基本介紹
- 中文名：焦蘊
- 國籍：中國
- 職業：教師
- 專業方向：神經影像
- 任職院校：東南大學醫學院
個人經歷,主講課程,研究方向,榮譽獎項,學術成果,
個人經歷
1、2017-今：中華醫學會放射學分會磁共振物理和工程學組委員
2、2018-今：中國電子學會生物醫學電子學分會委員
主講課程
1、《成像原理》，本科生專業必修課
2、《醫學影像圖像處理》，本科生專業選修課
3、《醫學信息學》，本科生選修課
4、《分子影像與功能影像》，研究生專業必修課
研究方向
1、神經影像：利用多模影像、圖像分析統計以及機器學習等技術，研究神經精神疾病、代謝性腦病、腦損傷等神經相關疾病的發生、發展以及轉歸的影像學標記物。
2、醫學影像大數據：建立醫學影像數據的標準化處理流程，構建心腦血管以及腫瘤等疾病的影像大數據平台。
3、影像組學及人工智慧：融合臨床指標、影像數據以及遺傳學等大數據資源，形成影像組學大數據，套用深度學習等人工智慧算法，建立相關疾病早期診斷模型及預後模型。
榮譽獎項
1、教育部科學技術進步一等獎：磁共振分子影像和功能影像研究和套用，2012（排名第七）
2、江蘇省科學技術三等獎：兒童孤獨症診斷有效性指標的篩選與早期干預，2012（排名第四）
3、江蘇省科學技術二等獎：功能磁共振新技術的研究及其在腦疾病中的套用，2014（排名第四）
4、國家科學技術進步二等獎：基於磁共振成像的多模態分子影像與功能影像的研究與套用，2016（排名第五）
學術成果
SCI:
1.Jiao Y, Chen R, Ke XY, Chu KK, Lu ZH, Herskovits EH. Predictive models of autism spectrum disorder based on brain regional cortical thickness.Neuroimage. 2010;50(2):589-99. doi:10.1016/j.neuroimage.2009.12.047.
2.Jiao Y, Chen R, Ke X, Cheng L, Chu K, Lu Z, et al. Predictive models for subtypes of autism spectrum disorder based on single-nucleotide polymorphisms and magnetic resonance imaging.Advances in Medical Sciences. 2011;56(2):334-42. doi:10.2478/v10039-011-0042-y. PubMed PMID: WOS:000300097400031.
3.ChenHJ, Zhu XQ,Jiao Y, Li PC, Wang Y,Teng GJ. Abnormal baseline brain activity in low-grade hepatic encephalopathy: A resting-state fMRI study.Journal ofthe Neurological Sciences. 2012;318(1-2):140-5. doi: 10.1016/j.jns.2012.02.019.
4.Jiao Y, Chen R, Ke XY, Cheng L, Chu KK, Lu ZH, et al. Single Nucleotide Polymorphisms Predict Symptom Severity of Autism Spectrum Disorder.J Autism Dev Disord. 2012;42(6):971-83.doi: 10.1007/s10803-011-1327-5.
5.Chen HJ,Jiao Y, Zhu XQ, Zhang HY, Liu JC, Wen S, et al. Brain Dysfunction Primarily Related to Previous Overt Hepatic Encephalopathy Compared with Minimal Hepatic Encephalopathy: Resting-State Functional MR Imaging Demonstration.Radiology. 2013;266(1):261-70. doi: 10.1148/radiol.12120026.
6.Tang TY,Jiao Y*（通訊）, Wang XH, Lu ZH. Gender versus brainsize effects on subcortical gray matter volumes in the human brain.Neuroscience Letters. 2013;556:79-83.doi: 10.1016/j.neulet.2013.09.060.
7.Wang X,Jiao Y*（通訊）, Tang T, Wang H, Lu Z. Investigating univariate temporal patterns for intrinsic connectivity networks based on complexity and low-frequency oscillation: a test-retest reliability study.Neuroscience. 2013;254:404-26. doi:10.1016/j.neuroscience.2013.09.009.
8.Wang XH,Jiao Y*（通訊）, Tang TY, Wang H, Lu ZH. Altered regional homogeneity patterns in adults with attention-deficit hyperactivity disorder.European Journal of Radiology. 2013;82(9):1552-7. doi: 10.1016/j.ejrad.2013.04.009.
9.Chen YC,Jiao Y, Cui Y, Shang SA, Ding J, Feng Y, et al. Aberrant Brain Functional Connectivity Related to Insulin Resistance in Type 2 Diabetes: A Resting-State fMRI Study.Diabetes Care. 2014;37(6):1689-96. doi: 10.2337/dc13-2127.
10.Cui Y,Jiao Y, Chen YC, Wang K, Gao B, Wen S, et al. Altered Spontaneous Brain Activity in Type 2 Diabetes: A Resting-State Functional MRI Study.Diabetes.2014;63(2):749-60. doi: 10.2337/db13-0519.
11.Cui Y,Jiao Y, Chen HJ, Ding J, Luo B, Peng CY, et al. Aberrant functional connectivity of default-mode network in type 2 diabetes patients.European Radiology. 2015;25(11):3238-46. doi: 10.1007/s00330-015-3746-8.
12.Li PC,Jiao Y, Ding J, Chen YC, Cui Y, Qian C, et al. Cystamine Improves Functional Recovery via Axon Remodeling and Neuroprotection after Stroke in Mice.Cns Neuroscience & Therapeutics. 2015;21(3):231-40. doi: 10.1111/cns.12343.
13.Li B,Jiao Y（共同一作）, Fu C, Xie B, Ma GS, Teng GJ, et al. Contralateralartery enlargement predicts carotid plaque progression based on machine learning algorithm models in apoE(-/-) mice.Biomedical Engineering Online. 2016;15. doi:10.1186/s12938-016-0265-z.
14.Peng CY, Chen YC, Cui Y, Zhao DL,Jiao Y, Tang TY, et al. Regional Coherence Alterations Revealed by Resting-State fMRI in Post-Stroke Patients with Cognitive Dysfunction.Plos One. 2016;11(7). doi: 10.1371/journal.pone.0159574.
15.Qian C, Li PC,Jiao Y, Yao HH, Chen YC,Yang J, et al. Precise Characterization of the Penumbra Revealed by MRI: A Modified Photothrombotic Stroke Model Study.Plos One. 2016;11(4). doi: 10.1371/journal.pone.0153756.
16. Zhang X, Wu F,Jiao Y, Tang T, Yang L, Lu C, Zhang Y, Zhang Y, Bai Y, Chao J, Teng G, Yao H. An Increase of Sigma-1 Receptor in the Penumbra Neuron after Acute Ischemic Stroke.J Stroke Cerebrovasc Dis. 2017 Sep;26(9):1981-1987. doi: 10.1016/j.jstrokecerebrovasdis.2017.06.013.
17. Wang XH,Jiao Y, Li L, Predicting clinical symptoms of attention deficit hyperactivity disorder based on temporal patterns between and within intrinsic connectivity networks.Neuroscience. 2017 Oct 24;362:60-69. doi: 10.1016/j.neuroscience.2017.08.038.
18.Jiao Y, Wang XH, Chen R, Tang TY, Zhu XQ, Teng GJ, Predictive models of minimal hepatic encephalopathy for cirrhotic patients based on large-scale brain intrinsic connectivity networks.Sci Rep. 2017 Sep 14;7(1):11512. doi: 10.1038/s41598-017-11196-y.
19. Tang TY,Jiao Y（共同一作）, Cui Y, Zeng CH, et al. Development and validation of a penumbra-based predictive model for thrombolysis outcome in acute ischemic stroke patients.EBioMedicine. 2018 Sep;35:251-259. doi: 10.1016/j.ebiom.2018.07.028.
20. Lu CQ,Jiao Y（共同一作）, Meng XP, Cai Y, et al. Structural change of thalamus in cirrhotic patients with or without minimal hepatic encephalopathy and the relationship between thalamus volume and clinical indexes related to cirrhosis.Neuroimage Clin. 2018;20:800-807. doi: 10.1016/j.nicl.2018.09.015.
21. Wang XH,Jiao Y, Li L. Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity.Sci Rep. 2018 Aug 7;8(1):11789. doi: 10.1038/s41598-018-30308-w.
22. Wang XH,Jiao Y, Li L. Mapping individual voxel-wise morphological connectivity using wavelet transform of voxel-based morphology.PLoS One. 2018 Jul 24;13(7):e0201243. doi: 10.1371/journal.pone.0201243.
23. Wang XH,Jiao Y, Li L. Diagnostic model for attention-deficit hyperactivity disorder based on interregional morphological connectivity.Neurosci Lett. 2018 Oct 15;685:30-34. doi: 10.1016/j.neulet.2018.07.029
24. Luan Y, Wang C,Jiao Y, Tang T, Zhang J, Lu C, Salvi R, Teng GJ. Abnormal functional connectivity and degree centrality in anterior cingulate cortex in patients with long-term sensorineural hearing loss.Brain Imaging Behav. 2018 Dec 3. doi: 10.1007/s11682-018-0004-0.
25. Xu XM,Jiao Y, Tang TY, Lu CQ, Zhang J, Salvi R, Teng GJ. Altered Spatial and Temporal Brain Connectivity in the Salience Network of Sensorineural Hearing Loss and Tinnitus.Front Neurosci. 2019 Mar 19;13:246. doi: 10.3389/fnins.2019.00246.
26. Xu XM,Jiao Y, Tang TY, Zhang J, Salvi R, Teng GJ. Inefficient Involvement of Insula in Sensorineural Hearing Loss.Front Neurosci. 2019 Feb 20;13:133. doi: 10.3389/fnins.2019.00133.
27. Luan Y, Wang C,Jiao Y, Tang T, Zhang J, Teng GJ. Prefrontal-Temporal Pathway Mediates the Cross-Modal and Cognitive Reorganization in Sensorineural Hearing Loss With or Without Tinnitus: A Multimodal MRI Study.Front Neurosci. 2019 Mar 12;13:222. doi: 10.3389/fnins.2019.00222
28. Luan Y, Wang C,Jiao Y, Tang T, Zhang J, Teng GJ. Dysconnectivity of Multiple Resting-State Networks Associated With Higher-Order Functions in Sensorineural Hearing Loss.Front Neurosci. 2019 Feb 5;13:55. doi: 10.3389/fnins.2019.00055.
29. Xu XM,Jiao Y, Tang TY, Zhang J, Lu CQ, Salvi R, Teng GJ. Sensorineural hearing loss and cognitive impairments: Contributions of thalamus using multiparametric MRI.J Magn Reson Imaging. 2019 Jan 29. doi: 10.1002/jmri.26665.
EI:
1.Jiao Y, Wang X, Tang T, Zhu X, Teng G. Discrimination forminimal hepatic encephalopathy based on Bayesian modeling of default modenetwork.Journal of Southeast University (English Edition). 2015;31(4):582-7. doi: 10.3969/j.issn.1003-7985.2015.04.026.
2.Jiao Y, Teng G-J, Wang X. Predictive model for minimalhepatic encephalopathy based on cerebral functional connectivity. 2013 6thInternational Conference on Biomedical Engineering and Informatics, BMEI 2013, December 16, 2013 - December 18,2013; 2013; Hangzhou, China: IEEE Computer Society.
核心：
- 焦蘊,湯天宇,王訓恆,朱西琪,滕皋軍.輕微型肝性腦病患者前後默認網路改變與神經認知損傷的關係.中華醫學雜誌. 2016;96(5):334-8.DOI: 10.3760/cma.j.issn.0376-2491.2016.05.004
主持項目
序號 | 課題編號 | 課題名稱 | 課題來源 | 起始時間 | 結束時間 |
1 | 81271739 | 基於多模磁MR神經影像的輕微型肝性腦病預測模型的研究 | 國家自然科學基金面上項目 | 2013-01-01 | 2013-12-31 |
2 | 2013CB733800 | 基於多模態影像的缺血性腦卒中診治新技術的關鍵科學問題研究 | 973項目 子課題負責人 | 2013-01-01 | 2017-12-31 |
3 | BK20141342 | 基於多模磁共振的輕微型肝性腦病的早期輔助診斷模型和縱向隨訪研 | 省自然科學基金面上項目（省科技廳） | 2014-07-01 | 2017-06-30 |
4 | 81501453 | 2型糖尿病認知障礙的多模磁共振研究 | 國家自然科學基金青年科學基金項目 | 2015-08-01 | 2018-12-31 |
5 | BL2013029 | 江蘇省醫學影像與介入放射診療臨床醫學研究中心 | 江蘇省臨床醫學科技專項-省臨床醫學研究中心項目 子課題負責人 | 2013-01 | 2016-12 |
6 | QNRC2016810 | 缺血性腦卒中半暗帶的精確量化雲平台研究 | 江蘇省青年醫學人才 | 2016-01 | 2020-12 |
7 | X2017002 | 醫聯體信息化建設策略和探討 | 江蘇省衛計委信息化項目 | 2017-01 | 2019-12 |
8 | 201805006 | 基於醫聯體信息化的智慧醫療和健康平台 | 南京市科技局項目 | 2018-01 | 2020-12 |
