region borders have been outlined in cyan. These results demonstrate benefits when using the subcortical tracts, with mapping of some regions (for instance $\mathit{vmPFC}$ and $OF{C}_r$) being improved more than others. However, in general, we observed an increase in cross-species similarity in the corresponding areas of interest, combined with a decrease in similarity everywhere else in the cortex, when we considered cortico-subcortical tracts (columns 2 and 3) compared to when we considered cortico-cortical tracts alone (column 1).

Figure 7 provides a further insight into these mappings, by plotting the connectivity patterns of the human regions against the pattern of its identified best match in the macaque brain. As can be observed, despite the relative proximity of these frontal regions, we have distinct patterns across them. With the exception of $\mathit{dmPFC}$, the connectivity patterns of all other regions have major contributions from the frontal striatal bundle, the extreme capsule and the AMF tract and connection patterns to these cortico-subcortical bundles enable better separation of these nearby regions. For instance, $\mathit{vmPFC}$ and $\mathit{dmPFC}$ have both connections through the cingulum bundle, the corpus callosum and the inferior fronto-occipital fasciculus. However, they connect differently to the striatal bundle, the AMF tract and the anterior thalamic radiation, and the addition of these tracts in the connectivity patterns allows the two regions to be better distinguished. The $FOp$ and $OF{C}_r$ have both relatively strong connection patterns to the uncinate and the inferior fronto-occipital fasciculi, but it is their different pattern of connections to extreme and external capsules and the AMF tract that enable their better separation.

Figure 7

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We introduced standardised dMRI tractography protocols for delineating cortico-subcortical connections between cortex and the amygdala, caudate, putamen, and the hippocampus, across humans and macaques. Building upon our previous work (Mars et al., 2018b; Warrington et al., 2020), which already provided protocols for cortico-thalamic radiations, and guided by the chemical tracer literature in the macaque, we devised the new protocols first for the macaque and then extended to humans. We demonstrated that our reconstructed tracts preserve topographical organisation principles, as suggested by tracers (Haber et al., 2006; Haber, 2016; Oldham and Ball, 2023).

As outlined in Schilling et al., 2020, tractography reconstructions can be highly accurate if information about where pathways go, and where they do not go is available. This is the philosophy behind the proposed protocols, which provide this type of constraints across different bundles. At the same time, these constraints are relatively coarse so that they are species-generalisable. We found that the proposed approaches yield tractography reconstruction across a range of datasets and respect individual similarities stemming from twinship. We further assessed the efficacy of these protocols in performing connectivity-based identification of homologous cortical and subcortical areas across the two species (Mars et al., 2018b; Mars et al., 2021; Warrington et al., 2022).

Mapping WM tracts that link cortical areas with deep brain structures (subcortical nuclei and hippocampus), as done here, enhances capabilities for studying neuroanatomy in many contexts, from evolution and development to mental health and neuropathology. As one of the (evolutionarily) older brain structures, the subcortex modulates brain functions including basic emotions, motivation, and movement control, providing a foundation upon which the more complex cognitive abilities of the cortex could develop and evolve (Haber et al., 2006; Pennartz et al., 2009; Haber, 2016; Sherman, 2016; Cruz et al., 2023). This modulatory function is mediated via WM bundles (Haber, 2016; Chumin et al., 2022). Consequently, their disruption is linked to abnormal function and pathology, in mental health, neurodegenerative, and neurodevelopmental disorders (Heller, 2016; Peters et al., 2016; Weerasekera et al., 2024). For example, in depression, fronto-thalamic (Bhatia et al., 2018), cortico-amygdalar (Arnsten and Rubia, 2012; Jalbrzikowski et al., 2017), and cortico-striatal (van Velzen et al., 2020) connectivity changes have been reported, while in schizophrenia there are associated fronto-striatal (Levitt et al., 2017) and hippocampal connectivity (Ikeda et al., 2023) changes. In Parkinson’s, there is impairment in fronto-striatal connectivity (Theilmann et al., 2013; Von Der Heide et al., 2013; Khan et al., 2019; Marecek et al., 2024), while fronto-thalamic and cingulate connectivity are impaired in Alzheimer’s disease (Von Der Heide et al., 2013; Bubb et al., 2018). Connectivity between the frontal lobe and the amygdala, thalamus, and striatum, as well as cingulum connectivity, is impaired in obsessive compulsive disorder, autism spectrum disorder, and attention deficit hyperactivity disorder (Langen et al., 2012; Arnsten and Rubia, 2012; Haber and Behrens, 2014; Kilroy et al., 2022). Therefore, reconstructing connectivity of these deep brain structures (striatum, thalamus, amygdala, and hippocampus) in a standardised manner, as enabled by our proposed tools, allows for further investigation into a wide range of disorders.

In addition, tractography of connections linking to/from deep brain structures has been used or proposed for guiding neuromodulation interventions, for example, deep brain stimulation (DBS) (Haber et al., 2021; Alagapan et al., 2023) or repetitive transcranial magnetic stimulation (rTMS) (Peters et al., 2016). DBS can inherently target subcortical structures and connectivity of subcortical circuits can be used to identify efficacious stimulation targets (Pouratian et al., 2011; Akram et al., 2017). rTMS, on the other hand, modulates subcortical function indirectly by targeting the structurally connected cortical areas. For example, dmPFC has been targeted to modulate the reward circuitry, in cases of anhedonia, negative symptoms in schizophrenia and major depression disorder (Dunlop et al., 2020; Gan et al., 2021; Bodén et al., 2021), while the vmPFC has been used as a target to modulate the prefrontal–striatal network (part of the limbic system) and regulate emotional arousal/anxiety (Chen et al., 2020; Kroker et al., 2022; Moses et al., 2025). Our results show a good mapping across species of both these cortical regions with specificity in their connectional patterns. Additionally, the motor cortex has been used as a target to modulate cortico-striatal connectivity in general anxiety disorder (Balderston et al., 2020; Fitzsimmons et al., 2024). We thus anticipate that having a standardised set of tracts linking the striatum, the hippocampus, the amygdala and the thalamus (all potential sites for stimulation) to specific cortical areas can assist the planning of interventions.

Our cross-species approach naturally lends itself to the study of evolutionary diversity. A number of comparative studies have revealed differences and similarities when comparing brain connectivity between humans and non-human primates (Barrett et al., 2020), including macaques (Mars et al., 2018b; Warrington et al., 2022) and chimpanzees (Bryant et al., 2025). Our work naturally extends these efforts and provides new tools for studying this diversity in deeper structures and subcortical nuclei. The ever-increasing availability of comparative MRI data (Bryant et al., 2021; Tendler et al., 2022) allows the definition of similar protocols in more species, such as the gibbon (Bryant et al., 2020; Bryant et al., 2024) or the marmoset monkey, and even across geometrically diverse brains depicting different stages of neurodevelopment (e.g. neonates vs adults) enabling concurrent studies of phylogeny and ontogeny (Warrington et al., 2022).

Our protocols have been developed and tested using FSL-XTRACT, but, in principle, are not specific to FSL. We have not evaluated performance with other tools, but these standard-space protocols could be translated into other tractography approaches. As described before, the protocols are recipes with anatomical constraints, including regions to which the corresponding WM pathways connect and regions they do not, constructed with cross-species generalisability in mind. Caution may be needed, however, if applying such protocols for segmenting whole-brain tractograms, as these can induce more false positives than tractography reconstructions from smaller seed regions and may require stricter exclusions.

Despite the potential demonstrated in this work, our study has limitations. As this is the first endeavour of this scale to map cortico-subcortical connections in a standardised manner and across two species, it is not exhaustive. Tracts linking the cortex to the striatum were prioritised as they are of increased relevance in human development and disease. However, expanding to include more tracts targeting other structures would provide a more holistic view. Our protocols were developed in the adult human brain. Future work will translate them to the infant brain (expanding on previous work Warrington et al., 2022) to interrogate cortico-subcortical connectivity across development. Tractography validation is a challenge, as is validation for any indirect and non-invasive imaging approach. We explored and demonstrated the generalisability of the proposed protocols, both within and across species. We also showed how the imaging-based reconstructions follow topographical organisation principles suggested by tracers.

Human in vivo diffusion MRI data are publicly available (https://www.humanconnectome.org) and provided by the Human Connectome Project (HCP; https://www.humanconnectome.org), WU-Minn Consortium (Principal Investigators: David van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centres that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Centre for Systems Neuroscience at Washington University (Van Essen et al., 2013). The UK Biobank (https://www.ukbiobank.ac.uk) data were used under UK Biobank Project 43822 (PI: Sotiropoulos). Macaque data are openly available (https://fcon_1000.projects.nitrc.org/indi/PRIME/oxford2.html) and provided via the PRIMatE Data Exchange (http://fcon_1000.projects.nitrc.org/indi/PRIME/oxford2.html) (Milham et al., 2018). Tractography protocols (https://github.com/SPMIC-UoN/xtract_data copy archived at Warrington, 2024) and white matter tract atlases (https://github.com/SPMIC-UoN/XTRACT_atlases copy archived at Warrington, 2021) are made available on GitHub. Tools for performing standardised and automated tractography (XTRACT), building connectivity blueprints (xtract blueprint), and performing divergence-based comparisons of connectivity blueprints (xtract divergence) are available on GitHub (https://github.com/SPMIC-UoN/xtract) and are released in FSL (v6.0.7.10 onwards, https://fsl.fmrib.ox.ac.uk/fsl/docs/#/diffusion/xtract). The cortico-subcortical protocols are available on GitHub (https://github.com/SPMIC-UoN/XTRACT_subcortex copy archived at Assimopoulos and Sotiropoulos, 2025).

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Author details

1. Stephania Assimopoulos

   Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7321-6297

2. Shaun Warrington

   Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom

   Contribution

   Data curation, Software, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7198-8162

3. Davide Folloni

   1. Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, United States
   2. Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Katherine Bryant

   1. Institute for Language, Cognition, and the Brain, CNRS, Université Aix-Marseille, Marseille, France
   2. Centre de Recherche en Psychologie et Neurosciences, UMR 7077, CNRS/Université Aix-Marseille, Marseille, France

   Contribution

   Resources, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1045-4543

5. Ali-Reza Mohammadi-Nejad

   1. Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
   2. NIHR Nottingham Biomedical Research Centre, Queen’s Medical Centre, University of Nottingham, Nottingham, United Kingdom

   Contribution

   Resources, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0710-6700

6. Wei Tang

   Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, United States

   Contribution

   Resources, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

7. Saad Jbabdi

   Oxford Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom

   Contribution

   Resources, Methodology, Writing – review and editing

   Competing interests

   Reviewing editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0003-3234-5639

8. Sarah R Heilbronner

   Baylor College of Medicine, Houston, United States

   Contribution

   Resources, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0893-5364

9. Rogier B Mars

   1. Oxford Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
   2. Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6302-8631

10. Stamatios N Sotiropoulos

    1. Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
    2. NIHR Nottingham Biomedical Research Centre, Queen’s Medical Centre, University of Nottingham, Nottingham, United Kingdom

    Contribution

    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Methodology, Writing – original draft, Writing – review and editing

    For correspondence

    Stamatios.Sotiropoulos@nottingham.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4735-5776

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