SpringerLink
Log in

Analysis of functional connectivity changes in attention networks and default mode networks in patients with depression and insomnia

  • Sleep Breathing Physiology and Disorders • Original Article
  • Published:
Sleep and Breathing Aims and scope Submit manuscript

Abstract

Purpose

Major Depressive Disorder (MDD) and Insomnia Disorder (ID) are prevalent psychiatric conditions often occurring concurrently, leading to substantial impairment in daily functioning. Understanding the neurobiological underpinnings of these disorders and their comorbidity is crucial for develo** effective interventions. This study aims to analyze changes in functional connectivity within attention networks and default mode networks in patients with depression and insomnia.

Methods

The functional connectivity alterations in individuals with MDD, ID, comorbid MDD and insomnia (iMDD), and healthy controls (HC) were assessed from a cohort of 174 participants. They underwent rs-fMRI scans, demographic assessments, and scale evaluations for depression and sleep quality. Functional connectivity analysis was conducted using region-of-interest (ROI) and whole-brain methods.

Results

The MDD and iMDD groups exhibited higher Hamilton Depression Scale (HAMD) scores compared to HC and ID groups (P < 0.001). Both ID and MDD groups displayed enhanced connectivity between the left and right orbital frontal cortex compared to HC (P < 0.05), while the iMDD group showed reduced connectivity compared to HC and ID groups (P < 0.05). In the left insula, reduced connectivity with the right medial superior frontal gyrus was observed across patient groups compared to HC (P < 0.05), with the iMDD group showing increased connectivity compared to MDD (P < 0.05). Moreover, alterations in functional connectivity between the left thalamus and left temporal pole were found in iMDD compared to HC and MDD (P < 0.05). Correlation analyses revealed associations between abnormal connectivity and symptom severity in MDD and ID groups.

Conclusions

Our findings demonstrate distinct patterns of altered functional connectivity in individuals with MDD, ID, and iMDD compared to healthy controls. These findings contribute to a better understanding of the pathophysiology of depression and insomnia, which could be used as a reference for the diagnosis and treatments of these patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

Data available upon request in line with relevant restrictions, e.g. privacy or ethical.

References

  1. Hoekzema E et al (2014) An independent components and functional connectivity analysis of resting state fMRI data points to neural network dysregulation in adult ADHD. Hum Brain Mapp 35(4):1261–1272

    Article  PubMed  Google Scholar 

  2. Ferri J et al (2018) Resting-state thalamic dysconnectivity in schizophrenia and relationships with symptoms. Psychol Med 48(15):2492–2499

    Article  CAS  PubMed  Google Scholar 

  3. Wang T et al (2016) Regional homogeneity changes in patients with primary insomnia. Eur Radiol 26(5):1292–1300

    Article  PubMed  Google Scholar 

  4. Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9(1):97–113

    Article  CAS  PubMed  Google Scholar 

  5. Petersen SE, Posner MI (2012) The attention system of the human brain: 20 years after. Annu Rev Neurosci 35:73–89

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Bagherzadeh-Azbari S et al (2019) Neuroimaging insights into the link between depression and Insomnia: A systematic review. J Affect Disord 258:133–143

    Article  PubMed  Google Scholar 

  7. Wang YF et al (2015) Reliable Attention Network Scores and Mutually Inhibited Inter-network Relationships Revealed by Mixed Design and Non-orthogonal Method. Sci Rep 5:10251

    Article  PubMed  PubMed Central  Google Scholar 

  8. Xuan B et al (2016) The activation of interactive attentional networks. Neuroimage 129:308–319

    Article  PubMed  Google Scholar 

  9. Joo EY et al (2013) Brain Gray Matter Deficits in Patients with Chronic Primary Insomnia. Sleep 36(7):999–1007

    Article  PubMed  PubMed Central  Google Scholar 

  10. Nofzinger EA et al (2004) Functional neuroimaging evidence for hyperarousal in insomnia. Am J Psychiatry 161(11):2126–2128

    Article  PubMed  Google Scholar 

  11. Keller AS, Ball TM, Williams LM (2020) Deep phenoty** of attention impairments and the “Inattention Biotype” in Major Depressive Disorder. Psychol Med 50(13):2203–2212

    Article  PubMed  Google Scholar 

  12. Rock PL et al (2014) Cognitive impairment in depression: a systematic review and meta-analysis. Psychol Med 44(10):2029–2040

    Article  CAS  PubMed  Google Scholar 

  13. (APA) APA (2013) Diagnostic and Statistical Manual of Mental Disorders V. Text Revision Washington, DC:American Psychiatric Press

  14. Wang H, Mo C, Fang F (2020) Dissociated deficits in attentional networks in social anxiety and depression. Sci China Life Sci 63(7):1071–1078

    Article  PubMed  Google Scholar 

  15. De Lissnyder E et al (2010) The association between depressive symptoms and executive control impairments in response to emotional and non-emotional information. Cogn Emot 24(2):264–28

  16. Jiang L et al (2018) Altered functional and causal connectivity in attention and default mode network of postconcussional syndrome patients. Chinese J Neurol 17(10):1008—1013

  17. Kaiser RH et al (2015) Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity. JAMA Psychiat 72(6):603–611

    Article  Google Scholar 

  18. Cheng W et al (2016) Medial reward and lateral non-reward orbitofrontal cortex circuits change in opposite directions in depression. Brain 139(Pt 12):3296–3309

    Article  PubMed  Google Scholar 

  19. Nie X et al (2015) Functional connectivity of paired default mode network subregions in primary insomnia. Neuropsychiatr Dis Treat 11:3085–3093

    PubMed  PubMed Central  Google Scholar 

  20. Lin P et al (2016) Static and dynamic posterior cingulate cortex nodal topology of default mode network predicts attention task performance. Brain Imaging Behav 10(1):212–225

    Article  PubMed  Google Scholar 

  21. Whitfield-Gabrieli S, Ford JM (2012) Default mode network activity and connectivity in psychopathology. Annu Rev Clin Psychol 8:49–76

    Article  PubMed  Google Scholar 

  22. Smith V, Mitchell DJ, Duncan J (2018) Role of the Default Mode Network in Cognitive Transitions. Cereb Cortex 28(10):3685–3696

    Article  PubMed  PubMed Central  Google Scholar 

  23. Zhu X et al (2017) Rumination and Default Mode Network Subsystems Connectivity in First-episode, Drug-Naive Young Patients with Major Depressive Disorder. Sci Rep 7:43105

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Brakowski J et al (2017) Resting state brain network function in major depression - Depression symptomatology, antidepressant treatment effects, future research. J Psychiatr Res 92:147–159

    Article  PubMed  Google Scholar 

  25. Philippi CL et al (2018) Neural and behavioral correlates of negative self-focused thought associated with depression. Hum Brain Mapp 39(5):2246–2257

    Article  PubMed  PubMed Central  Google Scholar 

  26. Guo W et al (2018) Increased anterior default-mode network homogeneity in first-episode, drug-naive major depressive disorder: A replication study. J Affect Disord 225:767–772

    Article  PubMed  Google Scholar 

  27. Rolls ET (2016) A non-reward attractor theory of depression. Neurosci Biobehav Rev 68:47–58

    Article  PubMed  Google Scholar 

  28. Rolls ET (2017) The roles of the orbitofrontal cortex via the habenula in non-reward and depression, and in the responses of serotonin and dopamine neurons. Neurosci Biobehav Rev 75:331–334

    Article  CAS  PubMed  Google Scholar 

  29. Feffer K et al (2018) 1Hz rTMS of the right orbitofrontal cortex for major depression: Safety, tolerability and clinical outcomes. Eur Neuropsychopharmacol 28(1):109–117

    Article  CAS  PubMed  Google Scholar 

  30. Fossati P (2013) Imaging autobiographical memory. Dialogues Clin Neurosci 15(4):487–490

    Article  PubMed  PubMed Central  Google Scholar 

  31. Freton M et al (2014) The eye of the self: precuneus volume and visual perspective during autobiographical memory retrieval. Brain Struct Funct 219(3):959–968

    Article  PubMed  Google Scholar 

  32. Delaveau P et al (2016) Antidepressant short-term and long-term brain effects during self-referential processing in major depression. Psychiatry Res Neuroimaging 247:17–24

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the programs of the National Natural Science Foundation of China International Cooperation and Exchange Project (81761128036); National Natural Science Foundation of China Regional Science Fund Project (81960262, 81560235, 31760294); Guizhou Provincial Precision Diagnosis and Treatment International Scientific Cooperation Base for Severe Depressive Disorders (Guizhou Science and Technology Cooperation Platform Talent [2018]5802); Guizhou Province High-Level Innovative Talent Training Program—Hundred-Level Talent (Guizhou Science and Technology Cooperation Platform Talent [2016]5679); Guiyang Science and Technology Plan Project (Zhu Ke Contract [2018]1-94)

Funding

This work was supported by the programs of the National Natural Science Foundation of China International Cooperation and Exchange Project (81761128036); National Natural Science Foundation of China Regional Science Fund Project (81960262, 81560235, 31760294); Guizhou Provincial Precision Diagnosis and Treatment International Scientific Cooperation Base for Severe Depressive Disorders (Guizhou Science and Technology Cooperation Platform Talent [2018]5802); Guizhou Province High-Level Innovative Talent Training Program—Hundred-Level Talent (Guizhou Science and Technology Cooperation Platform Talent [2016]5679); Guiyang Science and Technology Plan Project (Zhu Ke Contract [2018]1–94).

Author information

Authors and Affiliations

  1. Guizhou Medical University, Guiyang, 550004, Guizhou, China

    Yong-xue Hu & Long-fei Liu

  2. Department of Psychology, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, Guizhou, China

    Yong-xue Hu, Guang-yuan **a, Long-fei Liu, Pei-fan Li, Qing Shan & Yi-ming Wang

  3. Department of Neurology, The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, 550004, Guizhou, China

    **g-yu Shi

Authors
  1. Yong-xue Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. **g-yu Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guang-yuan **a
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Long-fei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pei-fan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qing Shan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yi-ming Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Yong-xue Hu, **g-yu Shi, Guang-yuan **a, Long-fei Liu, Pei-fan Li, Qing Shan and Yi-ming Wang designed the study. Yong-xue Hu and Yi-ming Wang participated in the conception of the study. **g-yu Shi, Guang-yuan **a, Long-fei Liu, Pei-fan Li and Qing Shan managed and conducted the statistical analyses and interpreted the data. All authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Yong-xue Hu or Yi-ming Wang.

Ethics declarations

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, Yx., Shi, Jy., **a, Gy. et al. Analysis of functional connectivity changes in attention networks and default mode networks in patients with depression and insomnia. Sleep Breath (2024). https://doi.org/10.1007/s11325-024-03064-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11325-024-03064-7

Keywords

Search

Navigation