基于磁共振的脑网络研究进展报告

Report of Advances in Studies of Brain Networks Based on Magnetic Resonance Imaging(MRI)

这篇报告主要介绍有关脑网络研究的最新进展,该研究主要研究范围包括:脑网络研究的理论与方法、小动物的实验研究和各种临床应用。在脑网络的理论与方法研究方面,主要研究动态因果模型(DCM),分析了一种基于随机滤波理论的新的DCM参数估计方法。也将基于机器学习的多体素模式识别方法应用于静息态脑功能网路的模式分类。该方法有望为精神类疾病的临床诊断提供潜在的生物学标记。其次,在小动物研究方面,基于DTI成像,深入分析了树鼩的解剖学网络,以期为精神类疾病的研究提供有效的动物模型。最后,运用脑网络的理论与方法,开展重度抑郁症、行为成瘾和吸烟成瘾等各类精神类疾病的病理学研究。获得的结果不仅验证了方法的可靠性,而且为这些疾病的病理成因和神经机制提供了新的认识。

This report aims at introducing the recent advances in study of brain networks,supported by the National Basic ResearchProgram of China(2011CB707802).Main research scopes of this project include theory and methodology applied in brain networks,experimental study of small animals,and the potential clinical applications.First,in the study for theory and methods of brainnetworks,we concentrated on the dynamic causal mode(DCM),and mainly researched a new parameter estimation approach of DCMbased stochastic filtering theory.We also applied multiple voxel pattern analysis approach based statistic learning theory into patternclassification of resting-state functional brain networks,which were suggested to provide the potential biomarkers for clinical diagnosesof various mental disorders.Second,in the experimental studies of animals,we mainly analyzed the anatomical networks based onDTI in Tupaia belangeri chinensis,which was expected to provide an effective animal mode for brain networks studies in psychiatry.Finally,we used the theory and methods of brain network to investigate pathological modes of a variety of mental disorder,includingmajor depression,behavioral addiction,and smoking addiction.These results not only validate the reliability and robustness of proposedmethods for brain network analyses,but shed some new lights on the pathological causes and neural mechanism of these disorders.