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Nonlinear disruption of integration-segregation balance in task-based fMRI brain networks of depression

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  • Ma, Wenzheng
  • Wang, Yu
  • Ma, Ningxin
  • Xiao, Hongbing

Abstract

Depression is often accompanied by impairments in reward-punishment feedback processing, yet the underlying complex-system mechanisms of brain functional networks remain unclear. Complex network theory provides a novel paradigm for characterizing the dynamics of integration and segregation in brain networks and for uncovering nonlinear topological alterations in psychiatric disorders. In this study, we constructed brain functional networks of patients with depression and healthy controls based on task-related fMRI data from a gambling reward-punishment paradigm. Using graph-theoretical and complex network approaches, we evaluated key topological measures, including global efficiency, local efficiency, and small-world properties, and further examined the coupling between network topology and behavioral reaction times. Individuals with depression exhibited significantly reduced network integration capacity, manifested by decreased global and local efficiency, which progressively worsened with clinical severity. In contrast, small-world properties in certain regions showed relative enhancement, which may reflect local reorganization or compensatory tendencies within the network rather than a definitive adaptive mechanism. Moreover, the degree of topological disruption was negatively correlated with reaction time, revealing a coupling between cognitive slowing and information transfer efficiency in complex networks. Overall, depression in reward-punishment contexts is characterized by a nonlinear disruption of the integration-segregation balance of brain networks, with impaired global transmission efficiency and adaptive modulation of local modularity and small-world organization. The results not only delineate the multilayered abnormalities of brain networks in depression but also indicate that complex network theory may provide novel topological biomarkers for the diagnosis and prediction of psychiatric disorders.

Suggested Citation

  • Ma, Wenzheng & Wang, Yu & Ma, Ningxin & Xiao, Hongbing, 2026. "Nonlinear disruption of integration-segregation balance in task-based fMRI brain networks of depression," Chaos, Solitons & Fractals, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:chsofr:v:204:y:2026:i:c:s0960077925017461
    DOI: 10.1016/j.chaos.2025.117733
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    1. Kurkin, S.A. & Mayorova, L.A. & Khorev, V.S. & Pitsik, E.N. & Radutnaya, M.L. & Bondar, E.L. & Hramov, A.E., 2025. "Multiscale fMRI analysis reveals hierarchical network disruptions underlying disorders of consciousness," Chaos, Solitons & Fractals, Elsevier, vol. 200(P1).
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    3. Pitsik, Elena N. & Maximenko, Vladimir A. & Kurkin, Semen A. & Sergeev, Alexander P. & Stoyanov, Drozdstoy & Paunova, Rositsa & Kandilarova, Sevdalina & Simeonova, Denitsa & Hramov, Alexander E., 2023. "The topology of fMRI-based networks defines the performance of a graph neural network for the classification of patients with major depressive disorder," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    4. Khorev, Vladimir S. & Kurkin, Semen A. & Zlateva, Gabriella & Paunova, Rositsa & Kandilarova, Sevdalina & Maes, Michael & Stoyanov, Drozdstoy & Hramov, Alexander E., 2024. "Disruptions in segregation mechanisms in fMRI-based brain functional network predict the major depressive disorder condition," Chaos, Solitons & Fractals, Elsevier, vol. 188(C).
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