山东大学学报 (医学版) ›› 2020, Vol. 58 ›› Issue (8): 28-33.doi: 10.6040/j.issn.1671-7554.0.2020.1012
Shuwei LIU1,2,*(),Yunxia LOU1,2,Yuchun TANG1,2
摘要:
人脑图谱是研究大脑结构和功能的基本工具,是进行脑结构和功能信息分析处理的重要手段。构建不同年龄段、不同性别、不同颅脑分型的标准化4D脑图谱,并利用计算机技术进行可视化和大脑结构的动态分析,可以为人脑结构和功能的发育及发展变化提供详实的形态学基础。概述4D数字脑图谱的构建、不对称性及其遗传倾向,并对未来的研究方向进行展望。
中图分类号:
1 |
Amunts K , Knoll AC , Lippert T , et al. The Human Brain Project-Synergy between neuroscience, computing, informatics, and brain-inspired technologies[J]. PLoS Biol, 2019, 17 (7): e3000344.
doi: 10.1371/journal.pbio.3000344 |
2 |
Bjerke IE , Ovsthus M , Papp EA , et al. Data integration through brain atlasing: Human Brain Project tools and strategies[J]. Eur Psychiat, 2018, 50: 70- 76.
doi: 10.1016/j.eurpsy |
3 |
Ewert S , Plettig P , Li NF , et al. Toward defining deep brain stimulation targets in MNI space: A subcortical atlas based on multimodal MRI, histology and structural connectivity[J]. Neuroimage, 2018, 170: 271- 282.
doi: 10.1016/j.neuroimage.2017.05.015 |
4 |
Oishi K , Chang LD , Huang H . Baby brain atlases[J]. Neuroimage, 2019, 185: 865- 880.
doi: 10.1016/j.neuroimage.2018.04.003 |
5 |
Kawasaki T , Shin M , Kimura Y , et al. Topographic anatomy of the subthalamic nucleus localized by high-resolution human brain atlas superimposing digital images of cross-sectioned surfaces and histological images of microscopic sections from frozen cadaveric brains[J]. J Clin Neurosci, 2018, 53: 193- 202.
doi: 10.1016/j.jocn |
6 | Sivaswamy J , Thottupattu AJ , Mehta R , et al. Construction of Indian human brain atlas[J]. Neurol India, 2019, 67 (1): 229- 234. |
7 |
Zhang YY , Wei HJ , Cronin MJ , et al. Longitudinal atlas for normative human brain development and aging over the lifespan using quantitative susceptibility mapping[J]. Neuroimage, 2018, 171: 176- 189.
doi: 10.1016/j.neuroimage.2018.01.008 |
8 |
Mazziotta JC , Toga AW , Evans A , et al. A probabilistic atlas of the human brain: theory and rationale for its development. The International Consortium for Brain Mapping (ICBM)[J]. Neuroimage, 1995, 2 (2): 89- 101.
doi: 10.1006/nimg.1995.1012 |
9 | Fujihara K , Takei Y . FreeSurfer as a platform for associating brain structure with function[J]. Brain Nerve, 2018, 70 (7): 841- 848. |
10 |
Guo CJ , Ferreira D , Fink K , et al. Repeatability and reproducibility of FreeSurfer, FSL-SIENAX and SPM brain volumetric measurements and the effect of lesion filling in multiple sclerosis[J]. Eur Radiol, 2019, 29 (3): 1355- 1364.
doi: 10.1007/s00330-018-5710-x |
11 |
Yokota H , Vijayasarathi A , Cekic M , et al. Diagnostic performance of glymphatic system evaluation using diffusion tensor imaging in idiopathic normal pressure hydrocephalus and mimickers[J]. Curr Gerontol Geriatr Res, 2019, 2019: 5675014.
doi: 10.1155/2019/5675014 |
12 |
Glasser MF , Coalson TS , Robinson EC , et al. A multi-modal parcellation of human cerebral cortex[J]. Nature, 2016, 536 (7615): 171- 178.
doi: 10.1038/nature18933 |
13 | Vogel JW , La Joie R , Grothe MJ , et al. A molecular gradient along the longitudinal axis of the human hippocampus informs large-scale behavioral systems[J]. Nat Commun, 2020, 11 (1): 960. |
14 |
Iglesias JE , Insausti R , Lerma-Usabiaga G , et al. A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology[J]. Neuroimage, 2018, 183: 314- 326.
doi: 10.1016/j.neuroimage.2018.08.012 |
15 |
Kunst M , Laurell E , Mokayes N , et al. A Cellular-Resolution Atlas of the Larval Zebrafish Brain[J]. Neuron, 2019, 103 (1): 21- 38.
doi: 10.1016/j.neuron.2019.04.034 |
16 |
Liu CR , Ye FQ , Yen CCC , et al. A digital 3D atlas of the marmoset brain based on multi-modal MRI[J]. Neuroimage, 2018, 169: 106- 116.
doi: 10.1016/j.neuroimage.2017.12.004 |
17 |
Murakami TC , Mano T , Saikawa S , et al. A three-dimensional single-cell-resolution whole-brain atlas using CUBIC-X expansion microscopy and tissue clearing[J]. Nat Neurosci, 2018, 21 (4): 625- 637.
doi: 10.1038/s41593-018-0109-1 |
18 | Oishi K , Mori S , Troncoso JC , et al. Mapping tracts in the human subthalamic area by 11.7T ex vivo diffusion tensor imaging[J]. Brain Struct Funct, 2020, 225 (4): 1293- 1312. |
19 |
Tang YC , Hojatkashani C , Dinov ID , et al. The construction of a Chinese MRI brain atlas: A morphometric comparison study between Chinese and Caucasian cohorts[J]. Neuroimage, 2010, 51 (1): 33- 41.
doi: 10.1016/j.neuroimage.2010.01.111 |
20 |
Zhan J , Dinov ID , Li J , et al. Spatial-temporal atlas of human fetal brain development during the early second trimester[J]. Neuroimage, 2013, 82: 115- 126.
doi: 10.1016/j.neuroimage.2013.05.063 |
21 |
Yu Q , Ouyang A , Chalak L , et al. Structural development of human fetal and preterm brain cortical plate based on population-averaged templates[J]. Cereb Cortex, 2016, 26 (11): 4381- 4391.
doi: 10.1093/cercor/bhv201 |
22 |
Liang PP , Shi L , Chen N , et al. Construction of brain atlases based on a multi-center MRI dataset of 2020 Chinese adults[J]. Sci Rep, 2015, 5: 18216.
doi: 10.1038/srep18216 |
23 |
Wang X , Chen N , Zuo Z , et al. Probabilistic MRI brain anatomical atlases based on 1, 000 Chinese subjects[J]. PLoS One, 2013, 8 (1): e50939.
doi: 10.1371/journal.pone.0050939 |
24 |
Fan L , Li H , Zhuo J , et al. The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture[J]. Cereb Cortex, 2016, 26 (8): 3508- 3526.
doi: 10.1093/cercor/bhw157 |
25 |
Hagen S , Jacques C , Maillard L , et al. Spatially dissociated intracerebral maps for face- and house-selective activity in the human ventral occipito-temporal cortex[J]. Cereb Cortex, 2020, 30 (7): 4026- 4043.
doi: 10.1093/cercor/bhaa022 |
26 | No authors listed . Focus on human brain mapping[J]. Nat Neurosci, 2017, 20 (3): 297. |
27 |
Sun B , Tang YC , Fan LZ , et al. The pineal region: thin sectional anatomy with MR correlation in the coronal plane[J]. Surg Radiol Anat, 2008, 30 (7): 575- 582.
doi: 10.1007/s00276-008-0375-9 |
28 |
Sun B , Wang D , Tang YC , et al. The pineal volume: a three-dimensional volumetric study in healthy young adults using 3.0 T MR data[J]. Int J Dev Neurosci, 2009, 27 (7): 655- 660.
doi: 10.1016/j.ijdevneu.2009.08.002 |
29 |
Tang YC , Sun W , Toga AW , et al. A probabilistic atlas of human brainstem pathways based on connectome imaging data[J]. Neuroimage, 2018, 169: 227- 239.
doi: 10.1016/j.neuroimage.2017.12.042 |
30 |
Novitskaya Y , Dumpelmann M , Vlachos A , et al. In vivo -assessment of the human temporal network: Evidence for asymmetrical effective connectivity[J]. Neuroimage, 2020, 214: 116769.
doi: 10.1016/j.neuroimage |
31 |
Tang YC , Zhao L , Lou YX , et al. Brain structure differences between Chinese and Caucasian cohorts: A comprehensive morphometry study[J]. Hum Brain Mapp, 2018, 39 (5): 2147- 2155.
doi: 10.1002/hbm.23994 |
32 |
Lou Y , Zhao L , Yu S , et al. Brain asymmetry differences between Chinese and Caucasian populations: a surface-based morphometric comparison study[J]. Brain Imaging Behav, 2019.
doi: 10.1007/s11682-019-00184-7 |
33 |
Sun B , Ge H , Tang Y , et al. Asymmetries of the central sulcus in young adults: Effects of gender, age and sulcal pattern[J]. Int J Dev Neurosci, 2015, 44: 65- 74.
doi: 10.1016/j.ijdevneu |
34 |
Fan L , Tang Y , Sun B , et al. Sexual dimorphism and asymmetry in human cerebellum: an MRI-based morphometric study[J]. Brain Res, 2010, 1353: 60- 73.
doi: 10.1016/j.brainres |
35 |
Yin X , Han Y , Ge H , et al. Inferior frontal white matter asymmetry correlates with executive control of attention[J]. Hum Brain Mapp, 2013, 34 (4): 796- 813.
doi: 10.1002/hbm.21477 |
36 |
Xiao M , Ge HT , Khundrakpam BS , et al. Attention performance measured by attention metwork test is correlated with global and regional efficiency of structural brain networks[J]. Front Behav Neurosci, 2016, 10: 194.
doi: 10.3389/fnbeh.2016.00194 |
37 |
Xu J , Rees G , Yin X , et al. Spontaneous neuronal activity predicts intersubject variations in executive control of attention[J]. Neuroscience, 2014, 263: 181- 192.
doi: 10.1016/j.neuroscience |
38 |
Xu JH , Yin XT , Ge HT , et al. Attentional performance is correlated with the local regional efficiency of intrinsic brain networks[J]. Front Behav Neurosci, 2015, 9: 200.
doi: 10.3389/fnbeh |
39 |
Xu J , Yin X , Ge H , et al. Heritability of the effective connectivity in the resting-state default mode metwork[J]. Cereb Cortex, 2017, 27 (12): 5626- 5634.
doi: 10.1093/cercor/bhw332 |
40 |
Yin XT , Zhao L , Xu JH , et al. Anatomical substrates of the alerting, orienting and executive control components of attention: Focus on the posterior parietal lobe[J]. PLoS One, 2012, 7 (11): e50590.
doi: 10.1371/journal.pone.0050590 |
41 |
Leng Y , Shi YG , Yu QW , et al. Phenotypic and genetic correlations between the lobar segments of the inferior fronto-occipital fasciculus and attention[J]. Sci Rep, 2016, 6: 33015.
doi: 10.1038/srep33015 |
42 |
Ge HT , Yin XT , Xu JH , et al. Fiber pathways of attention subnetworks revealed with tract-based spatial statistics (TBSS) and probabilistic tractography[J]. PLoS One, 2013, 8 (11): e78831.
doi: 10.1371/journal.pone.0078831 |
43 |
Grasby KL , Jahanshad N , Painter JN , et al. The genetic architecture of the human cerebral cortex[J]. Science, 2020, 367 (6484): 6690.
doi: 10.1126/science.aay6690 |
44 |
Reineberg AE , Hatoum AS , Hewitt JK , et al. Genetic and environmental influence on the human functional connectome[J]. Cereb Cortex, 2020, 30 (4): 2099- 2113.
doi: 10.1093/cercor/bhz225 |
No related articles found! |
|