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山东大学学报 (医学版) ›› 2020, Vol. 58 ›› Issue (8): 10-13.doi: 10.6040/j.issn.1671-7554.0.2020.0577

• 脑科学与类脑智能研究专题 • 上一篇    下一篇

类脑智能在脑科学的前沿应用

江涛1,2,*()   

  1. 1. 北京市神经外科研究所，北京100071
    2. 首都医科大学附属北京天坛医院神经外科，北京100071
  • 收稿日期:2020-04-13 出版日期:2020-08-07 发布日期:2020-08-07
  • 通讯作者: 江涛 E-mail:taojiang1964@163.com
  • 作者简介:江涛，教授，主任医师，博士研究生导师，现任北京市神经外科研究所副所长，首都医科大学附属北京天坛医院神经外科中心副主任。牵头创建了中国抗癌协会脑胶质瘤专业委员会、中国医师协会脑胶质瘤专业委员会，并担任两个委员会首任主任委员，是中国脑胶质瘤基因图谱计划(CGGA)发起者和创始人之一。担任《Neuro-Oncology》等国外期刊学术编辑。曾担任全球脑胶质瘤适应性临床创新试验体系(AGILE)中方主席。共发表SCI论文160余篇，代表论文发表在《Cell》《Neuro-oncology》《Genome Research》《PNAS》等知名期刊。曾以第一完成人获国家科学技术进步二等奖，2019年荣获“北京学者”称号。

The application of brain-like intelligence in the frontiers of brain science

Tao JIANG1,2,*()   

  1. 1. Beijing Neurosurgical Institute, Beijing 100070, China
    2. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
  • Received:2020-04-13 Online:2020-08-07 Published:2020-08-07
  • Contact: Tao JIANG E-mail:taojiang1964@163.com

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摘要：

脑科学与类脑科学研究，简称“脑计划”，是未来生命科学研究的重要方向。神经外科作为惟一能够直接接触到大脑的学科，在完成“脑计划”的工作中势必会起到至关重要的作用。神经外科功能区定位技术包括影像学技术和电生理技术。将人工智能技术与功能区定位技术相结合，有助于加快“脑计划”的完成。

关键词: 脑科学, 类脑智能, 脑胶质瘤, 脑计划, 脑功能

Abstract:

Brain science and brain-like intelligence technology are promising for life science research in the future. Neurosurgery, as the only discipline with direct access to the brain, is bound to play a crucial role in the human brain project. Identifying technologies in neurosurgery include neuro-radiological and electrophysiological techniques. Combination of artificial intelligence and identifying technologies helps to accelerate the completion of the human brain project.

Key words: Brain science, Brain-like intelligence, Glioma, Human brain project, Brain function

中图分类号: 

  • R651
1 张华, 詹启敏. 脑计划与精准医学[J]. 中国研究型医院, 2016, 3 (6): 18- 21.
2 江涛. 神经外科功能区定位技术助力"脑计划"研究[J]. 中华神经外科疾病研究杂志, 2018, 17 (5): 385- 386.
3 Duffau H . The error of Broca: from the traditional localizationist concept to a connectomal anatomy of human brain[J]. J Chem Neuroanat, 2017, 89: 73- 81.
doi: 10.1016/j.jchemneu.2017.04.003
4 Fang S , Bai HX , Fan X , et al. A novel sequence: ZOOMit-blood oxygen level-dependent for motor-cortex localization[J]. Neurosurgery, 2020, 86 (2): E124- E132.
5 江涛, 陈新忠, 谢坚, 等. 功能区胶质瘤的术中直接电刺激判断核心手术技术[J]. 中国微侵袭神经外科杂志, 2005, 10 (4): 148- 150.
6 Herbet G , Moritz-Gasser S , Boiseau M , et al. Converging evidence for a cortico-subcortical network mediating lexical retrieval[J]. Brain, 2016, 139 (11): 3007- 3021.
doi: 10.1093/brain/aww220
7 Rech F , Herbet G , Gaudeau Y , et al. A probabilistic map of negative motor areas of the upper limb and face: a brain stimulation study[J]. Brain, 2019, 142 (4): 952- 965.
doi: 10.1093/brain/awz021
8 Tate MC , Herbet G , Moritz-Gasserss S , et al. Probabilistic map of critical functional regions of the human cerebral cortex: Broca's area revisited[J]. Brain, 2014, 137 (Pt 10): 2773- 2782.
9 Liang X , Wang J , He Y . Human connectome: Structural and functional brain networks[J]. Chinese Science Bulletin, 2010, 55 (16): 1565- 1583.
doi: 10.1360/972009-2150
10 Fang S , Li Y , Wang Y. , et al. Awake craniotomy for gliomas involving motor-related areas: classification and function recovery[J]. J Neurooncol, 2020, 148 (2): 317- 325.
doi: 10.1007/s11060-020-03520-w
11 Li Y , Liang Y , Sun Z , et al. Radiogenomic analysis of PTEN mutation in glioblastoma using preoperative multi-parametric magnetic resonance imaging[J]. Neuroradiology, 2019, 61 (11): 1229- 1237.
doi: 10.1007/s00234-019-02244-7
12 Li Y , Liu X , Qian Z , et al. Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature[J]. Eur Radiol, 2018, 28 (7): 2960- 2968.
doi: 10.1007/s00330-017-5267-0
13 Li Y , Liu X , Xu K , et al. MRI features can predict EGFR expression in lower grade gliomas: A voxel-based radiomic analysis[J]. Eur Radiol, 2018, 28 (1): 356- 362.
doi: 10.1007/s00330-017-4964-z
14 Li Y , Qian Z , Xu K , et al. MRI features predict p53 status in lower-grade gliomas via a machine-learning approach[J]. Neuroimage Clin, 2018, 17: 306- 311.
doi: 10.1016/j.nicl.2017.10.030
15 Liu X , Li Y , Li S , et al. IDH mutation-specific radiomic signature in lower-grade gliomas[J]. Aging (Albany NY), 2019, 11 (2): 673- 696.
16 Wang K , Wang Y , Fan X , et al. Regional specificity of 1p/19q co-deletion combined with radiological features for predicting the survival outcomes of anaplastic oligodendroglial tumor patients[J]. J Neurooncol, 2018, 136 (3): 523- 531.
doi: 10.1007/s11060-017-2673-8
17 Qian Z , Li Y , Fan X , et al. Molecular and clinical characterization of IDH associated immune signature in lower-grade gliomas[J]. Oncoimmunology, 2018, 7 (6): e1434466.
doi: 10.1080/2162402X.2018.1434466
18 Liu X , Li Y , Qian Z , et al. A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas[J]. Neuroimage Clin, 2018, 20: 1070- 1077.
doi: 10.1016/j.nicl.2018.10.014
19 Qian Z , Li Y , Fan X , et al. Prognostic value of a microRNA signature as a novel biomarker in patients with lower-grade gliomas[J]. J Neurooncol, 2018, 137 (1): 127- 137.
doi: 10.1007/s11060-017-2704-5
20 Qian Z , Li Y , Sun Z , et al. Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction[J]. Aging (Albany NY), 2018, 10 (10): 2884- 2899.
21 Wang Y , Wei W , Liu Z , et al. Predicting the type of tumor-related epilepsy in patients with low-grade gliomas: a radiomics study[J]. Front Oncol, 2020, 10: 235.
doi: 10.3389/fonc.2020.00235
22 Murray NM , Unberath M , Hager GD , et al. Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review[J]. J Neurointerv Surg, 2020, 12 (2): 156- 164.
doi: 10.1136/neurintsurg-2019-015135
23 Zhou LQ , Wu XL , Huang SY , et al. Lymph node metastasis prediction from primary breast cancer US images using deep learning[J]. Radiology, 2020, 294 (1): 19- 28.
24 Fitzgerald RC . Big data is crucial to the early detection of cancer[J]. Nat Med, 2020, 26 (1): 19- 20.
doi: 10.1038/s41591-019-0725-7
25 Abbasi J . Artificial intelligence improves breast cancer screening in study[J]. JAMA, 2020, 323 (6): 499.
doi: 10.1001/jama.2020.0370
26 Vogel JW , Vachon-Presseau E , Binette AP , et al. Brain properties predict proximity to symptom onset in sporadic Alzheimer's disease[J]. Brain, 2018, 141 (6): 1871- 1883.
doi: 10.1093/brain/awy093
27 Wildman-Tobriner B , Buda M , Hoang JK , et al. Using artificial intelligence to revise ACR TI-RADS risk stratification of thyroid nodules: diagnostic accuracy and utility[J]. Radiology, 2019, 292 (1): 112- 119.
doi: 10.1148/radiol.2019182128
28 Lin L , Dou Q , Jin YM , et al. Deep learning for automated contouring of primary tumor volumes by MRI for nasopharyngeal carcinoma[J]. Radiology, 2019, 291 (3): 677- 686.
doi: 10.1148/radiol.2019182012
29 Abbasi J . "Electronic nose" predicts immunotherapy response[J]. JAMA, 2019, 322 (18): 1756.
30 Esteva A , Brett K , Novoa RA , et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542 (7639): 115- 118.
doi: 10.1038/nature21056
31 Leslie-Mazwi TM , Lev MH . Towards artificial intelligence for clinical stroke care[J]. Nat Rev Neurol, 2020, 16 (1): 5- 6.
doi: 10.1038/s41582-019-0287-9
32 McKinney SM , Sieniek M , Godbole V , et al. International evaluation of an AI system for breast cancer screening[J]. Nature, 2020, 577 (7788): 89- 94.
doi: 10.1038/s41586-019-1799-6
33 Park CM . Can artificial intelligence fix the reproducibility problem of radiomics?[J]. Radiology, 2019, 292 (2): 374- 375.
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