摘要
在解析大脑的功能连接(functional connectivity, FC)时,血液动力学响应函数(hemodynamic response function, HRF)的差异性往往被忽视,然而该差异会影响到FC的计算,进而混淆统计结果的准确性。该研究以糖尿病患者为例,基于他们与健康人群的静息态功能性磁共振成像(resting-state fMRI, rs-fMRI)数据,对比研究了两者HRF参数(响应高度、响应时间、全幅半宽)的差异,并以具有显著差异的脑区作为种子点构建全脑功能连接,进行解卷积前后组内和组间连接强度差异性的统计分析。研究结果表明,在一些关键大脑区域(如brain64),糖尿病患者组的HRF参数与健康人群的统计结果存在显著差异,t检验统计结果显示FC连接强度在解卷积前后具有显著差异的结果不同,反映出HRF差异性影响了FC连接强度的计算,进一步可能影响到对于糖尿病脑网络变化和其内在神经机制的理解。研究结果说明在脑代谢性疾病中以血氧水平依赖(blood oxygen level dependent, BOLD)时间序列为基础进行FC等分析时,需充分考虑到HRF差异性对结果的潜在影响,并对BOLD时间序列进行解卷积,以校正HRF差异,这将有助于更精准地捕捉大脑功能连接的变化和特征,更深刻地反映疾病机制,并且部分解决fMRI研究结果的可重复性问题。
When analyzing brain functional connectivity(FC),differences in the hemodynamic responsefunction(HRF)are often overlooked.However,these differences can affect the calculation of FC and mayconfound the accuracy of statistical results.This study uses diabetic patients as an example to compare HRFparameters(response amplitude,response time,and full width at half maximum)between diabetic patients andhealthy individuals based on their resting-state functional magnetic resonance imaging(rs-fMRI)data.Significantdifferences in HRF parameters were identified in specific brain regions,which were then used as seed points toconstruct whole-brain functional connectivity.Statistical analysis was performed to compare the differences inconnectivity strength both before and after deconvolution,within and between groups.The results indicate that insome key brain regions(such as brain64),significant differences in HRF parameters exist between the diabeticpatient group and the healthy population.T-test results show that FC connectivity strength exhibits differentsignificant patterns before and after deconvolution,reflecting that variability in HRF affects the calculation of FCconnectivity strength.This,in turn,may influence the understanding of changes in the brain network of individualswith diabetes and its underlying neural mechanisms.The findings suggest that when conducting FC analyses basedon blood oxygen level-dependent(BOLD)time series in metabolic brain diseases,it is essential to fully consider the potential impact of HRF variability on the results and to perform deconvolution on the BOLD time series tocorrect for HRF differences.This will help to more accurately capture changes and characteristics of brainfunctional connectivity,provide deeper insights into disease mechanisms,and partially address reproducibilityissues in fMRI research.
作者
王晓燕
张俊然
罗昌宇
何美荣
周丽君
WANG Xiaoyan;ZHANG Junran;LUO Changyu;HE Meirong;ZHOU Lijun(School of Electrical Engineering,Sichuan University,Chengdu 610065,China;School of Electrical Engineering,Northwest Minzu University,Lanzhou 730070,China)
出处
《电子科技大学学报》
北大核心
2025年第2期294-304,共11页
Journal of University of Electronic Science and Technology of China
基金
国家自然科学基金民航联合重点项目(U2333209)
四川省科技计划项目(2022YFS0109)
甘肃省自然基金(22JR11RA232)。
作者简介
王晓燕,博士生,副教授,主要从事医学影像数据处理和分析方面的研究;通信作者:张俊然,E-mail:zhangjunran@126.com。
