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DOI 10.1038/s41598-026-52086-6

Медицинская библиотека · Scientific reports · 2026

Visual information modulates brain network characteristics during static balance following ACL reconstruction - A graph theoretical analysis.

Grinberg Adam, Lehmann Tim, Strandberg Johan, Cobani Gjergji, Häger Charlotte K

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Аннотация

Long-term balance impairments are prevalent after anterior cruciate ligament (ACL) injury and are possibly linked to an overreliance on visual information and related cortical processing. We therefore aimed to explore characteristics of functional brain networks related to postural control with and without vision following ACL reconstruction (ACLR). Twenty-seven individuals after ACLR and 24 non-injured controls performed single-leg balance tasks under eyes-open/eyes-closed conditions. Graph-theoretical measures of functional network segregation (clustering coefficient, CC) and integration (path length, PL) were derived from mobile electroencephalography. Sway characteristics were calculated based on centre of pressure (CoP; area and velocity) and the mean distance between CoP and centre of mass (CoM). Knee antero-posterior kinematics were also explored. Group effects were analysed using permutation-based ANCOVA. During eyes-open only, the ACLR group exhibited greater cortical network segregation (higher CC; p = 0.025) in the alpha-1 band (8-10 Hz). While sway characteristics were similar between groups, the ACLR leg demonstrated greater knee flexion compared to their contralateral leg (p = 0.036). Individuals post-ACLR showed more efficient functional brain connectivity during eyes-open, combined with kinematic adaptations in their injured leg. These findings suggest post-ACLR neural adaptations of postural control mechanisms, particularly when visual information is available.

Для цитирования: Grinberg Adam, Lehmann Tim, Strandberg Johan, Cobani Gjergji, Häger Charlotte K. Visual information modulates brain network characteristics during static balance following ACL reconstruction - A graph theoretical analysis.. Scientific reports. 2026. DOI: 10.1038/s41598-026-52086-6.

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