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Original Article
Investigate the whole level alteration of dynamic functional connectivity of attention network in patients with major depressive disorder based on magnetic resonance imaging
LIU Sha  ZHANG Xun  HUANG Guimao  DU Biyin  CHEN Junhao  CHENG Xiaofang  ZOU Wenjin 

Cite this article as: Liu S, Zhang X, Huang GM, et al. Investigate the whole level alteration of dynamic functional connectivity of attention network in patients with major depressive disorder based on magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2022, 13(6): 40-44. DOI:10.12015/issn.1674-8034.2022.06.008.


[Abstract] Objective To explore the abnormalities of dynamic connectivity of attention network in patients with major depressive disorder (MDD) by using resting state-functional magnetic resonance imaging (rs-fMRI).Materials and Methods Totally, 73 MDD subjects and 71 healthy controls (HC) were included in this study. The rs-fMRI data were acquired for each subject, and the functional network of attention was established based on previous meta-analysis. Then, the attention network was temporally segmented according to a series of sliding windows, and the temporal variability of attention network connectivity was calculated and then compared between the MDD and HC groups.Results Compared with the HC group, significantly increased temporal variability of attention network across all the nodes of the whole network (P=0.019) was observed in the MDD group, while no significant difference of temporal variability was observed in any individual node between the two groups. No significant correlation between the mean temporal variability of attention network and the scores of the depression and anxiety was found.Conclusions The results may indicate that the abnormally increased dynamic connectivity of attention network in patients with MDD are at the whole network level, instead of a localized change driven by regional abnormalities.
[Keywords] major depressive disorder;attention network;functional magnetic resonance imaging;dynamic connectivity;temporal variability

LIU Sha1   ZHANG Xun2   HUANG Guimao1   DU Biyin1   CHEN Junhao1   CHENG Xiaofang1   ZOU Wenjin1*  

1 Department of Radiology, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China

2 Department of Neurosurgery, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China

Zou WJ, E-mail: 46016157@qq.com

Conflicts of interest   None.

Received  2021-12-17
Accepted  2022-05-12
DOI: 10.12015/issn.1674-8034.2022.06.008
Cite this article as: Liu S, Zhang X, Huang GM, et al. Investigate the whole level alteration of dynamic functional connectivity of attention network in patients with major depressive disorder based on magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2022, 13(6): 40-44.DOI:10.12015/issn.1674-8034.2022.06.008

[1]
Martin DM, Wollny-Huttarsch D, Nikolin S, et al. Neurocognitive subgroups in major depressive disorder[J]. Neuropsychology, 2020, 34(6): 726-734. DOI: 10.1037/neu0000626.
[2]
Schmidt GJ, Barbosa AO, de Assis SG, et al. Attentional subdomains' deficits in Brazilian patients with major depressive episodes[J]. Neuropsychology, 2021, 35(2): 232-240. DOI: 10.1037/neu0000719.
[3]
Shamai-Leshem D, Lazarov A, Pine DS, et al. A randomized controlled trial of gaze-contingent music reward therapy for major depressive disorder[J]. Depress Anxiety, 2021, 38(2): 134-145. DOI: 10.1002/da.23089.
[4]
Keller AS, Ball TM, Williams LM. Deep phenotyping of attention impairments and the 'inattention biotype' in major depressive disorder[J]. Psychol Med, 2020, 50(13): 2203-2212. DOI: 10.1017/S0033291719002290.
[5]
Giollabhui NM, Olino TM, Nielsen J, et al. Is worse attention a risk factor for or a consequence of depression, or are worse attention and depression better accounted for by stress? A prospective test of three hypotheses[J]. Clin Psychol Sci, 2019, 7(1): 93-109. DOI: 10.1177/2167702618794920.
[6]
Figueroa CA, DeJong H, Mocking RJT, et al. Attentional control, rumination and recurrence of depression[J]. J Affect Disord, 2019, 256: 364-372. DOI: 10.1016/j.jad.2019.05.072.
[7]
Liao XH, Yuan L, Zhao TD, et al. Spontaneous functional network dynamics and associated structural substrates in the human brain[J]. Front Hum Neurosci, 2015, 9: 478. DOI: 10.3389/fnhum.2015.00478.
[8]
Knyazev GG, Savostyanov AN, Bocharov AV, et al. Task-positive and task-negative networks in major depressive disorder: a combined fMRI and EEG study[J]. J Affect Disord, 2018, 235: 211-219. DOI: 10.1016/j.jad.2018.04.003.
[9]
Calhoun VD, Miller R, Pearlson G, et al. The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery[J]. Neuron, 2014, 84(2): 262-274. DOI: 10.1016/j.neuron.2014.10.015.
[10]
Chen H, Liu K, Zhang B, et al. More optimal but less regulated dorsal and ventral visual networks in patients with major depressive disorder[J]. J Psychiatr Res, 2019, 110: 172-178. DOI: 10.1016/j.jpsychires.2019.01.005.
[11]
Brancati GE, Perugi G, Milone A, et al. Development of bipolar disorder in patients with attention-deficit/hyperactivity disorder: a systematic review and meta-analysis of prospective studies[J]. J Affect Disord, 2021, 293: 186-196. DOI: 10.1016/j.jad.2021.06.033.
[12]
Chudal R, Brown AS, Gyllenberg D, et al. Maternal serum C-reactive protein (CRP) and offspring attention deficit hyperactivity disorder (ADHD)[J]. Eur Child Adolesc Psychiatry, 2020, 29(2): 239-247. DOI: 10.1007/s00787-019-01372-y.
[13]
Whitfield-Gabrieli S, Wendelken C, Nieto-Castañón A, et al. Association of intrinsic brain architecture with changes in attentional and mood symptoms during development[J]. JAMA Psychiatry, 2020, 77(4): 378-386. DOI: 10.1001/jamapsychiatry.2019.4208.
[14]
Deng YJ, Han SG, Cheng DL, et al. Simultaneously decreased temporal variability and enhanced variability-strength coupling of emotional network connectivities are related to positive symptoms in patients with schizophrenia[J]. Brain Imaging Behav, 2021, 15(1): 76-84. DOI: 10.1007/s11682-019-00234-0.
[15]
Eijlers AJC, Wink AM, Meijer KA, et al. Functional network dynamics on functional MRI: a primer on an emerging frontier in neuroscience[J]. Radiology, 2019, 292(2): 460-463. DOI: 10.1148/radiol.2019194009.
[16]
Yan CG, Wang XD, Zuo XN, et al. DPABI: data processing & analysis for (resting-state) brain imaging[J]. Neuroinformatics, 2016, 14(3): 339-351. DOI: 10.1007/s12021-016-9299-4.
[17]
Zhang J, Cheng W, Liu ZW, et al. Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders[J]. Brain, 2016, 139(Pt 8): 2307-2321. DOI: 10.1093/brain/aww143.
[18]
Kandilarova S, Stoyanov DS, Paunova R, et al. Effective connectivity between major nodes of the limbic system, salience and frontoparietal networks differentiates schizophrenia and mood disorders from healthy controls[J]. J Pers Med, 2021, 11(11): 1110. DOI: 10.3390/jpm11111110.
[19]
Dini H, Sendi MSE, Sui J, et al. Dynamic functional connectivity predicts treatment response to electroconvulsive therapy in major depressive disorder[J]. Front Hum Neurosci, 2021, 15: 689488. DOI: 10.3389/fnhum.2021.689488.
[20]
Wang X, Zhou H, Zhu XZ. Attention deficits in adults with Major depressive disorder: a systematic review and meta-analysis[J]. Asian J Psychiatr, 2020, 53: 102359. DOI: 10.1016/j.ajp.2020.102359.
[21]
Ai H, Opmeer EM, Marsman JC, et al. Longitudinal brain changes in MDD during emotional encoding: effects of presence and persistence of symptomatology[J]. Psychol Med, 2020, 50(8): 1316-1326. DOI: 10.1017/S0033291719001259.
[22]
Yan BY, Xu XP, Liu MW, et al. Quantitative identification of major depression based on resting-state dynamic functional connectivity: a machine learning approach[J]. Front Neurosci, 2020, 14: 191. DOI: 10.3389/fnins.2020.00191.
[23]
Wagner G, Li M, Sacchet MD, et al. Functional network alterations differently associated with suicidal ideas and acts in depressed patients: an indirect support to the transition model[J]. Transl Psychiatry, 2021, 11(1): 100. DOI: 10.1038/s41398-021-01232-x.
[24]
Zhu DM, Yang Y, Zhang Y, et al. Cerebellar-cerebral dynamic functional connectivity alterations in major depressive disorder[J]. J Affect Disord, 2020, 275: 319-328. DOI: 10.1016/j.jad.2020.06.062.
[25]
Bi K, Hua LL, Wei MB, et al. Dynamic functional-structural coupling within acute functional state change phases: evidence from a depression recognition study[J]. J Affect Disord, 2016, 191: 145-155. DOI: 10.1016/j.jad.2015.11.041.
[26]
Hou WL, Yin XL, Yin XY, et al. Association between stereopsis deficits and attention decline in patients with major depressive disorder[J]. Prog Neuropsychopharmacol Biol Psychiatry, 2021, 110: 110267. DOI: 10.1016/j.pnpbp.2021.110267.
[27]
Liu K, Zhao XH, Lu XB, et al. Effect of selective serotonin reuptake inhibitor on prefrontal-striatal connectivity is dependent on the level of TNF-α in patients with major depressive disorder[J]. Psychol Med, 2019, 49(15): 2608-2616. DOI: 10.1017/S0033291718003616.
[28]
Li XY, Zhang Y, Meng C, et al. Functional stability predicts depressive and cognitive improvement in major depressive disorder: a longitudinal functional MRI study[J]. Prog Neuropsychopharmacol Biol Psychiatry, 2021, 111: 110396. DOI: 10.1016/j.pnpbp.2021.110396.
[29]
Korgaonkar MS, Goldstein-Piekarski AN, Fornito A, et al. Intrinsic connectomes are a predictive biomarker of remission in major depressive disorder[J]. Mol Psychiatry, 2020, 25(7): 1537-1549. DOI: 10.1038/s41380-019-0574-2.
[30]
Figueroa CA, Cabral J, Mocking RJT, et al. Altered ability to access a clinically relevant control network in patients remitted from major depressive disorder[J]. Hum Brain Mapp, 2019, 40(9): 2771-2786. DOI: 10.1002/hbm.24559.

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