Event-related EEG microstate complexity reveals specific brain network connectivity during aesthetic perception in depression

Xianda Ma; Jun Ye; Ying Ding; Zhitang Chen; Xu Zhang; Zhaohui Lan; Yifang Kuang; Milos R. Popovic; Qian Zhao; Zhenghua Wang; Yuhua Shen; Yi Li; Yang Liu; Weidong Li

doi:10.59717/j.xinn-life.2026.100192

The Innovation Life > 2026 Vol. 4 > No. 1 > 100192

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Event-related EEG microstate complexity reveals specific brain network connectivity during aesthetic perception in depression

1.
Bio-X Institutes, Center for Brain Health & Brain Technology, Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai 200240, China
2.
WLA Laboratories, World Laureates Association, Shanghai 201100, China
3.
Toronto Rehabilitation Institute and The KITE Research Institute, University Health Network, University of Toronto, Toronto ON M5S 2E8, Canada
4.
The Fourth People’s Hospital of Wuhu, Wuhu 241000, China
5.
SJTU Paris Elite Institute of Technology, Shanghai Jiao Tong University, Shanghai 201100, China
6.
Department of Psychology, University of Warwick, Coventry CV4 7AL, United Kingdom
7.
Shanghai Normal University Tianhua College, Shanghai 201815, China
8.
These authors contributed equally

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Corresponding authors: liwd@sjtu.edu.cn (W.L.); yang.liu1@sjtu.edu.cn (Y.L.)
Received Date: January 17, 2025

Accepted Date: January 07, 2026

Published Online: January 16, 2026

The Innovation Life 4(1), https://doi.org/10.59717/j.xinn-life.2026.100192 | Cite this article

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PUBLIC SUMMARY

PUBLIC SUMMARY
1. Patients with major depressive disorder (MDD) show altered brain activity patterns during visual aesthetics.
  
  Electroencephalography (EEG) microstates reveal reduced frontal control and increased posterior activity in MDD.
  
  MDD shows abnormal timing and complexity of brain states, suggesting reduced flexibility in processing images.
  
  These EEG features may help identify depression and support treatment monitoring in the future.

ABSTRACT

ABSTRACT

Visual aesthetic experience recruits distributed brain networks encompassing perceptual, emotional, and cognitive processes. Individuals with major depressive disorder (MDD) often exhibit altered aesthetic preferences, typically biased toward negative stimuli, yet the underlying neural dynamics remain poorly understood. In this study, 25 patients with MDD and 27 healthy controls (HCs) were recruited and performed image-based aesthetic processing tasks while the EEG signals were collected using a 32-channel system. EEG microstate was obtained by iterative clustering and the duration, occurrence, coverage, Lempel-Ziv complexity, and functional connectivity were calculated and analyzed. Significant alterations in both the spatial and temporal characteristics of EEG microstates were observed in the MDD group. Specifically, individuals with MDD showed increased occurrence and coverage of microstates associated with posterior visual processing, and reduced coverage and duration of those linked to right prefrontal executive control networks. These findings suggest a general functional shift from higher-order cognitive regulation toward lower-level sensory processing across stimulus types. Notably, microstate transitions were less dynamic in individuals with MDD, indicating lower information entropy and a decreased frequency of microstate switching. Moreover, MDD patients exhibited prolonged engagement of anterior microstates, potentially reflecting sustained activation of self-referential and affective regulatory circuits, including the anterior cingulate cortex (ACC), independent of executive-control-associated networks. Functional connectivity analysis further revealed altered anterior–posterior coupling and disrupted integration within the fronto-central regions, which are considered critical for emotional appraisal. These findings suggest that MDD alters the balance between perceptual and regulatory networks during aesthetic processing, which may underlie the negative aesthetic preferences often observed in depression. Our study highlights EEG microstate analysis as a sensitive tool for capturing neural signatures of aesthetic experience and offers new insights into the dysfunctional brain dynamics associated with MDD. These results provide a foundation for developing neurophysiological markers and targeted interventions to modulate aesthetic-related brain activity in depressive disorders.
- Major depressive disorder /
- Aesthetic perception /
- EEG microstate /
- Microstate complexity /
- Functional connectivity

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REFERENCES

[1]	Pascual-Marqui R. D., Michel C. M. and Lehmann D. (1995). Segmentation of brain electrical activity into microstates: Model estimation and validation. IEEE Trans. Biomed. Eng. 42:658−665. DOI:10.1109/10.391164 View in Article CrossRef Google Scholar
[2]	Wu C., Mu Q., Gao W., et al. (2025). The characteristics of anhedonia in depression: A review from a clinically oriented perspective. Transl. Psychiatry 15:90. DOI:10.1038/s41398-025-03310-w View in Article CrossRef Google Scholar
[3]	McCrae R. R. (2024). A volitional account of aesthetic experience. Front. Psychol. 15:1480304. DOI:10.3389/fpsyg.2024.1480304 View in Article CrossRef Google Scholar
[4]	Chen Z., Wang Z., Shen Y., et al. (2023). Face-specific negative bias of aesthetic perception in depression: Behavioral and EEG evidence. Front. Psychiatry 14:1102843. DOI:10.3389/fpsyt.2023.1102843 View in Article CrossRef Google Scholar
[5]	Fan H., Li Q., Du Y., et al. (2024). Relationship of prefrontal cortex activity with anhedonia and cognitive function in major depressive disorder: An fNIRS study. Front. Psychiatry 15:1428425. DOI:10.3389/fpsyt.2024.1428425 View in Article CrossRef Google Scholar
[6]	Kawabata H. and Zeki S. (2004). Neural correlates of beauty. J. Neurophysiol 91:1699−1705. DOI:10.1152/jn.00696.2003 View in Article CrossRef Google Scholar
[7]	Salimpoor V. N., Benovoy M., Larcher K., et al. (2011). Anatomically distinct dopamine release during anticipation and experience of peak emotion to music. Nat. Neurosci. 14:257−262. DOI:10.1038/nn.2726 View in Article CrossRef Google Scholar
[8]	Chib V. S., Rangel A., Shimojo S., et al. (2009). Evidence for a common representation of decision values for dissimilar goods in human ventromedial prefrontal cortex. J. Neurosci. 29:12315−12320. DOI:10.1523/jneurosci.2575-09.2009 View in Article CrossRef Google Scholar
[9]	Lebreton M., Jorge S., Michel V., et al. (2009). An automatic valuation system in the human brain: Evidence from functional neuroimaging. Neuron 64:431−439. DOI:10.1016/j.neuron.2009.09.040 View in Article CrossRef Google Scholar
[10]	Hoenen M., Lübke K. T. and Pause B. M. (2017). Sensitivity of the human mirror neuron system for abstract traces of actions: An EEG-study. Biol. Psychol. 124:57−64. DOI:10.1016/j.biopsycho.2017.01.010 View in Article CrossRef Google Scholar
[11]	Jacobsen T., Schubotz R. I., Höfel L., et al. (2006). Brain correlates of aesthetic judgment of beauty. Neuroimage 29:276−285. DOI:10.1016/j.neuroimage.2005.07.010 View in Article CrossRef Google Scholar
[12]	Brown S., Gao X., Tisdelle L., et al. (2011). Naturalizing aesthetics: Brain areas for aesthetic appraisal across sensory modalities. Neuroimage 58:250−258. DOI:10.1016/j.neuroimage.2011.06.012 View in Article CrossRef Google Scholar
[13]	Ishizu T. and Zeki S. (2011). Toward a brain-based theory of beauty. PLoS One 6:e21852. DOI:10.1371/journal.pone.0021852 View in Article CrossRef Google Scholar
[14]	Nadal M. and Pearce M. T. (2011). The Copenhagen neuroaesthetics conference: Prospects and pitfalls for an emerging field. Brain Cogn. 76:172−183. DOI:10.1016/j.bandc.2011.01.009 View in Article CrossRef Google Scholar
[15]	Ferenczi E. A., Zalocusky K. A., Liston C., et al. (2016). Prefrontal cortical regulation of brainwide circuit dynamics and reward-related behavior. Science 351:aac9698. DOI:10.1126/science.aac9698 View in Article CrossRef Google Scholar
[16]	Vessel E. A., Starr G. G. and Rubin N. (2012). The brain on art: Intense aesthetic experience activates the default mode network. Front. Hum. Neurosci. 6:66. DOI:10.3389/fnhum.2012.00066 View in Article CrossRef Google Scholar
[17]	Vessel E. A., Starr G. G. and Rubin N. (2013). Art reaches within: Aesthetic experience, the self and the default mode network. Front. Neurosci. 7:258. DOI:10.3389/fnins.2013.00258 View in Article CrossRef Google Scholar
[18]	Vartanian O. and Skov M. (2014). Neural correlates of viewing paintings: Evidence from a quantitative meta-analysis of functional magnetic resonance imaging data. Brain Cogn. 87:52−56. DOI:10.1016/j.bandc.2014.03.004 View in Article CrossRef Google Scholar
[19]	Kirk U., Skov M., Christensen M. S., et al. (2009). Brain correlates of aesthetic expertise: A parametric fMRI study. Brain Cogn. 69:306−315. DOI:10.1016/j.bandc.2008.08.004 View in Article CrossRef Google Scholar
[20]	Aalbers S., Fusar-Poli L., Freeman R. E., et al. (2017). Music therapy for depression. Cochrane Database Syst. Rev. 11:Cd004517. DOI:10.1002/14651858.CD004517.pub3 View in Article CrossRef Google Scholar
[21]	Wang X., Huang W., Liu S., et al. (2024). Music therapy for depression: A narrative review. Brain‐X 2:e72. DOI:10.1002/brx2.72 View in Article CrossRef Google Scholar
[22]	Shukla A., Choudhari S. G., Gaidhane A. M., et al. (2022). Role of art therapy in the promotion of mental health: A critical review. Cureus 14:e28026. DOI:10.7759/cureus.28026 View in Article CrossRef Google Scholar
[23]	Fancourt D. and Finn S. (2019). WHO health evidence network synthesis reports. In what is the evidence on the role of the arts in improving health and well-being? A scoping review, (WHO regional office for europe© world health organization 2019). https://www.ncbi.nlm.nih.gov/books/NBK553773/. View in Article Google Scholar
[24]	Michel C. M. and Koenig T. (2018). EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review. Neuroimage 180:577−593. DOI:10.1016/j.neuroimage.2017.11.062 View in Article CrossRef Google Scholar
[25]	Battle D. E. (2013). Diagnostic and statistical manual of mental disorders (DSM). Codas 25:191−192. DOI:10.1590/s2317-17822013000200017 View in Article CrossRef Google Scholar
[26]	Smarr K. L. and Keefer A. L. (2011). Measures of depression and depressive symptoms: Beck depression inventory-II (BDI-II), center for epidemiologic studies depression scale (CES-D), geriatric depression scale (GDS), hospital anxiety and depression scale (HADS), and patient health questionnaire-9 (PHQ-9). Arthritis Care Res. (Hoboken) 63:S454−466. DOI:10.1002/acr.20556 View in Article CrossRef Google Scholar
[27]	Si Y., Jiang L., Tao Q., et al. (2019). Predicting individual decision-making responses based on the functional connectivity of resting-state EEG. J. Neural Eng. 16:066025. DOI:10.1088/1741-2552/ab39ce View in Article CrossRef Google Scholar
[28]	Nolte G., Bai O., Wheaton L., et al. (2004). Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin. Neurophysiol 115:2292−2307. DOI:10.1016/j.clinph.2004.04.029 View in Article CrossRef Google Scholar
[29]	Bouchard M., Lina J. M., Gaudreault P. O., et al. (2020). EEG connectivity across sleep cycles and age. Sleep 43. DOI:10.1093/sleep/zsz236 View in Article Google Scholar
[30]	Xia M., Wang J. and He Y. (2013). BrainNet viewer: A network visualization tool for human brain connectomics. PLoS One 8:e68910. DOI:10.1371/journal.pone.0068910 View in Article CrossRef Google Scholar
[31]	Britz J., Van De Ville D. and Michel C. M. (2010). BOLD correlates of EEG topography reveal rapid resting-state network dynamics. Neuroimage 52:1162−1170. DOI:10.1016/j.neuroimage.2010.02.052 View in Article CrossRef Google Scholar
[32]	Damborská A., Tomescu M. I., Honzírková E., et al. (2019). EEG resting-state large-scale brain network dynamics are related to depressive symptoms. Front. Psychiatry 10:548. DOI:10.3389/fpsyt.2019.00548 View in Article CrossRef Google Scholar
[33]	Northoff G. and Hayes D. J. (2011). Is our self nothing but reward. Biol. Psychiatry 69:1019−1025. DOI:10.1016/j.biopsych.2010.12.014 View in Article CrossRef Google Scholar
[34]	Disner S. G., Beevers C. G., Haigh E. A., et al. (2011). Neural mechanisms of the cognitive model of depression. Nat. Rev. Neurosci. 12:467−477. DOI:10.1038/nrn3027 View in Article CrossRef Google Scholar
[35]	Naghavi H. R. and Nyberg L. (2005). Common fronto-parietal activity in attention, memory, and consciousness: Shared demands on integration. Conscious Cogn. 14:390−425. DOI:10.1016/j.concog.2004.10.003 View in Article CrossRef Google Scholar
[36]	Lei L., Liu Z., Zhang Y., et al. (2022). EEG microstates as markers of major depressive disorder and predictors of response to SSRIs therapy. Prog. Neuropsychopharmacol Biol. Psychiatry 116:110514. DOI:10.1016/j.pnpbp.2022.110514 View in Article CrossRef Google Scholar
[37]	Zhou Y., Gong L., Yang Y., et al. (2023). Spatio-temporal dynamics of resting-state brain networks are associated with migraine disability. J. Headache Pain 24:13. DOI:10.1186/s10194-023-01551-y View in Article CrossRef Google Scholar
[38]	Chatterjee A. and Vartanian O. (2014). Neuroaesthetics. Trends Cogn. Sci. 18:370−375. DOI:10.1016/j.tics.2014.03.003 View in Article CrossRef Google Scholar
[39]	Rolls E. T. (2016). A non-reward attractor theory of depression. Neurosci. Biobehav. Rev. 68:47−58. DOI:10.1016/j.neubiorev.2016.05.007 View in Article CrossRef Google Scholar
[40]	Carballedo A., Scheuerecker J., Meisenzahl E., et al. (2011). Functional connectivity of emotional processing in depression. J. Affect. Disord. 134:272−279. DOI:10.1016/j.jad.2011.06.021 View in Article CrossRef Google Scholar
[41]	Gotlib I. H. and Joormann J. (2010). Cognition and depression: Current status and future directions. Annu. Rev. Clin. Psychol. 6:285−312. DOI:10.1146/annurev.clinpsy.121208.131305 View in Article CrossRef Google Scholar
[42]	Copeland J. R. M. (1970). Depression: Clinical, experimental and theoretical aspects. By Aaron T. Beck. London: Staples Press. 1969. Pp. 370. Price 65s. British Journal of Psychiatry 116:562-563. DOI:10.1192/bjp.116.534.562 View in Article Google Scholar
[43]	Davidson R. J., Lewis D. A., Alloy L. B., et al. (2002). Neural and behavioral substrates of mood and mood regulation. Biol. Psychiatry 52:478−502. DOI:10.1016/s0006-3223(02)01458-0 View in Article CrossRef Google Scholar
[44]	Mayberg H. S., Lozano A. M., Voon V., et al. (2005). Deep brain stimulation for treatment-resistant depression. Neuron 45:651−660. DOI:10.1016/j.neuron.2005.02.014 View in Article CrossRef Google Scholar
[45]	Greicius M. D., Flores B. H., Menon V., et al. (2007). Resting-state functional connectivity in major depression: Abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol. Psychiatry 62:429−437. DOI:10.1016/j.biopsych.2006.09.020 View in Article CrossRef Google Scholar
[46]	Whitfield-Gabrieli S. and Ford J. M. (2012). Default mode network activity and connectivity in psychopathology. Annu. Rev. Clin. Psychol. 8:49−76. DOI:10.1146/annurev-clinpsy-032511-143049 View in Article CrossRef Google Scholar
[47]	Rock P. L., Roiser J. P., Riedel W. J., et al. (2014). Cognitive impairment in depression: A systematic review and meta-analysis. Psychological Medicine 44:2029−2040. DOI:10.1017/S0033291713002535 View in Article CrossRef Google Scholar
[48]	Foti D., Kotov R., Bromet E., et al. (2012). Beyond the broken error-related negativity: Functional and diagnostic correlates of error processing in psychosis. Biol. Psychiatry 71:864−872. DOI:10.1016/j.biopsych.2012.01.007 View in Article CrossRef Google Scholar
[49]	Chatterjee A. (2003). Prospects for a cognitive neuroscience of visual aesthetics. Bull. Psychol. Arts 4:55−60. DOI:10.1037/e514602010-003 View in Article CrossRef Google Scholar
[50]	Cela-Conde C. J., García-Prieto J., Ramasco J. J., et al. (2013). Dynamics of brain networks in the aesthetic appreciation. Proc. Natl. Acad. Sci. USA 110:10454−10461. DOI:10.1073/pnas.1302855110 View in Article CrossRef Google Scholar
[51]	Cela-Conde C. J., Agnati L., Huston J. P., et al. (2011). The neural foundations of aesthetic appreciation. Prog. Neurobiol. 94:39−48. DOI:10.1016/j.pneurobio.2011.03.003 View in Article CrossRef Google Scholar
[52]	Orgo L., Bachmann M., Kalev K., et al. (2017). Resting EEG functional connectivity and graph theoretical measures for discrimination of depression. 2017 IEEE EMBS international conference on biomedical & health informatics (BHI). DOI:10.1109/BHI.2017.7897287 View in Article Google Scholar
[53]	Sheline Y. I., Price J. L., Yan Z., et al. (2010). Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proc. Natl. Acad. Sci. USA 107:11020−11025. DOI:10.1073/pnas.1000446107 View in Article CrossRef Google Scholar

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Ma X., Ye J., Ding Y., et al. (2026). Event-related EEG microstate complexity reveals specific brain network connectivity during aesthetic perception in depression. The Innovation Life 4:100192. https://doi.org/10.59717/j.xinn-life.2026.100192

Ma X., Ye J., Ding Y., et al. (2026). Event-related EEG microstate complexity reveals specific brain network connectivity during aesthetic perception in depression. The Innovation Life 4:100192. https://doi.org/10.59717/j.xinn-life.2026.100192

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Event-related EEG microstate complexity reveals specific brain network connectivity during aesthetic perception in depression

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Event-related EEG microstate complexity reveals specific brain network connectivity during aesthetic perception in depression

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