ArticleBiomedical engineering online2026
Resting-state brain network alterations in adolescent idiopathic scoliosis using functional near-infrared spectroscopy.
Article in Biomedical engineering online, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAdolescent Idiopathic Scoliosis (AIS) is a common spinal deformity arising during adolescence, a critical period for brain development. Although most AIS analyses predominantly focus on spinal or behavioral aspects, it remains unclear what brain functional alteration is associated with AIS. This gap in knowledge impedes a holistic understanding of the condition's pathophysiology. In this study, functional near-infrared spectroscopy (fNIRS) was used to explore the resting-state brain network in AIS.
methodsThe study recruited 25 AIS patients and 25 age-matched healthy controls and measured their brain activities during resting-state lying using fNIRS. Brain functional connectivity and network topology were evaluated for the two groups and their correlations with demographic and pathological variables were examined.
resultsThe functional connectivity in the patients, particularly single-curve patients, decreased and was sparser in the parietal and prefrontal regions in comparison to the healthy controls. The regional nodal metrics in the patients were significantly altered, with smaller nodal degrees and efficiency observed in certain nodes. Among the affected regions, the dorsolateral prefrontal cortex emerged repeatedly as a hub showing altered connectivity in patients, particularly in relation to parietal and sensorimotor regions. In addition, different correlations between brain network metrics and demographic as well as pathological parameters were identified within patients. For instance, a larger primary Cobb angle was associated with poorer small-world properties, suggesting greater structural deformity may be linked to less efficient functional network organization. Furthermore, a support vector machine-based classification model using functional connectivity achieved an average accuracy of 81.0%, demonstrating that fNIRS-based network features are sensitive to group-level neurofunctional differences. DISCUSSION: These findings provide insights into how scoliosis, including curvature, physical parameters, and pathological conditions, affects AIS patients in shaping abnormal brain network organization. Such network-based approaches may facilitate future studies aimed at assessing neurofunctional involvement associated with AIS rehabilitation interventions.
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