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Advances in imaging studies of senile depression
LI Xiaoling  YAO Chunli  WANG Feng  CAO Danna  ZHANG Kunyu  LIU Xiaohui  CAI Lina  JIANG Xiaoxu  NIE Shouping 

Cite this article as: Li XL, Yao CL, Wang F, et al. Advances in imaging studies of senile depression. Chin J Magn Reson Imaging, 2020, 11(3): 228-231. DOI:10.12015/issn.1674-8034.2020.03.015.


[Abstract] Late-life depression (LLD), as a heterogeneous syndrome, is a common clinical psychiatric disorder. At present, the pathophysiological mechanism of senile depression is not clear, and many previous studies often emphasize that its occurrence is related to vascular damage, nerve inflammation and other changes. With the development of imaging technology, more and more neuroimaging studies have shown that the pathogenesis of senile depression is closely related to changes in brain structure, brain function and brain metabolism. As a non-invasive examination method, imaging technology is becoming more and more mature. Compared with the widely used clinical diagnosis methods, it can provide more visual evidence and reduce the error caused by subjective factors. Now, imaging has been widely used in the study of neurobiological mechanism of senile depression, which provides new ideas and treatment methods for the diagnosis, treatment and prognosis evaluation of senile depression. This article will review the study of imaging in senile depression.
[Keywords] geriatric depression；imaging；brain structure；brain function；cerebral metabolic

LI Xiaoling Department of CT and Magnetic Resonance, First Hospital Affiliated to Heilongjiang University of Chinese Medicine, Harbin 150040, China

YAO Chunli Graduate School of Heilongjiang University of Chinese Medicine, Harbin 150040, China

WANG Feng Department of CT and Magnetic Resonance, First Hospital Affiliated to Heilongjiang University of Chinese Medicine, Harbin 150040, China; Graduate School of Heilongjiang University of Chinese Medicine, Harbin 150040, China

CAO Danna* Department of CT and Magnetic Resonance, First Hospital Affiliated to Heilongjiang University of Chinese Medicine, Harbin 150040, China; Graduate School of Heilongjiang University of Chinese Medicine, Harbin 150040, China

ZHANG Kunyu Graduate School of Heilongjiang University of Chinese Medicine, Harbin 150040, China

LIU Xiaohui Graduate School of Heilongjiang University of Chinese Medicine, Harbin 150040, China

CAI Lina Graduate School of Heilongjiang University of Chinese Medicine, Harbin 150040, China

JIANG Xiaoxu Graduate School of Heilongjiang University of Chinese Medicine, Harbin 150040, China

NIE Shouping Graduate School of Heilongjiang University of Chinese Medicine, Harbin 150040, China

*Corresponding to: Cao DN, E-mail: hljanna@126.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  National Natural Science Foundation of China No. 81973930, 81373714 Heilongjiang Province Natural Science Foundation Surface Project No. H2016081 Harbin Science and Technology Innovation Outstanding Academic Leaders Fund No. 2016RAXYJ096 Ministry of Education "Chunhui Plan" No. Z2009-1-15030 Harbin Science and Technology Innovation Talent Special Fund Project No. 2017RAQXJ180 Research Fund Project of Heilongjiang University of Traditional Chinese Medicine No. 201704 Innovative Research Project for Graduate Students of Heilongjiang University of Traditional Chinese Medicine No. 2019yjscx024
Received  2019-11-18
Accepted  2020-02-12
DOI: 10.12015/issn.1674-8034.2020.03.015
Cite this article as: Li XL, Yao CL, Wang F, et al. Advances in imaging studies of senile depression. Chin J Magn Reson Imaging, 2020, 11(3): 228-231. DOI:10.12015/issn.1674-8034.2020.03.015.

[1]
Zhang Y, Chen Y, Ma L. Depression and cardiovascular disease in elderly: current understanding. J Clin Neurosci, 2018, 47: 1-5.
[2]
Saracino RM, Weinberger MI, Roth AJ, et al. Assessing depression in a geriatric cancer population. Psychooncology, 2017, 26(10): 1484-1490.
[3]
Steffens DC. A multiplicity of approaches to characterize geriatric depression and its outcomes. Curr Opin Psychiatry, 2009, 22(6): 522-526.
[4]
Flint AJ, Gagnon N. Effective use of electroconvulsive therapy in late-life depression. Can J Psychiatry, 2002, 47(8): 734-741.
[5]
Gershenfeld HK, Philibert RA, Boehm GW. Looking forward in geriatric anxiety and depression: implications of basic science for the future. Am J Geriatr Psychiatry, 2005, 13(12): 1027-1040.
[6]
Murray CJ, Lopez AD. Alternative projections of mortality and disability by cause 1990-2020: global burden of disease study. Lancet, 1997, 349(9064): 1498-1504.
[7]
Li N, Chen G, Zeng P, et al. Prevalence and factors associated with mild cognitive impairment among Chinese older adults with depression. Geriatr Gerontol Int, 2018, 18(2): 263-268.
[8]
Vlasova RM, Siddarth P, Krause B, et al. Resilience and white matter integrity in geriatric depression. Am J Geriatr Psychiatry, 2018, 26(8): 874-883.
[9]
Sexton CE, Mackay CE, Ebmeier KP. A systematic review and meta-analysis of magnetic resonance imaging studies in late-life depression. Am J Geriatr Psychiatry, 2013, 21(2): 184-195.
[10]
Suzuki H, Matsumoto Y, Ota H, et al. Hippocampal blood flow abnormality associated with depressive symptoms and cognitive impairment in patients with chronic heart failure. Circ J, 2016, 80(8): 1773-1780.
[11]
Ashtari M, Greenwald BS, Kramer-Ginsberg E, et al. Hippocampal/amygdala volumes in geriatric depression. Psychol Med, 1999, 29(3): 629-638.
[12]
Harada K, Matsuo K, Nakashima M, et al. Disrupted orbitomedial prefrontal limbic network in individuals with later-life depression. J Affect Disord, 2016, 204: 112-119.
[13]
Egger K, Schocke M, Weiss E, et al. Pattern of brain atrophy in elderly patients with depression revealed by voxel-based morphometry. Psychiatry Res, 2008, 164(3): 237-244.
[14]
Du M, Liu J, Chen Z, et al. Brain grey matter volume alterations in late-life depression. J Psychiatry Neurosci, 2014, 39(6): 397-406.
[15]
Grieve SM, Korgaonkar MS, Koslow SH, et al. Widespread reductions in gray matter volume in depression. Neuroimage Clin, 2013, 3: 332-339.
[16]
Hwang JP, Lee TW, Tsai SJ, et al. Cortical and subcortical abnormalities in late-onset depression with history of suicide attempts investigated with MRI and voxel-based morphometry. J Geriatr Psychiatry Neurol, 2010, 23(3): 171-184.
[17]
Pasi M, Poggesi A, Salvadori E, et al. White matter microstructural damage and depressive symptoms in patients with mild cognitive impairment and cerebral small vessel disease: the VMCI-Tuscany Study. Int J Geriatr Psychiatry, 2016, 31(6): 611-618.
[18]
Bae JN, Macfall JR, Krishnan KR, et al. Dorsolateral prefrontal cortex and anterior cingulate cortex white matter alterations in late-life depression. Biol Psychiatry, 2006, 60(12): 1356-1363.
[19]
Nobuhara K, Okugawa G, Sugimoto T, et al. Frontal white matter anisotropy and symptom severity of late-life depression: a magnetic resonance diffusion tensor imaging study. J Neurol Neurosurg Psychiatry, 2006, 77(1): 120-122.
[20]
Brown GG, Perthen JE, Liu TT, et al. A primer on functional magnetic resonance imaging. Neuropsychol Rev, 2007, 17(2): 107-125.
[21]
Smitha KA, Akhil RK, Arun KM, et al. Resting state fMRI: a review on methods in resting state connectivity analysis and resting state networks. Neuroradiol J, 2017, 30(4): 305-317.
[22]
Jiang X, Fu S, Yin Z, et al. Common and distinct neural activities in frontoparietal network in first-episode bipolar disorder and major depressive disorder: preliminary findings from a follow-up resting state fMRI study. J Affect Disord, 2019, 260: 653-659.
[23]
Yao X, Yin Z, Liu F, et al. Shared and distinct regional homogeneity changes in bipolar and unipolar depression. Neurosci Lett, 2018, 673: 28-32.
[24]
Yao Z, Wang L, Lu Q, et al. Regional homogeneity in depression and its relationship with separate depressive symptom clusters: a resting-state fMRI study. J Affect Disord, 2009, 115(3): 430-438.
[25]
Yuan Y, Zhang Z, Bai F, et al. Abnormal neural activity in the patients with remitted geriatric depression: a resting-state functional magnetic resonance imaging study. J Affect Disord, 2008, 111(2-3): 145-152.
[26]
Tadayonnejad R, Ajilore O. Brain network dysfunction in late-life depression: a literature review. J Geriatr Psychiatry Neurol, 2014, 27(1): 5-12.
[27]
Menon V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci, 2011, 15(10): 483-506.
[28]
Yuen GS, Gunning-Dixon FM, Hoptman MJ, et al. The salience network in the apathy of late-life depression. Int J Geriatr Psychiatry, 2014, 29(11): 1116-1124.
[29]
Alexopoulos GS, Hoptman MJ, Kanellopoulos D, et al. Functional connectivity in the cognitive control network and the default mode network in late-life depression. J Affect Disord, 2012, 139(1): 56-65.
[30]
Steffens DC, Wang L, Pearlson GD. Functional connectivity predictors of acute depression treatment outcome. Int Psychogeriatr, 2019: 1-5.
[31]
Eyre HA, Yang H, Leaver AM, et al. Altered resting-state functional connectivity in late-life depression: a cross-sectional study. J Affect Disord, 2016, 189: 126-133.
[32]
Kumar A, Cook IA. White matter injury, neural connectivity and the pathophysiology of psychiatric disorders. Dev Neurosci, 2002, 24(4): 255-261.
[33]
De Crescenzo F, Ciliberto M, Menghini D, et al. Is (18)F-FDG- PET suitable to predict clinical response to the treatment of geriatric depression? A systematic review of PET studies. Aging Ment Health, 2017, 21(9): 889-894.
[34]
Sacher J, Neumann J, Funfstuck T, et al. Mapping the depressed brain: a meta-analysis of structural and functional alterations in major depressive disorder. J Affect Disord, 2012, 140(2): 142-148.
[35]
Marano CM, Workman CI, Kramer E, et al. Longitudinal studies of cerebral glucose metabolism in late-life depression and normal aging. Int J Geriatr Psychiatry, 2013, 28(4): 417-423.
[36]
Smith GS, Kramer E, Hermann C, et al. Serotonin modulation of cerebral glucose metabolism in depressed older adults. Biol Psychiatry, 2009, 66(3): 259-266.
[37]
Murata T, Kimura H, Omori M, et al. MRI white matter hyperintensities, (1)H-MR spectroscopy and cognitive function in geriatric depression: a comparison of early- and late-onset cases. Int J Geriatr Psychiatry, 2001, 16(12): 1129-1135.
[38]
Harper DG, Joe EB, Jensen JE, et al. Brain levels of high-energy phosphate metabolites and executive function in geriatric depression. Int J Geriatr Psychiatry, 2016, 31(11): 1241-1249.
[39]
Forester BP, Harper DG, Jensen JE, et al. 31Phosphorus magnetic resonance spectroscopy study of tissue specific changes in high energy phosphates before and after sertraline treatment of geriatric depression. Int J Geriatr Psychiatry, 2009, 24(8): 788-797.
[40]
Elderkin-Thompson V, Thomas MA, Binesh N, et al. Brain metabolites and cognitive function among older depressed and healthy individuals using 2D MR spectroscopy. Neuropsychopharmacology, 2004, 29(12): 2251-2257.

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