Understanding brain anatomy requires a multi-scale perspective. Regional variations in cell types and distributions underlie macro-scale patterns, whether they are reflective of functional dynamics, age, or disease states. For over 150 years (von Gudden, 1886), histological analysis of post mortem tissue has helped to reveal the microscopic architecture of the brain. Neuroanatomists observed a distinctive layered organisation of cells within the cortex (Baillarger, 1840), identified differences in the cellular composition (Betz, 1874), and developed principles of cortical organisation, including the definition of cortical types (Meynert, 1867) and areas (Brodmann, 1908; Von Economo and Koskinas, 1925). More recently, digitisation of post mortem tissue has allowed automated characterisation of cytoarchitecture and the definition of borders between areas (Schleicher et al., 1999).

Evidence has been provided that cortical organisation goes beyond a segregation into areas. For example, large-scale gradients that span areas and cytoarchitectonic heterogeneity within a cortical area have been reported (Amunts and Zilles, 2015; Goulas et al., 2018; Wang, 2020). Such progress became feasible through integration of classical techniques with computational methods, supporting more observer-independent evaluation of architectonic principles (Amunts et al., 2020; Paquola et al., 2019; Schiffer et al., 2020; Spitzer et al., 2018). This paves the way for novel investigations of the cellular landscape of the brain.

In vivo neuroimaging offers a complementary window into the structure and function of the brain. The non-invasive nature of magnetic resonance imaging (MRI) allows examination of large-scale, population-level variation, which is more limited in post mortem neuroanatomy. Human brain mapping research has furthermore established standard spaces, notably the MNI152 space for volumetric whole-brain analysis (Fonov et al., 2011b; Fonov et al., 2009; Mazziotta et al., 2001a; Mazziotta et al., 2001b) and ‘fsaverage’ and ‘fs_LR’ for surface-based cortical analyses (Fischl et al., 1999; Van Essen et al., 2012). Despite ongoing advances in attaining higher spatial resolution with higher field strength (Deistung et al., 2013; Holdsworth et al., 2019; Sitek et al., 2019; Trampel et al., 2019; Turner and De Haan, 2017), in vivo MRI researchers remain constrained by limited spatial resolution from making inferences on a cellular level. Establishing the relationship between macro-scale patterns and cellular architecture is crucial to substantiate physiological patterns observed with MRI and for further development of brain-inspired computational models.

BigBrain is a singular 3D volumetric reconstruction of a sliced and cell-body stained post mortem human brain (Amunts et al., 2013). This resource allows computational analysis of the entire organ in relation to cell staining at high resolutions (up to 20 µm). It is specially tailored for neuroimagers, as it is made available in common MRI formats (minc and NifTI), accompanied by cortical surface reconstructions (Lewis et al., 2014), and nonlinearly registered to standard MRI templates (ICBM152 and MNI-ADNI) (Fonov et al., 2011a). Furthermore, recent studies have expanded the resource by offering improved registrations to standard spaces (Lewis et al., 2020; Xiao et al., 2019), nuanced intracortical surface models, and laminar approximations (Wagstyl et al., 2018a; Wagstyl et al., 2020) as well as regional segmentations (DeKraker et al., 2019; Xiao et al., 2019). Several studies have already capitalised on this unique resource for integrative histological-neuroimaging analyses, including comparison of cytoarchitectural and functional gradients (Paquola et al., 2019), cross-validation of in vivo defined microstructural gradients in the insula with histological measures (Royer et al., 2020), mapping variations in functional connectivity along the histological axis of the mesiotemporal lobe (Paquola et al., 2020b), fMRI responses of the histologically defined auditory system (Sitek et al., 2019), comparison of cytoarchitectural similarity with MRI-derived estimates of structural connectivity (Wei et al., 2019), and analysis of the cytoarchitectural similarity of large-scale network hubs (Arnatkevičiute et al., 2020).

The present article introduces the BigBrainWarp toolbox. The aim of the toolbox is to facilitate integration of BigBrain with neuroimaging modalities, helping neuroscientists to utilise cytoarchitectural information in conjunction with in vivo imaging. The toolbox is open and includes (1) histological features and pre-transformed maps in BigBrain and imaging spaces, (2) code for performing data transformations, and (3) extensive tutorials. Toolbox functions and tutorials are documented on http://bigbrainwarp.readthedocs.io. Here, we introduce BigBrain to new users and demonstrate the utility of the BigBrainWarp toolbox. In the Materials and methods section, we overview the derivation of cytoarchitectural features from BigBrain and survey recent contributions to BigBrain-MRI integration. These include publication of histological cortical maps, regional segmentations, and registration efforts. Then, we detail the core functions of BigBrainWarp and the current contents of the toolbox. In the Results section, we share three tutorials to illustrate potential applications of BigBrainWarp.

The BigBrainWarp toolbox supports a range of integrative BigBrain–MRI analyses. The following tutorials outline three BigBrain–MRI analyses with unique types of transformations, specifically (1) BigBrain volume to ICBM2009sym, (2) BigBrain surface to fsaverage, and (3) fsaverage to BigBrain surface. Neither the forms nor the motivations are exhaustive but illustrate applications (see Figure 3 for all possible transformations). Code for each tutorial is available in the BigBrainWarp toolbox.

Tutorial 1: BigBrain → ICBM2009sym MNI152 space

Motivation: Despite MRI acquisitions at high and ultra-high fields reaching submillimeter resolutions with ongoing technical advances, certain brain structures and subregions remain difficult to identify (Kulaga-Yoskovitz et al., 2015; Wisse et al., 2017; Yushkevich et al., 2015). For example, there are challenges in reliably defining the subthalamic nucleus (not yet released for BigBrain) or hippocampal Cornu Ammonis subfields (manual segmentation available on BigBrain, https://osf.io/bqus3/; DeKraker et al., 2019). BigBrain-defined labels can be transformed to a standard imaging space for further investigation. Thus, this approach can support exploration of the functional architecture of histologically defined regions of interest.

Approach: (1) Create volumetric label in BigBrain space. (2) Perform nonlinear transformation to ICBM2009sym space using BigBrainWarp. (3) Transform individual resting-state functional MRI data to ICBM2009sym MNI152 space. (4) Sample timeseries from labelled voxels in this standard space.

Example: The mesiotemporal lobe plays important roles in multiple cognitive processes (Moscovitch et al., 2005; Squire et al., 2004; Vos de Wael et al., 2018) and is affected by multiple neurological and neuropsychiatric conditions (Ball et al., 1985; Bernhardt et al., 2016; Bernhardt et al., 2015; Calabresi et al., 2013). Increasing research suggests that this region shows complex subregional structural and functional organisation. Here, we illustrate how we can track resting-state functional connectivity changes along the latero-medial axis of the mesiotemporal lobe, from parahippocampal isocortex toward hippocampal allocortex, hereafter referred to as the iso-to-allocortical axis. For further details and additional motivation, please see Paquola et al., 2020a: (1) Our volumetric label represents the iso-to-allocortical axis of the mesiotemporal lobe. We constructed this axis by joining the isocortical (Lewis et al., 2014) and hippocampal (DeKraker et al., 2019) surface meshes in BigBrain histological space, creating the mesiotemporal confluence (available in BigBrainWarp, Figure 1G). Then, we calculated the distance of each vertex in the new surface model to the intersection of isocortical and hippocampal meshes, defining the iso-to-allocortical axis (Figure 4A). Next, we filled voxels in cortical ribbon according to the position of the iso-to-allocortical axis, producing a volumetric representation of the iso-to-allocortical axis in BigBrain histological space (Figure 4Bii). (2) We transform the volume from the BigBrain histological space to ICBM2009sym (Figure 4Biii).

bigbrainwarp --in_space bigbrain --out_space icbm --wd/project/ --desc confluence_axis --in_vol tpl-bigbrain_desc-confluence_axis.nii --interp linear

(3) aboveTo explore the functional architecture of this histologically defined axis, we obtained multi-modal MRI in 50 healthy adults from the MICs dataset (Royer et al., 2021). For each participant, we constructed an individualised transformation from ICBM2009sym to their native functional space, based on the inverse of the within-subject co-registration to the native T1-weighted imaging concatenated to the nonlinear between-subject registration to ICBM2009sym. (4) For each participant, BOLD timeseries were extracted from non-zero voxels of the transformed iso-to-allocortical axis, which are classified as grey matter (>50% probability) and collated in a 3D matrix (voxel × time × subject). Then, we sorted and analysed this matrix using the voxel-wise values of the iso-to-allocortical axis. For each subject, we averaged voxel-wise BOLD timeseries within 100 bins of the iso-to-allocortical axis and within 1000 isocortical parcels (Schaefer et al., 2018) and estimated the resting state functional connectivity of each iso-to-allocortical bin with each isocortical parcel. Then, we averaged resting state functional connectivity measures across subjects and performed product-moment correlations between the strength of resting state functional connectivity and bin position along the iso-to-allocortical axis. This analysis illustrates how functional connectivity varies along the histological axis for different areas of the isocortex (Figure 4C).

Figure 4

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Tutorial 2: BigBrain → fsaverage

Motivation: In vivo brain imaging reveals regionally variable effects of many demographic and clinical factors on brain structure and function. For example, prior studies of lifespan processes presented spatially variable patterns of cortical atrophy with advancing age, together with increased deposition of pathological aggregates, such as amyloid beta (Aβ) (Bilgel et al., 2018; Jansen et al., 2015; Knopman et al., 2018; Rodrigue et al., 2012; Sperling et al., 2011). Histological data provides a window into the cytoarchitectural features that align with imaging-derived phenotypes and that, in this instance, may predispose an area to specific aging-related processes. Essentially, we can evaluate whether regions with a certain cytoarchitecture overlap with those showing more marked aging effects. Furthermore, large-scale cytoarchitectural gradients can provide a unified framework to describe topographies, simplifying and standardising the reporting of imaging-derived phenotypes.

Approach: (1) Construct histological gradients using BigBrain and (2) transform to standard neuroimaging surface template using BigBrainWarp. (3) Plot the imaging-derived map against each histological gradient to understand the algebraic form of the relationship. Note, if imaging features are volumetric, one may use registration fusion to resample the data from ICBM2009sym to fsaverage (Wu et al., 2018). (4) Fit a statistical model to evaluate the relationship between the cytoarchitectural gradients and the imaging-derived map. For research questions with a more restricted region of interest, the cytoarchitectural gradient could be reconstructed within that field of view and the same procedure could be utilised. The optimal number of cytoarchitectural gradients should be evaluated.

Example: Cytoarchitectural correlates of age-related increases in Aβ deposition in a healthy lifespan cohort (Lowe et al., 2019; Park, 2018). (1–2) First, we obtained histological gradients on fsaverage from BigBrainWarp. The construction of histological gradients is detailed in Materials and methods (Figure 5A). The transformation from BigBrain to fsaverage was performed for the toolbox, like so,

bigbrainwarp --in_space bigbrain --out_space fsaverage --wd/project/ --desc Hist_G1 --in_lh Hist_G1_lh.txt --in_rh Hist_G1_rh.txt

For this analysis, we additionally smoothed the histological gradients on fsaverage (6 mm FWHM) to approximately match the smoothing kernel of the resting-state fMRI data. (3) We previously estimated the association of age with Aβ deposition across the cortical surface by combining positron emission tomography with MRI data in 102 adults (30–89 years), and assessed correspondence to functional connectivity gradients (Lowe et al., 2019). Here, we plot the vertex-wise t-statistics against Hist-G1 and Hist-G2 (Figure 5B). (4) We determine the optimal model via the Bayesian Information Criterion in univariate and multivariate regressions between the t-statistics and histological gradients (Figure 5C). The optimal model included only Hist-G2, indicating that Aβ preferentially accumulates towards the more agranular anchor of the sensory-fugal gradient.

Figure 5

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Tutorial 3: fsaverage → BigBrain

Motivation: A core aim of fMRI research is to map functional specialisation in the brain (Bassett et al., 2008; Eickhoff et al., 2018; Gordon et al., 2017; Raichle, 2015; Shine et al., 2019; Yeo et al., 2011). On the one hand, this work follows a long legacy of defining cortical areas, and on the other hand, it extends beyond the possibilities of post mortem research by capturing patterns of coordinated activity. For instance, clustering resting-state fMRI connectivity reveals a robust set of intrinsic functional networks (Beckmann and Smith, 2004; Gordon et al., 2017; Yeo et al., 2011). Nonetheless, there exists a gap in the literature between these well-characterised functional networks and their cytoarchitecture. BigBrain offers the opportunity to characterise and evaluate differences of cytoarchitecture for functionally defined atlases.

Approach: (1) Transform functionally-defined regions from a standard neuroimaging surface template to the BigBrain surface. Note, if the functional-defined regions are volumetric, one may use registration fusion to resample the data from ICBM2009sym to fsaverage (Wu et al., 2018). (2) Compile staining intensity profiles by functional class. (3) Assess discriminability of functional classes by staining intensity profiles.

Example: Cytoarchitectural differences of intrinsic functional networks. (1) Transform the 17-network functional atlas (Yeo et al., 2011) to the BigBrain surface.

bigbrainwarp --in_space fsaverage --out_space bigbrain --wd /project/ --desc Yeo2011_17Networks_1000 --in_lh lh.Yeo2011_17Networks_1000.annot --in_rh lh.Yeo2011_17Networks_1000.annot

(2) Stratify staining intensity profiles by network (Figure 6A). (3) Parameterise staining intensity profiles by the central moments and assess variation across functional networks (Figure 6B). For example, the mean and skewness illustrate distinct patterns of cytoarchitectural differentiation across the functional networks. Visual networks have the highest mean and lowest skewness. Somatomotor, dorsal attention and fronto-parietal networks contain most variable mean and skewness values. Ventral attention, limbic, and fronto-parietal networks harbour the lowest mean and highest skewness, whereas the default mode networks occupy an intermediary position. Notably, all the networks exhibit broad distribution of the moments, signifying substantial cytoarchitectural heterogeneity, as well as overlapping values. To quantify discriminability of functional networks by cytoarchitecture, we can attempt to classify the functional networks using the central moments. For this example, we z-standardised the central moments and split the vertices into five folds, each with an equal representation of the 17 functional networks. Then, we trained a one vs one linear support vector classification on 50% of each fold and tested the model on the remaining 50% of that fold. Functional networks were equally stratified across training and testing. Finally, for each fold, we generated a confusion matrix, showing the accurate predictions on the diagonal and the incorrect classification off the diagonal. Predictive ability provides insight into distinctiveness and homogeneity of functional networks. Visual networks harbour distinctive cytoarchitecture, reflected by relatively high accuracy and few incorrect predictions. Ventral attention, limbic, and temporo-parietal networks are relatively homogenous in cytoarchitecture, likely related to their restricted spatial distribution. The predictive accuracy did not appear to be negatively impacted by minor misalignments of the atlas, as the predictive accuracy was similar when excluding vertices within approximately 6 mm of the network boundaries (accuracy mean ± SD [%], original = 12.4 ± 15.4, excluding boundaries = 12.1 ± 13.3).

Figure 6

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Beyond cartography, a major aim of neuroanatomical research has been to understand the functioning of the human brain. Throughout the 20th century, cytoarchitectural studies were instrumental in demonstrating functional specialisation across the cortex, as well as the uniqueness of the human brain amongst mammals (Brodmann, 1909; Campbell et al., 1905; Sanides, 1962; Smith, 1907; Vogt and Vogt, 1919; Vogt, 1911). Fine-grained anatomical resolution maintains an important role in understanding brain function in the modern era, helping to bridge between microcircuit organisation and macroscale findings obtained with in vivo neuroimaging. BigBrain is the first ultra-high-resolution 3D histological dataset that can be readily integrated with in vivo neuroimaging. In this report, we presented BigBrainWarp, a simple and accessible toolbox comprising histological data, previously developed transformation functions between BigBrain and standard imaging spaces, and ready-to-use transformed cortical maps. The toolbox is containerised to eliminate software dependencies and to ensure reproducibility. An expandable documentation is available, alongside several tutorials, at http://bigbrainwarp.readthedocs.io.

Multimodal registrations are core to integrating BigBrain with in vivo neuroimaging data. Identifying optimal solutions is more difficult than intra- and inter-subject co-registrations of neuroimaging data, owing to histological artefacts, differences in intensity contrasts and morphological distortions. These challenges have been addressed by recent studies, which improved integration of BigBrain with standardised MRI spaces. An automated repair algorithm was specially devised for BigBrain, which involved nonlinear alignment of neighbouring sections, intensity normalisation, outlier detection using block averaging then artefact repair using the block averages (Lepage et al., 2010; Lewis et al., 2014). Following initial transformation of BigBrain to ICBM2009b, which was part of the initial BigBrain release (Amunts et al., 2013), a recent study optimised subcortical registrations by generating a T1-T2* fusion contrast that is more similar to the BigBrain intensity contrast than a T1-weighted image (Xiao et al., 2019). Additionally, that study involved manual segmentation of subcortical nuclei to use as shape priors in the registration, which benefits the alignment of subcortical structures between BigBrain and standard neuroimaging templates. Finally, inspired by advances in the alignment of surface-based MRI data (Robinson et al., 2018; Robinson et al., 2014), the BigBrain team has recently developed a multi-modal surface matching pipeline for BigBrain that involved re-tessellation of the BigBrain surface at a higher resolution, followed by alignment to standard surface templates using coordinate, sulcal depth and curvature maps (Lewis et al., 2020). The procedure significantly improves upon previous techniques, resulting in geometric distortions comparable to those seen for registrations between neuroimaging datasets of different individuals (Lewis et al., 2020). Cortical folding is variably associated with cytoarchitecture, however. The correspondence of morphology with cytoarchitectonic boundaries is stronger in primary sensory than association cortex (Fischl et al., 2008; Rajkowska and Goldman-Rakic, 1995a; Rajkowska and Goldman-Rakic, 1995b). Incorporating more anatomical information in the alignment algorithm, such as intracortical myelin or connectivity, may benefit registration, as has been shown in neuroimaging (Orasanu et al., 2016; Robinson et al., 2018; Tardif et al., 2015). Overall, evaluating the accuracy of volume- and surface-based transformations is important for selecting the optimal procedure given a specific research question and to gauge the degree of uncertainty in a registration.

Practically, 3D histological models provide an unrivalled level of precision and lend novel opportunities to cross-validate and contextualise findings from human neuroimaging. BigBrainWarp is particularly well-suited for investigations on the fundamental relationships between cytoarchitecture and function, which remains an elusive aspect of brain organisation. Our tutorials illustrate a range of use cases of BigBrain-MRI integration. In tutorial 1, we show how BigBrain can be used to initialise region of interest analyses, such as mapping resting-state functional connectivity along the iso-to-allocortical axis (Paquola et al., 2020b), enabling delineation of regions that are difficult to identify with in vivo imaging and functional interrogation of histological axes. In tutorial 2, we show how cytoarchitectural gradients can help to characterise large-scale cortical patterns, such as the association of aging with Aβ deposition (Lowe et al., 2019). This approach complements the tradition of reporting the cortical areas of significant clusters by offering a simplified topographical description of the spatial pattern. Furthermore, by comparing predictive power of various cytoarchitectural gradients, we may build towards hypotheses on the relationship between microcircuit properties and demographic or clinical factors. In tutorial 3, we discuss more specific histological features, namely moment-based parameterisation of staining intensity profiles (Schleicher et al., 1999; Zilles et al., 2002). These features depict the vast cytoarchitectural heterogeneity of the cortex and enable evaluation of homogeneity within imaging-based parcellations, for example macroscale functional communities (Yeo et al., 2011). The present analysis showed limited predictability of functional communities by cytoarchitectural profiles, even when accounting for uncertainty at the boundaries (Gordon et al., 2016). Together, these tutorials showcase how we can easily and robustly use BigBrain with BigBrainWarp to deepen our understanding of the human brain.

Despite all its promises, the singular nature of BigBrain currently prohibits replication and does not capture important inter-individual variation. While large-scale cytoarchitectural patterns are conserved across individuals, the position of areal boundaries relative to sulci vary, especially in association cortex (Amunts et al., 2020; Fischl et al., 2008; Zilles and Amunts, 2013). This can affect interpretation of BigBrain–MRI comparisons. For instance, in tutorial 3, low predictive accuracy of functional communities by cytoarchitecture may be attributable to the subject-specific topographies, which are well established in functional imaging (Benkarim et al., 2020; Braga and Buckner, 2017; Gordon et al., 2017; Kong et al., 2019). Future studies should consider the influence of inter-subject variability in concert with the precision of transformations, as these two elements of uncertainty can impact our interpretations, especially at higher granularity. Fortunately, the BigBrain team is working on new histology-based 3D models in the context of the HIBALL project (https://bigbrainproject.org/hiball.html). System neuroscience has dramatically benefitted from the availability of open resources (Di Martino et al., 2014; Milham et al., 2018; Poldrack et al., 2017; Van Essen et al., 2013). This path, together with ongoing refinements in multimodal data integration and efforts to make tools accessible, promises to further advance multi-scale neuroscience in the years to come.

All data generated or analysed during this study are included in the BigBrainWarp repository (https://github.com/caseypaquola/BigBrainWarp).

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Author details

1. Casey Paquola

   1. McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montréal, Canada
   2. Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany

   Contribution

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

   For correspondence

   casey.paquola@gmail.com

   Competing interests

   none

   "This ORCID iD identifies the author of this article:" 0000-0002-0190-4103

2. Jessica Royer

   McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montréal, Canada

   Contribution

   Validation, Writing – review and editing

   Competing interests

   none

3. Lindsay B Lewis

   McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montréal, Canada

   Contribution

   Data curation, Methodology, Writing – review and editing

   Competing interests

   none

4. Claude Lepage

   McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montréal, Canada

   Contribution

   Data curation, Methodology, Writing – review and editing

   Competing interests

   none

5. Tristan Glatard

   Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada

   Contribution

   Software, Writing – review and editing

   Competing interests

   none

6. Konrad Wagstyl

   Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom

   Contribution

   Methodology, Software

   Competing interests

   none

7. Jordan DeKraker

   1. McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montréal, Canada
   2. Brain and Mind Institute, University of Western Ontario, Ontario, Canada

   Contribution

   Resources, Writing – review and editing

   Competing interests

   none

8. Paule-J Toussaint

   McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montréal, Canada

   Contribution

   Project administration, Writing – review and editing

   Competing interests

   None

   "This ORCID iD identifies the author of this article:" 0000-0002-7446-150X

9. Sofie L Valk

   1. Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
   2. Institute of Neuroscience and Medicine (INM-7), Forschungszentrum Jülich, Jülich, Germany

   Contribution

   Validation, Writing – review and editing

   Competing interests

   none

   "This ORCID iD identifies the author of this article:" 0000-0003-2998-6849

10. Louis Collins

    McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montréal, Canada

    Contribution

    Methodology, Resources

    Competing interests

    none

    "This ORCID iD identifies the author of this article:" 0000-0002-8432-7021

11. Ali R Khan

    Department of Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western Ontario, London, Canada

    Contribution

    Resources, Writing – review and editing

    Competing interests

    none

12. Katrin Amunts

    Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany

    Contribution

    Conceptualization, Funding acquisition, Writing – review and editing

    Competing interests

    none

    "This ORCID iD identifies the author of this article:" 0000-0001-5828-0867

13. Alan C Evans

    McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montréal, Canada

    Contribution

    Conceptualization, Funding acquisition

    Competing interests

    none

14. Timo Dickscheid

    Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany

    Contribution

    Investigation, Methodology, Writing – review and editing

    Competing interests

    none

15. Boris Bernhardt

    McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montréal, Canada

    Contribution

    Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft

    For correspondence

    boris.bernhardt@mcgill.ca

    Competing interests

    none

    "This ORCID iD identifies the author of this article:" 0000-0001-9256-6041

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