Characterizing Static and Dynamic Fractional Amplitude of Low-Frequency Fluctuation and its Prediction of Clinical Dysfunction in Patients with Diffuse Axonal Injury

• Published in: Academic radiology Vol. 28; no. 3; p. e63
• Main Authors: Zhou, Fuqing, Zhan, Jie, Gong, Tao, Xu, Wenhua, Kuang, Hongmei, Li, Jian, Wang, Yinhua, Gong, Honghan
• Format: Journal Article
• Language: English
• Published: United States 01.03.2021
• Subjects: Artificial Intelligence; Brain; Brain Mapping; Diffuse Axonal Injury - diagnostic imaging; Humans; Magnetic Resonance Imaging; Diffuse axonal injury; Fractional amplitude of low-frequency fluctuation; Temporal variability
• ISSN: 1878-4046
• DOI: 10.1016/j.acra.2020.02.020

Recently, advanced magnetic resonance imaging has been widely adopted to investigate altered structure and functional activities in patients with diffuse axonal injury (DAI), this patient presumed to be caused by shearing forces and results in significant neurological effects. However, little is known regarding cerebral temporal dynamics and its predictive ability in the clinical dysfunction of DAI. In this study, static and dynamic fractional amplitude of low-frequency fluctuation (fALFF), an improved approach to detect the intensity of intrinsic neural activities, and their temporal variability were applied to examine the alteration between DAI patients (n = 24) and healthy controls (n = 26) at the voxel level. Then, the altered functional index was used to explore the clinical relationship and predict dysfunction in DAI patients. We discovered that, compared to healthy controls, DAI patients showed commonly altered regions of static fALFF, and its variability was mainly located in the left cerebellum posterior lobe. Furthermore, decreased static fALFF values over the left cerebellum posterior lobe and bilateral medial frontal gyrus showed significant correlations with disease duration and Mini-Mental State Examination scores. More important, the increased temporal variability of dynamic fALFF in the left caudate could predict the severity of the Glasgow Coma Scale score in DAI patients. Overall, these results suggested selective abnormalities in intrinsic neural activities with reduced intensity and increased variability, and this novel predictive marker may be developed as a useful indicator for future connectomics or artificial intelligence analyses.