Abnormalities in the default mode network in late-life depressiona study of resting-state fMRI

  1. Joan Guàrdia-Olmos 1
  2. Carles Soriano-Mas 1
  3. Lara Tormo-Rodríguez 1
  4. Cristina Cañete-Massé 1
  5. Inés del Cerro 1
  6. Mikel Urretavizcaya 1
  7. José M. Menchón 1
  8. Virgina Soria 1
  9. Maribel Peró-Cebollero 1
  1. 1 Universitat de Barcelona
    info

    Universitat de Barcelona

    Barcelona, España

    ROR https://ror.org/021018s57

Revista:
International journal of clinical and health psychology

ISSN: 1697-2600

Año de publicación: 2022

Volumen: 22

Número: 3

Páginas: 11-20

Tipo: Artículo

DOI: 10.1016/J.IJCHP.2022.100317 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: International journal of clinical and health psychology

Resumen

Background/Objective Neuroimaging studies have reported abnormalities in the examination of functional connectivity in late-life depression (LLD) in the default mode network (DMN). The present study aims to study resting-state functional connectivity within the DMN in people diagnosed with late-life major depressive disorder (MDD) compared to healthy controls (HCs). Moreover, we would like to differentiate these same connectivity patterns between participants with high vs. low anxiety levels. Method The sample comprised 56 participants between the ages of 60 and 75; 27 of them were patients with a diagnosis of MDD. Patients were further divided into two samples according to anxiety level: the four people with the highest anxiety level and the five with the lowest anxiety level. Clinical aspects were measured using psychological questionnaires. Each participant underwent functional magnetic resonance imaging (fMRI) acquisition in different regions of interest (ROIs) of the DMN. Results There was a greater correlation between pairs of ROIs in the control group than in patients with LLD, being this effect preferentially observed in patients with higher anxiety levels. Conclusions There are differences in functional connectivity within the DMN depending on the level of psychopathology. This can be reflected in these correlations and in the number of clusters and how the brain lateralizes (clustering).

Referencias bibliográficas

  • Alexopoulos, G. S. (2019). Mechanisms and treatment of late-life depression. Translational Psychiatry, 9 (1), 188.
  • Andreescu, C., Sheu, L. K., Tudorascu, D., Walker, S., & Aizenstein, H. (2014). The ages of anxiety—Differences across the lifespan in the default mode network functional connectivity in generalized anxiety disorder. International Journal of Geriatric Psychiatry, 29 (7), 704–712.
  • Beekman, A. T., De Beurs, E., Van Balkom, A. J., Deeg, D. J., Van Dyck, R., & Van Tilburg, W. (2000). Anxiety and depression in later life: Co-occurrence and communality of risk factors. American Journal of Psychiatry, 157 (1), 89–95.
  • Behzadi, Y., Restom, K., Liau, J., & Liu, T. T. (2007). A component-based noise correction method (CompCor) for BOLD and perfusion-based fMRI. NeuroImage, 37 (1), 90–101. https://doi.org/10.1016/j.neuroimage.2007.04.042
  • Damoiseaux, J. S., Beckmann, C. F., Sanz Arigita, E. S., Barkhof, F., Scheltens, P., Stam, C. J., et al. (2008). Reduced resting-state brain activity in the “default network” in normal aging. Cerebral Cortex, 18 (8), 1856–1864.
  • Demirtas, M., Tornador, C., Falcon, C., Lopez-Sola, M., Hernandez-Ribas, R., Pujol, J., et al. (2016). Dynamic functional connectivity reveals altered variability in functional connectivity among patients with major depressive disorder. Human Brain Mapping, 37 (8), 2918–2930.
  • Eyre, H. A., Yang, H., Leaver, A. M., Van Dyk, K., Siddarth, P., Cyr, N. S., et al. (2016). Altered resting-state functional connectivity in late-life depression: A cross-sectional study. Journal of Affective Disorders, 189, 126–133. https://doi.org/10.1016/j.jad.2015.09.011
  • Farras-Permanyer, L., Mancho-Fora, N., Montala-Flaquer, M., Bartres-Faz, D., Vaque-Alcazar, L., Pero-Cebollero, M., et al. (2019). Age-related changes in resting-state functional connectivity in older adults. Neural Regeneration Research, 14 (9), 1544.
  • Flandin, G., & Friston, K. J. (2019). Analysis of family-wise error rates in statistical parametric mapping using random field theory. Human Brain Mapping, 40 (7), 2052–2054. https://doi.org/10.1002/hbm.23839
  • Gandelman, J. A., Albert, K., Boyd, B. D., Park, J. W., Riddle, M., Woodward, N. D., et al. (2019). Intrinsic functional network connectivity is associated with clinical symptoms and cognition in late-life depression. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 4 (2), 160–170.
  • Geng, J., Yan, R., Shi, J., Chen, Y., Mo, Z., Shao, J., et al. (2019). Altered regional homogeneity in patients with somatic depression: A resting-state fMRI study. Journal of Affective Disorders, 246, 498–505.
  • Habes, M., Pomponio, R., Shou, H., Doshi, J., Mamourian, E., Erus, G., , & iSTAGING consortium, the Preclinical AD consortium, the ADNI, and the CARDIA studies. (2021). The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans. Alzheimer's & Dementia, 17 (1), 89–102.
  • Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry, 23, 56–62. https://doi.org/10.1136/jnnp.23.1.56
  • Harrison, B. J., Pujol, J., Lopez-Sola, M., Hernandez-Ribas, R., Deus, J., Ortiz, H., et al. (2008). Consistency and functional specialization in the default mode brain network. In Proceeding of National Academy of Sciences (pp. 9781-9786). https://doi.org/10.1073/pnas.0711791105
  • Huang, C. C., Hsieh, W. J., Lee, P. L., Peng, L. N., Liu, L. K., Lee, W. J., et al. (2015). Age-Related Changes in Resting-State Networks of A Large Sample Size of Healthy Elderly. CNS Neuro-science and Therapeutics, 21 (10), 817–825. https://doi.org/10.1111/cns.12396
  • Jacob, Y., Morris, L. S., Huang, K. H., Schneider, M., Rutter, S., Verma, G., et al. (2020). Neural correlates of rumination in major depressive disorder: A brain network analysis. Neuro-image Clinical, 25, 102142. https://doi.org/10.1016/j.nicl.2019.102142
  • Laird, K. T., Siddarth, P., Krause-Sorio, B., Kilpatrick, L., Milillo, M., Aguilar, Y., et al. (2019). Anxiety symptoms are associated with smaller insular and orbitofrontal cortex volumes in late-life depression. Journal of Affective Disorders, 256, 282–287.
  • Maarsingh, O. R., Heymans, M. W., Verhaak, P. F., Penninx, B. W. J. H., & Comijs, H. C. (2018). Development and external validation of a prediction rule for an unfavorable course of late-life depression: A multicenter cohort study. Journal of Affective Disorders, 235, 105–113.
  • Mancho-Fora, N., Montala-Flaquer, M., Farras-Permanyer, L., Zarabozo-Hurtado, D., Gallardo-Moreno, G. B., Gudayol-Farre, E., et al. (2020). Network change point detection in resting-state functional connectivity dynamics of mild cognitive impairment patients. International Journal of Clinical and Health Psychology, 20 (3), 200–212. https://doi.org/10.1016/j.ijchp.2020.07.005
  • Manning, K., Wang, L., & Steffens, D. (2019). Recent advances in the use of imaging in psychiatry: Functional magnetic resonance imaging of large-scale brain networks in late-life depression. F1000Research, 8. https://doi.org/10.12688/f1000research.17399.1
  • Northoff, G. (2020). Anxiety Disorders and the Brain’s Resting State Networks: From Altered Spatiotemporal Synchronization to Psychopathological Symptoms. In: Y. K. Kim (eds) Anxiety disorders. advances in experimental medicine and biology, vol 1191. Springer, Singapore. https://doi.org/10.1007/978-981-32-9705-0_5
  • Patek, A. X., Kundu, P., Rubinov, M., Jones, P. S., Vertes, P. E., Ersche, K. D., et al. (2014). A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series. NeuroImage, 95, 287–304. https://doi.org/10.1016/j.neuro-image.2014.03.012
  • Ponsoda, V., Martínez, K., Pineda-Pardo, J. A., Abad, F. J., Olea, J., Roman, F. J., et al. (2017). Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis. Human Brain Mapping, 38 (2), 803–816. https://doi.org/10.1002/hbm.23419.
  • Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. In Proceeding of the National Academy of Sciences USA (pp. 676-682). https://doi.org/10.1073/pnas.98.2.676
  • Shakil, S., Magnuson, M. E., Keilholz, S. D., & Lee, C. (2014). Cluster-based analysis for characterizing dynamic functional connectivity. 36th Annual International Conference of the IEEE Engineering in Medicince and Biology Society (pp. 982-985).
  • Sheehan, D. V., Lecrubier, Y., Sheehan, K. H., Amorim, P., Janavs, J., Weiller, E., et al. (1998). The Mini-International Neuropsychiatric Interview (MINI): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. Journal of Clinical Psychiatry, 59 (20), 22–33.
  • Sheikh, J. I., & Yesavage, J. A. (1986). Geriatric Depression Scale (GDS): Recent evidence and development of a shorter version. Clinical Gerontologist: The Journal of Aging and Mental Health, 5 (1-2), 165–173. https://doi.org/10.1300/J018v05n01_09.
  • Smith, J. M., & Alloy, L. B. (2009). A roadmap to rumination: A review of the definition, assessment, and conceptualization of this multifaceted construct. Clinical Psychology Review, 29, 116–128.
  • Spielberger, C. D. (1983). State-Trait Anxiety Inventory for Adults (STAI-AD) [Database record]. APA PsycTests. https://doi.org/10.1037/t06496-000.
  • Tumati,S.,Paulus,M.P.,&Northoff,G.(2021).Out-of-step:Brain-heart desynchronization in anxiety disorders. Molecular Psychiatry, 26, 1726–1737. https://doi.org/10.1038/s41380-021-01029-w.
  • Turner, B. O., Paul, E. J., Miller, M. B., & Barbey, A. K. (2018). Small sample sizes reduce the replicability of task-based fMRI studies. Communications Biology, 1 (1), 1–10. https://doi.org/10.1038/s42003-018-0073-z.
  • Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., et al. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15 (1), 273–289. https://doi.org/10.1006/nimg.2001.0978.
  • Van Den Heuvel, M. P., & Pol, H. E. H. (2010). Exploring the brain network: A review on resting-state fMRI functional connectivity. European Neuropsychopharmacology, 20 (8), 519–534.
  • Van Dijk, K. R. A., Hedden, T., Venkataraman, A., Evans, K. C., Lazar, S. W., & Buckner, R. L. (2010). Intrinsic functional connectivity as a tool for human connectomics: Theory, properties, and optimization. Journal of Neurophysiology, 103 (1), 297–321. https://doi.org/10.1152/jn.00783.2009.
  • Wang, R., Liu, M., Cheng, X., Wu, Y., Hildebrandt, A., & Zhou, C. (2021). Segregation, integration, and balance of large-scale resting brain networks conFig. different cognitive abilities. Proceedings of the National Academy of Sciences, 118 (23).
  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature, 393 (6684), 440–442. https://doi.org/10.1038/30918. Wechsler, D. (1999). WISC-III: Wechsler intelligence scale for children (finnish version). Psykologien Kustannus
  • Wei, Y., de Lange, S. C., Scholtens, L. H., Watanabe, K., Ardesch, D. J., Jansen, P. R., et al. (2019). Genetic mapping and evolutionary analysis of human-expanded cognitive networks. Nature Communications, 10 (1), 1–11.
  • Wen, J., Fu, C. H. Y., Tosun, D., Veturi, Y., Yang, Z., Abdulkadir, A., et al., , & iSTAGING consortium, ADNI, BIOCARD, and BLSA. (2022). Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression. JAMA psychiatry e220020. https://doi.org/10.1001/jamapsychiatry.2022.0020.
  • Wise, T., Marwood, L., Perkins, A. M., Herane-Vives, A., Joules, R., Lythgoe, D. J., et al. (2017). Instability of default mode network connectivity in major depression: A two-sample confirmation study. Translational Psychiatry, 7 (4). https://doi.org/10.1038/tp.2017.40e1105-e1105.
  • Xu, J., Van Dam, N. T., Feng, C., Luo, Y., Ai, H., Gu, R., et al. (2019). Anxious brain networks: A coordinate-based activation likelihood estimation meta-analysis of resting-state functional connectivity studies in anxiety. Neuroscience & Biobehavioral Reviews, 96, 21–30.
  • Xue, C., Yuan, B., Yue, Y., Xu, J., Wang, S., Wu, M., et al. (2019). Distinct disruptive patterns of default mode subnetwork connectivity across the spectrum of preclinical Alzheimer’s disease. Frontiers in Aging Neuroscience, 11, 307–321. https://doi.org/10.3389/fnagi.2019.00307.
  • Yesavage, J. A., Brink, T. L., Rose, T. L., Lum, O., Huang, V., Adey, M., et al. (1982). Development and validation of a geriatric depression screening scale: A preliminary report. Journal of Psychiatric Research, 17 (1), 37–49.
  • Yun, J. Y., & Kim, Y. K. (2021). Graph theory approach for the structural-functional brain connectome of depression. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 111, e110401.
  • Zhang, M., Bernhardt, B. C., Wang, X., Varga, D., Krieger-Redwood, K., Royer, J., et al. (2022). Perceptual coupling and decoupling of the default mode network during mind-wandering and reading. eLife, 11, e74011. https://doi.org/10.7554/eLife.74011
  • Zhang, Y., Chen, Y., & Ma, L. (2018). Depression and cardiovascular disease in elderly: Current understanding. Journal of Clinical Neuroscience, 47, 1–5.
  • Zhou, C., Zemanova, L., Zamora, G., Hilgetag, C. C., & Kurths, J. (2006). Hierarchical organization unveiled by functional connectivity in complex brain networks. Physical Review Letters, 97, (23) e238103.
  • Zugman, A., Harrewijn, A., Cardinale, E. M., Zwiebel, H., Freitag, G. F., Werwath, K. E., et al. (2022). Mega-analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group. Human Brain Mapping, 43 (1), 255–277.
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