| Peer-Reviewed

A Study of Subjective Cognitive Decline and Subclinical Depression Based on Dynamic Network Connectivity of Cerebral fMRI Data

Received: 17 October 2022     Accepted: 1 December 2022     Published: 8 December 2022
Views:       Downloads:
Abstract

A certain number of fMRI studies on subjective cognitive decline (SCD) have been widely debated. They mainly focus on the differences in brain structure and function between SCD and normal people, while more studies focus on objective cognitive decline. The relationship between psychological factors and SCD via cerebral fMRI data in the elderly is rarely discussed. In this study, we included 66 SCD patients and 63 normal controls (NC) to investigate the neural processes amid the psychological aspects of those with subclinical depression and SCD using dynamic network connectivity and to provide theoretical support for neuroimaging for improved Alzheimer's disease prevention and therapy. We calculated temporal flexibility and spatiotemporal diversity via fMRI data using Shen’s 268 brain template and No. 74 brain region was selected by t-test and correlation analysis. In the NC group, no significant correlation was observed in temporal flexibility value of No. 74–SCD and Hamilton depression scale HAMD–SCD, whereas No. 74–HAMD showed a significant correlation. In the SCD group, all of the three parameters exhibited significant correlation. Mediation analysis obtained the mediation model of No. 74 brain region, subclinical depression, and subjective cognitive decline (No. 74→HAMD→SCD). The results show that visual system plays an important role in subclinical depression, and subclinical depression increases the risk of SCD.

Published in American Journal of Biomedical and Life Sciences (Volume 10, Issue 6)
DOI 10.11648/j.ajbls.20221006.12
Page(s) 162-167
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Subjective Cognitive Decline, Subclinical Depression, Dynamic Network Connectivity, Temporal Flexibility, fMRI

References
[1] F. Jessen, R. Amariglio, M. Boxtel, M. Breteler, M. Ceccaldi, G. Chételat, B. Dubois, K. Ellis, W. Flier, L. Glodzik, A. Harten, M. Leon, P. McHugh, M. Mielke, J. Molinuevo, L. Mosconi, R. Osorio, A. Perrotin, and M. Wagner, “A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease,” Alzheimer's & dementia: the journal of the Alzheimer's Association, vol. 10, 05/02, 2014.
[2] G. Sinoff, and P. Werner, “Anxiety disorder and accompanying subjective memory loss in the elderly as a predictor of future cognitive decline,” International journal of geriatric psychiatry, vol. 18, pp. 951-9, 10/01, 2003.
[3] F. Jessen, L. Feyen, K. Freymann, R. Tepest, W. Maier, P. D. R. Heun, H. Schild, and L. Scheef, “Volume reduction of the entorhinal cortex in subjective memory impairment,” Neurobiology of aging, vol. 27, pp. 1751-6, 12/01, 2006.
[4] N. Hill, J. Mogle, R. Wion, E. Munoz, N. DePasquale, A. Yevchak, and J. Parisi, “Subjective Cognitive Impairment and Affective Symptoms: A Systematic Review,” The Gerontologist, vol. 56, pp. gnw091, 06/23, 2016.
[5] C. Amaefule, M. Dyrba, S. Wolfsgruber, A. Polcher, A. Schneider, K. Fliessbach, A. Spottke, D. Meiberth, L. Preis, O. Peters, E. Incesoy, E. Spruth, J. Priller, S. Altenstein, C. Bartels, J. Wiltfang, D. Janowitz, K. Bürger, C. Laske, and S. Teipel, “Association between composite scores of domain-specific cognitive functions and regional patterns of atrophy and functional connectivity in the Alzheimer’s disease spectrum,” NeuroImage: Clinical, vol. 29, pp. 102533, 01/01, 2021.
[6] S. Wang, J. Rao, Y. Yue, G. Hu, W. Qi, W. Ma, H. Ge, F. Zhang, X. Zhang, and J. Chen, “Altered Frequency-Dependent Brain Activation and White Matter Integrity Associated With Cognition in Characterizing Preclinical Alzheimer’s Disease Stages,” Frontiers in Human Neuroscience, vol. 15, pp. 625232, 02/01, 2021.
[7] R. Viviano, J. Hayes, P. Pruitt, Z. Fernandez, S. Rooden, J. van der Grond, S. Rombouts, and J. Damoiseaux, “Aberrant memory system connectivity and working memory performance in subjective cognitive decline,” NeuroImage, vol. 185, 10/01, 2018.
[8] K. Dillen, H. Jacobs, J. Kukolja, N. Richter, B. Reutern, O. Onur, K.-J. Langen, and G. Fink, “Functional Disintegration of the Default Mode Network in Prodromal Alzheimer’s Disease,” Journal of Alzheimer's Disease, vol. 59, pp. 1-19, 06/03, 2017.
[9] H. Chen, X. Sheng, C. Luo, R. Qin, Q. Ye, H. Zhao, Y. Xu, and F. Bai, “The compensatory phenomenon of the functional connectome related to pathological biomarkers in individuals with subjective cognitive decline,” Translational Neurodegeneration, vol. 9, 12/01, 2020.
[10] L. Liang, Y. Yuan, Y. Wei, B. Yu, W. Mai, G. Duan, X. Nong, C. Li, J. Su, L. Zhao, Z. Zhang, and D. Deng, “Recurrent and concurrent patterns of regional BOLD dynamics and functional connectivity dynamics in cognitive decline,” Alzheimer's Research & Therapy, vol. 13, 01/16, 2021.
[11] W. Qi, Q. Yuan, G. Hu, H. Ge, J. Rao, C. Xiao, and J. Chen, “Disrupted Dynamic Functional Connectivity in Distinguishing Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment Based on the Triple-Network Model,” Frontiers in Aging Neuroscience, vol. 13, 09/17, 2021.
[12] G. Dong, L. Yang, C.-s. Li, X. Wang, Y. Zhang, W. Du, Y. Han, and X. Tang, “Dynamic network connectivity predicts subjective cognitive decline: the Sino-Longitudinal Cognitive impairment and dementia study,” Brain Imaging and Behavior, vol. 14, 12/01, 2020.
[13] C.-G. Yan, X. Wang, X.-N. Zuo, and Y.-F. Zang, “DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging,” Neuroinformatics, vol. 14, 07/01, 2016.
[14] Chen, W. Cai, S. Ryali, K. Supekar, and V. Menon, “Distinct Global Brain Dynamics and Spatiotemporal Organization of the Salience Network,” PLOS Biology, vol. 14, no. 6, pp. e1002469, 2016.
[15] Zhang, S. Zhang, J. S. Ide, S. Hu, S. Zhornitsky, W. Wang, G. Dong, X. Tang, and C. R. Li, “Dynamic network dysfunction in cocaine dependence: Graph theoretical metrics and stop signal reaction time,” Neuroimage Clin, vol. 18, pp. 793-801, 2018.
[16] E. S. Finn, X. Shen, D. Scheinost, M. D. Rosenberg, J. Huang, M. M. Chun, X. Papademetris, and R. T. Constable, “Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity,” Nat Neurosci, vol. 18, no. 11, pp. 1664-71, Nov, 2015.
[17] A. Zalesky, A. Fornito, L. Cocchi, L. L. Gollo, and M. Breakspear, “Time-resolved resting-state brain networks,” Proceedings of the National Academy of Sciences, vol. 111, no. 28, pp. 10341-10346, 2014.
[18] A. Hayes, Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach, 2013.
[19] X. Shen, F. Tokoglu, X. Papademetris, and R. Constable, “Groupwise whole-brain parcellation from resting-state fMRI data for network node identification,” NeuroImage, vol. 82, 06/04, 2013.
[20] V. Salmela, L. Socada, J. Söderholm, R. Heikkilä, J. Lahti, J. Ekelund, and E. Isometsä, “Reduced visual contrast suppression during major depressive episodes,” Journal of Psychiatry and Neuroscience, vol. 46, 03/11, 2021.
[21] T. Heesterbeek, H. van der Aa, G. Rens, J. Twisk, and R. Nispen, “The incidence and predictors of depressive and anxiety symptoms in older adults with vision impairment: A longitudinal prospective cohort study,” Ophthalmic & physiological optics: the journal of the British College of Ophthalmic Opticians (Optometrists), vol. 37, 05/18, 2017.
[22] H. Choi, M. Lee, and S.-M. Lee, “Visual impairment and risk of depression: A longitudinal follow-up study using a national sample cohort,” Scientific Reports, vol. 8, 02/01, 2018.
[23] J. Renaud, and E. Bédard, “Depression in the elderly with visual impairment and its association with quality of life,” Clinical interventions in aging, vol. 8, pp. 931-43, 07/19, 2013.
[24] J. Renaud, M. Levasseur, J. Gresset, O. Overbury, M.-C. Wanet-Defalque, M.-F. Dubois, K. Temisjian, C. Vincent, M. Carignan, and J. Desrosiers, “Health-related and subjective quality of life of older adults with visual impairment,” Disability and rehabilitation, vol. 32, pp. 899-907, 10/01, 2009.
[25] I. Fort, L. Adoul, D. Holl, J. Kaddour, and K. Gana, “Psychometric Properties of the French Version of the Multifactorial Memory Questionnaire for Adults and the Elderly,” Canadian journal on aging = La revue canadienne du vieillissement, vol. 23, pp. 347-57, 02/01, 2004.
[26] J. Gaugler, M. Hovater, D. Roth, J. Johnston, R. Kane, and K. Sarsour, “Depressive, Functional Status, and Neuropsychiatric Symptom Trajectories Before an Alzheimer’s Disease Diagnosis,” Aging & mental health, vol. 18, 07/04, 2013.
[27] M. Dlugaj, A. Winkler, N. Dragano, S. Moebus, K.-H. Jöckel, R. Erbel, and C. Weimar, “Depression and Mild Cognitive Impairment in the General Population: Results of the Heinz Nixdorf Recall Study,” Journal of Alzheimer's disease: JAD, vol. 45, 12/02, 2014.
[28] E. Rolls, “The orbitofrontal cortex and emotion in health and disease, including depression,” Neuropsychologia, vol. 128, 09/01, 2017.
[29] F. Jessen, R. Amariglio, R. Buckley, W. Flier, Y. Han, J. Molinuevo, L. Rabin, D. Rentz, O. Rodriguez-Gomez, A. Saykin, S. Sikkes, C. Smart, S. Wolfsgruber, and M. Wagner, “The characterisation of subjective cognitive decline,” The Lancet Neurology, vol. 19, 01/17, 2020.
Cite This Article
  • APA Style

    Zhao Zhang, Guangfei Li, Zeyu Song, Xiaoying Tang. (2022). A Study of Subjective Cognitive Decline and Subclinical Depression Based on Dynamic Network Connectivity of Cerebral fMRI Data. American Journal of Biomedical and Life Sciences, 10(6), 162-167. https://doi.org/10.11648/j.ajbls.20221006.12

    Copy | Download

    ACS Style

    Zhao Zhang; Guangfei Li; Zeyu Song; Xiaoying Tang. A Study of Subjective Cognitive Decline and Subclinical Depression Based on Dynamic Network Connectivity of Cerebral fMRI Data. Am. J. Biomed. Life Sci. 2022, 10(6), 162-167. doi: 10.11648/j.ajbls.20221006.12

    Copy | Download

    AMA Style

    Zhao Zhang, Guangfei Li, Zeyu Song, Xiaoying Tang. A Study of Subjective Cognitive Decline and Subclinical Depression Based on Dynamic Network Connectivity of Cerebral fMRI Data. Am J Biomed Life Sci. 2022;10(6):162-167. doi: 10.11648/j.ajbls.20221006.12

    Copy | Download

  • @article{10.11648/j.ajbls.20221006.12,
      author = {Zhao Zhang and Guangfei Li and Zeyu Song and Xiaoying Tang},
      title = {A Study of Subjective Cognitive Decline and Subclinical Depression Based on Dynamic Network Connectivity of Cerebral fMRI Data},
      journal = {American Journal of Biomedical and Life Sciences},
      volume = {10},
      number = {6},
      pages = {162-167},
      doi = {10.11648/j.ajbls.20221006.12},
      url = {https://doi.org/10.11648/j.ajbls.20221006.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajbls.20221006.12},
      abstract = {A certain number of fMRI studies on subjective cognitive decline (SCD) have been widely debated. They mainly focus on the differences in brain structure and function between SCD and normal people, while more studies focus on objective cognitive decline. The relationship between psychological factors and SCD via cerebral fMRI data in the elderly is rarely discussed. In this study, we included 66 SCD patients and 63 normal controls (NC) to investigate the neural processes amid the psychological aspects of those with subclinical depression and SCD using dynamic network connectivity and to provide theoretical support for neuroimaging for improved Alzheimer's disease prevention and therapy. We calculated temporal flexibility and spatiotemporal diversity via fMRI data using Shen’s 268 brain template and No. 74 brain region was selected by t-test and correlation analysis. In the NC group, no significant correlation was observed in temporal flexibility value of No. 74–SCD and Hamilton depression scale HAMD–SCD, whereas No. 74–HAMD showed a significant correlation. In the SCD group, all of the three parameters exhibited significant correlation. Mediation analysis obtained the mediation model of No. 74 brain region, subclinical depression, and subjective cognitive decline (No. 74→HAMD→SCD). The results show that visual system plays an important role in subclinical depression, and subclinical depression increases the risk of SCD.},
     year = {2022}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - A Study of Subjective Cognitive Decline and Subclinical Depression Based on Dynamic Network Connectivity of Cerebral fMRI Data
    AU  - Zhao Zhang
    AU  - Guangfei Li
    AU  - Zeyu Song
    AU  - Xiaoying Tang
    Y1  - 2022/12/08
    PY  - 2022
    N1  - https://doi.org/10.11648/j.ajbls.20221006.12
    DO  - 10.11648/j.ajbls.20221006.12
    T2  - American Journal of Biomedical and Life Sciences
    JF  - American Journal of Biomedical and Life Sciences
    JO  - American Journal of Biomedical and Life Sciences
    SP  - 162
    EP  - 167
    PB  - Science Publishing Group
    SN  - 2330-880X
    UR  - https://doi.org/10.11648/j.ajbls.20221006.12
    AB  - A certain number of fMRI studies on subjective cognitive decline (SCD) have been widely debated. They mainly focus on the differences in brain structure and function between SCD and normal people, while more studies focus on objective cognitive decline. The relationship between psychological factors and SCD via cerebral fMRI data in the elderly is rarely discussed. In this study, we included 66 SCD patients and 63 normal controls (NC) to investigate the neural processes amid the psychological aspects of those with subclinical depression and SCD using dynamic network connectivity and to provide theoretical support for neuroimaging for improved Alzheimer's disease prevention and therapy. We calculated temporal flexibility and spatiotemporal diversity via fMRI data using Shen’s 268 brain template and No. 74 brain region was selected by t-test and correlation analysis. In the NC group, no significant correlation was observed in temporal flexibility value of No. 74–SCD and Hamilton depression scale HAMD–SCD, whereas No. 74–HAMD showed a significant correlation. In the SCD group, all of the three parameters exhibited significant correlation. Mediation analysis obtained the mediation model of No. 74 brain region, subclinical depression, and subjective cognitive decline (No. 74→HAMD→SCD). The results show that visual system plays an important role in subclinical depression, and subclinical depression increases the risk of SCD.
    VL  - 10
    IS  - 6
    ER  - 

    Copy | Download

Author Information
  • Department of Biomedical Engineering, School of Life Sciences, Beijing Institute of Technology, Beijing, China

  • Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China

  • Department of Biomedical Engineering, School of Life Sciences, Beijing Institute of Technology, Beijing, China

  • Department of Biomedical Engineering, School of Life Sciences, Beijing Institute of Technology, Beijing, China

  • Sections