Comparison of quantitative susceptibility mapping and blood-oxygen-level-dependent imaging in task-based functional MRI brain activation analysis

GUO Ying; LIN Liyuan; WANG Shumei; LI Wei; QIN Wen

doi:10.19300/j.2025.L21733

Abstract：Objective To compare the capability of functional quantitative susceptibility mapping(fQSM)and blood oxygen level dependent(BOLD)techniques in detecting brain activation during finger-tapping task functional MRI(fMRI).Methods Twenty-four healthy adult volunteers were prospectively recruited,and fMRI magnitude and phase images of the finger-tapping block task were synchronously acquired by a 3.0 T magnetic resonance imaging scanner.A general linear model(GLM)and independent component analysis(ICA)were used to derive task-evoked activation maps for BOLD and fQSM.Correlation analysis was used to investigate the similarity between time series of BOLD and fQSM fMRI activation components.Paired t-tests or permutation test were used to compare the dynamic range and fractional amplitudes of low-frequency fluctuations of the activation components between BOLD and fQSM.Results GLM showed that BOLD activation was mainly located in the bilateral precentral and postcentral gyri,cerebellar motor areas,while fQSM did not detect any statistically significant activation(voxel-level P＜0.001,cluster-level FWE corrected P＜0.05).Even at a lower threshold,activation detected by fQSM was substantially less than that detected by BOLD,and the time series of cerebellar motor areas showed a significant negative correlation between fQSM and BOLD.Head motion and the spatial smoothing levels had no significant effect on the activation intensity of either BOLD or fQSM.ICA with a fixed components(90)showed that BOLD could sensitively detect independent components closely related to the finger tapping task,whereas fQSM failed to identify task-related activation.The correlation between the time series of BOLD and fQSM activation components was weak,and the dynamic range and fractional amplitude of low frequeng fluctuation of BOLD were significantly higher than those of fQSM(P＜0.05,Bonferroni correction).Additionally,ICA with automatic component selection identified three fQSM activation components significantly associated with motor function,including the sensorimotor,medial prefrontal,and cerebellar motor networks,with the cerebellar motor network showing a significant negative correlation with the BOLD sensorimotor network.Conclusion The fQSM technique is less sensitive than the conventional BOLD technique in detecting brain activation related to finger movements.ICA with automated component selection may help improve the detectability of fQSM brain activation.