FNIRS based study of brain network characteristics in children with cerebral palsy during bilateral lower limb movement

• Published in: Medical physics (Lancaster) 2024-06, Vol.51 (6), p.4434-4446
• Main Authors: Xie, Ping, Nie, Zichao, Zhang, Tengyu, Xu, Gongcheng, Sun, Aiping, Chen, Tiandi, Lv, Yan
• Format: Article
• Language: English
• Subjects: Brain - diagnostic imaging; Brain - physiopathology; brain networks; cerebral palsy; Cerebral Palsy - diagnostic imaging; Cerebral Palsy - physiopathology; Child; Female; fNIRS; functional connectivity; Granger causality; Humans; Lower Extremity - diagnostic imaging; Lower Extremity - physiopathology; Male; Movement; Nerve Net - diagnostic imaging; Nerve Net - physiopathology; Spectroscopy, Near-Infrared - methods
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1002/mp.17106

Background Motor dysfunctions in children with cerebral palsy (CP) are caused by nonprogressive brain damage. Understanding the functional characteristics of the brain is important for rehabilitation. Purpose This paper aimed to study the brain networks of children with CP during bilateral lower limb movement using functional near‐infrared spectroscopy (fNIRS) and to explore effective fNIRS indices for reflecting functional brain activity. Methods Using fNIRS, cerebral oxygenation signals in the bilateral prefrontal cortex (LPFC/RPFC) and motor cortex (LMC/RMC) were recorded from fifteen children with spastic CP and seventeen children with typical development (CTDs) in the resting state and during bilateral lower limb movement. Functional connectivity matrices based on phase‐locking values (PLVs) were calculated using Hilbert transformation, and binary networks were constructed at different sparsity levels. Network metrics such as the clustering coefficient, global efficiency, local efficiency, and transitivity were calculated. Furthermore, the time‐varying curves of network metrics during movement were obtained by dividing the time window and using sparse inverse covariance matrices. Finally, conditional Granger causality (GC) was used to explore the causal relationships between different brain regions. Results Compared to CTDs, the connectivity between RMC‐RPFC (p = 0.017) and RMC‐LMC (p = 0.002) in the brain network was decreased in children with CP, and the clustering coefficient (p = 0.003), global efficiency (p = 0.034), local efficiency (p = 0.015), and transitivity (p = 0.009) were significantly lower. The standard deviation of the changes in global efficiency of children with CP during motion was also greater than that of CTDs. Using GC, it was found that there was a significant increase in causal strength from the RMC to the RPFC (p = 0.04) and from the RMC to the LMC (p = 0.042) in children with CP during motion. Additionally, there were significant negative correlations between the PLV of LMC‐RMC (p = 0.002) and the Gross Motor Function Classification System (GMFCS) and between the GMFCS and the clustering coefficient (p = 0.01). Conclusions During rehabilitation training of the lower limbs, there were significant differences in brain network indices between children with CP and CTDs. The indicators proposed in this paper are effective at evaluating motor function and the real‐time impact of rehabilitation training on the brain network and have