Methods

Sources and licences

Commercial use was assumed (the stricter setting): every bundled dataset is licensed for commercial use. Inputs are pinned by SHA-256 in data/raw-checksums.json.

Coordinate conventions

Source data are RAS in millimetres. fsaverage surface coordinates are MNI305: in fsaverage the scanner and surface (tkr) vox2ras matrices are equal and talairach.xfm is the identity, which the pipeline checks on every run. Positions are converted to MNI152 with the affine published in FreeSurfer's CoordinateSystems documentation, section 8a:

  0.9975   -0.0073    0.0176   -0.0429
  0.0146    1.0009   -0.0024    1.5496
 -0.0130   -0.0093    0.9971    1.1840

Reference points from the same page are unit-test fixtures: MNI305 (10, −20, 35) maps to MNI152 (10.695, −18.409, 36.137), and the published inverse maps MNI152 (10, −20, 35) to MNI305 (9.3131, −21.5849, 33.8345).

Scene mapping is three(x, y, z) = (R, S, −A), 1 scene unit = 1 mm. The mapping has determinant +1, so both systems are right-handed. The hover readout shows MNI152 coordinates in whole millimetres as x, y, z. On the inflated surface, which has no anatomical coordinates, it reports the corresponding pial point.

Display convention. 3D renders are never mirrored: the model always has its real handedness, and persistent L and R markers show the patient's sides. The neurological / radiological setting decides the side from which axial and coronal sections are viewed. Neurological: axial from above, coronal from behind, patient left on screen left. Radiological: axial from below, coronal from the front, patient left on screen right.

Asset pipeline

scripts/build_assets.py rebuilds every asset from the raw sources with one command and verifies its own output. fsaverage is a recursively subdivided icosahedron (ico7, 163,842 vertices per hemisphere). Its first 10,242 and 40,962 vertices are the ico5 and ico6 vertices, so the lower levels of detail are derived by reversing the subdivision. Vertex i is the same fsaverage vertex at every level. The derived fsaverage6 and fsaverage5 triangulations were compared with TemplateFlow's published meshes; face sets and winding were identical.

Each hemisphere and level of detail is one glTF binary file: pial positions, white and inflated morph targets, normals, sulcal depth, and three label attributes (Desikan-Killiany, Destrieux, Brodmann). Compression uses quantisation and meshopt without vertex reordering. A round-trip check decodes every compressed file and requires bit-identical labels, the same triangles in the same order with the same winding (the triangle codec may rotate indices), and positions within 0.05 mm. The measured maximum was under 0.01 mm. Label digests are re-checked by the unit tests.

Subcortical, ventricular, cerebellar and brainstem meshes come from aseg.mgz: marching cubes on each label mask (scikit-image), Taubin smoothing (30 iterations, λ 0.5, μ −0.53) and simplification with meshoptimizer. Every mesh is checked to be closed after simplification; one that would open is kept unsimplified. Closed meshes let clipped sections be capped: the cut face shows grey matter, white matter and deep nuclei in their own colours. White matter and cortex labels are not meshed because the surfaces represent them. Three labels with fewer than 100 voxels (vessel, white matter hypointensities) are excluded.

Atlases and colours

Label names and colours are read from the source files at build time: the colour tables embedded in ?h.aparc.annot and ?h.aparc.a2009s.annot, and FreeSurferColorLUT.txt for aseg and the Brodmann areas. Display names expand the source abbreviations mechanically, and the source name is always shown alongside.

Brodmann areas are approximate: each vertex gets the BA_exvivo area with the highest probability (Fischl et al. 2008), if that probability is at least 0.5. Twelve areas are available (BA1, 2, 3a, 3b, 4a, 4p, 6, 44, 45, V1, V2, MT).

Lobes are derived from Desikan-Killiany labels with FreeSurfer's documented lobe mapping (CorticalParcellation wiki, after Klein and Tourville 2012). Cingulate labels form their own class, and so does the insula, which the mapping does not assign to a lobe. Lobe colours are the Okabe-Ito colour-blind-safe palette.

The colour-blind-safe alternative for parcel atlases assigns one of five CIELAB lightness levels (pairwise at least 15 L* apart) by exact graph colouring of the parcel adjacency graph. Neighbouring parcels therefore always differ in lightness, and hue alternates on a blue–orange axis where possible.

Tracts

Tracts are the HCP-1065 population-averaged tractography atlas (Yeh 2022) in ICBM 2009a space. For each of the 87 tracts, up to 160 streamlines are drawn at random with a fixed seed and resampled to 24 points by arc length. Mean lengths on the Data page use every streamline in the atlas file. Colour encodes local direction with the DTI convention (Pajevic and Pierpaoli 1999): red left–right, green anterior–posterior, blue superior–inferior. The source abbreviation table gives left-hemisphere names for two right-hemisphere tracts (C_FP_R, TR_S_R). The file name is treated as authoritative for side, and the correction is recorded.

MRI sections

A clipped section can be filled with the MNI ICBM 2009a nonlinear asymmetric T1 template (Fonov et al. 2009, 2011), the template the tracts are registered to. The template is brain-masked, cropped to the mask and stored on its own 1 mm grid (147 × 184 × 156 voxels), so no voxel is resampled: the shader samples the volume at each pixel's MNI152 position on the plane, with trilinear interpolation. Intensities are scaled linearly from 0 to the 99.5th percentile inside the mask; 0 marks voxels outside the mask, which are not drawn. The volume (1,587 KiB transferred) loads only when the MRI fill is chosen.

The pial and white surfaces are outlined where they cross the plane, so the template MRI and the fsaverage surfaces can be compared directly. They come from different templates: fsaverage was averaged in MNI305 space and is mapped to MNI152 with a linear transform. Every pial and white vertex lies inside the template brain mask (100% and 100%), and 98.1% of the displayed tract points do (most of the rest are cranial nerves leaving the brain).

Measured against the template's own tissue probability maps, white-surface vertices lie a median 1.3 mm (90th percentile 2.5 mm) from its white-matter boundary. On gyral crowns, pial vertices lie a median 4.0 mm (90th percentile 7.5 mm) inside its outer grey-matter boundary: the fsaverage surfaces are an average of surfaces, the template an average of images, and the two represent the outer cortex differently. Medial-wall vertices are excluded (scripts/measure_surface_template_fit.py).

Arterial territories

The arterial territory colour mode uses the Digital 3D Brain MRI Arterial Territories Atlas (Liu et al. 2023). It was built from the lesion distributions of 1,298 patients with acute or early subacute ischaemic stroke confined to one major arterial territory; voxels in border zones were assigned to the territory with the highest probability. Level 1 has 15 territories per hemisphere (plus the lateral ventricles), grouped at level 2 into the anterior, middle and posterior cerebral arteries and the vertebrobasilar system. Names, acronyms and levels come from the atlas lookup table; colours are a display choice (hue by level-2 family, lightness separating neighbouring territories by at least 15 L*), because the source has none.

Each cortical vertex takes the territory of the voxel at mid-thickness, halfway between the white and pial surfaces, considering only territories of its own hemisphere. 621 of 299,879 cortical vertices (0.21%) fall in unlabelled voxels and take the nearest territory within 3 mm; none remain unassigned. The medial wall is left uncoloured. On an MRI section the territories are drawn over the T1, with 1 mm borders, including the deep territories that do not reach the cortex.

The atlas files carry orientation but no origin, so the FSL MNI152 1 mm grid named in their README is assumed (voxel (90, 126, 72) at MNI (0, 0, 0)). Checks: every left-named territory lies at x < 0 and every right-named one at x > 0; the atlas overlaps the FSL MNI152 brain mask with a Dice coefficient of 0.946; the level-1 and level-2 images agree on 99.986% of labelled voxels. Moving the origin by 1 mm along any axis would change the territory of 1.5%–2.1% of cortical vertices, all at territory borders. The source acronyms of four right-hemisphere territories end in “L”; the descriptive names, which the voxel data confirm, are used, and the source text is kept in arterial/art.json.

Content governance

Every statement about function, supply, drainage, deficits, surgical relevance or eloquence is a claim with at least one source. The build fails on an unsourced claim, an unknown reference, a missing atlas label, or dominance wording other than “dominant hemisphere (usually left)”. Every reference was checked against Crossref (DOI, title, authors, journal, volume, pages). Clinical reviewer: none yet. All 10 curated structures carry an “Awaiting clinical review” badge. Quantitative values come only from the cited datasets; there are no placeholder numbers.

Known limitations

• The model is a template, an average of 40 subjects. It is not patient-specific and cannot show an individual's anatomy, variability, or language dominance.
• Average surfaces are smoother than individual cortex. Template areas are smaller than individual measurements and are not norms.
• The medial walls of the average hemispheres meet at the midline. 2,722 left pial vertices lie up to 2.1 mm right of the midline, 65 of them labelled (paracentral, up to 0.6 mm). No labelled white-surface vertex crosses.
• Lower levels of detail subsample the fsaverage surface. FreeSurfer's separately averaged fsaverage5 and fsaverage6 surfaces differ from them by up to 4.8 mm at deep sulcal vertices.
• Subcortical meshes are smoothed segmentations of a template volume at 1 mm. Thin structures such as the choroid plexus and the inferior horn are approximate.
• Brodmann areas are probabilistic projections from ex vivo brains and are approximate on any template.
• Arterial territories are population maps derived from stroke lesions, not angiography; individual territories, collaterals and border zones vary and are not shown. The less stroke-prone medial lenticulostriate and anterior choroidal/thalamoperforating territories were defined from prior anatomical knowledge (Liu et al. 2023).
• The MRI sections, the tracts and the arterial atlas are on MNI152 templates; the cortex is fsaverage mapped linearly from MNI305. Against the MRI template, white surfaces are a median 1.3 mm from its white-matter boundary and pial surfaces on gyral crowns a median 4.0 mm inside its outer boundary; the surface outlines on the MRI show these differences.

Required notices

Human Connectome Project: Data were provided in part by the Human Connectome Project, WU-Minn Consortium (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.

Arterial territories adapted from the Digital 3D Brain MRI Arterial Territories Atlas, © 2021 The Johns Hopkins University (Liu et al. 2023, https://doi.org/10.1038/s41597-022-01923-0), distributed under CC BY-SA 4.0. Changes: sampled onto the cortical surfaces, resampled onto the MRI grid, display colours added, two spellings and four side letters corrected. The adapted files are distributed under CC BY-SA 4.0.

The files in public/assets/mri/ are derived from the MNI ICBM 2009a nonlinear asymmetric
template (Fonov et al. 2009, 2011), McConnell Brain Imaging Centre, Montreal Neurological
Institute. Changes: brain-masked, cropped to the mask, intensities rescaled to 8 bits.
The copyright notice shipped with the template (file COPYING) follows verbatim.

Copyright (C) 1993-2004 Louis Collins, McConnell Brain
Imaging Centre, Montreal Neurological Institute, McGill University.
Permission to use, copy, modify, and distribute this software and
its documentation for any purpose and without fee is hereby granted,
provided that the above copyright notice appear in all copies.  The
authors and McGill University make no representations about the
suitability of this software for any purpose.  It is provided "as
is" without express or implied warranty.  The authors are not
responsible for any data loss, equipment damage, property loss, or
injury to subjects or patients resulting from the use or misuse of
this software package. 

Tract data adapted from HCP-1065 (Yeh 2022) are distributed under CC BY-SA 4.0. The tract files were modified: subsampled, resampled and quantised.

PART B. DOWNLOADING AGREEMENT - License from MGH with Right to Sublicense
("Software License").

1. As used in this Software License, "you" means the individual
   downloading and/or using, reproducing, modifying, displaying and/or
   distributing the Software and the institution or entity which
   employs or is otherwise affiliated with such individual in
   connection therewith. The General Hospital Corporation ("MGH")
   hereby grants you, with right to sublicense, with
   respect to MGH's rights in the software, and data, if any,
   which is the subject of this Software License (collectively, the
   "Software"), a royalty-free, non-exclusive license to use,
   reproduce, make derivative works of, display and distribute the
   Software, provided that:

(a) you accept and adhere to all of the terms and conditions of this
Software License;

(b) in connection with any copy of or sublicense of all or any portion
of the Software, all of the terms and conditions in this Software
License shall appear in and shall apply to such copy and such
sublicense, including without limitation all source and executable
forms and on any user documentation, prefaced with the following
words: "All or portions of this licensed product (such portions are
the "Software") have been obtained under license from The General Hospital
Corporation "MGH" and are subject to the following terms and
conditions:"

(c) you preserve and maintain all applicable attributions, copyright
notices and licenses included in or applicable to the Software;

(d) modified versions of the Software must be clearly identified and
marked as such, and must not be misrepresented as being the original
Software; and

(e) you consider making, but are under no obligation to make, the
source code of any of your modifications to the Software freely
available to others on an open source basis.

2. The license granted in this Software License includes without
   limitation the right to (i) incorporate the Software into
   proprietary programs (subject to any restrictions applicable to
   such programs), (ii) add your own copyright statement to your
   modifications of the Software, and (iii) provide additional or
   different license terms and conditions in your sublicenses of
   modifications of the Software; provided that in each case your use,
   reproduction or distribution of such modifications otherwise
   complies with the conditions stated in this Software License.

3. This Software License does not grant any rights with respect to
   third party software, except those rights that MGH has been
   authorized by a third party to grant to you, and accordingly you
   are solely responsible for (i) obtaining any permissions from third
   parties that you need to use, reproduce, make derivative works of,
   display and distribute the Software, and (ii) informing your
   sublicensees, including without limitation your end-users, of their
   obligations to secure any such required permissions.

4. The Software has been designed for research purposes only and has
   not been reviewed or approved by the Food and Drug Administration
   or by any other agency. YOU ACKNOWLEDGE AND AGREE THAT CLINICAL
   APPLICATIONS ARE NEITHER RECOMMENDED NOR ADVISED. Any
   commercialization of the Software is at the sole risk of the party
   or parties engaged in such commercialization. You further agree to
   use, reproduce, make derivative works of, display and distribute
   the Software in compliance with all applicable governmental laws,
   regulations and orders, including without limitation those relating
   to export and import control.

5. The Software is provided "AS IS" and neither MGH nor any
   contributor to the software (each a "Contributor") shall have any
   obligation to provide maintenance, support, updates, enhancements
   or modifications thereto. MGH AND ALL CONTRIBUTORS SPECIFICALLY
   DISCLAIM ALL EXPRESS AND IMPLIED WARRANTIES OF ANY KIND INCLUDING,
   BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR
   A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL
   MGH OR ANY CONTRIBUTOR BE LIABLE TO ANY PARTY FOR DIRECT,
   INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY OR CONSEQUENTIAL DAMAGES
   HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY ARISING IN ANY WAY
   RELATED TO THE SOFTWARE, EVEN IF MGH OR ANY CONTRIBUTOR HAS
   BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. TO THE MAXIMUM
   EXTENT NOT PROHIBITED BY LAW OR REGULATION, YOU FURTHER ASSUME ALL
   LIABILITY FOR YOUR USE, REPRODUCTION, MAKING OF DERIVATIVE WORKS,
   DISPLAY, LICENSE OR DISTRIBUTION OF THE SOFTWARE AND AGREE TO
   INDEMNIFY AND HOLD HARMLESS MGH AND ALL CONTRIBUTORS FROM AND
   AGAINST ANY AND ALL CLAIMS, SUITS, ACTIONS, DEMANDS AND JUDGMENTS
   ARISING THEREFROM.

6. None of the names, logos or trademarks of MGH or any of
   MGH's affiliates or any of the Contributors, or any funding
   agency, may be used to endorse or promote products produced in
   whole or in part by operation of the Software or derived from or
   based on the Software without specific prior written permission
   from the applicable party.

7. Any use, reproduction or distribution of the Software which is not
   in accordance with this Software License shall automatically revoke
   all rights granted to you under this Software License and render
   Paragraphs 1 and 2 of this Software License null and void.

8. This Software License does not grant any rights in or to any
   intellectual property owned by MGH or any Contributor except
   those rights expressly granted hereunder.

References

1. Amunts K, Schleicher A, B�rgel U, Mohlberg H, Uylings HBM, Zilles K. Broca's region revisited: Cytoarchitecture and intersubject variability. The Journal of Comparative Neurology 1999;412(2):319-341. doi:10.1002/(SICI)1096-9861(19990920)412:2<319::AID-CNE10>3.0.CO;2-7
2. Berman JI, Berger MS, Mukherjee P, Henry RG. Diffusion-tensor imaging-guided tracking of fibers of the pyramidal tract combined with intraoperative cortical stimulation mapping in patients with gliomas. Journal of Neurosurgery 2004;101(1):66-72. doi:10.3171/jns.2004.101.1.0066
3. Binder JR. The Wernicke area. Neurology 2015;85(24):2170-2175. doi:10.1212/WNL.0000000000002219
4. Catani M, Jones DK, ffytche DH. Perisylvian language networks of the human brain. Annals of Neurology 2005;57(1):8-16. doi:10.1002/ana.20319
5. Catani M, Thiebautdeschotten M. A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex 2008;44(8):1105-1132. doi:10.1016/j.cortex.2008.05.004
6. Corkin S. Somatosensory Thresholds. Archives of Neurology 1970;23(1):41. doi:10.1001/archneur.1970.00480250045007
7. Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al.. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 2006;31(3):968-980. doi:10.1016/j.neuroimage.2006.01.021
8. Destrieux C, Fischl B, Dale A, Halgren E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage 2010;53(1):1-15. doi:10.1016/j.neuroimage.2010.06.010
9. Ebeling U, Reulen HJ. Neurosurgical topography of the optic radiation in the temporal lobe. Acta Neurochirurgica 1988;92(1-4):29-36. doi:10.1007/BF01401969
10. Fischl B, Sereno MI, Tootell RBH, Dale AM. High-resolution intersubject averaging and a coordinate system for the cortical surface. Human Brain Mapping 1999;8(4):272-284. doi:10.1002/(SICI)1097-0193(1999)8:4<272::AID-HBM10>3.0.CO;2-4
11. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al.. Whole Brain Segmentation. Neuron 2002;33(3):341-355. doi:10.1016/S0896-6273(02)00569-X
12. Fischl B, Rajendran N, Busa E, Augustinack J, Hinds O, Yeo BTT, et al.. Cortical Folding Patterns and Predicting Cytoarchitecture. Cerebral Cortex 2008;18(8):1973-1980. doi:10.1093/cercor/bhm225
13. Fischl B. FreeSurfer. NeuroImage 2012;62(2):774-781. doi:10.1016/j.neuroimage.2012.01.021
14. Fonov VS, Evans AC, McKinstry RC, Almli CR, Collins DL. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage 2009;47:S102. doi:10.1016/S1053-8119(09)70884-5
15. Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL. Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 2011;54(1):313-327. doi:10.1016/j.neuroimage.2010.07.033
16. Fontaine D, Capelle L, Duffau H. Somatotopy of the Supplementary Motor Area: Evidence from Correlation of the Extent of Surgical Resection with the Clinical Patterns of Deficit. Neurosurgery 2002;50(2):297-305. doi:10.1097/00006123-200202000-00011
17. FreeSurfer developers. CoordinateSystems (FreeSurfer wiki), sections 8a and 8b. FreeSurfer wiki. https://surfer.nmr.mgh.harvard.edu/fswiki/CoordinateSystems
18. FreeSurfer developers. CorticalParcellation (FreeSurfer wiki), section "Lobe mapping". FreeSurfer wiki. https://surfer.nmr.mgh.harvard.edu/fswiki/CorticalParcellation
19. The General Hospital Corporation. FreeSurfer Software License Agreement, version 1.0. 2011. https://raw.githubusercontent.com/freesurfer/freesurfer/dev/LICENSE.txt
20. Yeh F-C. Population-Probability Atlas and Tract-to-Region Connectome (HCP-1065), data release. brain.labsolver.org 2022. https://brain.labsolver.org/hcp_trk_atlas.html
21. Hickok G, Poeppel D. The cortical organization of speech processing. Nature Reviews Neuroscience 2007;8(5):393-402. doi:10.1038/nrn2113
22. Keles GE, Lundin DA, Lamborn KR, Chang EF, Ojemann G, Berger MS. Intraoperative subcortical stimulation mapping for hemispheric perirolandic gliomas located within or adjacent to the descending motor pathways: evaluation of morbidity and assessment of functional outcome in 294 patients. Journal of Neurosurgery 2004;100(3):369-375. doi:10.3171/jns.2004.100.3.0369
23. Klein A, Tourville J. 101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol. Frontiers in Neuroscience 2012;6:171. doi:10.3389/fnins.2012.00171
24. Knecht S. Handedness and hemispheric language dominance in healthy humans. Brain 2000;123(12):2512-2518. doi:10.1093/brain/123.12.2512
25. Krainik A, Lehéricy S, Duffau H, Capelle L, Chainay H, Cornu P, et al.. Postoperative speech disorder after medial frontal surgery. Neurology 2003;60(4):587-594. doi:10.1212/01.WNL.0000048206.07837.59
26. Laplane D, Talairach J, Meininger V, Bancaud J, Orgogozo JM. Clinical consequences of corticectomies involving the supplementary motor area in man. Journal of the Neurological Sciences 1977;34(3):301-314. doi:10.1016/0022-510X(77)90148-4
27. Lewiner T, Lopes H, Vieira AW, Tavares G. Efficient Implementation of Marching Cubes' Cases with Topological Guarantees. Journal of Graphics Tools 2003;8(2):1-15. doi:10.1080/10867651.2003.10487582
28. Liu CF, Hsu J, Xu X, Kim G, Sheppard SM, Meier EL, Miller MI, Hillis AE, Faria AV. Digital 3D Brain MRI Arterial Territories Atlas. Scientific Data 2023;10(1):74. doi:10.1038/s41597-022-01923-0
29. Morel A, Magnin M, Jeanmonod D. Multiarchitectonic and stereotactic atlas of the human thalamus. The Journal of Comparative Neurology 1997;387(4):588-630. doi:10.1002/(SICI)1096-9861(19971103)387:4<588::AID-CNE8>3.0.CO;2-Z
30. Mori S, Oishi K, Jiang H, Jiang L, Li X, Akhter K, Hua K, Faria AV, Mahmood A, Woods R, Toga AW, Pike GB, Neto PR, Evans A, Zhang J, Huang H, Miller MI, van Zijl P, Mazziotta J. Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. NeuroImage 2008;40(2):570-582. doi:10.1016/j.neuroimage.2007.12.035
31. Ojemann G, Ojemann J, Lettich E, Berger M. Cortical language localization in left, dominant hemisphere. Journal of Neurosurgery 1989;71(3):316-326. doi:10.3171/jns.1989.71.3.0316
32. Okabe M, Ito K. Color Universal Design (CUD): how to make figures and presentations that are friendly to colorblind people. J*Fly 2008. https://jfly.uni-koeln.de/color/
33. Pajevic S, Pierpaoli C. Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: Application to white matter fiber tract mapping in the human brain. Magnetic Resonance in Medicine 1999;42(3):526-540. doi:10.1002/(SICI)1522-2594(199909)42:3<526::AID-MRM15>3.0.CO;2-J
34. Penfield W, Boldrey E. Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation. Brain 1937;60(4):389-443. doi:10.1093/brain/60.4.389
35. Rhoton AL. The Supratentorial Arteries. Neurosurgery 2002;51(suppl_4):S1-53-S1-120. doi:10.1097/00006123-200210001-00003
36. Rhoton AL. The Cerebrum. Neurosurgery 2002;51(suppl_4):S1-1-S1-52. doi:10.1097/00006123-200210001-00002
37. Rhoton AL. The Cerebral Veins. Neurosurgery 2002;51(suppl_4):S1-159-S1-205. doi:10.1097/00006123-200210001-00005
38. Ritchie SJ, Cox SR, Shen X, Lombardo MV, Reus LM, Alloza C, Harris MA, Alderson HL, Hunter S, Neilson E, Liewald DCM, Auyeung B, Whalley HC, Lawrie SM, Gale CR, Bastin ME, McIntosh AM, Deary IJ. Sex Differences in the Adult Human Brain: Evidence from 5216 UK Biobank Participants. Cerebral Cortex 2018;28(8):2959-2975. doi:10.1093/cercor/bhy109
39. Sanai N, Mirzadeh Z, Berger MS. Functional Outcome after Language Mapping for Glioma Resection. New England Journal of Medicine 2008;358(1):18-27. doi:10.1056/NEJMoa067819
40. Sanai N, Polley MY, Berger MS. Insular glioma resection: assessment of patient morbidity, survival, and tumor progression. Journal of Neurosurgery 2010;112(1):1-9. doi:10.3171/2009.6.JNS0952
41. Sawaya R, Hammoud M, Schoppa D, Hess KR, Wu SZ, Shi WM, et al.. Neurosurgical Outcomes in a Modern Series of 400 Craniotomies for Treatment of Parenchymal Tumors. Neurosurgery 1998;42(5):1044-1055. doi:10.1097/00006123-199805000-00054
42. Schmahmann JD. Vascular Syndromes of the Thalamus. Stroke 2003;34(9):2264-2278. doi:10.1161/01.STR.0000087786.38997.9E
43. Schmahmann JD, Pandya DN. Fiber Pathways of the Brain. Oxford University Press 2006. doi:10.1093/acprof:oso/9780195104233.001.0001
44. Spetzler RF, Martin NA. A proposed grading system for arteriovenous malformations. Journal of Neurosurgery 1986;65(4):476-483. doi:10.3171/jns.1986.65.4.0476
45. Tatu L, Moulin T, Bogousslavsky J, Duvernoy H. Arterial territories of human brain. Neurology 1996;47(5):1125-1135. doi:10.1212/WNL.47.5.1125
46. Tatu L, Moulin T, Bogousslavsky J, Duvernoy H. Arterial territories of the human brain. Neurology 1998;50(6):1699-1708. doi:10.1212/WNL.50.6.1699
47. Taubin G. A signal processing approach to fair surface design. Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH 95) 1995:351-358. doi:10.1145/218380.218473
48. Türe U, Yaşargil DCH, Al-Mefty O, Yaşargil MG. Topographic anatomy of the insular region. Journal of Neurosurgery 1999;90(4):720-733. doi:10.3171/jns.1999.90.4.0720
49. Türe U, Yaşargil MG, Al-Mefty O, Yaşargil DCH. Arteries of the insula. Journal of Neurosurgery 2000;92(4):676-687. doi:10.3171/jns.2000.92.4.0676
50. Van Essen DC, Smith SM, Barch DM, Behrens TEJ, Yacoub E, Ugurbil K. The WU-Minn Human Connectome Project: An overview. NeuroImage 2013;80:62-79. doi:10.1016/j.neuroimage.2013.05.041
51. Yeh FC. Population-based tract-to-region connectome of the human brain and its hierarchical topology. Nature Communications 2022;13(1):4933. doi:10.1038/s41467-022-32595-4
52. Yousry T. Localization of the motor hand area to a knob on the precentral gyrus. A new landmark. Brain 1997;120(1):141-157. doi:10.1093/brain/120.1.141