Abnormal characteristic static and dynamic functional network connectivity in idiopathic normal pressure hydrocephalus

• Published in: CNS neuroscience & therapeutics Vol. 30; no. 3; pp. e14178 - n/a
• Main Authors: Huang, Wenjun, Fang, Xuhao, Li, Shihong, Mao, Renling, Ye, Chuntao, Liu, Wei, Deng, Yao, Lin, Guangwu
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.03.2024; John Wiley and Sons Inc
• Subjects: Alzheimer's disease; Biomarkers; Clustering; Correlation analysis; Dementia disorders; Disease; dynamic; functional network connectivity; Gender; Hydrocephalus; idiopathic normal pressure hydrocephalus; independent component analysis; Movement disorders; Neural networks; Neurodegenerative diseases; Original; resting‐state functional magnetic resonance imaging; Software; static; Surgery; Time series; resting-state functional magnetic resonance imaging; independent component analysis; dynamic; static; functional network connectivity; idiopathic normal pressure hydrocephalus
• ISSN: 1755-5930; 1755-5949; 1755-5949
• DOI: 10.1111/cns.14178

Aims Idiopathic Normal pressure hydrocephalus (iNPH) is a neurodegenerative disease characterized by gait disturbance, dementia, and urinary dysfunction. The neural network mechanisms underlying this phenomenon is currently unknown. Methods To investigate the resting‐state functional connectivity (rs‐FC) abnormalities of iNPH‐related brain connectivity from static and dynamic perspectives and the correlation of these abnormalities with clinical symptoms before and 3‐month after shunt. We investigated both static and dynamic functional network connectivity (sFNC and dFNC, respectively) in 33 iNPH patients and 23 healthy controls (HCs). Results The sFNC and dFNC of networks were generally decreased in iNPH patients. The reduction in sFNC within the default mode network (DMN) and between the somatomotor network (SMN) and visual network (VN) were related to symptoms. The temporal properties of dFNC and its temporal variability in state‐4 were sensitive to the identification of iNPH and were correlated with symptoms. The temporal variability in the dorsal attention network (DAN) increased, and the average instantaneous FC was altered among networks in iNPH. These features were partially associated with clinical symptoms. Conclusion The dFNC may be a more sensitive biomarker for altered network function in iNPH, providing us with extra information on the mechanisms of iNPH. The current study is the first study in iNPH cohort to combine a dynamic analysis framework with classical sFNC to investigate changes in brain connectivity from both static and dynamic perspectives, providing extra information on the underlying mechanisms of iNPH.