Share:
Share this content in WeChat
X
Review
Progress in the study of functional magnetic resonance imaging (fMRI) brain networks in the depression
ZHANG Qi  WANG Bin 

DOI:10.12015/issn.1674-8034.2018.04.010.


[Abstract] In recent years, the development of functional magnetic resonance imaging technology has enabled researchers to study the structure and function of living brain tissue without invasive research, and the study of large scale brain structure and function network is necessary to study the occurrence and development mechanism of certain diseases. Currently, the study of brain network in patients with depression is still in the preliminary stage of exploration, the topological properties of brain network play an auxiliary role in early diagnosis and differential diagnosis of depressed patients, it can also be used as a measure of the severity of depression. Abnormal patterns of brain structure and function can also be used as sensitive features to diagnose related brain diseases, therefore, this paper will review the research results of brain structure and function of depressed patients from the perspective of brain network.
[Keywords] Depression;Brain network;Magnetic resonance imaging, functional

ZHANG Qi Department of Medical Imaging, Clinical College of Binzhou Medical College, Department of Imaging of Yantai Affiliated Hospital, Yantai 264003, China

WANG Bin* Department of Medical Imaging, Clinical College of Binzhou Medical College, Department of Imaging of Yantai Affiliated Hospital, Yantai 264003, China

*Corresponding to: Wang B, E-mail: binwang001@aliyun.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This work was part of National Natural Science Foundation of China No. 81641074 Shandong Provincial Natural Science Foundation No. ZR2016HL40 Shandong Provincial Science and Technology Achievements Key Promotion Plan No. 2017GSF18121
Received  2018-01-18
Accepted  2018-03-13
DOI: 10.12015/issn.1674-8034.2018.04.010
DOI:10.12015/issn.1674-8034.2018.04.010.

[1]
陈建淮,姚志剑.基于图论的复杂脑网络理论在抑郁症中的研究进展.临床精神医学杂志, 2016, 26(4): 272-274.
[2]
夏明睿,贺永.多模态脑磁共振成像计算方法在抑郁症研究中的应用进展.中华精神科杂志, 2016, 49(4): 255-260.
[3]
Humphries MD, Gurney K, Prescott TJ. The brainstem reticular formation is a small world, not scale-free, network. Proc Biol Sci, 2006, 273(1585): 503-511.
[4]
van den Heuvel MP, van Soelen IL, Stam CJ, et al. Genetic control of functional brain network efficiency in children. Eur Neuropsychopharmacol, 2013, 23(1): 19-23.
[5]
房俊芳,李旭日,王滨,等. VBM联合ReHo在评价抑郁症脑功能及结构异常中的应用.广东医学, 2015, 36(14): 2167-2171.
[6]
Zhao YJ, Du MY, Huang XQ, et al. Brain grey matter abnormalities in medication-free patients with major depressive disorder: a meta-analysis. Psychol Med, 2014, 44(14): 2927-2937.
[7]
Goodkind M, Eickhoff SB, Oathes DJ, et al. Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry, 2015, 72(4): 305-315.
[8]
Xu K, Jing W, Ren L, et al. Impaired interhemispheric connectivity in medicationnaive patients with major depressive disorder. Psychiatry Neurosci, 2013, 38(1): 43-48.
[9]
Jia ZY, Huang XQ, Wu QZ, et al. High-field magnetic resonance imaging of suicidality in patients with major depressive disorder. Am J Psychiatry, 2010, 167(11): 1381-1390.
[10]
Taylor WD, Mac Fall JR, Boyd B, et al. One year change in anterior cingulate cortex white matter microstructure: relationship with late-life depression outcomes. Am J Geriatr Psychiatry, 2011, 19(1): 43-52.
[11]
Singh MK, Kesler SR, Hadi Hosseini SM, et al. Anomalous gray matter structural networks in major depressive disorder. Biological psychiatry, 2013, 74(10): 777-785.
[12]
Ryan KA, Hsu DT, Welsh RC, et al. Decoupling of the amygdala to other salience network regions in adolescent-onset recurrent major depressive disorder. Psychol Med, 2016, 46(5): 1055-1067.
[13]
Korgaonkar MS, Fornito A, Williams LM, et al. Abnormal structural networks characterize major depressive disorder: a connectome analysis. Biol Psychiatry, 2014, 76(7): 567-574.
[14]
Lim HK, Jung WS, Aizenstein HJ. Aberrant topographical organization in gray matter structural network in late life depression: a graph theoretical analysis. Int Psychogeriatr, 2013, 25(12): 1929-1940.
[15]
Menon V. Large-scale brain network and psychopathology: a unifying triple network model. Trends Cogn Sci, 2011, 15(10): 483-506.
[16]
Kaiser RH, Andrews-Hanna JR, Wager TD, et al. Large-scale network dysfunction in major depressive disorder: A meta-analysis of resting-state functional connectivity. JAMA Psychiatry, 2015, 72(6): 603-611.
[17]
van den Heuvel MP, Stam CJ, Boersma M, et al. Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain. Neuroimage, 2008, 43(3): 528-539.
[18]
Zhu X, Wang X, Xiao J, et al. Evidence of a dissociation pattern in resting-state default mode networkconnectivity in first-episode, treatment-naive major depression patients. Biol Psychiatry, 2012, 71(7): 611-617.
[19]
Wang L, Hermens DF, Hickie IB. A systematic review of resting-state functional-MRI studies in major depression. J Affect Disord, 2012, 142(1-3): 6-12.
[20]
Grimm S, Boesiger P, Beck J, et al. Altered negative BOLD responses in the default-mode network during emotion processing in depressed subjects. Neuropsychopharmacology, 2009, 34(4): 932-943.
[21]
Sikora M, Heffernan J, Avery ET, et al. Salience network functional connectivity predicts placebo effects in major depression. Biol Psychiatry Cogn Neurosci Neuroimaging, 2016, 1(1): 68-76.
[22]
陈佳杰,刘洁荣,魏璇,等.海洛因成瘾者大脑突显性网络异常的独立成分分析.磁共振成像, 2017, 8(2): 100-104.
[23]
Manoliu A, Meng C, Brandl F, et al. Insular dysfunction within the salience network is associated with severity of symptoms and aberrant inter-network connectivity in major depressive disorder. Frontiers in Human Neuroscience, 2013, 7(2): 930.
[24]
Corbetta M. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci, 2002, 3(3): 201-215.
[25]
Rogers MA, Kasai K, Koji M, et al. Executive and prefrontal dysfunction in unipolar depression: a review of neuropsychological and imaging evidence. Neuroscience Res, 2004, 50(1): 1-11.
[26]
朱俊娟,彭代辉,江开达,等.脑网络在抑郁症中的应用研究进展.中国神经精神疾病杂志, 2011, 37(12): 760-763.
[27]
Frodl T, Bokde AL, Scheuerecker J, et al. Functional connectivity bias of the orbitofrontal cortex in drug-free patients with major depression. Biol Psychiatry, 2010, 67(2): 161-167.
[28]
Li SY, Zhu Y, Wang YL, et al. Dysfunctional resting-state connectivity of default mode network in adolescent patients with first-episode drug-naive major depressive disorder. Natl Med J China, 2017, 97(45): 3538-3542.
[29]
Hama S, Yamashita H, Yamawaki S, et al. Post-stroke depression and apathy: Interactions between functional recovery, lesion location, and emotional response. Psychogeriatrics, 2011, 11(1): 68-76.
[30]
Cieri F, Esposito R, Cera N, et al. Late-life depression: Modifications of brain resting state activity. J Geriatr Psychiatry Neurol, 2017, 30(3): 140-150.
[31]
朱雪玲,袁福来.基于区域的抑郁症默认网络内部功能连接研究.中国临床心理学杂志, 2016, 24(2): 218-220, 212.
[32]
Li L, Li B, Bai Y, et al. Abnormal resting state effective connectivity within the default mode network in major depressive disorder: A spectral dynamic causal modeling study. Brain and Behavior, 2017, 7(7): e00732.
[33]
Rodríguez-Cano E, Alonso-Lana S, Sarró S, et al. Differential failure to deactivate the default mode network in unipolar and bipolar depression. Bipolar Disord, 2017, 19(5): 386-395.
[34]
Dutta A, McKie S, Deakin JF. Resting state networks in major depressive disorder. Psychiatry Res, 2014, 224(3): 139-151.

PREV Application of quantitative magnetic susceptibility weighted imaging in acute ischemic stroke
NEXT Progress of multimodal magnetic resonance imaging in left atrial appendage
  



Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn