基本资料
姓名：龙治良
性别：男
学历：工学博士
联系邮箱：longzhiliang@swu.edu.cn
本人兴趣及方向是抑郁机制和干预研究、多模态分析技术发展以及抑郁症个体化治疗研究。擅长脑影像数据分析，机器学习，元分析以及多体素模式分析。是Biological psychiatry, molecular psychiatry等国际期刊的审稿人。研究成果发表在Nature communications, Movement Disorders, Schizophrenia Bulletin等期刊上。
工作及受教育经历
2022/07－至今，西南大学，副教授
2017/07－2022/06，西南大学，讲师
2014/10－2015/09，英国牛津大学实验心理学系Dante Mantini教授团队做访问学者.
2012/09－2017/06，电子科技大学，生命科学与技术学院，博士研究生，导师：陈华富
2010/09－2012/06，电子科技大学，数学科学学院，硕士研究生，导师：曾翎
2006/09－2010/06，电子科技大学，数学科学学院，本科/学士
主持项目
国家青年科学基金项目：基于多模态神经影像的首发重度抑郁症抗抑郁药物治疗前后的脑网络变化研究，已结题。
中央高校基本科研业务费专项：基于多模态脑影像的睡眠剥夺研究，已结题。
横向项目：情绪障碍多模态脑影像研究，在研。
本科授课课程
《高等数学》、《心理统计学》、《心理学中的定量法》
计算机软件成果
独立开发了基于脑皮层的数据分析软件SPAMRI（获得国家软件著作权），下载地址：https://www.nitrc.org/projects/longzhiliangmri/
近年来学术文章成果（*共同一作，†共同通讯）
2026
1.Huiqing Meng, Yahui Yang, Qian Cui, Zhiliang Long. Regional and network-level convergent structural effects of electroconvulsive therapy in depression: a neuroimaging meta-analysis. Journal of Psychiatric Research. 2026 Sep:200:1-9. doi: 10.1016/j.jpsychires.2026.05.024.
2.Lan Ou, Jia Yang, Linfeng Shi, Xingang Wang, Peiling Ou, Yonghua Huang, Hui Chen, Congwei Li, Rui Fan, Bijia Wang, YiCheng Hsu, Guolin Ma, Wei Chen, Jian Wang, Zhiliang Long†, Chen Liu. Putamen Structural-Functional Decoupling as an Early-Stage Candidate Imaging Marker for Motor Severity in Spinocerebellar Ataxia Type 3. Movement Disorders. 2026 Jun 29. doi: 10.1002/mds.70402.
3.Lan Ou, Chaoyang Zhou, Xingang Wang, Linfeng Shi, Peiling Ou, Chaodong Xiang, Hui Chen, Xi Chen, Bijia Wang, Wei Chen, Jian Wang, Zhiliang Long†, Chen Liu. Mapping the Disrupted Connectome in Spinocerebellar Ataxia Type 3: A Network-Based Statistics Study Identifying Novel Therapeutic Targets for Neuromodulation. CNS Neuroscience & Therapeutics. 2026 Jul;32(7):e71016. doi: 10.1002/cns.71016.
4.Haitao Guo, Xuexia Mao, Yahui Yang, Xiaoqin Li, Jingyi Cheng, Ruihan Fu, Furong Lyu, Zhiliang Long†, Hao Wu, Lian Du. Exploring the relationship between structural and functional connectivity of the sgACC and different brain regions in relation to dimensional symptoms of major depressive disorder. Journal of Psychiatric Research. 2026 Aug:199:385-392. doi: 10.1016/j.jpsychires.2026.05.006.
2025
1.Jiao Li, Zhiliang Long*, Gong-Jun Ji, Shaoqiang Han, Yuan Chen, Guanqun Yao, Yong Xu, Kerang Zhang, Yong Zhang, Jingliang Cheng, Kai Wang, Huafu Chen, Wei Liao. Major depressive disorder on a neuromorphic continuum. Nature Communications. 2025 Mar 11;16(1):2405. doi: 10.1038/s41467-025-57682-0.
2.Zhiliang Long, Jiao Li, Marco Marino. Functional connectivity between subgenual anterior cingulate cortex and dorsomedial prefrontal cortex predicts clinical symptom improvement following total sleep deprivation in treatment resistant depression. Progress in Neuro-Psychopharmacology & Biological Psychiatry. 2025 Apr 2:138:111351. doi: 10.1016/j.pnpbp.2025.111351.
3.Zhiliang Long, Jiao Li, Marco Marino. Brain structural changes underlying clinical symptom improvement following fast-acting treatments in treatment resistant depression. Journal of affective disorders. 2025 Jan 15:369:52-60. doi: 10.1016/j.jad.2024.09.150.
2024
1.Zhiliang Long, Lian Du, Marco Marino. Individual resting-state network functional connectivity predicts treatment improvement of repetitive transcranial magnetic stimulation in major depressive disorder: A pilot study. Psychiatry Research. 2024 Jan:331:115616. doi: 10.1016/j.psychres.2023.115616.
2023
1.Zhiliang Long, Danni Chen, Xu Lei. Enhanced rich club connectivity in mild or moderate depression after nonpharmacological treatment: A preliminary study. Brain and Behavior. 2023 Oct;13(10):e3198. doi: 10.1002/brb3.3198.
2022
1.Danni Chen, Xu Lei, Lian Du, Zhiliang Long. Use of machine learning in predicting the efficacy of repetitive transcranial magnetic stimulation on treating depression based on functional and structural thalamo-prefrontal connectivity: A pilot study. Journal of Psychiatric Research. 2022 Apr:148:88-94. doi: 10.1016/j.jpsychires.2022.01.064.
2.Zhiliang Long. SPAMRI: A MATLAB Toolbox for Surface-Based Processing and Analysis of Magnetic Resonance Imaging. Frontiers in Human Neuroscience. 2022 Jul 7:16:946156. doi: 10.3389/fnhum.2022.946156.
