抑郁症相关的静息态脑功能网络异常
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摘要
近年来,静息态脑功能磁共振成像(resting-state functional magnetic resonance imaging, rf MRI)被大量用于揭示抑郁个体脑功能网络的异常,主要体现在默认网络、认知控制网络和情绪网络各自内部及三大网络之间交互作用方面.与正常人相比,抑郁个体默认网络内部的异常主要表现为前部功能连接增强而后部功能连接减弱,前后两部分的异常可能有着分离的模式;认知控制网络内部的异常表现为功能连接减弱;而在情绪网络内部,抑郁个体的异常主要表现为边缘系统功能连接增强以及奖赏回路功能连接减弱.抑郁症对不同网络之间交互作用的影响主要体现在各个网络代表节点之间的功能连接异常.这些功能网络之间的交互异常可能反映了抑郁个体大脑在资源分配以及信息整合两方面存在缺陷.基于当前研究存在的不足,未来研究可关注抑郁症的多维度大数据整合和个体化研究,并将抑郁症与其他精神疾病脑网络异常的共性与特异性进行比较,在更深入揭示抑郁症神经机制的基础上为临床诊断和干预提供有效的生物学标记.
Numerous resting-state functional magnetic resonance imaging studies have revealed that major depressive disorder(MDD) is associated with abnormal functional connectivity(FC) within and between large-scale functional networks such as the default mode network(DMN), cognitive control network(CCN) and affective network(AN). Compared with healthy controls, individuals with MDD usually show(i) increased FC within the anterior DMN and decreased FC within the posterior DMN,(ii) decreased FC within the CCN and(iii) increased FC within limbic system and decreased FC in the reward system in the AN. Depression related interactive changes between networks have also been reported:(i) decreased FC between DMN and CCN,(ii) increased FC between DMN and AN, and(iii) decreased FC between CCN and AN. These findings on network interaction may represent impaired resource allocation and information integration in MDD. Major weakness in the present rfMRI studies of depression resides in small sample and lack of multidimensional features. Meanwhile, as several brain disorders may show commonly disrupted functional architectures, depressionrelated specific alterations are typically lacking. We suggest that future studies may advance by combining multidimensional big data and individualized characterization, as well as examining shared and distinct functional network mechanisms of MDD in the spectrum of psychiatric disorders.
Numerous resting-state functional magnetic resonance imaging studies have revealed that major depressive disorder(MDD) is associated with abnormal functional connectivity(FC) within and between large-scale functional networks such as the default mode network(DMN), cognitive control network(CCN) and affective network(AN). Compared with healthy controls, individuals with MDD usually show(i) increased FC within the anterior DMN and decreased FC within the posterior DMN,(ii) decreased FC within the CCN and(iii) increased FC within limbic system and decreased FC in the reward system in the AN. Depression related interactive changes between networks have also been reported:(i) decreased FC between DMN and CCN,(ii) increased FC between DMN and AN, and(iii) decreased FC between CCN and AN. These findings on network interaction may represent impaired resource allocation and information integration in MDD. Major weakness in the present rfMRI studies of depression resides in small sample and lack of multidimensional features. Meanwhile, as several brain disorders may show commonly disrupted functional architectures, depressionrelated specific alterations are typically lacking. We suggest that future studies may advance by combining multidimensional big data and individualized characterization, as well as examining shared and distinct functional network mechanisms of MDD in the spectrum of psychiatric disorders.
引文
1 Chen Y, Wang C, Zhu X, et al. Aberrant connectivity within the default mode network in first-episode, treatment-na?ve major depressive disorder.J Affect Disorders, 2015, 183:49–56
2 WHO. Depression and Other Common Mental Disorders:Global Health Estimates. Geneva:World Health Organization, 2017
3 Beevers C G, Clasen P, Stice E, et al. Depression symptoms and cognitive control of emotion cues:a functional magnetic resonance imaging study. Neuroscience, 2010, 167:97–103
4 Surguladze S, Brammer M J, Keedwell P, et al. A differential pattern of neural response toward sad versus happy facial expressions in major depressive disorder. Biol Psychiatry, 2005, 57:201–209
5 Mulders P C, van Eijndhoven P F, Schene A H, et al. Resting-state functional connectivity in major depressive disorder:a review. Neurosci Biobehaval Rev, 2015, 56:330–344
6 Cole D M. Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Front Syst Neurosci, 2010, 4:8
7 Golestani A M, Goodyear B G. Regions of interest for resting-state fMRI analysis determined by inter-voxel cross-correlation. NeuroImage,2011, 56:246–251
8 Calhoun V D, Liu J, Adali T. A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. NeuroImage,2009, 45:S163–S172
9 Li Z, Prudente C N, Stilla R, et al. Alterations of resting-state fMRI measurements in individuals with cervical dystonia. Hum Brain Mapp, 2017,38 :4098–4108
10 Zang Y, Jiang T, Lu Y, et al. Regional homogeneity approach to fMRI data analysis. NeuroImage, 2004, 22:394–400
11 Guo W, Liu F, Zhang J, et al. Abnormal default-mode network homogeneity in first-episode, drug-naive major depressive disorder. PLoS ONE,2014, 9:e91102
12 Zou Q H, Zhu C Z, Yang Y, et al. An improved approach to detection of amplitude of low-frequency fluctuation(ALFF)for resting-state fMRI:fractional ALFF. J Neurosci Methods, 2008, 172:137–141
13 Küblb?ck M, Woletz M, H?flich A, et al. Stability of low-frequency fluctuation amplitudes in prolonged resting-state fMRI. NeuroImage, 2014,103 :249–257
14 Zuo X N, Xing X X. Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics:a systems neuroscience perspective. Neurosci Biobehaval Rev, 2014, 45:100–118
15 Gong Q, He Y. Depression, neuroimaging and connectomics:a selective overview. Biol Psychiatry, 2015, 77:223–235
16 Drysdale A T, Grosenick L, Downar J, et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med,2017, 23:28–38
17 Wager T D, Lindquist M A, Nichols T E, et al. Evaluating the consistency and specificity of neuroimaging data using meta-analysis. NeuroImage,2009, 45:S210–S221
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22 Sheline Y I, Price J L, Yan Z, et al. Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proc Natl Acad Sci USA, 2010, 107:11020–11025
23 Berman M G, Peltier S, Nee D E, et al. Depression, rumination and the default network. Social Cogn Affect Neurosci, 2011, 6:548–555
24 Manoliu A, Riedl V, Doll A, et al. Insular dysfunction reflects altered between-network connectivity and severity of negative symptoms in schizophrenia during psychotic remission. Front Hum Neurosci, 2013, 7:930
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27 Lemogne C, Delaveau P, Freton M, et al. Medial prefrontal cortex and the self in major depression. J Affect Disorders, 2012, 136:e1–e11
28 Li B, Liu L, Friston K J, et al. A treatment-resistant default mode subnetwork in major depression. Biol Psychiatry, 2013, 74:48–54
29 van Tol M J, Li M, Metzger C D, et al. Local cortical thinning links to resting-state disconnectivity in major depressive disorder. Psychol Med,2014, 44:2053–2065
30 Qiu H, Liu H, He Q, et al. Research on amplitude of low-frequency fluctuation in patients with major depression based on resting-state functional magnetic resonance imaging. J Biomed Eng, 2014, 31:97–102
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35 Greicius M D, Flores B H, Menon V, et al. Resting-state functional connectivity in major depression:abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol Psychiatry, 2007, 62:429–437
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2 WHO. Depression and Other Common Mental Disorders:Global Health Estimates. Geneva:World Health Organization, 2017
3 Beevers C G, Clasen P, Stice E, et al. Depression symptoms and cognitive control of emotion cues:a functional magnetic resonance imaging study. Neuroscience, 2010, 167:97–103
4 Surguladze S, Brammer M J, Keedwell P, et al. A differential pattern of neural response toward sad versus happy facial expressions in major depressive disorder. Biol Psychiatry, 2005, 57:201–209
5 Mulders P C, van Eijndhoven P F, Schene A H, et al. Resting-state functional connectivity in major depressive disorder:a review. Neurosci Biobehaval Rev, 2015, 56:330–344
6 Cole D M. Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Front Syst Neurosci, 2010, 4:8
7 Golestani A M, Goodyear B G. Regions of interest for resting-state fMRI analysis determined by inter-voxel cross-correlation. NeuroImage,2011, 56:246–251
8 Calhoun V D, Liu J, Adali T. A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. NeuroImage,2009, 45:S163–S172
9 Li Z, Prudente C N, Stilla R, et al. Alterations of resting-state fMRI measurements in individuals with cervical dystonia. Hum Brain Mapp, 2017,38 :4098–4108
10 Zang Y, Jiang T, Lu Y, et al. Regional homogeneity approach to fMRI data analysis. NeuroImage, 2004, 22:394–400
11 Guo W, Liu F, Zhang J, et al. Abnormal default-mode network homogeneity in first-episode, drug-naive major depressive disorder. PLoS ONE,2014, 9:e91102
12 Zou Q H, Zhu C Z, Yang Y, et al. An improved approach to detection of amplitude of low-frequency fluctuation(ALFF)for resting-state fMRI:fractional ALFF. J Neurosci Methods, 2008, 172:137–141
13 Küblb?ck M, Woletz M, H?flich A, et al. Stability of low-frequency fluctuation amplitudes in prolonged resting-state fMRI. NeuroImage, 2014,103 :249–257
14 Zuo X N, Xing X X. Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics:a systems neuroscience perspective. Neurosci Biobehaval Rev, 2014, 45:100–118
15 Gong Q, He Y. Depression, neuroimaging and connectomics:a selective overview. Biol Psychiatry, 2015, 77:223–235
16 Drysdale A T, Grosenick L, Downar J, et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med,2017, 23:28–38
17 Wager T D, Lindquist M A, Nichols T E, et al. Evaluating the consistency and specificity of neuroimaging data using meta-analysis. NeuroImage,2009, 45:S210–S221
18 Zhu X, Wang X, Xiao J, et al. Evidence of a dissociation pattern in resting-state default mode network connectivity in first-episode, treatmentnaive major depression patients. Biol Psychiatry, 2012, 71:611–617
19 Liston C, Chen A C, Zebley B D, et al. Default mode network mechanisms of transcranial magnetic stimulation in depression. Biol Psychiatry,2014, 76:517–526
20 Veer I M, Beckmann C, Van T M J, et al. Whole brain resting-state analysis reveals decreased functional connectivity in major depression. Front Syst Neurosci, 2010, 4:1–10
21 Wang Y, Yang S, Sun W, et al. Altered functional interaction hub between affective network and cognitive control network in patients with major depressive disorder. Behaviour Brain Res, 2016, 298:301–309
22 Sheline Y I, Price J L, Yan Z, et al. Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proc Natl Acad Sci USA, 2010, 107:11020–11025
23 Berman M G, Peltier S, Nee D E, et al. Depression, rumination and the default network. Social Cogn Affect Neurosci, 2011, 6:548–555
24 Manoliu A, Riedl V, Doll A, et al. Insular dysfunction reflects altered between-network connectivity and severity of negative symptoms in schizophrenia during psychotic remission. Front Hum Neurosci, 2013, 7:930
25 Tang Y, Kong L, Wu F, et al. Decreased functional connectivity between the amygdala and the left ventral prefrontal cortex in treatment-naive patients with major depressive disorder:a resting-state functional magnetic resonance imaging study. Psychol Med, 2013, 43:1921–1927
26 Andrews-Hanna J R, Smallwood J, Spreng R N. The default network and self-generated thought:component processes, dynamic control, and clinical relevance. Ann NY Acad Sci, 2014, 1316:29–52
27 Lemogne C, Delaveau P, Freton M, et al. Medial prefrontal cortex and the self in major depression. J Affect Disorders, 2012, 136:e1–e11
28 Li B, Liu L, Friston K J, et al. A treatment-resistant default mode subnetwork in major depression. Biol Psychiatry, 2013, 74:48–54
29 van Tol M J, Li M, Metzger C D, et al. Local cortical thinning links to resting-state disconnectivity in major depressive disorder. Psychol Med,2014, 44:2053–2065
30 Qiu H, Liu H, He Q, et al. Research on amplitude of low-frequency fluctuation in patients with major depression based on resting-state functional magnetic resonance imaging. J Biomed Eng, 2014, 31:97–102
31 Guo W, Liu F, Xun G, et al. Reversal alterations of amplitude of low-frequency fluctuations in early and late onset, first-episode, drug-naive depression. Prog Neuro-Psychopharmacol Biol Psychiatry, 2013, 40:153–159
32 Guo W, Liu F, Zhang J, et al. Dissociation of regional activity in the default mode network in first-episode, drug-naive major depressive disorder at rest. J Affect Disorders, 2013, 151:1097–1101
33 Iwabuchi S J, Krishnadas R, Li C, et al. Localized connectivity in depression:a meta-analysis of resting state functional imaging studies.Neurosci Biobehaval Rev, 2015, 51:77–86
34 Zhang J, Wang J, Wu Q, et al. Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biol Psychiatry,2011, 70:334–342
35 Greicius M D, Flores B H, Menon V, et al. Resting-state functional connectivity in major depression:abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol Psychiatry, 2007, 62:429–437
36 Damoiseaux J S, Rombouts S A R B, Barkhof F, et al. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci USA, 2006,103 :13848–13853
37 Damoiseaux J S, Beckmann C F, Arigita E J S, et al. Reduced resting-state brain activity in the “default network” in normal aging. Cerebral Cortex, 2008, 18:1856–1864
38 Abbott C C, Lemke N T, Gopal S, et al. Electroconvulsive therapy response in major depressive disorder:a pilot functional network connectivity resting state FMRI investigation. Front Psychiatry, 2013, 4:10
39 Wu M, Andreescu C, Butters M A, et al. Default-mode network connectivity and white matter burden in late-life depression. Psychiatry ResNeuroimag, 2011, 194:39–46
40 Zhou Y, Yu C, Zheng H, et al. Increased neural resources recruitment in the intrinsic organization in major depression. J Affect Disorders, 2010,121 :220–230
41 Wise T, Marwood L, Perkins A M, et al. Instability of default mode network connectivity in major depression:a two-sample confirmation study.Transl Psychiatry, 2017, 7:e1105
42 Hutchison R M, Womelsdorf T, Allen E A, et al. Dynamic functional connectivity:promise, issues, and interpretations. NeuroImage, 2013, 80:360 –378
43 Lee R S C, Hermens D F, Porter M A, et al. A meta-analysis of cognitive deficits in first-episode Major Depressive Disorder. J Affect Disorders,2012, 140:113–124
44 Alexopoulos G S, Hoptman M J, Kanellopoulos D, et al. Functional connectivity in the cognitive control network and the default mode network in late-life depression. J Affect Disorders, 2012, 139:56–65
45 Ye T, Peng J, Nie B, et al. Altered functional connectivity of the dorsolateral prefrontal cortex in first-episode patients with major depressive disorder. Eur J Rad, 2012, 81:4035–4040
46 Lui S, Wu Q, Qiu L, et al. Resting-state functional connectivity in treatment-resistant depression. Am J Psychiatry, 2011, 168:642–648
47 Ye M, Yang T, Qing P, et al. Changes of functional brain networks in major depressive disorder:a graph theoretical analysis of resting-state fMRI. PLoS ONE, 2015, 10:e0133775
48 Avery J A, Drevets W C, Moseman S E, et al. Major depressive disorder is associated with abnormal interoceptive activity and functional connectivity in the insula. Biol Psychiatry, 2014, 76:258–266
49 Yue Y, Yuan Y, Hou Z, et al. Abnormal functional connectivity of amygdala in late-onset depression was associated with cognitive deficits. PLoS One, 2013, 8:e75058
50 Ramasubbu R, Konduru N, Cortese F, et al. Reduced intrinsic connectivity of amygdala in adults with major depressive disorder. Front Psychiatry, 2014, 5:17
51 Pannekoek J N, van der Werff S J A, Meens P H F, et al. Aberrant resting-state functional connectivity in limbic and salience networks in treatment-na?ve clinically depressed adolescents. J Child Psychol Psychiatr, 2014, 55:1317–1327
52 Albert K, Gau V, Taylor W D, et al. Attention bias in older women with remitted depression is associated with enhanced amygdala activity and functional connectivity. J Affect Disorders, 2017, 210:49–56
53 Davey C G, Harrison B J, Yücel M, et al. Regionally specific alterations in functional connectivity of the anterior cingulate cortex in major depressive disorder. Psychol Med, 2012, 42:2071–2081
54 de Kwaasteniet B, Ruhe E, Caan M, et al. Relation between structural and functional connectivity in major depressive disorder. Biol Psychiatry,2013, 74:40–47
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