Chinese Journal of Intelligent Science and Technology ›› 2020, Vol. 2 ›› Issue (2): 116-125.doi: 10.11959/j.issn.2096-6652.202013
• Surveys and Prospectives • Previous Articles Next Articles
Shan LI1,Yongchao LI1,Ying ZOU1,Lin YANG1,Yin WANG1,Zhijun YAO1,Ubin H1,2,3,4()
Revised:
2020-05-06
Online:
2020-06-20
Published:
2020-07-14
Supported by:
CLC Number:
Shan LI, Yongchao LI, Ying ZOU, et al. Brain abnormalities in depression based on multimodal imaging[J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(2): 116-125.
[1] | GOTLIB I H , JOORMANN J . Cognition and depression:current status and future directions[J]. Annual Review of Clinical Psychology, 2010,6: 285-312. |
[2] | KESSLER R C , BERGLUND P , DEMLER O ,et al. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the national comorbidity survey replication[J]. Archives of General Psychiatry, 2005,62(6): 593-602. |
[3] | STEINERT C , HOFMANN M , KRUSE J ,et al. The prospective long-term course of adult depression in general practice and the community:a systematic literature review[J]. Journal of Affective Disorders, 2014(152-154): 65-75. |
[4] | RIHMER Z . Can better recognition and treatment of depression reduce suicide rates? A brief review[J]. European Psychiatry, 2001,16(7): 406-409. |
[5] | MURRAY C J L , LOPEZ A D . Evidence-based health policy-lessons from the global burden of disease study[J]. Science, 1996,274(5288): 740-743. |
[6] | ZHUO C J , LI G Y , LIN X D ,et al. The rise and fall of MRI studies in major depressive disorder[J]. Translational Psychiatry, 2019,9(1): 1-14. |
[7] | QIU H , LI J . Major depressive disorder and magnetic resonance imaging:a mini-review of recent progress[J]. Current Pharmaceutical Design, 2018,24(22): 2524-2529. |
[8] | SCHMAAL L , VELTMAN D J , VAN ERP T G M ,et al. Subcortical brain alterations in major depressive disorder:findings from the enigma major depressive disorder working group[J]. Molecular Psychiatry, 2016,21(6): 806-812. |
[9] | SCHMAAL L , HIBAR D,S?MANN P ,et al. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the enigma major depressive disorder working group[J]. Molecular Psychiatry, 2017,22(6): 900-909. |
[10] | CARDINALE F , CHINNICI G , BRAMERIO M ,et al. Validation of freesurfer-estimated brain cortical thickness:comparison with histologic measurements[J]. Neuroinformatics, 2014,12(4): 535-542. |
[11] | BRODMANN K . Vergleichende lokalisationslehre der grobhirnrinde[J]. Journal of Nervous & Mental disease, 1909,37: 783-784. |
[12] | TZOURIO-MAZOYER N , LANDEAU B , PAPATHANASSIOU D ,et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain[J]. Neuroimage, 2002,15(1): 273-289. |
[13] | SMITH S M , JENKINSON M , WOOLRICH M W ,et al. Advances in functional and structural MR image analysis and implementation as FSL[J]. Neuroimage, 2004(23): S208-S219. |
[14] | FAN L , LI H , ZHUO J J ,et al. The human brainnetome atlas:a new brain atlas based on connectional architecture[J]. Cerebral Cortex, 2016,26(8): 3508-3526. |
[15] | NUGENT A C , FARMER C , EVANS J W ,et al. Multimodal imaging reveals a complex pattern of dysfunction in corticolimbic pathways in major depressive disorder[J]. Human Brain Mapping, 2019,40(13): 3940-3950. |
[16] | CHEN H J , CHEN Q F , YANG Z T ,et al. Aberrant topological organization of the functional brain network associated with prior overt hepatic encephalopathy in cirrhotic patients[J]. Brain Imaging, 2019,13(3): 771-780. |
[17] | 倪冰洁, 李炜, 陈曦 . 阿尔兹海默症脑网络演化建模[J]. 智能科学与技术学报, 2019,1(4): 369-378. |
NI B J , LI W , CHEN X . Brain network evolution modeling based on alzheimer’s disease[J]. Chinese Journal of Intelligent Science and Technology, 2019,1(4): 369-378. | |
[18] | WISE T , MARWOOD L , PERKINS A ,et al. Instability of default mode network connectivity in major depression:a two-sample confirmation study[J]. Translational Psychiatry, 2017,7(4):e1105. |
[19] | ZHU X L , ZHU Q L , SHEN H Z ,et al. Rumination and default mode network subsystems connectivity in first-episode,drug-naive young patients with major depressive disorder[J]. Scientific Reports, 2017,7(1): 569-582. |
[20] | WANG Y L , YANG S Z , SUN W L ,et al. Altered functional interaction hub between affective network and cognitive control network in patients with major depressive disorder[J]. Behavioural Brain Research, 2016,298: 301-309. |
[21] | CRANE N A , JENKINS L M , DION C ,et al. Comorbid anxiety increases cognitive control activation in major depressive disorder[J]. Depression and Anxiety, 2016,33(10): 967-977. |
[22] | LIU C H , MA X , SONG L P ,et al. Alteration of spontaneous neuronal activity within the salience network in partially remitted depression[J]. Brain Research, 2015,1599: 93-102. |
[23] | LI B J , LIU L , FRISTON K J ,et al. A treatment-resistant default mode subnetwork in major depression[J]. Biological Psychiatry, 2013,74(1): 48-54. |
[24] | ANDREWS-HANNA J R , SMALLWOOD J , SPRENG R N . The default network and self-generated thought:component processes,dynamic control,and clinical relevance[J]. Annals of the New York Academy of Sciences, 2014,1316(1): 29-52. |
[25] | MULDERS P C , VAN EIJNDHOVEN P F , SCHENE A H ,et al. Resting-state functional connectivity in major depressive disorder:a review[J]. Neuroscience & Biobehavioral Reviews, 2015,56: 330-344. |
[26] | 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[J]. Proceedings of the National Academy of Sciences, 2010,107(24): 11020-11025. |
[27] | NEJAD A B , FOSSATI P , LEMOGNE C . Self-referential processing,rumination,and cortical midline structures in major depression[J]. Frontiers in Human Neuroscience, 2013,7:666. |
[28] | BRAKOWSKI J , SPINELLI S , D?RIG N ,et al. Resting state brain network function in major depression-depression symptomatology,antidepressant treatment effects,future research[J]. Journal of Psychiatric Research, 2017,92: 147-159. |
[29] | BREUKELAAR I A , ANTEES C , GRIEVE S M ,et al. Cognitive control network anatomy correlates with neurocognitive behavior:a longitudinal study[J]. Human Brain Mapping, 2017,38(2): 631-643. |
[30] | ALEXOPOULOS G S , MURPHY C F , GUNNING-DIXON F M ,et al. Serotonin transporter polymorphisms,microstructural white matter abnormalities and remission of geriatric depression[J]. Journal of Affective Disorders, 2009,119(1-3): 132-141. |
[31] | YE T , PENG J , NIE B B ,et al. Altered functional connectivity of the dorsolateral prefrontal cortex in first-episode patients with major depressive disorder[J]. European Journal of Radiology, 2012,81(12): 4035-4040. |
[32] | KAISER R H , ANDREWS-HANNA J R , WAGER T D ,et al. Large-scale network dysfunction in major depressive disorder:a meta-analysis of resting-state functional connectivity[J]. JAMA Psychiatry, 2015,72(6): 603-611. |
[33] | 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]. Journal of Affective Disorders, 2012,139(1): 56-65. |
[34] | HOLROYD C B , NIEUWENHUIS S , YEUNG N ,et al. Dorsal anterior cingulate cortex shows fMRI response to internal and external error signals[J]. Nature Neuroscience, 2004,7(5): 497-498. |
[35] | HAM T , LEFF A , DE BOISSEZON X ,et al. Cognitive control and the salience network:an investigation of error processing and effective connectivity[J]. Journal of Neuroscience, 2013,33(16): 7091-7098. |
[36] | GOULDEN N , KHUSNULINA A , DAVIS N J ,et al. The salience network is responsible for switching between the default mode network and the central executive network:replication from DCM[J]. Neuroimage, 2014,99: 180-190. |
[37] | SRIDHARAN D , LEVITIN D J , MENON V . A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks[J]. Proceedings of the National Academy of Sciences, 2008,105(34): 12569-12574. |
[38] | 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[J]. Frontiers in Human Neuroscience, 2014,7:930. |
[39] | MA K S , WON E , KANG J ,et al. Brain-derived neurotrophic factor promoter methylation and cortical thickness in recurrent major depressive disorder[J]. Scientific Reports, 2016,6(1): 1-10. |
[40] | VAN EIJNDHOVEN P , VAN WINGEN G , KATZENBAUER M ,et al. Paralimbic cortical thickness in first-episode depression:evidence for trait-related differences in mood regulation[J]. American Journal of Psychiatry, 2013,170(12): 1477-1486. |
[41] | QIU L , LUI S , KUANG W ,et al. Regional increases of cortical thickness in untreated,first-episode major depressive disorder[J]. Translational Psychiatry, 2014,4(4):e378. |
[42] | GRIEVE S M , KORGAONKAR M S , KOSLOW S H ,et al. Widespread reductions in gray matter volume in depression[J]. NeuroImage:Clinical, 2013,3: 332-339. |
[43] | PENG J , LIU J T , NIE B B ,et al. Cerebral and cerebellar gray matter reduction in first-episode patients with major depressive disorder:a voxel-based morphometry study[J]. European Journal of Radiology, 2011,80(2): 395-399. |
[44] | XU L Y , XU F C , LIU C ,et al. Relationship between cerebellar structure and emotional memory in depression[J]. Brain and Behavior, 2017,7(7):e00738. |
[45] | MORRIS J S , ?HMAN A , DOLAN R J . Conscious and unconscious emotional learning in the human amygdala[J]. Nature, 1998,393(6684): 467-470. |
[46] | ADOLPHS R , TRANEL D , DAMASIO H ,et al. Impaired recognition of emotion in facial expressions following bilateral damage to the human amygdala[J]. Nature, 1994,372(6507): 669-672. |
[47] | WINOCUR G , WOJTOWICZ J M , SEKERES M ,et al. Inhibition of neurogenesis interferes with hippocampus-dependent memory function[J]. Hippocampus, 2006,16(3): 296-304. |
[48] | RAJARETHINAM R , DEQUARDO J R , MIEDLER J ,et al. Hippocampus and amygdala in schizophrenia:assessment of the relationship of neuroanatomy to psychopathology[J]. Psychiatry Research:Neuroimaging, 2001,108(2): 79-87. |
[49] | BASSO M , YANG J , WARREN L ,et al. Volumetry of amygdala and hippocampus and memory performance in alzheimer’s disease[J]. Psychiatry Research:Neuroimaging, 2006,146(3): 251-261. |
[50] | YAO Z J , FU Y , WU J F ,et al. Morphological changes in subregions of hippocampus and amygdala in major depressive disorder patients[J]. Brain Imaging, 2018: 1-15. |
[51] | JOKO T , WASHIZUKA S , SASAYAMA D ,et al. Patterns of hippocampal atrophy differ among alzheimer’s disease,amnestic mild cognitive impairment,and late-life depression[J]. Psychogeriatrics, 2016,16(6): 355-361. |
[52] | OTA M , SATO N , HIDESE S ,et al. Structural differences in hippocampal subfields among schizophrenia patients,major depressive disorder patients,and healthy subjects[J]. Psychiatry Research:Neuroimaging, 2017,259: 54-59. |
[53] | MAHON P B , ELDRIDGE H , CROCKER B ,et al. An MRI study of amygdala in schizophrenia and psychotic bipolar disorder[J]. Schizophrenia Research, 2012,138(2-3): 188-191. |
[54] | BROWN G G , LEE J S , STRIGO I A ,et al. Voxel-based morphometry of patients with schizophrenia or bipolar I disorder:a matched control study[J]. Psychiatry Research:Neuroimaging, 2011,194(2): 149-156. |
[55] | LI C T , LIN C P , CHOU K H ,et al. Structural and cognitive deficits in remitting and non-remitting recurrent depression:a voxel-based morphometric study[J]. Neuroimage, 2010,50(1): 347-356. |
[56] | FANG P , ZENG L L , SHEN H ,et al. Increased cortical-limbic anatomical network connectivity in major depression revealed by diffusion tensor imaging[J]. PloS One, 2012,7(9):e45972. |
[57] | YAO Z J , ZOU Y , ZHENG W ,et al. Structural alterations of the brain preceded functional alterations in major depressive disorder patients:evidence from multimodal connectivity[J]. Journal of Affective Disorders, 2019,253: 107-117. |
[58] | AJILORE O , LAMAR M , KUMAR A . Association of brain network efficiency with aging,depression,and cognition[J]. The American Journal of Geriatric Psychiatry, 2014,22(2): 102-110. |
[59] | ZHU X X , WANG X , XIAO J ,et al. Evidence of a dissociation pattern in resting-state default mode network connectivity in first-episode,treatment-naive major depression patients[J]. Biological Psychiatry, 2012,71(7): 611-617. |
[60] | CHEN Y , WANG C , ZHU X ,et al. Aberrant connectivity within the default mode network in first-episode,treatment-naive major depressive disorder[J]. Journal of Affective Disorders, 2015,183: 49-56. |
[61] | XIA M , SI T , SUN X Y ,et al. Reproducibility of functional brain alterations in major depressive disorder:Evidence from a multisite resting-state functional MRI study with 1 434 individuals[J]. Neuroimage, 2019,189: 700-714. |
[62] | ZHANG R B , KRANZ G S , ZOU W J ,et al. Rumination network dysfunction in major depression:a brain connectome study[J]. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 2020,98:109819. |
[63] | 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[J]. PloS One, 2015,10(9):e0133775. |
[64] | YAO Z , SHI J , ZHANG Z ,et al. Altered dynamic functional connectivity in weakly-connected state in major depressive disorder[J]. Clinical Neurophysiology, 2019,130(11): 2096-2104. |
[65] | ZHI D M , CALHOUN V D , LV L ,et al. Aberrant dynamic functional network connectivity and graph properties in major depressive disorder[J]. Frontiers in Psychiatry, 2018(9):339. |
[66] | ZHENG H N , LI F , BO Q J ,et al. The dynamic characteristics of the anterior cingulate cortex in resting-state fMRI of patients with depression[J]. Journal of Affective Disorders, 2018,227: 391-397. |
[67] | ZWEERINGS J , ZVYAGINTSEV M , TURETSKY B I ,et al. Fronto-parietal and temporal brain dysfunction in depression:a fMRI investigation of auditory mismatch processing[J]. Human Brain Mapping, 2019,40(12): 3657-3668. |
[68] | LI G S , LIU Y J , ZHENG Y T ,et al. Large-scale dynamic causal modeling of major depressive disorder based on resting-state functional magnetic resonance imaging[J]. Human Brain Mapping, 2019,41: 865-881. |
[69] | DE KWAASTENIET B , RUHE E , CAAN M ,et al. Relation between structural and functional connectivity in major depressive disorder[J]. Biological Psychiatry, 2013,74(1): 40-47. |
[70] | WANG W N , ZHAO Y J , HU X Y ,et al. Conjoint and dissociated structural and functional abnormalities in first-episode drug-naive patients with major depressive disorder:a multimodal meta-analysis[J]. Scientific Reports, 2017,7(1): 1-13. |
[71] | GAO S , CALHOUN V D , SUI J ,et al. Machine learning in major depression:from classification to treatment outcome prediction[J]. CNS Neuroscience, 2018,24(11): 1037-1052. |
[72] | 韩冬, 李其花, 蔡巍 ,等. 人工智能在医学影像中的研究与应用[J]. 大数据, 2019,5(1): 42-70. |
HAN D , LI Q H , CAI W ,et al. Research and application of artificial intelligence in medical imaging[J]. Big Data Research, 2019,5(1): 42-70. | |
[73] | 李伟凯, 高欣, 纪同俭 ,等. 用于轻度认知障碍诊断的群体相似约束功能脑网络建模方法[J]. 智能科学与技术学报, 2019,1(2): 145-153. |
LI W K , GAO X , JI T J ,et al. Method of functional brain network modeling with group similarity constraint for mild cognitive impairment classification[J]. Chinese Journal of Intelligent Science and Technology, 2019,1(2): 145-153. | |
[74] | WONG T T . Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation[J]. Pattern Recognition, 2015,48(9): 2839-2846. |
[75] | MWANGI B , EBMEIER K P , MATTHEWS K ,et al. Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder[J]. Brain, 2012,135(5): 1508-1521. |
[76] | LIU F , GUO W B , YU D M ,et al. Classification of different therapeutic responses of major depressive disorder with multivariate pattern analysis method based on structural MR scans[J]. PloS One, 2012,7(7):e40968. |
[77] | PATEL M J , ANDREESCU C , PRICE J C ,et al. Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction[J]. International journal of geriatric psychiatry, 2015,30(10): 1056-1067. |
[78] | PATEL M J , KHALAF A , AIZENSTEIN H J . Studying depression using imaging and machine learning methods[J]. NeuroImage:Clinical, 2016,10: 115-123. |
[79] | WEI M B , QIN J L , YAN R ,et al. Identifying major depressive disorder using Hurst exponent of resting-state brain networks[J]. Psychiatry Research:Neuroimaging, 2013,214(3): 306-312. |
[80] | ROSA M J , PORTUGAL L , HAHN T ,et al. Sparse network-based models for patient classification using fMRI[J]. Neuroimage, 2015,105: 493-506. |
[81] | ZHENG Y T , CHEN X B , LI D N ,et al. Treatment-naive first episode depression classification based on high-order brain functional network[J]. Journal of Affective Disorders, 2019,256: 33-41. |
[82] | ALI N S , AZAM I S , ALI B S ,et al. Frequency and associated factors for anxiety and depression in pregnant women:a hospital-based cross-sectional study[J]. The Scientific World Journal, 2012,2012: 1-9. |
[83] | MURRAY L , ARTECHE A , FEARON P ,et al. Maternal postnatal depression and the development of depression in offspring up to 16 years of age[J]. Journal of the American Academy of Child, 2011,50(5): 460-470. |
[84] | HE B Y , FAN J Y , LIU N ,et al. Depression risk of ‘left-behind children’ in rural China[J]. Psychiatry Research, 2012,200(2-3): 306-312. |
[85] | KESSING L V . Depression and the risk for dementia[J]. Current Opinion in Psychiatry, 2012,25(6): 457-461. |
[86] | MENG L , CHEN D , YANG Y ,et al. Depression increases the risk of hypertension incidence:a meta-analysis of prospective cohort studies[J]. Journal of Hypertension, 2012,30(5): 842-851. |
[87] | FRASURE-SMITH N , LESPéRANCE F . Depression and cardiac risk:present status and future directions[J]. The Fellowship of Postgraduate Medicine, 2010,86: 193-196. |
[88] | BLAZER D , BURCHETT B , SERVICE C ,et al. The association of age and depression among the elderly:an epidemiologic exploration[J]. Journal of Gerontology, 1991,46(6): M210-M215. |
[89] | NOLEN-HOEKSEMA S . Gender differences in depression[J]. Current Directions in Psychological Science, 2001,10(5): 173-176. |
[90] | LUPPINO F S , DE WIT L M , BOUVY P F ,et al. Overweight,obesity,and depression:a systematic review and meta-analysis of longitudinal studies[J]. Archives of General Psychiatry, 2010,67(3): 220-229. |
[91] | MCKENZIE D P , TOUMBOUROU J W , FORBES A B ,et al. Predicting future depression in adolescents using the Short Mood and Feelings Questionnaire:a two-nation study[J]. Journal of Affective Disorders, 2011,134(1-3): 151-159. |
[92] | LI Y , LV M R , WEI Y J ,et al. Dietary patterns and depression risk:a meta-analysis[J]. Psychiatry Research, 2017,253: 373-382. |
[93] | YAMA M F , TOVEY S L , FOGAS B S . Childhood family environment and sexual abuse as predictors of anxiety and depression in adult women[J]. American Journal of Orthopsychiatry, 1993,63(1): 136-141. |
[94] | BLAIR-WEST G W , CANTOR C H , MELLSOP G W ,et al. Lifetime suicide risk in major depression:sex and age determinants[J]. Journal of Affective Disorders, 1999,55(2-3): 171-178. |
[1] | Yan CHEN, Xueqin LUO, Wei LIANG, Yongfang XIE. Depression recognition based on emotional information fused with attentional mechanism [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(4): 600-609. |
[2] | Bingjie NI,Wei LI,Xi CHEN. Brain network evolution modeling based on Alzheimer’s disease [J]. Chinese Journal of Intelligent Science and Technology, 2019, 1(4): 369-378. |
[3] | Weikai LI,Xin GAO,Tongjian JI,Zhengxia WANG. Method of functional brain network modeling with group similarity constraint for mild cognitive impairment classification [J]. Chinese Journal of Intelligent Science and Technology, 2019, 1(2): 145-153. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||
|