Fast detection and reduction of local transient artifacts in resting-state fMRI.
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ABSTRACT:
SUBMITTER: Jo HJ
PROVIDER: S-EPMC8056358 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Recent studies have illustrated that motion-related artifacts remain in resting-state fMRI (rs-fMRI) data even after common corrective processing procedures have been applied, but the extent to which head motion distorts the data may be modulated by the corrective approach taken. We compare two different methods for estimating nuisance signals from tissues not expected to exhibit BOLD fMRI signals of neuronal origin: 1) the more commonly used mean signal method and 2) the principal components analysis approach (aCompCor: Behzadi et al., 2007). Further, we investigate the added benefit of "scrubbing" (Power et al., 2012) following both methods. We demonstrate that the use of aCompCor removes motion artifacts more effectively than tissue-mean signal regression. In addition, inclusion of more components from anatomically defined regions of no interest better mitigates motion-related artifacts and improves the specificity of functional connectivity estimates. While scrubbing further attenuates motion-related artifacts when mean signals are used, scrubbing provides no additional benefit in terms of motion artifact reduction or connectivity specificity when using aCompCor.
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Project description:Resting-state functional magnetic resonance imaging (rs-fMRI) combined with optogenetics and electrophysiological/calcium recordings in animal models is becoming a popular platform to investigate brain dynamics under specific neurological states. Physiological noise originating from the cardiac and respiration signal is the dominant interference in human rs-fMRI and extensive efforts have been made to reduce these artifacts from the human data. In animal fMRI studies, physiological noise sources including the respiratory and cardiorespiratory artifacts to the rs-fMRI signal fluctuation have typically been less investigated. In this article, we demonstrate evidence of aliasing effects into the low-frequency rs-fMRI signal fluctuation mainly due to respiration-induced B0 offsets in anesthetized rats. This aliased signal was examined by systematically altering the fMRI sampling rate, i.e., the time of repetition (TR), in free-breathing conditions and by adjusting the rate of ventilation. Anesthetized rats under ventilation showed a significantly narrower frequency bandwidth of the aliasing effect than free-breathing animals. It was found that the aliasing effect could be further reduced in ventilated animals with a muscle relaxant. This work elucidates the respiration-related aliasing effects on the rs-fMRI signal fluctuation from anesthetized rats, indicating non-negligible physiological noise needed to be taken care of in both awake and anesthetized animal rs-fMRI studies.
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Project description:Background: Transient ischemic attack (TIA) is an important risk factor for stroke. Despite the transient episodes of clinical symptoms, brain alterations are still observed in patients with TIA. However, the functional mechanism of transient ischemia is still unclear. Here, we employed resting-state functional magnetic resonance imaging (rs-fMRI) to explore the functional abnormalities in patients with TIA. Methods: 48 TIA patients and 41 age- and sex-matched healthy controls (HCs) were enrolled in the study. For each participant, we collected rs-fMRI data and clinical/physiological/biochemical data. Amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) were then calculated. Two sample t-tests were performed to compare the ALFF, ReHo, and DC maps between the two groups. Furthermore, a correlation analysis was performed to explore the relationship between local brain abnormalities and clinical/physiological/biochemical characteristics tests in TIA patients. Results: Compared with the HCs, the TIA patients exhibited decreased ALFF in the left middle temporal gyrus, decreased DC in the triangular part of right inferior frontal gyrus, and no significant statistical difference in ReHo. No correlation was found between local abnormalities and clinical/physiological/biochemical scores in the patients with TIA. Conclusion: Collectively, we found decreased ALFF and DC in patients with TIA which provide evidence for local brain dysfunctions and may help to understand the pathological mechanism for the disease.
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Project description:Artifactual sources of resting-state (RS) FMRI can originate from head motion, physiology, and hardware. Of these sources, motion has received considerable attention and was found to induce corrupting effects by differentially biasing correlations between regions depending on their distance. Numerous corrective approaches have relied on the identification and censoring of high-motion time points and the use of the brain-wide average time series as a nuisance regressor to which the data are orthogonalized (Global Signal Regression, GSReg). We first replicate the previously reported head-motion bias on correlation coefficients using data generously contributed by Power et al. (2012). We then show that while motion can be the source of artifact in correlations, the distance-dependent bias-taken to be a manifestation of the motion effect on correlation-is exacerbated by the use of GSReg. Put differently, correlation estimates obtained after GSReg are more susceptible to the presence of motion and by extension to the levels of censoring. More generally, the effect of motion on correlation estimates depends on the preprocessing steps leading to the correlation estimate, with certain approaches performing markedly worse than others. For this purpose, we consider various models for RS FMRI preprocessing and show that WMeLOCAL, as subset of the ANATICOR discussed by Jo et al. (2010), denoising approach results in minimal sensitivity to motion and reduces by extension the dependence of correlation results on censoring.
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Project description:Functional magnetic resonance imaging (fMRI) has proven to be a powerful tool for noninvasively measuring human brain activity; yet, thus far, fMRI has been relatively limited in its temporal resolution. A key challenge is understanding the relationship between neural activity and the blood-oxygenation-level-dependent (BOLD) signal obtained from fMRI, generally modeled by the hemodynamic response function (HRF). The timing of the HRF varies across the brain and individuals, confounding our ability to make inferences about the timing of the underlying neural processes. Here, we show that resting-state fMRI signals contain information about HRF temporal dynamics that can be leveraged to understand and characterize variations in HRF timing across both cortical and subcortical regions. We found that the frequency spectrum of resting-state fMRI signals significantly differs between voxels with fast versus slow HRFs in human visual cortex. These spectral differences extended to subcortex as well, revealing significantly faster hemodynamic timing in the lateral geniculate nucleus of the thalamus. Ultimately, our results demonstrate that the temporal properties of the HRF impact the spectral content of resting-state fMRI signals and enable voxel-wise characterization of relative hemodynamic response timing. Furthermore, our results show that caution should be used in studies of resting-state fMRI spectral properties, because differences in fMRI frequency content can arise from purely vascular origins. This finding provides new insight into the temporal properties of fMRI signals across voxels, which is crucial for accurate fMRI analyses, and enhances the ability of fast fMRI to identify and track fast neural dynamics.
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Project description:Current studies of resting-state connectivity rely on coherent signal fluctuations at frequencies below 0.1 Hz, however, recent studies using high-speed fMRI have shown that fluctuations above 0.5 Hz may exist. This study replicates the feasibility of measuring high frequency (HF) correlations in six healthy controls and a patient with a brain tumor while analyzing non-physiological signal sources via simulation. Resting-state data were acquired using a high-speed multi-slab echo-volumar imaging pulse sequence with 136 ms temporal resolution. Bandpass frequency filtering in combination with sliding window seed-based connectivity analysis using running mean of the correlation maps was employed to map HF correlations up to 3.7 Hz. Computer simulations of Rician noise and the underlying point spread function were analyzed to estimate baseline spatial autocorrelation levels in four major networks (auditory, sensorimotor, visual, and default-mode). Using seed regions based on Brodmann areas, the auditory and default-mode networks were observed to have significant frequency band dependent HF correlations above baseline spatial autocorrelation levels. Correlations in the sensorimotor network were at trend level. The auditory network was still observed using a unilateral single voxel seed. In the patient, HF auditory correlations showed a spatial displacement near the tumor consistent with the displacement seen at low frequencies. In conclusion, our data suggest that HF connectivity in the human brain may be observable with high-speed fMRI, however, the detection sensitivity may depend on the network observed, data acquisition technique, and analysis method.
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Project description:We present a technique for predicting cardiac and respiratory phase on a time point by time point basis, from fMRI image data. These predictions have utility in attempts to detrend effects of the physiological cycles from fMRI image data. We demonstrate the technique both in the case where it can be trained on a subject's own data, and when it cannot. The prediction scheme uses a multiclass support vector machine algorithm. Predictions are demonstrated to have a close fit to recorded physiological phase, with median Pearson correlation scores between recorded and predicted values of 0.99 for the best case scenario (cardiac cycle trained on a subject's own data) down to 0.83 for the worst case scenario (respiratory predictions trained on group data), as compared to random chance correlation score of 0.70. When predictions were used with RETROICOR--a popular physiological noise removal tool--the effects are compared to using recorded phase values. Using Fourier transforms and seed based correlation analysis, RETROICOR is shown to produce similar effects whether recorded physiological phase values are used, or they are predicted using this technique. This was seen by similar levels of noise reduction noise in the same regions of the Fourier spectra, and changes in seed based correlation scores in similar regions of the brain. This technique has a use in situations where data from direct monitoring of the cardiac and respiratory cycles are incomplete or absent, but researchers still wish to reduce this source of noise in the image data.
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Project description:Epilepsy is a disease characterized by abnormal spontaneous activity in the brain. Resting-state functional magnetic resonance imaging (RS-fMRI) is a powerful technique for exploring this activity. With good spatial and temporal resolution, RS-fMRI is a promising approach for accurate localization of the focus of seizure activity. Although simultaneous electroencephalogram-fMRI has been performed with patients in the resting state, most studies focused on activation. This mini-review focuses on RS-fMRI alone, including its computational methods and its application to epilepsy.
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Project description:The human brain at rest exhibits intrinsic dynamics transitioning among the multiple metastable states of the inter-regional functional connectivity. Accordingly, the demand for exploring the state-specific functional connectivity increases for a deeper understanding of mental diseases. Functional connectivity, however, lacks information about the directed causal influences among the brain regions, called effective connectivity. This study presents the dynamic causal modeling (DCM) framework to explore the state-dependent effective connectivity using spectral DCM for the resting-state functional MRI (rsfMRI). We established the sequence of brain states using the hidden Markov model with the multivariate autoregressive coefficients of rsfMRI, summarizing the functional connectivity. We decomposed the state-dependent effective connectivity using a parametric empirical Bayes scheme that models the effective connectivity of consecutive windows with the time course of the discrete states as regressors. We showed the plausibility of the state-dependent effective connectivity analysis in a simulation setting. To test the clinical applicability, we applied the proposed method to characterize the state- and subtype-dependent effective connectivity of the default mode network in children with combined-type attention deficit hyperactivity disorder (ADHD-C) compared with age-matched, typically developed children (TDC). All 88 children were subtyped according to the occupation times (i.e., dwell times) of the three dominant functional connectivity states, independently of clinical diagnosis. The state-dependent effective connectivity differences between ADHD-C and TDC according to the subtypes and those between the subtypes of ADHD-C were expressed mainly in self-inhibition, magnifying the importance of excitation inhibition balance in the subtyping. These findings provide a clear motivation for decomposing the state-dependent dynamic effective connectivity and state-dependent analysis of the directed coupling in exploring mental diseases.
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Project description:Spontaneous fluctuations in activity in different parts of the brain can be used to study functional brain networks. We review the use of resting-state functional MRI (rfMRI) for the purpose of mapping the macroscopic functional connectome. After describing MRI acquisition and image-processing methods commonly used to generate data in a form amenable to connectomics network analysis, we discuss different approaches for estimating network structure from that data. Finally, we describe new possibilities resulting from the high-quality rfMRI data being generated by the Human Connectome Project and highlight some upcoming challenges in functional connectomics.
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