摘要
抑郁症是当今社会上造成首要危害且病因和病理机制最为复杂的精神疾病之一,寻找抑郁症的客观生物学标志物一直是精神医学研究和临床实践的重点和难点,而结合人工智能技术的磁共振影像(magnetic resonance imaging,MRI)技术被认为是目前抑郁症等精神疾病中最有可能率先取得突破进展的客观生物学标志物.然而,当前基于精神影像学的潜在抑郁症客观生物学标志物还未得到一致结论 .本文从精神影像学和以机器学习(machine learning,ML)与深度学习(deep learning, DL)等为代表的人工智能技术相结合的角度,首次从疾病诊断、预防和治疗等三大临床实践环节对抑郁症辅助诊疗的相关研究进行归纳分析,我们发现:a.具有诊断价值的脑区主要集中在楔前叶、扣带回、顶下缘角回、脑岛、丘脑以及海马等;b.具有预防价值的脑区主要集中在楔前叶、中央后回、背外侧前额叶、眶额叶、颞中回等;c.具有预测治疗反应价值的脑区主要集中在楔前叶、扣带回、顶下缘角回、额中回、枕中回、枕下回、舌回等.未来的研究可以通过多中心协作和数据变换提高样本量,同时将多元化的非影像学数据应用于数据挖掘,这将有利于提高人工智能模型的辅助分类能力,为探寻抑郁症的精神影像学客观生物学标志物及其临床应用提供科学证据和参考依据.
Depression is one of the most complex psychiatric diseases that cause the most serious harm in today’s society.Searching for objective biomarkers of depression has always been the focus and difficulty of psychiatric research and clinical practice.Numerous studies have shown that magnetic resonance imaging(MRI)combined with artificial intelligence technology might be currently the most likely biologic marker to find breakthroughly in mental illness such as depression.However,the current potential objective biomarkers of depression based on psychiatric imaging have not been consistently concluded.From the perspective of combining psychoradiology with artificial intelligence technology represented by machine learning(ML)and deep learning(DL),this paper summarizes and analyses the related studies on depression from three components of the clinical practice including disease diagnosis,prevention and treatment for the first time.We found that a.the brain areas with diagnostic value are mainly concentrated in:precuneus,cingulate gyrus,inferior parietal lobule,insula,thalamus and hippocampus;b.the brain regions with preventive value are mainly concentrated in:precuneus,central posterior gyrus,dorsolateral prefrontal cortex,orbitofrontal cortex,middle temporal gyri;c.brain regions with predictive therapeutic response are mainly concentrated in:precuneus,cingulate gyrus,inferior parietal lobule,middle frontal gyrus,middle occipital gyrus,lingual gyrus.Future research can be improved by enlarging the sample size through multi-center collaboration and data transformation,and at the same time non-imaging data can be applied to data mining,which will help to improve the classification accuracy of artificial intelligence models,and provide scientific evidence and reference for the studies on exploring psychoradiological objective biomarkers for depression and its clinical application.
作者
孙也婷
陈桃林
何度
董再全
程勃超
王淞
汤万杰
况伟宏
龚启勇
SUN Ye-Ting;CHEN Tao-Lin;HE Du;DONG Zai-Quan;CHENG Bo-Chao;WANG Song;TANGWan-Jie;KUANGWei-Hong;GONG Qi-Yong(Huaxi MR Research Center(HMRRC,Department of Radiology,West China Hospital of Sichuan University,Chengdu610041,China;West China School of Medicine,Sichuan University,Chengdu610041,China;Department of Sociality and Psychology,School of Public Administration,Sichuan University,Chengdu610065,China;Department of Pathology,West China Hospital of Sichuan University,Chengdu610041,China;Center for Educational and Health Psychology Sichuan University,Chengdu610041,China;Department of Radiology,West China Second University Hospital,Sichuan University,Chengdu610041,China)
出处
《生物化学与生物物理进展》
SCIE
CAS
CSCD
北大核心
2019年第9期879-899,共21页
Progress In Biochemistry and Biophysics
基金
国家自然科学基金青年科学基金(81401398)
四川省科技厅应用基础研究计划项目(2019YJ0049)
四川省卫生健康委员会普及应用项目(19PJ080,16PJ244)
中国博士后科学基金(一等资助)(2013M530401)
成都医学院四川应用心理学研究中心(重点项目)(CSXL-171001)资助~~
关键词
抑郁症
脑成像
精神影像学
人工智能
机器学习
深度学习
生物学标志物
major depression disorder
brain imaging
psychoradiology
artificial intelligence
machine learning
deep learning
biological marker
