Hostname: page-component-848d4c4894-x5gtn Total loading time: 0 Render date: 2024-05-28T10:04:46.866Z Has data issue: false hasContentIssue false

Abnormal intrinsic brain functional network dynamics in first-episode drug-naïve adolescent major depressive disorder

Published online by Cambridge University Press:  04 January 2024

Baolin Wu
Affiliation:
Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
Xipeng Long
Affiliation:
Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, China
Yuan Cao
Affiliation:
Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, China
Hongsheng Xie
Affiliation:
Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, China
Xiuli Wang
Affiliation:
Department of Clinical Psychology, The Fourth People's Hospital of Chengdu, Chengdu, China
Neil Roberts
Affiliation:
The Queens Medical Research Institute (QMRI), School of Clinical Sciences, University of Edinburgh, Edinburgh, UK
Qiyong Gong*
Affiliation:
Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, China
Zhiyun Jia*
Affiliation:
Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, China
*
Corresponding authors: Qiyong Gong; Email: qiyonggong@hmrrc.org.cn; Zhiyun Jia; Email: zhiyunjia@hotmail.com
Corresponding authors: Qiyong Gong; Email: qiyonggong@hmrrc.org.cn; Zhiyun Jia; Email: zhiyunjia@hotmail.com

Abstract

Background

Alterations in brain functional connectivity (FC) have been frequently reported in adolescent major depressive disorder (MDD). However, there are few studies of dynamic FC analysis, which can provide information about fluctuations in neural activity related to cognition and behavior. The goal of the present study was therefore to investigate the dynamic aspects of FC in adolescent MDD patients.

Methods

Resting-state functional magnetic resonance imaging data were acquired from 94 adolescents with MDD and 78 healthy controls. Independent component analysis, a sliding-window approach, and graph-theory methods were used to investigate the potential differences in dynamic FC properties between the adolescent MDD patients and controls.

Results

Three main FC states were identified, State 1 which was predominant, and State 2 and State 3 which occurred less frequently. Adolescent MDD patients spent significantly more time in the weakly-connected and relatively highly-modularized State 1, spent significantly less time in the strongly-connected and low-modularized State 2, and had significantly higher variability of both global and local efficiency, compared to the controls. Classification of patients with adolescent MDD was most readily performed based on State 1 which exhibited disrupted intra- and inter-network FC involving multiple functional networks.

Conclusions

Our study suggests local segregation and global integration impairments and segregation-integration imbalance of functional networks in adolescent MDD patients from the perspectives of dynamic FC. These findings may provide new insights into the neurobiology of adolescent MDD.

Type
Original Article
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aalto-Setälä, T., Marttunen, M., Tuulio-Henriksson, A., Poikolainen, K., & Lönnqvist, J. (2002). Depressive symptoms in adolescence as predictors of early adulthood depressive disorders and maladjustment. The American Journal of Psychiatry, 159(7), 12351237. doi: 10.1176/appi.ajp.159.7.1235CrossRefGoogle ScholarPubMed
Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., & Calhoun, V. D. (2014). Tracking whole-brain connectivity dynamics in the resting state. Cerebral Cortex (New York, N.Y.: 1991), 24(3), 663676. doi: 10.1093/cercor/bhs352CrossRefGoogle ScholarPubMed
Allen, E. A., Erhardt, E. B., Wei, Y., Eichele, T., & Calhoun, V. D. (2012). Capturing inter-subject variability with group independent component analysis of fMRI data: A simulation study. NeuroImage, 59(4), 41414159. doi: 10.1016/j.neuroimage.2011.10.010CrossRefGoogle ScholarPubMed
Bassett, D. S., & Bullmore, E. T. (2017). Small-world brain networks revisited. The Neuroscientist: A Review Journal Bringing Neurobiology, Neurology and Psychiatry, 23(5), 499516. doi: 10.1177/1073858416667720CrossRefGoogle ScholarPubMed
Bell, A. J., & Sejnowski, T. J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 11291159. doi: 10.1162/neco.1995.7.6.1129CrossRefGoogle ScholarPubMed
Biswal, B., Yetkin, F. Z., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine, 34(4), 537541. doi: 10.1002/mrm.1910340409CrossRefGoogle ScholarPubMed
Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001). A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping, 14(3), 140151. doi: 10.1002/hbm.1048CrossRefGoogle ScholarPubMed
Calhoun, V. D., Miller, R., Pearlson, G., & Adalı, T. (2014). The chronnectome: Time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron, 84(2), 262274. doi: 10.1016/j.neuron.2014.10.015CrossRefGoogle ScholarPubMed
Chang, C., & Glover, G. H. (2010). Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage, 50(1), 8198. doi: 10.1016/j.neuroimage.2009.12.011CrossRefGoogle ScholarPubMed
Cole, M. W., & Schneider, W. (2007). The cognitive control network: Integrated cortical regions with dissociable functions. NeuroImage, 37(1), 343360. doi: 10.1016/j.neuroimage.2007.03.071CrossRefGoogle ScholarPubMed
Connolly, C. G., Ho, T. C., Blom, E. H., LeWinn, K. Z., Sacchet, M. D., Tymofiyeva, O., … Yang, T. T. (2017). Resting-state functional connectivity of the amygdala and longitudinal changes in depression severity in adolescent depression. Journal of Affective Disorders, 207, 8694. doi: 10.1016/j.jad.2016.09.026CrossRefGoogle ScholarPubMed
Cordes, D., Haughton, V. M., Arfanakis, K., Wendt, G. J., Turski, P. A., Moritz, C. H., … Meyerand, M. E. (2000). Mapping functionally related regions of brain with functional connectivity MR imaging. AJNR. American Journal of Neuroradiology, 21(9), 16361644.Google ScholarPubMed
Cullen, K. R., Westlund, M. K., Klimes-Dougan, B., Mueller, B. A., Houri, A., Eberly, L. E., & Lim, K. O. (2014). Abnormal amygdala resting-state functional connectivity in adolescent depression. JAMA Psychiatry, 71(10), 11381147. doi: 10.1001/jamapsychiatry.2014.1087CrossRefGoogle ScholarPubMed
Deco, G., Tononi, G., Boly, M., & Kringelbach, M. L. (2015). Rethinking segregation and integration: Contributions of whole-brain modelling. Nature Reviews. Neuroscience, 16(7), 430439. doi: 10.1038/nrn3963CrossRefGoogle ScholarPubMed
Fair, D. A., Dosenbach, N. U., Church, J. A., Cohen, A. L., Brahmbhatt, S., Miezin, F. M., … Schlaggar, B. L. (2007). Development of distinct control networks through segregation and integration. Proceedings of the National Academy of Sciences of the United States of America, 104(33), 1350713512. doi: 10.1073/pnas.0705843104CrossRefGoogle ScholarPubMed
Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics (Oxford, England), 9(3), 432441. doi: 10.1093/biostatistics/kxm045CrossRefGoogle ScholarPubMed
Friston, K. (2002). Beyond phrenology: What can neuroimaging tell us about distributed circuitry? Annual Review of Neuroscience, 25, 221250. doi: 10.1146/annurev.neuro.25.112701.142846CrossRefGoogle ScholarPubMed
Gao, W., Biswal, B., Yang, J., Li, S., Wang, Y., Chen, S., & Yuan, J. (2023). Temporal dynamic patterns of the ventromedial prefrontal cortex underlie the association between rumination and depression. Cerebral Cortex (New York, N.Y.: 1991), 33(4), 969982. doi: 10.1093/cercor/bhac115CrossRefGoogle ScholarPubMed
Gazzaley, A., Cooney, J. W., McEvoy, K., Knight, R. T., & D'Esposito, M. (2005a). Top-down enhancement and suppression of the magnitude and speed of neural activity. Journal of Cognitive Neuroscience, 17(3), 507517. doi: 10.1162/0898929053279522CrossRefGoogle ScholarPubMed
Gazzaley, A., Cooney, J. W., Rissman, J., & D'Esposito, M. (2005b). Top-down suppression deficit underlies working memory impairment in normal aging. Nature Neuroscience, 8(10), 12981300. doi: 10.1038/nn1543CrossRefGoogle ScholarPubMed
Geng, H., Wu, F., Kong, L., Tang, Y., Zhou, Q., Chang, M., … Wang, F. (2016). Disrupted structural and functional connectivity in prefrontal-hippocampus circuitry in first-episode medication-naïve adolescent depression. PloS One, 11(2), e0148345. doi: 10.1371/journal.pone.0148345CrossRefGoogle ScholarPubMed
Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., … Rapoport, J. L. (1999). Brain development during childhood and adolescence: A longitudinal MRI study. Nature Neuroscience, 2(10), 861863. doi: 10.1038/13158CrossRefGoogle ScholarPubMed
Himberg, J., Hyvärinen, A., & Esposito, F. (2004). Validating the independent components of neuroimaging time series via clustering and visualization. NeuroImage, 22(3), 12141222. doi: 10.1016/j.neuroimage.2004.03.027CrossRefGoogle ScholarPubMed
Hutchison, R. M., & Morton, J. B. (2015). Tracking the brain's functional coupling dynamics over development. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 35(17), 68496859. doi: 10.1523/jneurosci.4638-14.2015CrossRefGoogle ScholarPubMed
Kaiser, R. H., Kang, M. S., Lew, Y., Van Der Feen, J., Aguirre, B., Clegg, R., … Pizzagalli, D. A. (2019). Abnormal frontoinsular-default network dynamics in adolescent depression and rumination: A preliminary resting-state co-activation pattern analysis. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 44(9), 16041612. doi: 10.1038/s41386-019-0399-3CrossRefGoogle ScholarPubMed
Kaiser, R. H., Whitfield-Gabrieli, S., Dillon, D. G., Goer, F., Beltzer, M., Minkel, J., … Pizzagalli, D. A. (2016). Dynamic resting-state functional connectivity in major depression. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 41(7), 18221830. doi: 10.1038/npp.2015.352CrossRefGoogle ScholarPubMed
Kang, J., Wang, L., Yan, C., Wang, J., Liang, X., & He, Y. (2011). Characterizing dynamic functional connectivity in the resting brain using variable parameter regression and Kalman filtering approaches. NeuroImage, 56(3), 12221234. doi: 10.1016/j.neuroimage.2011.03.033CrossRefGoogle ScholarPubMed
Kessler, R. C., Avenevoli, S., Costello, E. J., Georgiades, K., Green, J. G., Gruber, M. J., … Merikangas, K. R. (2012). Prevalence, persistence, and sociodemographic correlates of DSM-IV disorders in the national comorbidity survey replication adolescent supplement. Archives of General Psychiatry, 69(4), 372380. doi: 10.1001/archgenpsychiatry.2011.160Google ScholarPubMed
Kim, J., Criaud, M., Cho, S. S., Díez-Cirarda, M., Mihaescu, A., Coakeley, S., … Strafella, A. P. (2017). Abnormal intrinsic brain functional network dynamics in Parkinson's disease. Brain: A Journal of Neurology, 140(11), 29552967. doi: 10.1093/brain/awx233CrossRefGoogle ScholarPubMed
Kiviruusu, O., Strandholm, T., Karlsson, L., & Marttunen, M. (2020). Outcome of depressive mood disorder among adolescent outpatients in an eight-year follow-up. Journal of Affective Disorders, 266, 520527. doi: 10.1016/j.jad.2020.01.174CrossRefGoogle Scholar
Liu, F., Guo, W., Fouche, J. P., Wang, Y., Wang, W., Ding, J., … Chen, H. (2015). Multivariate classification of social anxiety disorder using whole brain functional connectivity. Brain Structure & Function, 220(1), 101115. doi: 10.1007/s00429-013-0641-4CrossRefGoogle ScholarPubMed
Malhi, G. S., & Mann, J. J. (2018). Depression. Lancet (London, England), 392(10161), 22992312. doi: 10.1016/s0140-6736(18)31948-2CrossRefGoogle ScholarPubMed
Marchitelli, R., Paillère-Martinot, M. L., Bourvis, N., Guerin-Langlois, C., Kipman, A., Trichard, C., … Artiges, E. (2022). Dynamic functional connectivity in adolescence-onset major depression: Relationships with severity and symptom Dimensions. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 7(4), 385396. doi: 10.1016/j.bpsc.2021.05.003CrossRefGoogle ScholarPubMed
Moeller, S., Yacoub, E., Olman, C. A., Auerbach, E., Strupp, J., Harel, N., & Uğurbil, K. (2010). Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magnetic Resonance in Medicine, 63(5), 11441153. doi: 10.1002/mrm.22361CrossRefGoogle ScholarPubMed
Pannekoek, J. N., van der Werff, S. J., Meens, P. H., van den Bulk, B. G., Jolles, D. D., Veer, I. M., … Vermeiren, R. R. (2014). Aberrant resting-state functional connectivity in limbic and salience networks in treatment--naïve clinically depressed adolescents. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 55(12), 13171327. doi: 10.1111/jcpp.12266CrossRefGoogle ScholarPubMed
Pine, D. S., Cohen, P., Gurley, D., Brook, J., & Ma, Y. (1998). The risk for early-adulthood anxiety and depressive disorders in adolescents with anxiety and depressive disorders. Archives of General Psychiatry, 55(1), 5664. doi: 10.1001/archpsyc.55.1.56CrossRefGoogle ScholarPubMed
Preti, M. G., Bolton, T. A., & Van De Ville, D. (2017). The dynamic functional connectome: State-of-the-art and perspectives. NeuroImage, 160, 4154. doi: 10.1016/j.neuroimage.2016.12.061CrossRefGoogle Scholar
Sacchet, M. D., Ho, T. C., Connolly, C. G., Tymofiyeva, O., Lewinn, K. Z., Han, L. K., … Yang, T. T. (2016). Large-scale hypoconnectivity between resting-state functional networks in unmedicated adolescent major depressive disorder. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 41(12), 29512960. doi: 10.1038/npp.2016.76CrossRefGoogle ScholarPubMed
Schmaal, L., Hibar, D. P., Sämann, P. G., Hall, G. B., Baune, B. T., Jahanshad, N., … Veltman, D. J. (2017). 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. Molecular Psychiatry, 22(6), 900909. doi: 10.1038/mp.2016.60CrossRefGoogle ScholarPubMed
Shine, J. M. (2019). Neuromodulatory influences on integration and segregation in the brain. Trends in Cognitive Sciences, 23(7), 572583. doi: 10.1016/j.tics.2019.04.002CrossRefGoogle ScholarPubMed
Sporns, O. (2013). Network attributes for segregation and integration in the human brain. Current Opinion in Neurobiology, 23(2), 162171. doi: 10.1016/j.conb.2012.11.015CrossRefGoogle ScholarPubMed
Tu, Y., Fu, Z., Mao, C., Falahpour, M., Gollub, R. L., Park, J., … Kong, J. (2020). Distinct thalamocortical network dynamics are associated with the pathophysiology of chronic low back pain. Nature Communications, 11(1), 3948. doi: 10.1038/s41467-020-17788-zCrossRefGoogle ScholarPubMed
Tu, Y., Fu, Z., Zeng, F., Maleki, N., Lan, L., Li, Z., … Kong, J. (2019). Abnormal thalamocortical network dynamics in migraine. Neurology, 92(23), e2706e2716. doi: 10.1212/wnl.0000000000007607CrossRefGoogle ScholarPubMed
Wang, R., Liu, M., Cheng, X., Wu, Y., Hildebrandt, A., & Zhou, C. (2021). Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities. Proceedings of the National Academy of Sciences of the United States of America, 118(23), e2022288118. doi: 10.1073/pnas.2022288118CrossRefGoogle ScholarPubMed
Wu, B., Li, X., Zhang, M., Zhang, F., Long, X., Gong, Q., & Jia, Z. (2020a). Disrupted brain functional networks in patients with end-stage renal disease undergoing hemodialysis. Journal of Neuroscience Research, 98(12), 25662578. doi: 10.1002/jnr.24725CrossRefGoogle ScholarPubMed
Wu, B., Li, X., Zhou, J., Zhang, M., & Long, Q. (2020b). Altered whole-brain functional networks in drug-naïve, first-episode adolescents with major depression disorder. Journal of Magnetic Resonance Imaging: JMRI, 52(6), 17901798. doi: 10.1002/jmri.27270CrossRefGoogle ScholarPubMed
Wu, F., Tu, Z., Sun, J., Geng, H., Zhou, Y., Jiang, X., … Kong, L. (2019a). Abnormal functional and structural connectivity of amygdala-prefrontal circuit in first-episode adolescent depression: A combined fMRI and DTI study. Frontiers in Psychiatry, 10, 983. doi: 10.3389/fpsyt.2019.00983CrossRefGoogle ScholarPubMed
Wu, X., He, H., Shi, L., Xia, Y., Zuang, K., Feng, Q., … Qiu, J. (2019b). Personality traits are related with dynamic functional connectivity in major depression disorder: A resting-state analysis. Journal of Affective Disorders, 245, 10321042. doi: 10.1016/j.jad.2018.11.002CrossRefGoogle ScholarPubMed
Yan, C.-G., Wang, X.-D., Zuo, X.-N., & Zang, Y.-F. (2016). DPABI: Data processing & analysis for (resting-state) brain imaging. Neuroinformatics, 14(3), 339351.CrossRefGoogle ScholarPubMed
Yao, Z., Shi, J., Zhang, Z., Zheng, W., Hu, T., Li, Y., … Hu, B. (2019). Altered dynamic functional connectivity in weakly-connected state in major depressive disorder. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 130(11), 20962104. doi: 10.1016/j.clinph.2019.08.009CrossRefGoogle Scholar
Yu, Q., Erhardt, E. B., Sui, J., Du, Y., He, H., Hjelm, D., … Calhoun, V. D. (2015). Assessing dynamic brain graphs of time-varying connectivity in fMRI data: Application to healthy controls and patients with schizophrenia. NeuroImage, 107, 345355. doi: 10.1016/j.neuroimage.2014.12.020CrossRefGoogle ScholarPubMed
Yue, Z., Wang, P., Li, X., Ren, J., & Wu, B. (2021). Abnormal brain functional networks in end-stage renal disease patients with cognitive impairment. Brain and Behavior, 11(4), e02076. doi: 10.1002/brb3.2076CrossRefGoogle ScholarPubMed
Zhang, J., Wang, J., Wu, Q., Kuang, W., Huang, X., He, Y., & Gong, Q. (2011). Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biological Psychiatry, 70(4), 334342. doi: 10.1016/j.biopsych.2011.05.018CrossRefGoogle ScholarPubMed
Zheng, R., Chen, Y., Jiang, Y., Zhou, B., Li, S., Wei, Y., … Cheng, J. (2022). Abnormal dynamic functional connectivity in first-episode, drug-naïve adolescents with major depressive disorder. Journal of Neuroscience Research, 100(7), 14631475. doi: 10.1002/jnr.25047CrossRefGoogle ScholarPubMed
Zhi, D., Calhoun, V. D., Lv, L., Ma, X., Ke, Q., Fu, Z., … Sui, J. (2018). Aberrant dynamic functional network connectivity and graph properties in major depressive disorder. Frontiers in Psychiatry, 9, 339. doi: 10.3389/fpsyt.2018.00339CrossRefGoogle ScholarPubMed
Supplementary material: File

Wu et al. supplementary material

Wu et al. supplementary material
Download Wu et al. supplementary material(File)
File 26.5 MB