Improved Functionnectome by dissociating the contributions of white matter fiber classes to functional activation

<jats:title>Abstract</jats:title>         <jats:p>Integrating the underlying brain circuit's structural and functional architecture is required to explore the functional organization of cognitive networks properly. In that regard, we recently introduced the Functionnectome. This structural-functional method combines an fMRI acquisition with tractography-derived white matter connectivity data to map cognitive processes onto the white matter. However, this multimodal integration faces three significant challenges: 1) the definition of the interface between grey and white matter, as the tractography streamlines may fail to reach the grey matter properly; 2) the scrambling effect of crossing fibers on functional signal, as a single voxel in such regions can be structurally connected to several cognitive networks with heterogeneous functional signals; and 3) the difficulty of interpretation of the resulting cognitive maps, as crossing and overlapping white matter tracts can obscure the organization of the studied network. In the present study, we tackled these problems by developing a streamline-extension procedure and dividing the white matter anatomical priors between association, commissural, and projection fibers. This approach significantly improved the characterization of the white matter involvement in the studied cognitive processes. The new Functionnectome priors produced are now readily available, and the analysis workflow highlighted here should also be generalizable to other structural-functional approaches.</jats:p>

section_content: Magnetic resonance imaging (MRI) is one of the best tools to study the human brain in vivo (Bandettini 2012 ; Lerch et al. 2017 ). It offers a wealth of modalities, leveraging different properties of matter to infer and contrast brain tissues' state and inform us of their structural and functional characteristics. Notably, the blood-oxygen-level-dependent (BOLD) signal of functional MRI (fMRI) (Ogawa et al. 1990 ) has shown tremendous success in mapping cognitive areas and networks in the grey matter (Yarkoni et al. 2011 ). 

Analysis of fMRI data traditionally involves leveraging the link between the BOLD signal and hemodynamic response to physiological processes. It reveals brain areas in which the BOLD signal displays a speci c pattern related to cognition or other functional organization of the brain. Likewise, diffusion MRI (dMRI) data, in tandem with tractography algorithms (Conturo et al. 1999; Jeurissen et al. 2019), have been harnessed to tackle the challenge of charting the white matter tracts de ning the brain's structural connectivity. Diffusion MRI yields information about the local directions of water diffusion across the brain (Le Bihan et al. 1986 ). Because water tends to diffuse in the same direction as axonal bers in the white matter, tractography uses dMRI data to try and reconstruct ber bundles -or "streamlines" -that can be employed to estimate structural connectivity in the brain (Jeurissen et al.
section_name: Introduction

section_content: Over the past decade, the exploration of the brain's function using MRI has shifted from unimodal fMRI to the combination of functional and structural information (Douaud et al. 2011; Hermundstad et al. 2013 ). 

One domain that has particularly bene ted from this is the functional study of white matter in the brain. 

While there is emerging evidence that the white matter holds meaningful BOLD signals (Gore et al. 2019 ;

Li et al. 2019), it is too weak in comparison to the grey matter. Yet, the white matter plays a major role in cognition: it is the structural foundation of the functional networks classically observed in grey matter (for a review see (Thiebaut de Schotten and Forkel 2022)). Assessing the functional involvement of the white matter in cognitive processes is thus crucial to better understand the brain and to shed light on how diseases and damage to the white matter affect it. Traditionally, this aspect has mainly been investigated through lesion-symptom mapping, correlating cognitive de cit with the location of brain lesions. In this framework, and more generally in the study of brain connectivity, the white matter bers are classi ed among three groups: association, commissural, and projection bers (Catani and Thiebaut de Schotten Yet, relying solely on the clinical approach to study the function of the white matter greatly limits our insights into the subject as symptoms cannot re ect all the brain's cognitive processes. By undertaking a multimodal approach and complementing the grey matter functional information with structural connectivity derived from tractography, it is possible to map cognitive processes onto the white matter of the healthy human brain. In this regard, we recently introduced the Functionnectome (Nozais et al. 2021), a structural-functional method -and an open-source program -that combines an fMRI acquisition with whole-brain, tractography-derived, white matter connectivity data. It has been successfully applied to task fMRI (t-fMRI) -mapping white matter domains linked to speci c cognitive tasks -and to resting-state fMRI (rs-fMRI) -mapping the white matter and grey matter joint contribution to resting-state networks (Nozais et al. 2023 ). 

However, integrating fMRI and tractography data is not trivial, and multiple factors may impact the results and their interpretability. Among them, three potential pitfalls stand out: 1) The proper de nition of the interface between grey and white matter is crucial to effectively combine the grey matter functional signal with the related white matter connectivity. The resulting structural-functional relationship between the two could be underestimated if the streamlines do not fully reach the grey matter. 

2) The structural-functional integration in white matter areas with crossing bers can be problematic. In these regions, a single voxel can be structurally connected to several functional networks with widely different cognitive roles, effectively reducing its apparent involvement with any speci c cognitive process (i. e., locally lowering the signal-to-noise ratio). 3) Probably the most important point lies in the interpretability of the brain maps derived from structural-functional integration. Indeed, using whole-brain tractograms to estimate the structural connectivity necessarily leads to maps involving the whole studied networks without distinctions between which ber tract is being activated. This is a problem when these tracts are close or even overlapping, as it would lead to ambiguity regarding the architecture of the studied functional network. 

In the present study, we explored the effect of these three points on structural-functional analysis using the Functionnectome. More speci cally, we rst implemented and tested a procedure to allow streamlines to reach deeper into grey matter to help improve the interface between grey and white matter. Second, we adopted the framework mentioned above, categorizing the connectivity data by ber class (association, commissural, and projection bers). By ltering the tractograms using these categories, we could tackle the two remaining issues together: it reduced the occurrence of ber-crossing areas and allowed the examination of the cognitive networks across their different components (and ber tracts) separately. 

The results signi cantly improved the Functionnectome priors and the analysis work ow. They also offer some general advice for the eld of structural-functional analysis.
section_name: 2019).

section_content: The processing and analysis work ow is summarized in Fig. 1
section_name: Methods

section_content: The tractography was performed using a probabilistic tractography approach. We used TractoFlow (Theaud et al. 2020 ), an automated dMRI processing pipeline for probabilistic tractography. TractoFlow was run with its two tracking algorithms: a classical local tracking and the Particle Filter Tracking (PFT) (Girard et al. 2014 ). The streamlines generated by the two probabilistic tracking algorithms were combined to form each whole-brain probabilistic tractogram. Most of the settings used for the tracking were the default settings proposed by TractoFlow: the shape factor for the elongated symmetric diffusion tensor of the FRF was 1. 5 x 10 - 3 mm 2. s - 1 ; spherical harmonics order was 8; all the shells were used (b = 1000, 2000, and 3000) for the fODF; step-size was 0. 5 mm; and maximal angle between steps was 20°. 

The seeding was done on the whole white matter, with 10 seeds per voxel for the PFT and 5 seeds per voxel for the local tracking. Each tractogram was then registered to the MNI space by applying the linear and nonlinear transformations used to register the anatomical scan.
section_name: Tractography

section_content: When combining volumetric grey matter data with tractography, a signi cant risk lies in the streamlines not properly reaching the grey matter voxels. More speci cally, the stopping criteria of the tractography algorithm (i. e., the criteria de ning when and where a streamline should stop) may not allow the streamlines to reach deep enough into the grey matter. Note that we are not discussing the challenge of properly connecting the whole grey matter using tractography. This topic and the related gyral bias (Rheault et al. 2020; St-Onge et al. 2018 ) are mentioned in the discussion but they go beyond the current proposed improvements. In the present study, we focus on the termination location of streamlines and their relation to structural-functional coupling. Indeed, in methods combining grey matter functional and tractography (like the Functionnectome), this coupling could be underestimated if the streamlines stop before properly reaching the grey matter voxel they should be related to. To test this effect, we devised a simple post-hoc lengthening procedure that was applied on each tractogram, effectively allowing the streamlines to fully reach the grey matter if it was close to its termination points. The extension procedure is as follows: all streamlines are linearly extended by 5 mm at both ends in the same direction as the streamline's ending segments; if the extension doesn't reach the grey matter, the extension is canceled; if the extension reaches the grey matter but changes area (as per the FreeSurfer grey matter parcellation) or gets out of the grey matter, the extended part is cut, keeping only the part in the rst grey matter area (Fig. 2 ). This last point reduces the risk that the procedure extends the streamline to a different functional domain. Such a procedure is similar to tractography toolboxes when building connectomes, like the tck2connectome function from MRtrix3 (Smith et al. 2015; Tournier et al. 2019 ). However, the current method differs from these approaches as they usually only allow the association of a streamline to a whole grey matter region, while our method was speci cally tailored to our needs by associating the streamlines to the grey matter voxels they reach. It was necessary for testing the effect of such extension on Functionnectome analyses.
section_name: Streamline extension

section_content: When combining functional information directly on the white matter using connectivity information from whole-brain tractography, as the Functionnectome does, two potential issues arise: First, the resulting activation maps do not differentiate between ber tracts, which limits our ability to interpret the data and precisely identify the tracts involved in the circuit. Second, whole-brain tractograms present crossingbers regions (i. e., regions of the brains where multiple white matter tracts, part of different functional networks, intersect). Using structural-functional combination methods in these areas can mix unrelated functional signals, arti cially lowering the local signal-to-noise ratio and thus the ability to precisely estimate the involvement of the white matter in speci c cognitive functions. 

To circumvent these problems, each tractogram was split into three categories of streamlines: association, commissural, and projection streamlines (Catani and Thiebaut de Schotten 2012). To further reduce the occurrence of crossing bers, association streamlines were also split into three length categories: "short bers" with streamlines below 40 mm in length, "medium bers" with streamlines between 40 and 80 mm, and "long bers" with streamlines longer than 80 mm ( ), that can be summarized as follow. First, every cortical area is linked with other cortical and subcortical regions by pathways grouped into three distinct categories: association, commissural, and projection bers. Second, the commissural, projection, and long-range association bers travel within the central WM part of the core of a gyrus, namely the gyral stem, and therefore never from the side of a gyrus by crossing a sulcus. Third, the shorter U-shaped association bers connect adjacent gyri by running in a thin band immediately beneath the GM, constituting the most external part of the gyral stem. Following these anatomical principles, regions of interest (ROIs) from the Johns Hopkins University (JHU) template (Oishi et al. 2009 ) are used to de ne ROI-based sequential ltering conditions. ExTractor ow starts, therefore, by considering a streamline as anatomicallyimplausible either when shorter than 20 mm, making a 360° loop, terminating along the ventricles, or even being broken within the deep WM structures as de ned in (Oishi et al. 2009 ), e. g., the streamlines ending within the corpus callosum, corona radiata, internal capsule, external capsule, parts of association bundles, etc. Interestingly, the JHU template includes both cortical grey matter and underlying super cial white matter parts for each gyrus. By manipulating these different ROIs, it is possible to create additional ROIs that optimize the ltering of anatomically plausible bers while applying the principles of WM ber organization emanating from a given gyrus. Two additional ROIs were thus used for each gyrus and each hemisphere. The rst one corresponds to the outermost part of the gyrus, namely its GM shell. The second corresponds to the "entrance/exit door" of the gyrus, namely its WM stem, which was manually drawn between the shell boundaries corresponding to the fundus of the sulci delimiting the gyrus. An essential step in ExTractor ow is to consider as plausible bers only those ending in a gyrus while having passed through the stem and not crossing the shell. A second stage of anatomical Boolean rules uses the JHU template ROIs to keep anatomically plausible streamlines strictly. 

Finally, the different anatomical rules used in ExTractor ow were also used in the context of the current study to split the remaining plausible streamlines into the association, commissural, and projection streamlines (Fig. 3 ).
section_name: Streamline ltering and categorization

section_content: To evaluate the effect of splitting the tractograms by ber class on a structural-functional analysis, we used each set of tractograms in Functionnectome analyses (Nozais et al. 2021 ). The whole procedure is described in the original paper, but we summarized it below. First, the white matter priors are computed using the tractograms (n = 100). These priors correspond to maps, one per brain voxel, giving the probability of structural connectivity from this voxel to the other brain voxels. These priors can then be used by the Functionnectome program (available at http://www. bcblab. com) to project grey matter BOLD signals onto the related white matter. Essentially, for a given white matter voxel, the Functionnectome uses the structural connectivity information from the white matter priors to do a weighted average of the BOLD time-series from the grey matter voxels connected to the said white matter voxel. This results in a new functional volume (with 4 dimensions), with functional time-series on the white matter voxels, that can then be statistically analyzed in the same way as a classical fMRI volume. Here, we used the Functionnectome program to project the signal of two task-fMRI datasets onto the white matter ( ngertapping dataset, n = 46; working-memory dataset, n = 45). The functionnectome volumes of each participant were then statistically analyzed by generalized linear modeling (GLM) to reveal the corresponding activation maps. Both rst-and second-level analyses were done using FEAT (FMRI Expert Analysis Tool, v6. 00), from the FMRIB Software Library (FSL), yielding a group-level activation z-map per task. 

In the present study, we created one set of white matter priors per set of tractograms, for a total of eight sets of priors, namely: two whole-brain sets, extended and non-extended; four association extended sets (all bers, and separately short, medium, and long association extended bers); one commissural extended set; and one projection extended set. Together, the association, commissural, and projection priors are referred to as the split priors, in opposition to the whole-brain priors. Note that all the split priors we generated with tractograms that underwent the streamline-extension procedure. The functional analyses described above (for the two functional datasets) were repeated for each of these 8 sets of priors.
section_name: Functionnectome, white matter priors, and activation maps

section_content: All the activation z-maps were analyzed and displayed after applying a white matter mask to keep only white matter voxels, and using an arbitrarily high threshold of z = 3 (equivalent to p < 0. 0013, uncorrected). We kept this uncorrected threshold to make the displayed map clearer and easier to read, as applying a false discovery rate correction did not change the signi cance of the results with the chosen threshold. The white matter mask was de ned as the voxels attributed to white matter in at least 10% of the study's participants. All the presented maps are z-maps, displaying voxelwise z-scores for the task activation being studied. The slices shown in the results were selected to display the main white matter functional pathways revealed by the Functionnectome activation mapping. The unthresholded z-maps are freely available on Neurovault (https://identi ers. org/neurovault. collection:13538). 

To compare the results obtained from using whole-brain priors and those obtained using split priors, the corresponding z-maps (association, commissural, and projection) were combined, keeping, for each voxel, the maximum z-score across the three maps. These maps are referred to as the z-max maps. Similarly, the activation z-maps generated using the three association priors split by length (short, medium, and long) were recombined in the same manner to create association z-max maps. Note that the whole-brain z-max map mentioned above uses these association z-max maps instead of the basic association z-map. 

The statistical comparison between the maps was done on the voxels with z-scores above 3 in at least one of the maps, to only compare regions that are considered activated. The p-values displayed are the results from the two-sided paired t-test on the selected voxels.
section_name: Maps analyses and statistics

section_name: Results

section_content: We rst explored the effect of extending the WM streamlines in the GM on the structural-functional analysis. We therefore analyzed the involvement of the white matter in the right-hand nger-tapping task by contrasting the activation maps from the Functionnectome analysis using the priors generated with and without streamline extension. The resulting maps (Fig. 4A-B ) showed that using the extension method signi cantly improved the statistics of the activated regions (i. e., with z > 3), resulting in an average increase of 12% (p < 0. 0001) of the z-values across the map. The analysis was repeated with the working-memory dataset (Fig. 5A-B ), which also demonstrated a signi cant increase of 4% (p < 0. 0001) in z-values across the map when using the extended tractograms. 

We interpreted these increases as proof that using our simple method to extend the streamlines improved the structural-function coupling in the analysis. As a result, we used the extension method in all the following analyses. 

While the increase in z-values from the extension procedure is relatively modest, the improvement from splitting the white matter priors and recombining the activation maps are quite striking. First, compared to the z-maps from the extended priors, we note an 83% increase (p < 0. 0001) in the mean z-values of the zmax map for the nger-tapping task (Fig. 4B-C ), and a 31% increase (p < 0. 0001) for the working memory task (Fig. 5B-C ). The z-max map from the nger-tapping task also shows a clear recovery of the signal in areas of crossing bers, very distinct from the disappearance of the "gaps" in the activation map. This is especially obvious around the centrum semiovale, where the superior longitudinal fasciculus (SLF, not involved in the task) intersects with callosal bers and the cortico-spinal tract (both involved in the task). 

Likewise, when focusing on the split priors for the association bers, splitting them again by length category allowed reducing further the effect of the remaining crossing regions. In the z-maps from the nger-tapping task (Fig. 6 ), using the z-max approach with the three split association priors increases the z-values by 103% on average (p < 0. 0001). For the working-memory task, we observe a 34% increase on average (p < 0. 0001) with the z-max approach. Also, similarly to what is shown above with the wholebrain z-max map on the nger-tapping task, the association z-max map displays a recovery of the activation in ber-crossing regions, lling the visible gap where the SLF intersects with shorter association bers.
section_name: Enhancement of the activity detection in the white matter

section_content: Using the split priors also allows us to differentiate between the functional integration mediated by different classes of bers and, thus, different tracts. In the case of the nger-tapping task (Fig. 7 ), using the split priors helps differentiate the functional involvement of multiple ber tracts, both near grey matter hubs and in the deep white matter. For example, we can see that while most of the bers involved in activations around the thalamic nuclei are from the projection category, some of this activity is also related to association bers connecting the insula with the motor cortex. Similarly, this separation helps determine that the strong activation observed near the motor cortex from the left hemisphere is mostly related to association bers. 

Likewise, in the activation maps from the working-memory task (Fig. 8 ), we can better differentiate between the strong activations related to either the association or commissural bers. Most interestingly, the split also reveals the involvement of the projection bers, which is not obvious when using wholebrain priors. 

Similarly, to the split between ber classes, splitting the association bers by length category reveals the different scales in connectivity that govern the functional organization of the cognitive networks. In the case of the nger-tapping task (Fig. 9A-B-C ), we observe that most of the strong activation near the motor cortex is related to short and medium-length (U-shaped) bers. Also, the connectivity of the insula to the other parts of the network is mostly mediated by medium-length bers, but also in part by long association bers. 

As for the working-memory task (Fig. 9D-E-F ), the split reveals the strong activations related to small and medium-length bers, especially in the frontal lobe, likely driving the local integration of functional information. It also unveils the exact connectivity pro le speci cally related to the long-distance association in the network, with both fronto-parietal and parieto-temporal connectivity.
section_name: Clarifying the structure of the networks

section_content: We explored two approaches to signi cantly enhance structural-functional analysis to study the function of white matter and improved the Functionnectome framework accordingly. Our results highlight three main factors that in uence the analyses and their interpretation: elongating the streamlines at the interface between grey matter voxels and white matter tracts improves the structural-functional integration; splitting the bers by category upstream of the analysis, then recombining the resulting maps vastly increase the ability to detect the involvement of the white matter in cognition; and the same split also strikingly clarify the structural-functional architecture of the studied networks. Moreover, these improvements should be equally applicable to other structural-functional methods (besides the Functionnectome), which would give our approach a global impact on the domain. 

Structural-functional analyses in neuroimaging, combining cortical functional information and tractography data, have been successfully used to explore the structure-function relationship between the brain's grey and white matter. However, the de nition of the interface between the two is not a trivial step, and two notable approaches have been used. One consists in using functionally de ned ROI as a seed for the tractography (Javad et al. 2014), while the other consists in doing whole brain tractography and then selecting the streamlines terminating in the studied grey matter areas (Wang et al. 2015). The method de ning the termination point of the streamlines also plays a major role in shaping the interface. Here, three main methods exist: a streamline can stop when it reaches the edge of a prede ned white matter mask (Theaud et al. 2020) ; it can stop when it reaches a voxel associated with speci c diffusion metrics (e. g., fractional anisotropy lower than a given threshold) (Thiebaut de Schotten et al. 2011), or can be guided to the cortical surface by a geometrical model when approaching the grey matter (St-Onge et al. 2018; St-Onge et al. 2021). In each case, there is no guarantee that the streamline will actually fully reach the grey matter it should be linked to. Even in the case when streamlines are guided to the cortical surface, which will improve the estimation of cortical connectivity (St-Onge et al. 2021), the streamlines may not reach all the functionally related grey matter voxels. This is especially true for voxels on the outer side of the cortex or deep inside subcortical nuclei. 

In our study, we showed that a simple elongation of the streamline (constricted by grey matter boundaries) is enough to correct this bias and to signi cantly improve the results of a structuralfunctional analysis. While the increase in the signi cance of the activations was somewhat modest, it should be noted that the tractography used in the present study was very "aggressive", attempting to maximize the estimation of the brain connectivity by generating a vast number of streamlines. It is entirely possible that the streamline-extension procedure would have a much stronger effect on less dense tractograms. This method has thus the potential to become a standard step applied to tractograms when used in this framework. It should be noted, however, that for the same region the connectivity changes according to the layer of the cortex studied (Pandya et al. 2015). While such estimation is beyond the resolution available nowadays, future endeavors based on higher resolution diffusion weighted imaging datasets should adapt streamline elongation to the anatomical principles of the cortical architecture. Additionally, to ensure a proper streamline elongation, the orientation of the terminal segments of a streamline must be correct. This may not always be the case, as can be illustrated by the existence of the gyral bias (Schilling et al. 2018 ). Ideally, this elongation step should be part of the tractography itself and coupled with a method that ensures the proper orientation of the streamlines (e. The other avenue we explored to improve structural-functional analyses was to split the analysis by ber class, independently studying association, commissural and projection bers. Depending on the method used to perform the structural and functional integration, a detection bias may arise from bers crossing and mixing unrelated functional signals together. Splitting the analysis by ber category and reassembling the resulting maps can correct this bias. In the case of the Functionnectome analysis, which directly combines the functional information from grey matter sources onto white matter voxels, we showed that this correction effect is present and very strong. Here, splitting the analysis enabled us to both ll the gap in the activation maps located in regions of crossing bers, and to increase the signi cance of the studied activation globally. These two points are very bene cial from a technical standpoint and a clear improvement in our ability to map the involvement of white matter in cognitive processes fully. Nevertheless, the aspect that may be the most important when splitting the analysis by ber category is the ability it gives us to disentangle different bers' roles in a white matter activation. Indeed, each set of split priors, whether by ber class or ber length, revealed details about the organization of the studied cognitive circuits, which would have been obscured in the analyses using whole-brain priors. For example, the working memory task revealed projection bers connecting the frontal lobe with thalamic nuclei. This activity was effectively hidden by the cortico-cortical parts of the circuit in the whole-brain analysis, and is extremely interesting, especially in the light of the mounting evidence of the involvement of the thalamus in working memory (Piras et We expect the improvement derived from splitting the tractograms to be generalizable to other volumetric structural-functional methods that integrate information directly on white matter voxels, such as trackweighted imaging (Calamante 2017) or white-matter interpolation of fMRI (Tarun et al. 2020 ). However, ber crossing can still happen in split tractograms, especially with association bers (e. g., the SLF crossing the frontal aslant tract). To further improve this aspect, one conceivable approach would be to use an approach akin to using xels (Dhollander et al. 2021) instead of voxels in the analysis. With a type of volumetric data that keeps the information about the direction of the bers from which the signal was extracted, it should help separate unrelated signals when integrating them onto the white matter, which in turn would remove the need to split the tractograms. However, this type of analysis would require a complete remake of the current methods, is more complicated to implement, would likely be much more computationally intensive, and its results would de nitely be harder to interpret. 

The seven sets of extended priors (whole-brain, association, commissural, projection, and short-/medium-/long-association bers), are all freely available online and are now part of the updated Functionnectome. These new priors, and especially the split priors, will help disentangle the involvement of different ber tract populations in cognitive functions. The priors can be easily selected and downloaded from the Functionnectome interface, then the analysis has to be run separately for each set of priors.
section_name: Discussion

section_content: We showed that structural-functional analyses combining fMRI and tractography can be dramatically improved through two relatively easy-to-implement steps: By ensuring a proper reach of each streamline in the grey matter and by grouping streamlines into classes that minimize streamline crossings. In the present study, we used the Functionnectome as an example of structural-functional analysis, but we believe our results are generalizable to many other similar methods and could thus help improve results obtained in this eld.
section_name: Conclusion

section_content: Graphical summary of the processing and analysis. In orange, each processing step. In green, the new priors now provided with the Functionnectome to improve the integration of functional and structural information on the white matter. Here, "class" refers to the classes of white matter bers included in the tractograms (as listed below the "Filtered tractograms" box). Effect of streamline extension and ber categorization on the group-level functionnectome z-map of the right-hand nger-tapping task. A: Z-map generated using whole-brain priors from non-extended tractograms. B: Z-map generated using whole-brain priors from extended tractograms. C: Z-max map generated by recombining the z-maps obtained from using the priors split by ber class. 

Effect of streamline extension and ber splitting on the group-level functionnectome z-map of the working memory task. A: Z-map generated using whole-brain priors from non-extended tractograms. B: Zmap generated using whole-brain priors from extended tractograms. C: Z-max map generated by recombining the z-maps obtained from using the priors split by ber class. 

Effect of categorization by the length of association bers on the group-level functionnectome z-map of the right-hand nger-tapping task. A: Z-map generated using association priors. B: Z-max map generated by recombining the z-maps obtained from using the association priors split according to ber length (short, medium, and long association bers). 

White matter activation maps for each class of bers in the right-hand nger-tapping task. A: Z-max map generated by recombining the z-maps generated using the three sets of association priors (split by length), the asterisk (*) indicates that this is a Z-max map, contrary to the other two maps. B: Z-map generated using commissural priors. C: Z-map generated using projection priors.
section_name: Declarations Figures

section_content: Acknowledgements: This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement No. 818521 ). Data were provided by the Human Connectome Project, WU-Minn Con-sortium (Principal Investigators: David Van Essen and Kamil Ugurbil ; 1U54MH091657 ) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research and by the McDonnell Center for Systems Neuroscience at Washington University. Victor Nozais received nancial support for this project from the "Consulat Général de France à Québec" and the "Conseil Franco-Québécois de Coopération Universitaire" as part of the Samuel de Champlain Program for the Development of Strategic Partnerships in Education and Research. References

section_name: Supplementary Files

section_content: extension leaves the grey matter. c: Part of the extension crosses to another grey matter parcel. d: The extension doesn't reach the grey matter. Projection bers comprise all the bers connected to subcortical structures and the brainstem. 

White matter activation maps for each class of bers in the working-memory task. A: Z-max map generated by recombining the z-maps generated using the three sets of association priors (split by length), the asterisk (*) indicates that this is a Z-max map, contrary to the other two maps. B: Z-map generated using commissural priors. C: Z-map generated using projection priors. 

White matter activation maps for each length category of association bers in the right-hand ngertapping task and working-memory task. A-B-C: Finger-tapping Z-maps generated using priors for the short, medium, and long bers category, respectively. D-E-F: Working-memory Z-maps generated using priors for the short, medium, and long bers category, respectively.

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section_name: Supplementary Files