摘要
人脑是现实世界中最为复杂的网络系统之一。其复杂性不仅体现在数以亿计的神经元及连接的数量,更体现其在不同尺度下的构成以及这些连接在认知功能、思想、感觉及行为时所表现出来的不同模式。近年来,将复杂网络理论应用在神经认知科学中,利用复杂网络基本原理等方法进行属性分析,以期发现网络基本属性及节点间潜在的拓扑关系。复杂网络理论使我们从一个不同的角度来看待人脑这一复杂系统,也为人脑的研究提供了一个新的方向。
本文基于复杂网络理论,探讨静息态功能脑网络构建、分析及比较方法,并在此基础上完成静息态脑网络构建分析软件平台的开发；利用功能脑网络进行网络指标组间比较,从全局属性、局部属性、社团化分析等多角度进行组间差异分析；利用抑郁症作为疾病模型验证上述方法的临床可用性,寻找在脑疾病状态及基因影响下的变化规律,探索发现抑郁症早期诊断的影像学标志,突破脑影像技术在精神疾病临床诊断应用所面临的瓶颈问题；针对网络表征,利用机器学习算法,建立抑郁症辅助模型,以辅助临床诊断应用。
本文主要创新工作包括有：
(1)提出抑郁症静息态功能脑网络指标差异分析方法,并构建分类模型
本文分别对抑郁症患者及健康人群的静息态功能脑网络拓扑属性从多个角度进行刻画及比较分析,寻找组间差异,揭示抑郁症在网络层面的指标变化规律。利用多种机器学习方法,将所发现的差异指标作为分类特征,进行分类模型构建及性能评价。并利用敏感性分析,判定其在分类模型中的贡献度,以验证研究方法的合理性。
(2)利用复杂网络模块划分方法进行静息态功能脑模块划分,并提出抑郁症模块结构差异分析技术
本文利用基于贪婪思想的CNM模块划分算法,完成抑郁组及对照组的静息态功能脑网络模块划分,并从模块的组成、模块角色、模块间的连接等多个角度,挖掘抑郁症在模块结构上的差异。最后,利用差异模块指标进行分类研究,以验证方法的可靠性,最高正确率可达到90%以上。
(3)提出基于基因的抑郁症脑网络拓扑属性差异分析技术
前人研究证明,基因对于脑网络的拓扑属性则存在不同程度的影响。本文利用功能脑网络方法,挖掘GSK3β基因对于抑郁症患者及正常对照的网络拓扑属性差异,以探讨脑网络的基因基础。
(4)提出抑郁症局部一致性指标差异分类技术,构建分类模型并提出特征评价标准
局部一致性方法反映了脑区中某个局部的神经元活动在时间上的一致性和同步性。本文利用局部一致性指标,进行抑郁症组间差异分析。利用机器学习方法,验证局部一致性方法的可靠性,并提出通过敏感性分析方法对所选指标进行量化评价。
本文是国家自然科学基金项目《抑郁症fMRI数据分析方法及辅助诊断治疗模型研究》(No.61170136)的主要组成部分。研究工作还得到了山西省教育厅高校科技项目《多模态脑网络拓扑属性分析方法研究》(No.20121003)以及太原理工大学青年基金项目《抑郁症静息态功能脑网络拓扑属性差异分析研究》(No.2012L014)的支持。本文重点研究静息态功能脑网络的构建、分析方法及其软件平台的开发,以及脑疾病状态下脑网络的变化规律,在此基础上探索抑郁症等重大脑疾病早期诊断的影像学标志,并建立辅助诊断模型。这不仅是国际前沿基础科学问题,也是国家重大需求。
The human brain is one of the most complex system in the world. Its complexity is not only reflected in the number of neurons and connections, but also in how the brain is wired on different scales and how such patterns of their connections produce cognitive functions, thoughts, feelings, and behaviors. Recently, a number of researchers have applied complex network methods in cognitive neuroscience, especially for the study of psychiatric disorders. Using complex network principles and statistical physics methods, researchers can analyze and discover basic network properties and the potential topological relationships between nodes. Complex network theory provides a new perspective and method for brain research.
The current study explores the resting state functional brain network, compares methods based on complex network theory, and develops a software platform. On the basis of these observations, different network metrics, including global, local, and modular properties, are compared to explore the differences between groups. We then confirm the clinical availability of major depressive disorder (MDD) as a disease model. The aim is to determine the imageology symbol for the clinical early diagnosis and treatment of MDD at the network level. Finally, referring to the network metrics, the current study constructs an aided diagnosis model of MDD using a machine learning algorithm that could be helpful in clinical applications.
The current study includes the following research outcomes:
(1) Construction, analysis and classification of a resting state functional brain network in MDD
Global and local metrics are calculated using graph theory-based approaches. Non-parametric permutation tests are then used for group comparisons of topological metrics, which are used as classified features in different algorithms. A sensitivity analysis is used to calculate the change in variance of each feature in the target category.
(2) Research of the different community structures of resting state functional brain networks in MDD
A greedy algorithm is used to divide the community structures. In terms of the modularity of the brain network, differences in the modularity metrics of normal control subjects and MDD patients are found, including the modular components, modular roles, and connections between modules. These differences are used as classification features in the machine learning method, the results of which exhibit a highest accuracy of90.50%.
(3) Gene effects for the resting state functional brain network in MDD
Convergent evidence from multimodal imaging studies has demonstrated that brain networks are both structurally and functionally heritable. A general linear2×2analysis of variance test is performed to investigate the main effects of genotype and prevalence, as well as their interaction. The metrics with significant differences are selected as features in the classification research.
(4) Feature selection and classifier using regional homogeneity method in MDD
The current study explores the regional homogeneity of brain regions in the resting state to test the abnormality hypothesis in MDD patients. Classification and sensitivity analysis are also used to determine changes in the variance of each feature in the target category.
The current study is the main component of the National Natural Science Foundation Project "The study of fMRI data analysis methods and diagnosis treatment models in major depressive disorder (No.61170136)", and is also supported by the University Science Research and Development Project of Shanxi Province (No.20121003) and the Special/Youth Foundation of Taiyuan University of Technology (No.2012L014).
In brief, the current study focuses on resting state funcaional brain network construction and analysis techniques, the development of a software platform, and topological network changes in the condition of brain disease. On this basis, the current study explores the imageology symbols of early diagnosis and prognostic evaluation of major brain diseases, and constructs a diagnostic model. This is not only at the frontier of international scientific issues, but is also a major national requirement.
引文
[1]. Spijker, J., et al., Functional disability and depression in the general population. Results from the Netherlands Mental Health Survey and Incidence Study (NEMESIS). Acta Psychiatr Scand, 2004.110(3):p.208-14.
[2]. Ustun, T.B., et al., Global burden of depressive disorders in the year 2000. Br J Psychiatry, 2004.184:p.386-92.
[3]. Murray, C.J. and A.D. Lopez, Evidence-based health policy-lessons from the Global Burden of Disease Study. Science,1996.274(5288):p.740-743.
[4]. Murray, C. and A. Lopez, Summary:The global burden of disease:A comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020,1996, Harvard University Press Cambridge eMA MA.
[5]. Sporns, O., G. Tononi, and R. Kotter, The human connectome:a structural description of the human brain. PLoS computational biology,2005.1(4):p. e42.
[6]. Lichtman, J.W. and J.R. Sanes, Ome sweet ome:what can the genome tell us about the connectome? Current opinion in neurobiology,2008.18(3):p.346-353.
[7]. Lichtman, J. W., J. Livet, and J.R. Sanes, A technicolour approach to the connectome. Nature Reviews Neuroscience,2008.9(6):p.417-422.
[8]. Varela, F., et al., The brainweb:phase synchronization and large-scale integration. Nature Reviews Neuroscience,2001.2(4):p.229-239.
[9]. Lehrer, J., Neuroscience:Making connections, nature,2009.457(7229):p.524.
[10]. ERDdS, P. and A. WI, On random graphs I. Publ. Math. Debrecen,1959.6:p.290-297.
[11]. Albert, R. and A.-L. Barabasi, Statistical mechanics of complex networks. Reviews of modern physics,2002.74(1):p.47.
[12]. Mechelli, A., et al., Structural covariance in the human cortex. The Journal of neuroscience, 2005.25(36):p.8303-8310.
[13]. Lerch, J.P., et al., Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MR1. Neuroimage,2006.31(3):p.993-1003.
[14]. Thompson, P.M., et al., Genetic influences on brain structure. Nature neuroscience,2001. 4(12):p.1253-1258.
[15]. Mechelli, A., et al., Neurolinguistics:structural plasticity in the bilingual brain. nature,2004. 431(7010):p.757-757.
[16]. He, Y., Z.J. Chen, and A.C. Evans, Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cerebral cortex,2007.17(10):p.2407-2419.
[17]. Chen, Z.J., et al., Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. Cerebral cortex,2008.18(10):p.2374-2381.
[18]. Salvador, R., et al., Neurophysiological architecture of functional magnetic resonance images of human brain. Cerebral Cortex,2005.15(9):p.1332-1342.
[19]. Achard, S., et al., A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. The Journal of Neuroscience,2006.26(1):p. 63-72.
[20]. Achard, S. and E. Bullmore, Efficiency and cost of economical brain functional networks. PLoS Computational Biology,2007.3(2):p. e17.
[21]. Meunier, D., et al., Age-related changes in modular organization of human brain functional networks. Neuroimage,2009.44(3):p.715-723.
[22]. Liu, Y., et al., Disrupted small-world networks in schizophrenia. Brain,2008.131(4):p. 945-961.
[23]. Lynall, M.-E., et al., Functional connectivity and brain networks in schizophrenia. The Journal of Neuroscience,2010.30(28):p.9477-9487.
[24]. Micheloyannis, S., et al., Small-world networks and disturbed functional connectivity in schizophrenia. Schizophrenia research,2006.87(1):p.60-66.
[25]. Rubinov, M., et al., Small-world properties of nonlinear brain activity in schizophrenia. Human brain mapping,2009.30(2):p.403-416.
[26]. He, Y., Z. Chen, and A. Evans, Structural insights into aberrant topologicalpatterns of large-scale cortical networks in Alzheimer's disease. The Journal of Neuroscience,2008. 28(18):p.4756-4766.
[27]. Stam, C., Use of magnetoencephalography (MEG) to study functional brain networks in neurodegenerative disorders. Journal of the neurological sciences,2010.289(1):p.128-134.
[28]. Supekar, K., et al., Network analysis of intrinsic functional brain connectivity in Alzheimer's disease. PLoS computational biology,2008.4(6):p. e1000100.
[29]. Horstmann, M.-T., et al., State dependent properties of epileptic brain networks:Comparative graph-theoretical analyses of simultaneously recorded EEG and MEG. Clinical neurophysiology,2010.121(2):p.172-185.
[30]. Raj, A., et al., Network-level analysis of cortical thickness of the epileptic brain. Neuroimage, 2010.52(4):p.1302-1313.
[31]. van Dellen, E., et al., Long-term effects of temporal lobe epilepsy on local neural networks:a graph theoretical analysis of corticography recordings. PLoS One,2009.4(11):p. e8081.
[32]. Wang, L., et al., Altered small-world brain functional networks in children with attention-deficit/hyperactivity disorder. Human brain mapping,2009.30(2):p.638-649.
[33]. De Vico Fallani, F., et al., Evaluation of the brain network organization from EEG signals:a preliminary evidence in stroke patient. The Anatomical Record,2009.292(12):p.2023-2031.
[34]. Wang, J., et al., Parcellation-dependent small-world brain functional networks:A resting-state fMR1 study. Human brain mapping,2009.30(5):p.1511-1523.
[35]. Tzourio-Mazoyer, N., et al., Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage,2002. 15(1):p.273-289.
[36]. He, Y, et al., Uncovering intrinsic modular organization of spontaneous brain activity in humans. PLoS One,2009.4(4):p. e5226.
[37]. Meunier, D., et al., Hierarchical modularity in human brain functional networks. Frontiers in neuroinformatics,2009.3.
[38]. Ferrarini, L., et al., Hierarchical functional modularity in the resting-state human brain. Human brain mapping,2009.30(7):p.2220-2231.
[39]. Van den Heuvel, M., et al., Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain. Neuroimage,2008.43(3):p.528.
[40]. van den Heuvel, M., R. Mandl, and H.H. Pol, Normalized cut group clustering of resting-state FMR1 data. PLoS One,2008.3(4):p. e2001.
[41]. Zuo, X.-N., et al., Reliable intrinsic connectivity networks:test-retest evaluation using ICA and dual regression approach. Neuroimage,2010.49(3):p.2163-2177.
[42]. Stam, C., et al., Small-world networks and functional connectivity in Alzheimer's disease. Cerebral cortex,2007.17(1):p.92-99.
[43]. Pachou, E., et al., Working memory in schizophrenia:an EEG study using power spectrum and coherence analysis to estimate cortical activation and network behavior. Brain topography, 2008.21(2):p.128-137.
[44]. Liao, W., et al., Altered functional connectivity and small-world in mesial temporal lobe epilepsy. PLoS One,2010.5(1):p. e8525.
[45]. Fox, M.D. and M.E. Raichle, Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews Neuroscience,2007.8(9):p. 700-711.
[46]. Greicius, M.D., et al., Resting-state functional connectivity in major depression:abnormally increased contributions from subgenual cingulate cortex and thalamus. Biological psychiatry, 2007.62(5):p.429-437.
[47]. Anand, A., et al., Resting state corticolimbic connectivity abnormalities in unmedicated bipolar disorder and unipolar depression. Psychiatry research,2009.171(3):p.189.
[48]. Cullen, K.R., et al., A preliminary study of functional connectivity in comorbid adolescent depression. Neuroscience letters,2009.460(3):p.227-231.
[49]. Bluhm, R., et al., Resting state default-mode network connectivity in early depression using a seed region-of interest analysis:Decreased connectivity with caudate nucleus. Psychiatry and clinical neurosciences,2009.63(6):p.754-761.
[50]. Sheline, Y.I., et al., Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proceedings of the National Academy of Sciences, 2010.107(24):p.11020-11025.
[51]. Guo, W.-b., et al., Disrupted regional homogeneity in treatment-resistant depression:a resting-state fMRI study. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 2011.35(5):p.1297-1302.
[52]. Liu, Z., et al., Decreased regional homogeneity in insula and cerebellum:a resting-state fMRI study in patients with major depression and subjects at high risk for major depression. Psychiatry Research:Neuroimaging,2010.182(3):p.211-215.
[53]. Wu, Q.Z., et al., Abnormal regional spontaneous neural activity in treatment-refractory depression revealed by resting-state fMRI. Human brain mapping,2011.32(8):p.1290-1299.
[54]. Yao, Z., et al., Regional homogeneity in depression and its relationship with separate depressive symptom clusters:a resting-state fMRI study. Journal of affective disorders,2009. 115(3):p.430-438.
[55]. Jin, C., et al., A preliminary study of the dysregulation of the resting networks in first-episode medication-naive adolescent depression. Neuroscience letters,2011.503(2):p.105-109.
[56]. Tao, H., et al., Depression uncouples brain hate circuit. Molecular psychiatry,2011.18(1):p. 101-111.
[57]. O'Toole, A.J., et al., Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data. Journal of cognitive neuroscience,2007.19(11):p.1735-1752.
[58]. Mitchell, T.M., et al., Learning to decode cognitive states from brain images. Machine Learning,2004.57(1-2):p.145-175.
[59]. Haynes, J.-D. and G. Rees, Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nature neuroscience,2005.8(5):p.686-691.
[60]. Mourao-Miranda, J., et al., Classifying brain states and determining the discriminating activation patterns:support vector machine on functional MR1 data. Neuroimage,2005.28(4): p.980-995.
[61]. Davatzikos, C., et al., Classifying spatial patterns of brain activity with machine learning methods:application to lie detection. Neuroimage,2005.28(3):p.663-668.
[62]. Costafreda, S.G., et al., Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS One,2009.4(7):p. e6353.
[63]. Fu, C.H., et al., Pattern classification of sad facial processing:toward the development of neurobiological markers in depression. Biological psychiatry,2008.63(7):p.656-662.
[64]. Gong, Q., et al., Prognostic prediction of therapeutic response in depression using high-field MR imaging. Neuroimage,2011.55(4):p.1497-1503.
[65]. First, M.B. and M. Gibbon, User's guide for the structured clinical interview for DSM-IV axis I disorders:SCID-1 clinician version1997:American Psychiatric Pub.
[66]. Williams, J.B., A structured interview guide for the Hamilton Depression Rating Scale. Archives of general psychiatry,1988.45(8):p.742.
[67]. Guy, W., ECDEU assessment manual for psychopharmacology1991.
[68]. Friston, K.J., et al., Statistical Parametric Mapping:The Analysis of Functional Brain Images: The Analysis of Functional Brain Images2011:Academic Press.
[69]. Butts, C.T., Social network analysis:A methodological introduction. Asian Journal of Social Psychology,2008.11(1):p.13-41.
[70]. Butts, C.T., Revisiting the foundations of network analysis. Science,2009.325(5939):p. 414-416.
[71]. Rubinov, M. and O. Sporns, Complex network measures of brain connectivity:uses and interpretations. Neuroimage,2010.52(3):p.1059-1069.
[72]. Bassett, D.S., et al., Efficient physical embedding of topologically complex information processing networks in brains and computer circuits. PLoS Computational Biology,2010. 6(4):p. e1000748.
[73]. Stam, C.J., G. Nolte, and A. Daffertshofer, Phase lag index:assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sourees. Human brain mapping,2007.28(11):p.1178-1193.
[74]. Zalesky, A., et al., Whole-brain anatomical networks:does the choice of nodes matter? Neuroimage,2010.50(3):p.970.
[75], Eguiluz, V.M., et al., Scale-free brain functional networks. Physical review letters,2005.94(1): p.018102.
[76]. Kaiser, M., and Hilgetag, C., Nonoptimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems.. PLoS Computational Biology 2006:p. 7:e95.
[77]. Sporns, O., et al., Organization, development and function of complex brain networks. Trends in cognitive sciences,2004.8(9):p.418-425.
[78]. Baccala, L.A. and K. Sameshima, Partial directed coherence:a new concept in neural structure determination. Biological Cybernetics,2001.84(6):p.463-474.
[79]. Friston, K.J., L. Harrison, and W. Penny, Dynamic causal modelling. Neuroimage,2003.19(4): p.1273-1302.
[80]. Bernasconi, C. and P. KoEnig, On the directionality of cortical interactions studied by structural analysis of electrophysiological recordings. Biological Cybernetics,1999.81(3):p. 199-210.
[81]. Bassett, D.S., et al., Cognitive fitness of cost-efficient brain functional networks. Proceedings of the National Academy of Sciences,2009.106(28):p.11747-11752.
[82]. Kitzbichler, M.G., et al., Broadband criticality of human brain network synchronization. PLoS Computational Biology,2009.5(3):p. e1000314.
[83]. Palva, J.M., et al., Neuronal synchrony reveals working memory networks and predicts individual memory capacity. Proceedings of the National Academy of Sciences,2010.107(16): p.7580-7585.
[84]. Stam, C.J., Functional connectivity patterns of human magnetoencephalographic recordings: a'small-world'network? Neuroscience letters,2004.355(1-2):p.25-28.
[85]. Bullmore, E., et al., Wavelets and functional magnetic resonance imaging of the human brain. Neuroimage,2004.23(Suppl 1):p. S234-S249.
[86]. Salvador, R., et al., Undirected graphs of frequency-dependent functional connectivity in whole brain networks. Philosophical Transactions of the Royal Society B:Biological Sciences, 2005.360(1457):p.937-946.
[87]. Bollobas, B., Random graphs. Vol.73.2001:Cambridge university press.
[88]. Bullmore, E.T. and D.S. Bassett, Brain graphs:graphical models of the human brain connectome. Annual review of clinical psychology,2011.7:p.113-140.
[89]. Erd6s, P. and A. Renyi, On the evolution of random graphs. Publ. Math. Inst. Hungar. Acad. Sci,1960.5:p.17-61.
[90]. Watts, D.J. and S.H. Strogatz, Collective dynamics of 'small-world' networks, nature,1998. 393(6684):p.440-442.
[91]. Onnela, J.-P., et al., Intensity and coherence of motifs in weighted complex networks. Physical Review E,2005.71(6):p.065103.
[92]. Freeman, L.C., A set of measures of centrality based on betweenness. Sociometry,1977:p. 35-41.
[93]. Maslov, S. and K. Sneppen, Specificity and stability in topology of protein networks. Science, 2002.296(5569):p.910-913.
[94]. Sporns, O. and J.D. Zwi, The small world of the cerebral cortex. Neuroinformatics,2004.2(2): p.145-162.
[95]. Humphries, M.D., K. Gurney, and T.J. Prescott, The brainstem reticularformation is a small-world, not scale-free, network. Proceedings of the Royal Society B:Biological Sciences, 2006.273(1585):p.503-511.
[96]. Latora, V. and M. Marchiori, Efficient behavior of small-world networks. Physical review letters,2001.87(19):p.198701.
[97]. Latora, V. and M. Marchiori, Economic small-world behavior in weighted networks. The European Physical Journal B-Condensed Matter and Complex Systems,2003.32(2):p. 249-263.
[98]. Bullmore, E. and O. Sporns, Complex brain networks:graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience,2009.10(3):p.186-198.
[99]. He, Y. and A. Evans, Graph theoretical modeling of brain connectivity. Current opinion in neurology,2010.23(4):p.341-350.
[100]. Stam, C, et al., Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. Brain,2009.132(1):p.213-224.
[101]. Sheline, Y.I., Neuroimaging studies of mood disorder effects on the brain. Biological psychiatry,2003.54(3):p.338-352.
[102]. Kronmuller, K.-T., et al., Hippocampal volume in first episode and recurrent depression. Psychiatry Research:Neuroimaging,2009.174(1):p.62-66.
[103]. ALEXOPOULOS, G.S. and R.E. KELLY, Research advances in geriatric depression. World Psychiatry,2009.8(3):p.140-149.
[104]. Abe, O., et al., Voxel-based analyses of gray/white matter volume and diffusion tensor data in major depression. Psychiatry Research:Neuroimaging,2010.181(1):p.64-70.
[105]. Davidson, R. J., et al., The neural substrates of affective processing in depressed patients treated with venlafaxine. American journal of psychiatry,2003.160(1):p.64-75.
[106]. Lui, S., et al., Resting-state functional connectivity in treatment-resistant depression. American journal of psychiatry,2011.168(6):p.642-648.
[107]. Steele, J.D., et al., Prefrontal cortical functional abnormality in major depressive disorder:a stereotactic meta-analysis. Journal of affective disorders,2007.101(1-3):p.1-11.
[108]. Li, L., et al., Prefrontal white matter abnormalities in young adult with major depressive disorder:a diffusion tensor imaging study. Brain research,2007.1168:p.124-128.
[109]. Coryell, W., et al., Subgenual prefrontal cortex volumes in major depressive disorder and schizophrenia:diagnostic specificity and prognostic implications. American journal of psychiatry,2005.162(9):p.1706-1712.
[110]. Matsuo, K., et al., Prefrontal hyperactivation during working memory task in untreated individuals with major depressive disorder. Molecular psychiatry,2006.12(2):p.158-166.
[111]. Lai, T.-J., et al., Reduction of orbital frontal cortex volume in geriatric depression. Biological psychiatry,2000.48(10):p.971-975.
[112]. Lacerda, A.L., et al., Anatomic evaluation of the orbitofrontal cortex in major depressive disorder. Biological psychiatry,2004.55(4):p.353-358.
[113]. Haldane, M., et al., Structural brain correlates of response inhibition in Bipolar Disorder Ⅰ. Journal of Psychopharmacology,2008.22(2):p.138-143.
[114]. Ma, N., et al., White matter abnormalities in first-episode, treatment-naive young adults with major depressive disorder. American journal of psychiatry,2007.164(5):p.823-826.
[115]. Cheng, Y.-q., et al., Brain volume alteration and the correlations with the clinical characteristics in drug-naive first-episode MDD patients:a voxel-based morphometry study. Neuroscience letters,2010.480(1):p.30-34.
[116]. Maller, J.J., Z.J. Daskalakis, and P.B. Fitzgerald, Hippocampal volumetrics in depression:the importance of the posterior tail. Hippocampus,2007.17(11):p.1023-1027.
[117]. Young, K.A., et al., Elevated neuron number in the limbic thalamus in major depression. American journal of psychiatry,2004.161(7):p.1270-1277.
[118]. Pereira, F., T. Mitchell, and M. Botvinick, Machine learning classifiers and fMRI:a tutorial overview. Neuroimage,2009.45(1 Suppl):p. S199.
[119]. Cox, D.D. and R.L. Savoy, Functional magnetic resonance imaging (fMRI) "brain reading " detecting and classifying distributed patterns of fMRI activity in human visual cortex. Neuroimage,2003.19(2):p.261-270.
[120]. Raudys, S.J. and A.K. Jain, Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Transactions on pattern analysis and machine intelligence,1991.13(3):p.252-264.
[121]. Kanal, L. and B. Chandrasekaran, On dimensionality and sample size in statistical pattern classification. Pattern recognition,1971.3(3):p.225-234.
[122]. Jain, A.K. and W.G. Waller, On the optimal number of features in the classification of multivariate Gaussian data. Pattern recognition,1978.10(5):p.365-374.
[123]. Hua, J., et al., Optimal number of features as a function of sample size for various classification rules. Bioinformatics,2005.21(8):p.1509-1515.
[124]. Saltelli, A., Making best use of model evaluations to compute sensitivity indices. Computer Physics Communications,2002.145(2):p.280-297.
[125]. Lui, S., et al., Depressive Disorders:Focally Altered Cerebral Perfusion Measured with Arterial Spin-labeling MR Imaging]. Radiology,2009.251(2):p.476-484.
[126]. Scheuerecker, J., et al., Orbitofrontal volume reductions during emotion recognition in patients with major depression. Journal of psychiatry & neuroscience:JPN,2010.35(5):p. 311.
[127]. Dichter, G.S., et al., The effects of psychotherapy on neural responses to rewards in major depression. Biological psychiatry,2009.66(9):p.886-897.
[128]. Surguladze, S.A., et al., Depression is associated with increased sensitivity to signals of disgust:a functional magnetic resonance imaging study. Journal of psychiatric research,2010. 44(14):p.894-902.
[129]. Murphy, M.L. and T. Frodl, Meta-analysis of diffusion tensor imaging studies shows altered fractional anisotropy occurring in distinct brain areas in association with depression. Biology of mood & anxiety disorders,2011.1(1):p.1-12.
[130]. Zhang, J., et al., Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biological psychiatry,2011.70(4):p.334-342.
[131]. Girvan, M. and M.E. Newman, Community structure in social and biological networks. Proceedings of the National Academy of Sciences,2002.99(12):p.7821-7826.
[132]. Newman, M.E., Modularity and community structure in networks. Proceedings of the National Academy of Sciences,2006.103(23):p.8577-8582.
[133]. Kernighan B W, L.S., An efficient heuristic procedure for partitioning graphs. Bell System Technical Journal,1970.49:p.291-306.
[134]. Pothen, A., H.D. Simon, and K.-P. Liou, Partitioning sparse matrices with eigenvectors of graphs. SIAM Journal on Matrix Analysis and Applications,1990.11(3):p.430-452.
[135]. Newman, M.E., Fast algorithm for detecting community structure in networks. Physical Review E,2004.69(6):p.066133.
[136]. Clauset, A., M.E. Newman, and C. Moore, Finding community structure in very large networks. Physical Review E,2004.70(6):p.066111.
[137]. Blondel, V.D., et al., Fast unfolding of communities in large networks. Journal of Statistical Mechanics:Theory and Experiment,2008.2008(10):p. P10008.
[138]. Leicht, E.A. and M.E. Newman, Community structure in directed networks. Physical review letters,2008.100(11):p.118703.
[139]. Newman, M.E. and M. Girvan, Finding and evaluating community structure in networks. Physical Review E,2004.69(2):p.026113.
[140]. Guimera, R., M. Sales-Pardo, and L.A. Amaral, Classes of complex networks defined by role-to-role connectivity profiles. Nature physics,2006.3(1):p.63-69.
[141]. Berman, M.G., et al., Depression, rumination and the default network. Social cognitive and affective neuroscience,2011.6(5):p.548-555.
[142]. Taylor, J.G. and N.R. Taylor, Analysis of recurrent cortico-basal ganglia-thalamic loops for working memory. Biological Cybernetics,2000.82(5):p.415-432.
[143]. Zatorre, R.J., et al., Functional localization and lateralization of human olfactory cortex. 1992.
[144].梁秦川,et al.,后脑底核脑深部电刺激治疗帕金森病抑郁障碍的初步临床研究中国神经精神疾病杂志,2006.32(1)：p.67-68.
[145]. Cavanna, A.E. and M.R. Trimble, The precuneus:a review of its functional anatomy and behavioural correlates. Brain,2006.129(3):p.564-583.
[146]. Werner, N.S., et al., Functional MRI study of memory-related brain regions in patients with depressive disorder. Journal of affective disorders,2009.119(1):p.124-131.
[147]. Wicker, B., et al., Both of Us Disgusted in<ⅰ-> My Insula:The Common Neural Basis of Seeing and Feeling Disgust. Neuron,2003.40(3):p.655-664.
[148]. Squire, L.R., C.E. Stark, and R.E. Clark, The medial temporal lobe*. Annu. Rev. Neurosci., 2004.27:p.279-306.
[149]. Milner, B., Disorders of learning and memory after temporal lobe lesions in man. Clinical neurosurgery,1971.19:p.421-446.
[150]. Morris, J.S., et al., A differential neural response in the human amygdala to fearful and happy facial expressions.1996.
[151]. Moser, M.B. and E.I. Moser, Functional differentiation in the hippocampus. Hippocampus, 1998.8(6):p.608-619.
[152].樊晓燕and郭春彦,从认知神经科学的角度看熟悉性和回想.心理科学进展,2005.13(2)：p.314-319.
[153]. Meyer-Lindenberg, A., Neural connectivity as an intermediate phenotype:brain networks under genetic control. Human brain mapping,2009.30(7):p.1938-1946.
[154]. Chiang, M.-C., et al., Genetics of white matter development:a DTI study of 705 twins and their siblings aged 12 to 29. Neuroimage,2011.54(3):p.2308-2317.
[155]. Glahn, D., et al., Genetic control over the resting brain. Proceedings of the National Academy of Sciences,2010.107(3):p.1223-1228.
[156]. van den Heuvel, M.P., et al., Genetic control of functional brain network efficiency in children. European Neuropsychopharmacology,2012.
[157]. Esslinger, C., et al., Neural mechanisms of a genome-wide supported psychosis variant. Science,2009.324(5927):p.605-605.
[158]. Rasetti, R., et al., Altered cortical network dynamics:a potential intermediate phenotype for schizophrenia and association with ZNF804A. Archives of General Psychiatry,2011.68(12):p. 1207.
[159]. Filippini, N., et al., Distinct patterns of brain activity in young carriers of the APOE-ε4 allele. Proceedings of the National Academy of Sciences,2009.106(17):p.7209-7214.
[160]. Sheline, Y.I., et al., APOE4 allele disrupts resting state fMRI connectivity in the absence of amyloid plaques or decreased CSF Aβ42. The Journal of neuroscience,2010.30(50):p. 17035-17040.
[161]. Liu, B., et al., Prefrontal-related functional connectivities within the default network are modulated by COMT vall58met in healthy young adults. The Journal of neuroscience,2010. 30(1):p.64-69.
[162]. Liu, B., et al., Haplotypes of catechol-O-methyltransferase modulate intelligence-related brain white matter integrity. Neuroimage,2010.50(1):p.243-249.
[163]. Thomason, M.E., et al., COMT genotype affects prefrontal white matter pathways in children and adolescents. Neuroimage,2010.53(3):p.926-934.
[164]. Sprooten, E., et al., Association of white matter integrity with genetic variation in an exonic DISCI SNP. Molecular psychiatry,2011.16(7):p.688-689.
[165]. Li, Y., et al., Less efficient information transfer in Cys-allele carriers of DISCI:A brain network study based on diffusion MRI. Cerebral cortex,2013.23(7):p.1715-1723.
[166]. Gould, T.D. and H.K. Manji, Glycogen synthase kinase-3:a putative molecular target for lithium mimetic drugs. Neuropsychopharmacology,2005.30(7):p.1223-1237.
[167]. Muyllaert, D., et al., Glycogen synthase kinase-3β, or a link between amyloid and tau pathology? Genes, Brain and Behavior,2008.7(s1):p.57-66.
[168]. Yang, C., et al., The combined effects of the< i> BDNF and< i> GSK3B genes modulate the relationship between negative life events and major depressive disorder. Brain research,2010.1355:p.1-6.
[169]. Li, X., et al., In vivo regulation of glycogen synthase kinase-3fi (GSK3β) by serotonergic activity in mouse brain. Neuropsychopharmacology:official publication of the American College of Neuropsychopharmacology,2004.29(8):p.1426.
[170]. Roh, M.-S., et al., Hypoxia activates glycogen synthase kinase-3 in mouse brain in vivo: protection by mood stabilizers and imipramine. Biological psychiatry,2005.57(3):p. 278-286.
[171]. Klein, P.S. and D.A. Melton, A molecular mechanism for the effect of lithium on development. Proceedings of the National Academy of Sciences,1996.93(16):p.8455-8459.
[172]. Stambolic, V., L. Ruel, and J.R. Woodgett, Lithium inhibits glycogen synthase kinase-3 activity and mimics wingless signalling in intact cells. Current Biology,1996.6(12):p. 1664-1669.
[173]. Li, X., et al., Lithium regulates glycogen synthase kinase-3β in human peripheral blood mononuclear cells:implication in the treatment of bipolar disorder. Biological psychiatry, 2007.61(2):p.216-222.
[174]. Tsai, S., et al., Glycogen synthase kinase-3β gene is associated with antidepressant treatment response in Chinese major depressive disorder. The pharmacogenomics journal,2008.8(6):p. 384-390.
[175]. Inkster, B., et al., Association of GSK3{beta} Polymorphisms With Brain Structural Changes in Major Depressive Disorder. Archives of General Psychiatry,2009.66(7):p.721.
[176]. Kwok, J. B., et al., GSK3B polymorph isms alter transcription and splicing in Parkinson's disease. Annals of neurology,2005.58(6):p.829-839.
[177]. Radua, J., et al., Neural response to specific components of fearful faces in healthy and schizophrenic adults. Neuroimage,2010.49(1):p.939-946.
[178]. Yao, H.-B., et al., Expression of glycogen synthase kinase-3 isoforms in mouse tissues and their transcription in the brain. Journal of chemical neuroanatomy,2002.23(4):p.291-297.
[179]. Park, H. J., et al., Corpus callosal connection mapping using cortical gray matter parcellation and DT-MRI. Human brain mapping,2008.29(5):p.503-516.
[180]. Guo, W., et al., Decreased interhemispheric resting-state functional connectivity in first-episode, drug-naive major depressive disorder. Progress in Neuro-Psychopharmacology and Biological Psychiatry,2012.
[181]. Lord, A., et al., Changes in community structure of resting state functional connectivity in unipolar depression. PLoS One,2012.7(8):p. e41282.
[182]. Cao, X., et al., Disrupted resting-state functional connectivity of the hippocampus in medication-naive patients with major depressive disorder. Journal of affective disorders,2012. 141(2):p.194-203.
[183]. Bigler, E.D., et al., Superior temporal gyrus, language function, and autism. Developmental neuropsychology,2007.31(2):p.217-238.
[184]. Fitzgerald, P.B., et al., A meta-analytic study of changes in brain activation in depression. Human brain mapping,2008.29(6):p.683-695.
[185]. Canli, T., et al., Brain activation to emotional words in depressed vs healthy subjects. Neuroreport,2004.15(17):p.2585-2588.
[186]. Biswal, B., et al., Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magnetic resonance in medicine,1995.34(4):p.537-541.
[187]. Kiviniemi, V., et al., Independent component analysis of nondeterministic fMRI signal sources. Neuroimage,2003.19(2):p.253-260.
[188]. Zang, Y., et al., Regional homogeneity approach to fMRI data analysis. Neuroimage,2004. 22(1):p.394-400.
[189].龙淼淼,et al.,采用MR低频振幅图像和低频振幅分数图像观察阿尔茨海默病患者静息态脑功能改变.中华放射学杂志,2013.47(001)：p.44-48.
[190]. Yu-Feng, Z., et al., Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain and Development,2007.29(2):p.83-91.
[191]. Cao, Q., et al., Abnormal neural activity in children with attention deficit hyperactivity disorder:a resting-state functional magnetic resonance imaging study. Neuroreport,2006. 17(10):p.1033-1036.
[192]. Liu, H., et al., Decreased regional homogeneity in schizophrenia:a resting state functional magnetic resonance imaging study. Neuroreport,2006.17(1):p.19-22.
[193]. Hoptman, M.J., et al., Amplitude of low-frequency oscillations in schizophrenia:a resting state fMR1 study. Schizophrenia research,2010.117(1):p.13-20.
[194]. He, Y., et al., Regional coherence changes in the early stages of Alzheimer's disease:a combined structural and resting-state functional MRI study. Neuroimage,2007.35(2):p. 488-500.
[195]. Liu, Y., et al., Regional homogeneity, functional connectivity and imaging markers of Alzheimer's disease:a review of resting-state fMRI studies. Neuropsychologia,2008.46(6):p. 1648-1656.
[196]. Wu, T., et al., Regional homogeneity changes in patients with Parkinson's disease. Human brain mapping,2009.30(5):p.1502-1510.
[197]. Zhang, Z., et al., Impaired attention network in temporal lobe epilepsy:a resting FMRI study. Neuroscience letters,2009.458(3):p.97-101.
[198]. Ballmaier, M., et al., Anterior cingulate, gyrus rectus, and orbitofrontal abnormalities in elderly depressed patients:an MRI-based parcellation of the prefrontal cortex. American Journal of Psychiatry,2004.161(1):p.99-108.
[199]. Wu, F., et al., Whiter matter abnormalities in medication-naive subjects with a single short-duration episode of major depressive disorder. Psychiatry Research:Neuroimaging, 2011.191(1):p.80-83.
[200]. Aron, A.R., et al., Stop-signal inhibition disrupted by damage to right inferior frontal gyrus in humans. Nature neuroscience,2003.6(2):p.115-116.
[201]. Ettlin, D.A., et al.,Interindividual differences in the perception of dental stimulation and related brain activity. European journal of oral sciences,2009.117(1):p.27-33.
[202]. Stoeckel, C., et al., Supramarginal gyrus involvement in visual word recognition. Cortex,2009. 45(9):p.1091-1096.
[203]. Sandrini, M., P.M. Rossini, and C. Miniussi, The differential involvement of inferior parietal lobule in number comparison:a rTMS study. Neuropsychologia,2004.42(14):p.1902-1909.
[204]. Vickery, T.J. and Y.V. Jiang, Inferior parietal lobule supports decision making under uncertainty in humans. Cerebral Cortex,2009.19(4):p.916-925.
[205]. Nagaratnam, N., et al., Angular gyrus syndrome mimicking depressive pseudodementia. Journal of Psychiatry and Neuroscience,2002.27(5):p.364.
[206]. Mayberg, H.S., et al., Selective hypometabolism in the inferior frontal lobe in depressed patients with Parkinson's disease. Annals of neurology,1990.28(1):p.57-64.
[207]. Bench, C., R. Frackowiak, and R. Dolan, Changes in regional cerebral blood flow on recovery from depression. Psychological Medicine,1995.25(2):p.247-262.
[208]. Perico, C.A.-M., et al., Relationship between regional cerebral blood flow and separate symptom clusters of major depression:a single photon emission computed tomography study using statistical parametric mapping. Neuroscience letters,2005.384(3):p.265-270.
[209]. Cavanna, A.E., The precuneus and consciousness. CNS spectrums,2007.
[210]. Maddock, R., A. Garrett, and M. Buonocore, Remembering familiar people:the posterior cingulate cortex and autobiographical memory retrieval. Neuroscience,2001.104(3):p. 667-676.
[211]. Maddock, R.J., The retrosplenial cortex and emotion:new insights from functional neuroimaging of the human brain. Trends in neurosciences,1999.22(7):p.310-316.
[212]. Maddock, R.J., A.S. Garrett, and M.H. Buonocore, Posterior cingulate cortex activation by emotional words:fMRI evidence from a valence decision task. Human brain mapping,2003. 18(1):p.30-41.
[213]. Caetano, S.C., et al., Smaller cingulate volumes in unipolar depressed patients. Biological psychiatry,2006.59(8):p.702-706.
[214]. Drevets, W.C., Neuroimaging studies of mood disorders. Biological psychiatry,2000.48(8):p. 813-829.
[215]. Hayasaka, S. and P.J. Laurienti, Comparison of characteristics between region-and voxel-based network analyses in resting-state fMRI data. Neuroimage,2010.50(2):p.499.
[216]. Fomito, A., A. Zalesky, and E.T. Bullmore, Network scaling effects in graph analytic studies of human resting-state FMRI data. Frontiers in systems neuroscience,2010.4.