A Quantitative Data-Driven Analysis Framework for Resting-State Functional Magnetic Resonance Imaging: A Study of the Impact of Adult Age

Frontiers in Neuroscience

Kristoffer N. T. Månsson¹  Tie-Qiang Li³  Amirhossein Manzouri⁴  Xia Li⁵  Håkan Fischer⁶ 

[1] Centre of Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden;Department of Clinical Science, Intervention, and Technology, Karolinska Institute, Solna, Sweden;Department of Medical Radiation and Nuclear Medicine, Karolinska University Hospital, Solna, Sweden;Department of Psychology, Stockholm University, Stockholm, Sweden;Institute of Informatics Engineering, China Jiliang University, Hangzhou, China;Stockholm University Brain Imaging Centre, Stockholm, Sweden;

关键词: quantitative data-driven analysis (QDA);    resting-state functional magnetic resonance imaging (R-fMRI);    resting-state functional connectivity (RFC);    connectivity strength index (CSI);    connectivity density index (CDI);    adult age;   

DOI  :  10.3389/fnins.2021.768418

来源: DOAJ

【 摘 要 】

The objective of this study is to introduce a new quantitative data-driven analysis (QDA) framework for the analysis of resting-state fMRI (R-fMRI) and use it to investigate the effect of adult age on resting-state functional connectivity (RFC). Whole-brain R-fMRI measurements were conducted on a 3T clinical MRI scanner in 227 healthy adult volunteers (N = 227, aged 18–76 years old, male/female = 99/128). With the proposed QDA framework we derived two types of voxel-wise RFC metrics: the connectivity strength index and connectivity density index utilizing the convolutions of the cross-correlation histogram with different kernels. Furthermore, we assessed the negative and positive portions of these metrics separately. With the QDA framework we found age-related declines of RFC metrics in the superior and middle frontal gyri, posterior cingulate cortex (PCC), right insula and inferior parietal lobule of the default mode network (DMN), which resembles previously reported results using other types of RFC data processing methods. Importantly, our new findings complement previously undocumented results in the following aspects: (1) the PCC and right insula are anti-correlated and tend to manifest simultaneously declines of both the negative and positive connectivity strength with subjects’ age; (2) separate assessment of the negative and positive RFC metrics provides enhanced sensitivity to the aging effect; and (3) the sensorimotor network depicts enhanced negative connectivity strength with the adult age. The proposed QDA framework can produce threshold-free and voxel-wise RFC metrics from R-fMRI data. The detected adult age effect is largely consistent with previously reported studies using different R-fMRI analysis approaches. Moreover, the separate assessment of the negative and positive contributions to the RFC metrics can enhance the RFC sensitivity and clarify some of the mixed results in the literature regarding to the DMN and sensorimotor network involvement in adult aging.

【 授权许可】

Unknown