Data Analysis Of Magnetic Resonance Functional Imaging Based On Hemodynamics And Echo-planar Technique

Functional imaging from MRI(magnetic resonance imaging)provides tissue function information complementary to anatomical imaging and plays an essential role in the diagnosis of diseases,evaluation of treatment effects,and neuropsychological research.In this study,we focused on two fast MRI functional imaging based on hemodynamics and echo planar imaging,namely dynamic susceptibility contrast-enhanced MRI(DSC-MRI)and resting-state functional MRI(RS-fMRI).Then,we investigated the T1 effect contamination,reliability,and new method for calculating the parameter in the data analysis of DSC-MRI and RS-fMRI.DSC-MRI is a functional imaging technique and widely used in clinical.Recently,researches demonstrated that relative recirculation(rR)is a promising surrogate permeability biomarker derived from DSC-MRI.However,when the blood brain barrier is disrupted,the contrast agent leaks into the extravascular extracellular space,which leads to unignorable T1 effects.The T1 effects will further elevate the DSM-MRI signal.So far,the influence of T1 effects on rR and its correction have not been reported,yet.In this work,we simulated the influence of T1 effects on rR under different imaging conditions and proposed a method for correcting the T1 effects.With the proposed method,the obtained rR can be adjusted to a level in accordance with known vascular physiology.RS-fMRI is explosively applied to neuropsychological and clinical researches.However,due to low contrast-to-noise ratio and high variation among subjects,reliability of the data analysis method is rather important for RS-fMRI.Moreover,the intra-and inter-scanner reliability is an important metric for quantification of fMRI measurement reliability,given increasing research interest relying on the ability to combine the data from multiple scanners into larger,integrative data sets.Scaled subprofile model of principal component analysis(SSM-PCA)was one of the earliest and widely-used multivariate statistical method,proposed to analyze data from positron emission tomography(PET).Recently,SSM-PCA was applied to investigate Parkinson's disease-related covariance brain pattern with a RS-fMRI metric,namely amplitude of low frequency fluctuation(ALFF),founded the Parkinson's disease related pattern,and successfully differentiated the healthy controls and patients with the Parkinson's disease.So far,the intra-and inter-scanner reliability of SSM-PCA in RS-fMRI is not investigated systematically,yet.In this work,we demonstrated that both the intra-and inter-scanner reliability of SSM-PCA were fair to good for ALFF under eyes-open and eyes-closed conditions,the intra-class correlation coefficient(ICC)for intra-scanner reliability was 0.49 and ICC for inter-scanner reliability was 0.65?0.66,which suggested that SSM-PCA can be further applied to clinical studies.Moreover,SSM-PCA and conventional univariate T-test are complementary for neuroimaging studies.ALFF is a widely-used voxel-wise parameter from RS-fMRI.Commonly,ALFF is computed from T2*-weighted fMRI signals and affected by R2*fluctuation,echo time,spin density,and mean R2*.Multi-echo fMRI(ME-fMRI)acquires multiple images with different echo times in one repetition time.By fitting to a monoexponential decay function,quantitative R2 can be obtained from the multiple signals with different T2*weightings.To make ALFF more reliable and comparable between researches,in this work,we proposed a new method for calculating ALFF based on R2*(ALFF-R2)from ME-fMRI.Results showed that ALFF-R2*provided higher specificity than conventional ALFF in the eyes-open and eyes-closed dataset,which suggested that ALFF-R2*was potential for neuropsychiatric and clinical researches.