Research Progress on Tractography of Superficial White Matter Based on Diffusion Magnetic Resonance Imaging

doi:10.11938/cjmr20243126

Abstract

Abstract:

In recent years, significant progress has been made in tractography of superficial white matter in the brain based on diffusion magnetic resonance imaging. Superficial white matter fiber tracts serve as critical pathways connecting cortical and subcortical structures, playing a vital role in the construction of complete human connectome and neuropathological studies. This paper first summarizes the development of superficial white matter tractography techniques. Subsequently, it evaluates the strengths and weaknesses of different methods employed in superficial white matter fiber tract segmentation. Afterwards, the construction process of superficial white matter fiber tract atlas is discussed. Finally, this review concludes with an outlook on future research direction of superficial white matter tractography technology and segmentation methods.

Key words: diffusion magnetic resonance imaging, superficial white matter, tractography, segmentation, atlas

CLC Number:

O482.53

MENG Jingxin, WANG Yuanjun. Research Progress on Tractography of Superficial White Matter Based on Diffusion Magnetic Resonance Imaging[J]. Chinese Journal of Magnetic Resonance, 2025, 42(2): 205-220.

Figures/Tables 6

Fig. 1

Fig. 2

Table 1

SWM fiber tract segmentation method studies

第一作者	研究区域	分割方法	主要连接和发现	定量评价
第一作者	研究区域	分割方法	主要连接和发现	分割数量	准确率%
Catani^[7]	枕叶、颞叶	ROI/手动选择	枕颞外侧区相邻回的下纵束	1	/
Wu^[14]	颞叶、顶叶、枕叶	ROI/手动选择	上纵束后段连接颞中回和颞下回的后部与角回和缘上回；垂直枕束连接下顶叶、颞叶和枕叶；新的颞顶叶连接，将颞下回、颞中回、枕颞外侧回以及枕叶下部与顶叶上部互连	3	/
Wakana^[33]	全脑	ROI/手动选择	上纵束的一部分；枕叶束	2	/
Catani^[34]	额叶、中央沟、中央前沟、岛沟、额缘沟	ROI/手动选择	PrCG-PoCG，PrCG-MFG，SFG-IFG，SFG-MFG，FOP，FMT，FSL，FIL，Ins-Or/Tr/Op/PrCG/SuCG	13	/
Rojkova^[35]	额叶	ROI/手动选择	连接中央前回和中央后回的U型纤维；额叶斜束；连接额叶和岛叶的五个U型纤维；额叶上纵束和下纵束；额哐束和额边缘束	30	/
Burks^[36]	顶下小叶	ROI/手动选择	连接缘上回和角回的U型纤维；连接颞上沟边缘正下方和颞叶的U型纤维；连接侧裂末端和额叶的U型纤维	3	/
Catani^[37]	顶叶	ROI/手动选择	SMG-SPL，AG-SPL，PoCG-AG，PoCG-SMG，PoCG-SPL，AG-SMG，SMG-SMG，aPrCu-pPrCu，SPL的前后连接和内外侧连接	9	/
Shinohara^[38]	全脑	ROI/手动选择	脑回内和脑回间U型纤维从各个方向汇聚到白质脊的交界处，构成了“金字塔形交叉”	/	/
Oishi^[39]	全脑	ROI/自动选择	SFG-IFG，MFG-PrCG，PrCG-PoCG，psaf	4	/
Zhang^[40]	全脑	ROI/自动选择	SFG-IFG，MFG-PrCG，PrCG-PoCG，SPG-SMG，SPG-PoCG，SPG-AG，SPG-PrCu，SPG-SOG，SPG-MOG，CG-SFG，CG-PrCu，SFG-MFG，SFG-PrCG，MFG-IFG，IFG-PrCG，PoCG-SMG，AG-MOG，AG-SMG，Cu-LG，Cu-SOG，Cu-MOG，FuG-IOG，FuG-MOG，SOG-MOG，IOG-MOG，STG-MTG，STG-SMG，ITG-MTG，LFOG-MFOG	29	/
Pardo^[41]	全脑	ROI/自动选择	研究其SWM束的变异性	80	/
Ouyang^[42]	全脑	ROI/自动选择	没有特定的束，短关联纤维根据它们连接的两个相邻回进行分组	/	/
Movahedian^[43]	初级和次级视觉皮层区域	ROI/自动选择	初级和次级视觉皮层区域的短关联纤维束连接	/	/
Vergani^[44]	辅助运动区	ROI/半自动选择	SMA-PrCG，SMA-CG	2	/
Magro^[45]	中央前回和中央后回	ROI/半自动选择	中央前回和中央后回9条纤维束	9	/
Guevara^[47]	全脑	流线标记/几何距离	在中央沟和颞上沟发现了不同人群的纤维组织的变异性	/	/
Vindas^[48]	全脑	流线标记/几何距离	所提出的方法在两个数据集中都发现了更多的SWM纤维束	/	/
Zhang^[13]	中央沟、中央前沟、中央后沟、颞上沟、额下沟和顶内沟	流线标记/聚类	三种数据类型共有：SFG-MFG，MFG-IFG，PrCG-PoCG，SPG-IFG，PoCG-SPG；DSI：MFG-IPL，SFG-IPL，MFG-SMG，IFG-MTG，PoCG-IPL，SPG-SMG，SMG-MTG，MTG-ITG，IPL-MOG，SFG-SPG，MFG-MTG，IFG-SPG，PrCG-SPG，SPG-IPL，SPG-SOG，SPG-MTG，STG-MTG； HARDI：SPG-SMG，MFG-MTG，SFG-IFG，SOG-MOG，MFG-PrCG，PoCG-SMG，SPG-MTG，STG-MTG，SPG-IPL，SMG-PrCG，IPL-SMG，IPL-MTG； DTI：SFG-IFG，MFG-PrCG，IFG-PrCG，SMG-MTG，PrCG-SPG，SFG-PrCG，SFG-PoCG，SFG-SPG，PrCG-MTG，SPG-SOG，IPL-MTG，IFG-STG，PoCG-IPL，PrCG-IPL，PoCG-IPL，IPL-MOG，SMG-MOG，STG-SMG	/	/
Guevara^[49]	全脑	流线标记/聚类	左半球与右半球一致：SFG-IFG(ant，mid，post)，SFG-MFG(ant，mid，post)，MFG-IFG，MFG(mid，mid2，post，post2)，IFG-Ins，IFG(post，inf)，LFOG(inf，sup)，MFOG，MFOG-CG，SFG-CG(mid)，MFG-PrCG(sup，mid)，PrCG-PoCG(sup，inf)，PrCG-Ins，PrCG-SMG，PaCG-PrCu，PoCG-SMG，SMG，SPG，AG(sup，inf)，STG-AG，MTG-AG，STG(post)，MTG-Ins，STG-Ins，ITG-MOG，Cu，Cu-Li，LG，FuG(ant，mid，post)，PrCu-CG，PrCu-SFG，CG(ant，mid，post)	94	/
Román^[50]	全脑	流线标记/聚类	两半球共有：SPL_SPL_0i，PrCG_SFG_0i，PoCG_PrCG_0-3i，Op_SFG_0i，CMFG_PrCG_0-1i，MTG_MTG_0-1i，PrCG_SMG_0-1i，CMFG_CMFG_0i，FuG_ITG_0i，IPL_SPL_0i，MTG_STG_0i，LorFG_LorFG_0i，CMFG_Op_0i，RMFG_SFG_0-1i，Tr_SFG_0i，SMG_SMG_0-2i，RMFG_RMFG_0-1i，PoCG_SMG_0i，FuG_FuG_0i，STG_STG_0i，Tr_RMFG_0i，LOG_LOG_0-1i, 左半球：ITG_ITG_0-1l，SFG_SFG_0l，FuG_FuG_1l，PrCG_PrCG_0l，STG_STG_1l，Cu_LG_0l，PrCu_PrCu_0l，MTG_MTG_1l，LOG_LOG_2l，PrCG_Ins_0l 右半球：Tr_Tr_0r，Tr_Ins_0r，MTG_MTG_0r，SFG_SFG_1-2r，RMFG_SFG_0r，RMFG_RMFG_0-1r，PoCG_PoCG_1r，PoCG_PrCG_1r，SPL_SPL_0r，PrCu_PrCu_0r，IPL_LOG_0r，IPL_IPL_0r，LoFG_LoFG_1r，Tr_SFG_1r	左：44；右：49	/
Zhang^[52]	全脑	流线标记/聚类	198短纤维簇连接：颞叶、顶叶-颞叶、顶叶-枕叶、顶叶、枕颞叶、枕叶、额叶-顶叶和额叶区域	198	/
Pron^[53]	中央沟	流线标记/聚类	左半球五条U型纤维连接中央前回和中央后回	左：5	/
Pron^[54]	中央沟	流线标记/聚类	左右半球各有五条U型纤维连接中央前回和中央后回	左：5；右：5	/
Zhang^[15]	全脑	流线标记/深度学习	198短纤维簇连接：颞叶、顶叶-颞叶、顶叶-枕叶、顶叶、枕颞叶、枕叶、额叶-顶叶和额叶区域	198	98.42
Xue^[55]	全脑	流线标记/深度学习	198短纤维簇连接：颞叶、顶叶-颞叶、顶叶-枕叶、顶叶、枕颞叶、枕叶、额叶-顶叶和额叶区域	198	96.79
Guevara^[56]	全脑	ROI选择和聚类相结合	两半球共有：CACG-PrCu_0，CMFG-PrCG_0-1，CMFG-RMFG_0，CMFG-SFG_0，IC-PrCu_0，IPL-ITG_0，IPL-MTG_0，IPL-SMG_0，IPL-SPL_0，LOFG-RMFG_0-1，LOFG-STG_0，MOFG-STG_0，MTG-SMG_0，MTG-STG_0，Op-Ins_0，Op-PrCG_0，Op-SFG_0，Or-Ins_0，PoCiG-PrCu_1，PoCiG-RACG_0，PoCG-PrCG_0-2，PoCG-SMG_0，PrCG-Ins_0，PrCG-SMG_0，RMFG-SFG_0-1，SMG-Ins_0，SPL-SMG_0，STG-TTG_0，Tr-Ins_0，Tr-SFG_0 左半球：CMFG-Op_0，CMFG-PoCG_0，Fu-LOG_0，IPL-LOG_1，IPL-SPL_1，ITG-MTG_0，LOFG-Or_0，PoCG-Ins_0，PoCiG-PrCu_0，PoCiG-SFG_0，PoCG-PrCG_3，PoCG-SMG_1，PrCG-SFG_0，RACG-SFG_1，STG-Ins_0 右半球：CACG-PoCiG_0，CMFG-SFG_1，Cu-LG_0，Fu-LOG_1，IPL-LOG_0，ITG-MTG_1-2，LOFG-MOFG_0，LOG-SPL_0，Op-Tr_0，PoCiG-PrCu_2，PoCG-SPL_0，PrCG-SPL_0，RACG-SFG_0	100	/
Román^[57]	全脑	ROI选择和聚类相结合	图谱由整个大脑的525束短关联纤维组成，其中384束连接不同ROI部分，141束连接相同ROI部分	525	/

Table 1

Fig. 3

Table 2

Table A1

Chinese and English full names for corresponding abbreviations

缩写	英文全称	中文全称
PrCG	precentral gyrus	中央前回
PoCG	postcentral gyrus	中央后回
SFG	superior frontal gyrus	额上回
MFG	middle frontal gyrus	额中回
IFG	inferior frontal gyrus	额下回
FOP	fronto-orbitopolar tract	额眶极束
FMT	fronto-marginal tract	额叶边缘束
FSL	frontal superior longitudinal tract	额叶上纵束
FIL	frontal inferior longitudinal tract	额叶下纵束
Ins	insular	脑岛
Or	orbital	眶部
Tr	triangular	三角部
Op	opercular	岛盖部
SuCG	sub-central gyrus	亚中央回
SMG	supramarginal gyrus	缘上回
SPL	superior parietal lobule	顶上小叶
IPL	inferior parietal lobule	顶下小叶
AG	angular gyrus	角回
PrCu	precuneus	楔前叶
psaf	parietal short association fibers	顶叶短关联纤维
SPG	superior parietal gyrus	顶上回
SOG	superior occipital gyrus	枕上回
MOG	medial occipital gyrus	枕中回
CG	cingulate gyrus	扣带回
SFG	superior frontal gyrus	额上回
MFG	medial frontal gyrus	额中回
IFG	inferior frontal gyrus	额下回
Cu	cuneus	楔叶
LG	lingual gyrus	舌回
FuG	fusigorm gyrus	枕颞外侧回
IOG	inferior occipital gyrus	枕下回
STG	superior temporal gyrus	颞上回
MTG	middle temporal gyrus	颞中回
ITG	inferior temporal gyrus	额下回
LFOG	lateral fronto-orbital gyrus	内侧眶额回
MFOG	medial fronto-orbital gyrus	外侧眶额回
SMA	supplementary motor area	辅助运动区
CMFG	caudal middle frontal gyrus	额中回尾部
LoFG	lateral orbito frontal gyrus	额眶外侧回
RMFG	rostral middle frontal gyrus	额中回头部
LOG	lateral occipital gyrus	枕外侧回
CACG	caudal anterior cingulate gyrus	尾侧前扣带回
IC	isthmus cingulate	扣带峡部
MOFG	medial orbito frontal gyrus	额眶内侧回
RACG	rostal anterior cingulate gyrus	前扣带回头部
PoCiG	posterior cingulate gyrus	后扣带回
TTG	transverse temporal gyrus	颞横回

Table A1

References 58

[1]	VERGANI F, MAHMOOD S, MORRIS C M, et al. Intralobar fibres of the occipital lobe: a post mortem dissection study[J]. Cortex, 2014, 56: 145-156. doi: 10.1016/j.cortex.2014.03.002 pmid: 24768339
[2]	REVELEY C, SETH A K, PIERPAOLI C, et al. Superficial white matter fiber systems impede detection of long-range cortical connections in diffusion MR tractography[J]. Proc Natl Acad Sci, 2015, 112(21): E2820-E2828.
[3]	VAN DYKEN P C, KHAN A R, PALANIYAPPAN L. Imaging of the superficial white matter in health and disease[J]. Imaging Neurosci, 2024, 2: 1-35.
[4]	KIRILINA E, HELBLING S, MORAWSKI M, et al. Superficial white matter imaging: Contrast mechanisms and whole-brain in vivo mapping[J]. Sci Adv, 2020, 6(41): eaaz9281.
[5]	ZHU Y, WANG Y. Brain fiber structure estimation based on principal component analysis and RINLM filter[J]. Med Biol Eng Comput, 2024, 62(3): 751-771. doi: 10.1007/s11517-023-02972-2 pmid: 37996628
[6]	CONTURO T E, LORI N F, CULL T S, et al. Tracking neuronal fiber pathways in the living human brain[J]. Proc Natl Acad Sci, 1999, 96(18): 10422-10427.
[7]	CATANI M, JONES D K, DONATO R, et al. Occipito-temporal connections in the human brain[J]. Brain, 2003, 126(9): 2093-2107.
[8]	GUEVARA P, DUCLAP D, POUPON C, et al. Segmentation of short association bundles in massive tractography datasets using a multi-subject bundle atlas[C]// Iberoamerican Congress on Pattern Recognition, Berlin, Heidelberg: Springer Berlin Heidelberg, 2011: 701-708.
[9]	XUE T, ZHANG F, ZHANG C, et al. Supwma: consistent and efficient tractography parcellation of superficial white matter with deep learning[C]// International Symposium on Biomedical Imaging, ITC Royal Bengal, Kolkata, India: IEEE, 2022: 1-5.
[10]	FENG Y J, WANG Z J, ZHANG G J, et al. Global white matter tractography using swarm optimization[J]. J Image Graph, 2012, 17(10): 1312-1318.
	冯远静, 王哲进, 张贵军, 等. 全局脑白质纤维群智能跟踪算法[J]. 中国图象图形学报, 2012, 17(10): 1312-1318.
[11]	张军. 基于邻域字典基模型的脑纤维流线微分方程跟踪算法[D]. 杭州: 浙江工业大学, 2017.
[12]	YUE Q, WANG Y J. A fiber tracking algorithm based on non-local constrained spherical deconvolution[J]. Chinese J Magn Reson, 2020, 37(4): 422-433.
	岳晴, 王远军. 基于非局部约束球面反卷积模型的纤维追踪算法[J]. 波谱学杂志, 2020, 37(4): 422-433. doi: 10.11938/cjmr20192798
[13]	ZHANG T, CHEN H, GUO L, et al. Characterization of U-shape streamline fibers: methods and applications[J]. Med Image Anal, 2014, 18(5): 795-807. doi: 10.1016/j.media.2014.04.005 pmid: 24835185
[14]	WU Y, SUN D, WANG Y, et al. Tracing short connections of the temporo-parieto-occipital region in the human brain using diffusion spectrum imaging and fiber dissection[J]. Brain Res, 2016, 1646: 152-159. doi: S0006-8993(16)30408-5 pmid: 27235864
[15]	ZHANG D, ZONG F, ZHANG Q, et al. Anat-SFSeg: Anatomically-guided superficial fiber segmentation with point-cloud deep learning[J]. Med Image Anal, 2024, 95: 103165.
[16]	LI M, HE J Z, FENG Y J. Research progress of neural fiber tracking[J]. J Image Graph, 2020, 25(8): 1513-1528.
	李茂, 何建忠, 冯远静. 神经纤维跟踪算法研究进展[J]. 中国图象图形学报, 2020, 25(8): 1513-1528.
[17]	NIE X, RUAN J, OTADUY M C G, et al. Surface-based probabilistic fiber tracking in superficial white matter[J]. IEEE Trans Med Imaging, 2024, 43(3): 1113-1124.
[18]	GUEVARA M, GUEVARA P, ROMÁN C, et al. Superficial white matter: A review on the dMRI analysis methods and applications[J]. NeuroImage, 2020, 212: 116673.
[19]	UĞURBIL K, XU J, AUERBACH E J, et al. Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project[J]. NeuroImage, 2013, 80: 80-104. doi: 10.1016/j.neuroimage.2013.05.012 pmid: 23702417
[20]	YE W H, WANG Y J. Review of neural fiber tracking with diffusion magnetic resonance imaging[J]. J Chinese Comput Sys, 2022, 43(7): 1458-1463.
	叶伟红, 王远军. 扩散磁共振图像的神经纤维追踪算法研究综述[J]. 小型微型计算机系统, 2022, 43(7): 1458-1463.
[21]	TOURNIER J D, CALAMANTE F, KING M D, et al. Limitations and requirements of diffusion tensor fiber tracking: an assessment using simulations[J]. Magn Reson Med, 2002, 47(4): 701-708. pmid: 11948731
[22]	AYDOGAN D B, SHI Y. Parallel transport tractography[J]. IEEE Trans Med Imaging, 2020, 40(2): 635-647.
[23]	GAHM J K, SHI Y. Surface-based tracking of U-fibers in the superficial white matter[C]// Medical Image Computing and Computer-assisted Intervention, Shenzhen, China: Springer International Publishing, 2019: 538-546.
[24]	SCHILLING K, GAO Y, JANVE V, et al. Confirmation of a gyral bias in diffusion MRI fiber tractography[J]. Hum Brain Mapp, 2018, 39(3): 1449-1466. doi: 10.1002/hbm.23936 pmid: 29266522
[25]	SOTIROPOULOS S N, HERNÁNDEZ-FERNÁNDEZ M, VU A T, et al. Fusion in diffusion MRI for improved fibre orientation estimation: An application to the 3T and 7T data of the Human Connectome Project[J]. NeuroImage, 2016, 134: 396-409. doi: S1053-8119(16)30047-7 pmid: 27071694
[26]	SONG A W, CHANG H C, PETTY C, et al. Improved delineation of short cortical association fibers and gray/white matter boundary using whole-brain three-dimensional diffusion tensor imaging at submillimeter spatial resolution[J]. Brain Connect, 2014, 4(9): 636-640. doi: 10.1089/brain.2014.0270 pmid: 25264168
[27]	ST-ONGE E, DADUCCI A, GIRARD G, et al. Surface-enhanced tractography (SET)[J]. NeuroImage, 2018, 169: 524-539. doi: S1053-8119(17)31058-3 pmid: 29258891
[28]	BASTIANI M, COTTAAR M, DIKRANIAN K, et al. Improved tractography using asymmetric fibre orientation distributions[J]. NeuroImage, 2017, 158: 205-218. doi: S1053-8119(17)30521-9 pmid: 28669902
[29]	WU Y, HONG Y, FENG Y, et al. Mitigating gyral bias in cortical tractography via asymmetric fiber orientation distributions[J]. Med Image Anal, 2020, 59: 101543.
[30]	SHASTIN D, GENC S, PARKER G D, et al. Surface-based tracking for short association fibre tractography[J]. NeuroImage, 2022, 260: 119423.
[31]	COTTAAR M, BASTIANI M, BODDU N, et al. Modelling white matter in gyral blades as a continuous vector field[J]. NeuroImage, 2021, 227: 117693.
[32]	RHEAULT F, DE BENEDICTIS A, DADUCCI A, et al. Tractostorm: The what, why, and how of tractography dissection reproducibility[J]. Hum Brain Mapp, 2020, 41(7): 1859-1874. doi: 10.1002/hbm.24917 pmid: 31925871
[33]	WAKANA S, JIANG H, NAGAE-POETSCHER L M, et al. Fiber tract-based atlas of human white matter anatomy[J]. Radiology, 2004, 230(1): 77-87. doi: 10.1148/radiol.2301021640 pmid: 14645885
[34]	CATANI M, DELL’ACQUA F, VERGANI F, et al. Short frontal lobe connections of the human brain[J]. Cortex, 2012, 48(2): 273-291. doi: 10.1016/j.cortex.2011.12.001 pmid: 22209688
[35]	ROJKOVA K, VOLLE E, URBANSKI M, et al. Atlasing the frontal lobe connections and their variability due to age and education: a spherical deconvolution tractography study[J]. Brain Struct Funct, 2016, 221: 1751-1766. doi: 10.1007/s00429-015-1001-3 pmid: 25682261
[36]	BURKS J D, BOETTCHER L B, CONNER A K, et al. White matter connections of the inferior parietal lobule: a study of surgical anatomy[J]. Brain Behav, 2017, 7(4): e00640.
[37]	CATANI M, ROBERTSSON N, BEYH A, et al. Short parietal lobe connections of the human and monkey brain[J]. Cortex, 2017, 97: 339-357. doi: S0010-9452(17)30370-2 pmid: 29157936
[38]	SHINOHARA H, LIU X, NAKAJIMA R, et al. Pyramid-shape crossings and intercrossing fibers are key elements for construction of the neural network in the superficial white matter of the human cerebrum[J]. Cereb Cortex, 2020, 30(10): 5218-5228. doi: 10.1093/cercor/bhaa080 pmid: 32324856
[39]	OISHI K, ZILLES K, AMUNTS K, et al. Human brain white matter atlas: identification and assignment of common anatomical structures in superficial white matter[J]. NeuroImage, 2008, 43(3): 447-457. doi: 10.1016/j.neuroimage.2008.07.009 pmid: 18692144
[40]	ZHANG Y, ZHANG J, OISHI K, et al. Atlas-guided tract reconstruction for automated and comprehensive examination of the white matter anatomy[J]. NeuroImage, 2010, 52(4): 1289-1301. doi: 10.1016/j.neuroimage.2010.05.049 pmid: 20570617
[41]	PARDO E, GUEVARA P, DUCLAP D, et al. Study of the variability of short association bundles on a HARDI database[C]// Engineering in Medicine and Biology Society, Osaka, Japan: IEEE, 2013: 77-80.
[42]	OUYANG M, JEON T, MISHRA V, et al. Global and regional cortical connectivity maturation index (CCMI) of developmental human brain with quantification of short-range association tracts[C]// International Society for Optical Engineering, San Diego, CA, United States: SPIE, 2016, 9788: 328-334.
[43]	MOVAHEDIAN ATTAR F, KIRILINA E, HAENELT D, et al. Mapping short association fibers in the early cortical visual processing stream using in vivo diffusion tractography[J]. Cereb Cortex, 2020, 30(8): 4496-4514.
[44]	VERGANI F, LACERDA L, MARTINO J, et al. White matter connections of the supplementary motor area in humans[J]. J Neurol Neurosurg Psychiatry, 2014, 85(12): 1377-1385. doi: 10.1136/jnnp-2013-307492 pmid: 24741063
[45]	MAGRO E, MOREAU T, SEIZEUR R, et al. Characterization of short white matter fiber bundles in the central area from diffusion tensor MRI[J]. Neuroradiology, 2012, 54: 1275-1285. doi: 10.1007/s00234-012-1073-1 pmid: 22854806
[46]	YANG Z, LI X, ZHOU J, et al. Functional clustering of whole brain white matter fibers[J]. J Neurosci Meth, 2020, 335: 108626.
[47]	GUEVARA M, SUN Z Y, GUEVARA P, et al. Disentangling the variability of the superficial white matter organization using regional-tractogram-based population stratification[J]. NeuroImage, 2022, 255: 119197.
[48]	VINDAS N, AVILA N L, ZHANG F, et al. GeoLab: geometry-based tractography parcellation of superficial white matter[C]// International Symposium on Biomedical Imaging, Cartagena de Indias, Colombia: IEEE, 2023: 1-5.
[49]	GUEVARA P, DUCLAP D, POUPON C, et al. Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas[J]. NeuroImage, 2012, 61(4): 1083-1099. doi: 10.1016/j.neuroimage.2012.02.071 pmid: 22414992
[50]	ROMÁN C, GUEVARA M, VALENZUELA R, et al. Clustering of whole-brain white matter short association bundles using HARDI data[J]. Front Neuroinform, 2017, 11: 73. doi: 10.3389/fninf.2017.00073 pmid: 29311886
[51]	DESIKAN R S, SÉGONNE F, FISCHL B, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest[J]. NeuroImage, 2006, 31(3): 968-980. doi: 10.1016/j.neuroimage.2006.01.021 pmid: 16530430
[52]	ZHANG F, WU Y, NORTON I, et al. An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan[J]. NeuroImage, 2018, 179: 429-447. doi: S1053-8119(18)30534-2 pmid: 29920375
[53]	PRON A, BRUN L, DERUELLE C, et al. Dense and structured representations of U-shape fiber connectivity in the central sulcus[C]// International Symposium on Biomedical Imaging, Washington, DC, USA: IEEE, 2018: 700-703.
[54]	PRON A, DERUELLE C, COULON O. U-shape short-range extrinsic connectivity organisation around the human central sulcus[J]. Brain Struct Funct, 2021, 226(1): 179-193. doi: 10.1007/s00429-020-02177-5 pmid: 33245395
[55]	XUE T, ZHANG F, ZHANG C, et al. Superficial white matter analysis: An efficient point-cloud-based deep learning framework with supervised contrastive learning for consistent tractography parcellation across populations and dMRI acquisitions[J]. Med Image Anal, 2023, 85: 102759.
[56]	GUEVARA M, ROMÁN C, HOUENOU J, et al. Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography[J]. NeuroImage, 2017, 147: 703-725. doi: S1053-8119(16)30684-X pmid: 28034765
[57]	ROMÁN C, HERNÁNDEZ C, FIGUEROA M, et al. Superficial white matter bundle atlas based on hierarchical fiber clustering over probabilistic tractography data[J]. NeuroImage, 2022, 262: 119550.
[58]	JIANG F, WANG Y J. Construction of human brain templates with diffusion tensor imaging data: A review[J]. Chinese J Magn Reson, 2018, 35(4): 520-530.
	蒋帆, 王远军. 扩散张量成像的人脑模板构建[J]. 波谱学杂志, 2018, 35(4): 520-530. doi: 10.11938/cjmr20182662

第一作者	图谱构成	构建数据	测试数据	纤维束追踪方法	概率性/确定性追踪	构建方法
Guevara^[49]	36个DWM束，94个SWM束	12 NMR	20 HARDI	Q-ball+正则化粒子轨迹	确定性	层次聚类
Román^[50]	93个SWM束	74 CONNECT/Archi	78 HARDI	Q-ball+正则化粒子轨迹	确定性	层次聚类
Zhang^[52]	58 DWM束，198个SWM束	100名健康受试者	584名患有多种健康状况的受试者	双张量无迹卡尔曼滤波	确定性	谱聚类
Guevara^[56]	100个SWM束	79 CONNECT/Archi	26 HARDI	Q-ball+正则化粒子轨迹	确定性	自动ROI选择+层次聚类
Román^[57]	525个SWM束	100 HCP	79 HARDI	CSD+iFOD2	概率性	自动ROI选择+FFClust聚类+层次聚类