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Introduction: The Iranian Brain Imaging Database (IBID) was initiated in 2017, with 5 major goals: provide researchers easy access to a neuroimaging database, provide normative quantitative measures of the brain for clinical research purposes, study the aging profile of the brain, examine the association of brain structure and function, and join the ENIGMA consortium. Many prestigious databases with similar goals are available. However, they were not done on an Iranian population, and the battery of their tests (e.g. cognitive tests) is selected based on their specific questions and needs. Methods: The IBID will include 300 participants (50% female) in the age range of 20 to 70 years old, with an equal number of participants (#60) in each age decade. It comprises a battery of cognitive, lifestyle, medical, and mental health tests, in addition to several Magnetic Resonance Imaging (MRI) protocols. Each participant completes the assessments on two referral days. Results: The study currently has a cross-sectional design, but longitudinal assessments are considered for the future phases of the study. Here, details of the methodology and the initial results of assessing the first 152 participants of the study are provided. Conclusion: IBID is established to enable research into human brain function, to aid clinicians in disease diagnosis research, and also to unite the Iranian researchers with interests in the brain.
Motion correction is an essential step in the preprocessing of functional magnetic resonance imaging (fMRI) data, improving the temporal signal to noise ratio (tSNR) and removing unwanted variance. Because of the characteristics of the spinal cord (non-rigidity, surrounded by moving organs), motion correction becomes especially challenging. We compared the efficiency of different motion correction protocols and suggest a preferred method for spinal cord fMRI data. Here we acquired gradient-echo echo-planar-imaging axial lumbar spinal cord fMRI data during painful mechanical stimulation of the left lower extremity of 15 healthy volunteers on a 3T scanner. We compared multiple motion correction techniques: 2D and 3D FLIRT realignment with and without slice-wise regulation, SliceCorr (implemented in the Spinal Cord Toolbox) andproposed a method 3D FLIRT in addition to Slice Regulation (SLiceReg) along the spinal cord.TSNR, image entropy, DVARS, image Sum of Absolute Differences and number of activated voxels in the spinal cord from GLM analysis to evaluate the performance of multiple motion correction procedures. The tSNR and DVARS 3D FLIRT + SLiceReg were significantly improved over other realignment methods (p<0.001). In comparison, tSNR=14.20±0.02 and DVARS=165.77±1.54 were higher than other methods. Additionally, the number of activated voxels of the statistical map in our suggested method was higher than the other realignment methods (p<0.05). Our results illustrated the proposed motion correction algorithm that integrated 3D motion correction and 2D slicewise regularization along spinal cord curvature could improve subject-level processing outputs by reducing the effects of motions. Our proposed protocols can improve subject-level analysis, especially in lumbar region that suffers from involuntary motions and signal loss due to susceptibility effect more than other spinal cord regions.3
Introduction: Functional magnetic resonance imaging (fMRI) methods have been used to study sensorimotor processing in the spinal cord. However, these techniques confront unwanted noises to the measured signal from the physiological fluctuations. In the spinal cord imaging, most of the challenges are consequences of cardiac and respiratory movement artifacts that are considered as significant sources of noise, especially in the thoracolumbar region. In this study, we investigated the effect of each source of physiological noise and their contribution to the outcome of the analysis of the blood-oxygen-level-dependent signal in the human thoracolumbar spinal cord. Methods: Fifteen young healthy male volunteers participated in the study, and pain stimuli were delivered on the L5 dermatome between the two malleoli. Respiratory and cardiac signals were recorded during the imaging session, and the generated respiration and cardiac regressors were included in the general linear model for quantification of the effect of each of them on the task-analysis results. The sum of active voxels of the clusters was calculated in the spinal cord in three correction states (respiration correction only, cardiac correction only, and respiration and cardiac noise corrections) and analyzed with analysis of variance statistical test and receiver operating characteristic curve. Results: The results illustrated that cardiac noise correction had an effective role in increasing the active voxels (Mean±SD= 23.46±9.46) compared to other noise correction methods. Cardiac effects were higher than other physiological noise sources Conclusion: In summary, our results indicate great respiration effects on the lumbar and thoracolumbar spinal cord fMRI, and its contribution to the heartbeat effect can be a significant variable in the individual fMRI data analysis. Displacement of the spinal cord and the effects of this noise in the thoracolumbar and lumbar spinal cord fMRI results are significant and cannot be ignored.
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