Person: Tong, Yunjie
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Tong
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Yunjie
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Tong, Yunjie
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Publication Studying the Spatial Distribution of Physiological Effects on BOLD Signals Using Ultrafast fMRI(Frontiers Media S.A., 2014) Tong, Yunjie; Frederick, BlaiseThe blood-oxygen-level dependent (BOLD) signal in functional MRI (fMRI) reflects both neuronal activations and global physiological fluctuations. These physiological fluctuations can be attributed to physiological low frequency oscillations (pLFOs), respiration, and cardiac pulsation. With typical TR values, i.e., 2 s or longer, the high frequency physiological signals (i.e., from respiration and cardiac pulsation) are aliased into the low frequency band, making it hard to study the individual effect of these physiological processes on BOLD. Recently developed multiband EPI sequences, which offer full brain coverage with extremely short TR values (400 ms or less) allow these physiological signals to be spectrally separated. In this study, we applied multiband resting state scans on nine healthy participants with TR = 0.4 s. The spatial distribution of each physiological process on BOLD fMRI was explored using their spectral features and independent component analysis (ICA). We found that the spatial distributions of different physiological processes are distinct. First, cardiac pulsation affects mostly the base of the brain, where high density of arteries exists. Second, respiration affects prefrontal and occipital areas, suggesting the motion associated with breathing might contribute to the noise. Finally, and most importantly, we found that the effects of pLFOs dominated many prominent ICA components, which suggests that, contrary to the popular belief that aliased cardiac and respiration signals are the main physiological noise source in BOLD fMRI, pLFOs may be the most influential physiological signals. Understanding and measuring these pLFOs are important for denoising and accurately modeling BOLD signals.Publication Correcting for Blood Arrival Time in Global Mean Regression Enhances Functional Connectivity Analysis of Resting State fMRI-BOLD Signals(Frontiers Media S.A., 2016) Erdoğan, Sinem B.; Tong, Yunjie; Hocke, Lia M.; Lindsey, Kimberly P.; deB Frederick, BlaiseResting state functional connectivity analysis is a widely used method for mapping intrinsic functional organization of the brain. Global signal regression (GSR) is commonly employed for removing systemic global variance from resting state BOLD-fMRI data; however, recent studies have demonstrated that GSR may introduce spurious negative correlations within and between functional networks, calling into question the meaning of anticorrelations reported between some networks. In the present study, we propose that global signal from resting state fMRI is composed primarily of systemic low frequency oscillations (sLFOs) that propagate with cerebral blood circulation throughout the brain. We introduce a novel systemic noise removal strategy for resting state fMRI data, “dynamic global signal regression” (dGSR), which applies a voxel-specific optimal time delay to the global signal prior to regression from voxel-wise time series. We test our hypothesis on two functional systems that are suggested to be intrinsically organized into anticorrelated networks: the default mode network (DMN) and task positive network (TPN). We evaluate the efficacy of dGSR and compare its performance with the conventional “static” global regression (sGSR) method in terms of (i) explaining systemic variance in the data and (ii) enhancing specificity and sensitivity of functional connectivity measures. dGSR increases the amount of BOLD signal variance being modeled and removed relative to sGSR while reducing spurious negative correlations introduced in reference regions by sGSR, and attenuating inflated positive connectivity measures. We conclude that incorporating time delay information for sLFOs into global noise removal strategies is of crucial importance for optimal noise removal from resting state functional connectivity maps.Publication Systemic Low-Frequency Oscillations in BOLD Signal Vary with Tissue Type(Frontiers Media S.A., 2016) Tong, Yunjie; Hocke, Lia M.; Lindsey, Kimberly P.; Erdoğan, Sinem B.; Vitaliano, Gordana; Caine, Carolyn E.; Frederick, BlaiseBlood-oxygen-level dependent (BOLD) signals are widely used in functional magnetic resonance imaging (fMRI) as a proxy measure of brain activation. However, because these signals are blood-related, they are also influenced by other physiological processes. This is especially true in resting state fMRI, during which no experimental stimulation occurs. Previous studies have found that the amplitude of resting state BOLD is closely related to regional vascular density. In this study, we investigated how some of the temporal fluctuations of the BOLD signal also possibly relate to regional vascular density. We began by identifying the blood-bound systemic low-frequency oscillation (sLFO). We then assessed the distribution of all voxels based on their correlations with this sLFO. We found that sLFO signals are widely present in resting state BOLD signals and that the proportion of these sLFOs in each voxel correlates with different tissue types, which vary significantly in underlying vascular density. These results deepen our understanding of the BOLD signal and suggest new imaging biomarkers based on fMRI data, such as amplitude of low-frequency fluctuation (ALFF) and sLFO, a combination of both, for assessing vascular density.Publication Can apparent resting state connectivity arise from systemic fluctuations?(Frontiers Media S.A., 2015) Tong, Yunjie; Hocke, Lia M.; Fan, Xiaoying; Janes, Amy; Frederick, BlaiseIt is widely accepted that the fluctuations in resting state blood oxygenation level dependent (BOLD) functional MRI (fMRI) reflect baseline neuronal activation through neurovascular coupling; this data is used to infer functional connectivity in the human brain during rest. Consistent activation patterns, i.e., resting state networks (RSN) are seen across groups, conditions, and even species. In this study, we show that some of these patterns can also be generated from the dynamic, systemic, non-neuronal physiological low frequency oscillations (sLFOs) in the BOLD signal alone. We have previously used multimodal imaging to demonstrate the wide presence of the same sLFOs in the brain (BOLD) and periphery with different time delays. This study shows that these sLFOs from BOLD signals alone can give rise to stable spatial patterns, which can be detected during resting state analyses. We generated synthetic resting state data for 11 subjects based only on subject-specific, dynamic sLFO information obtained from resting state data using concurrent peripheral optical imaging or a novel recursive procedure. We compared the results obtained by performing a group independent component analysis (ICA) on this synthetic data (i.e., the result from simulation) to the results obtained from analysis of the real data. ICA detected most of the eight well-known RSNs, including visual, motor, and default mode networks (DMNs), in both the real and the synthetic data sets. These findings suggest that RSNs may reflect, to some extent, vascular anatomy associated with systemic fluctuations, rather than neuronal connectivity.