Echo Time Dependency of Local Activity Metrics of Resting-State Functional MRI

Frontiers in Neuroscience

Hongjian He¹  Na Zhao⁴  Xiu-Qin Wang⁵  Ya-Ting Lv⁵  Li-Xia Yuan⁵ 

[1] Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China;Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China;Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, China;Unit of Psychiatry, Faculty of Health Sciences, Center for Cognition and Brain Sciences, Institute of Translational Medicine, University of Macau, Macao, China;Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China;

关键词: echo time;    amplitude of low-frequency fluctuation;    fractional amplitude of low-frequency fluctuation;    regional homogeneity;    degree centrality;    resting-state fMRI;   

DOI  :  10.3389/fnins.2021.619412

来源: DOAJ

【 摘 要 】

Local activity metrics of resting-state functional MRI (RS-fMRI), such as the amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and degree centrality (DC), are widely used to detect brain abnormalities based on signal fluctuations. Although signal changes with echo time (TE) have been widely studied, the effect of TE on local activity metrics has not been investigated. RS-fMRI datasets from 12 healthy subjects with eyes open (EO) and eyes closed (EC) were obtained with a four-echo gradient-echo-planar imaging pulse sequence with the following parameters: repetition time/TE1/TE2/TE3/TE4 = 2,000/13/30.93/48.86/66.79 ms. Six representative regions were selected for simulating the spatial feature of TE dependency of local activity metrics. Moreover, whole-brain local activity metrics were calculated from each echo dataset and compared between EO and EC conditions. Dice overlap coefficient (DOC) was then employed to calculate the overlap between the T maps. We found that all the local activity metrics displayed different TE dependency characteristics, while their overall change patterns were similar: an initial large change followed by a slow variation. The T maps for local activity metrics also varied greatly with TE. For ALFF, fALFF, ReHo, and DC, the DOCs for voxels in four TE datasets were 6.87, 0.73, 5.08, and 0.93%, respectively. Collectively, these findings demonstrate that local metrics are greatly dependent on TE. Therefore, TE should be carefully considered for the optimization of data acquisition and multi-center data analysis in RS-fMRI.

【 授权许可】

Unknown