Decoding the brain state-dependent relationship between pupil dynamics and resting state fMRI signal fluctuation.
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ABSTRACT:
SUBMITTER: Sobczak F
PROVIDER: S-EPMC8460262 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The amplitude of low-frequency fluctuation (ALFF) measures resting-state functional magnetic resonance imaging (RS-fMRI) signal of each voxel. However, the unit of blood oxygenation level-dependent (BOLD) signal is arbitrary and hence ALFF is sensitive to the scale of raw signal. A well-accepted standardization procedure is to divide each voxel's ALFF by the global mean ALFF, named mALFF. Although fractional ALFF (fALFF), a ratio of the ALFF to the total amplitude within the full frequency band, offers possible solution of the standardization, it actually mixes with the fluctuation power within the full frequency band and thus cannot reveal the true amplitude characteristics of a given frequency band. The current study borrowed the percent signal change in task fMRI studies and proposed percent amplitude of fluctuation (PerAF) for RS-fMRI. We firstly applied PerAF and mPerAF (i.e., divided by global mean PerAF) to eyes open (EO) vs. eyes closed (EC) RS-fMRI data. PerAF and mPerAF yielded prominently difference between EO and EC, being well consistent with previous studies. We secondly performed test-retest reliability analysis and found that (PerAF ? mPerAF ? mALFF) > (fALFF ? mfALFF). Head motion regression (Friston-24) increased the reliability of PerAF, but decreased all other metrics (e.g. mPerAF, mALFF, fALFF, and mfALFF). The above results suggest that mPerAF is a valid, more reliable, more straightforward, and hence a promising metric for voxel-level RS-fMRI studies. Future study could use both PerAF and mPerAF metrics. For prompting future application of PerAF, we implemented PerAF in a new version of REST package named RESTplus.
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Project description:Pupillometry, a noninvasive measure of arousal, complements human functional MRI (fMRI) to detect periods of variable cognitive processing and identify networks that relate to particular attentional states. Even under anesthesia, pupil dynamics correlate with brain-state fluctuations, and extended dilations mark the transition to more arousable states. However, cross-scale neuronal activation patterns are seldom linked to brain state-dependent pupil dynamics. Here, we complemented resting-state fMRI in rats with cortical calcium recording (GCaMP-mediated) and pupillometry to tackle the linkage between brain-state changes and neural dynamics across different scales. This multimodal platform allowed us to identify a global brain network that covaried with pupil size, which served to generate an index indicative of the brain-state fluctuation during anesthesia. Besides, a specific correlation pattern was detected in the brainstem, at a location consistent with noradrenergic cell group 5 (A5), which appeared to be dependent on the coupling between different frequencies of cortical activity, possibly further indicating particular brain-state dynamics. The multimodal fMRI combining concurrent calcium recordings and pupillometry enables tracking brain state-dependent pupil dynamics and identifying unique cross-scale neuronal dynamic patterns under anesthesia.
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Project description:Most studies of resting-state functional connectivity using fMRI employ methods that assume temporal stationarity, such as correlation and data-driven decompositions computed across the duration of the scan. However, evidence from both task-based fMRI studies and animal electrophysiology suggests that functional connectivity may exhibit dynamic changes within time scales of seconds to minutes. In the present study, we investigated the dynamic behavior of resting-state connectivity across the course of a single scan, performing a time-frequency coherence analysis based on the wavelet transform. We focused on the connectivity of the posterior cingulate cortex (PCC), a primary node of the default-mode network, examining its relationship with both the "anticorrelated" ("task-positive") network as well as other nodes of the default-mode network. It was observed that coherence and phase between the PCC and the anticorrelated network was variable in time and frequency, and statistical testing based on Monte Carlo simulations revealed the presence of significant scale-dependent temporal variability. In addition, a sliding-window correlation procedure identified other regions across the brain that exhibited variable connectivity with the PCC across the scan, which included areas previously implicated in attention and salience processing. Although it is unclear whether the observed coherence and phase variability can be attributed to residual noise or modulation of cognitive state, the present results illustrate that resting-state functional connectivity is not static, and it may therefore prove valuable to consider measures of variability, in addition to average quantities, when characterizing resting-state networks.
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Project description:One of the most controversial practices in resting-state fMRI functional connectivity studies is whether or not to regress out the global average brain signal (GS) during artifact removal. Some groups have argued that it is absolutely essential to regress out the GS in order to fully remove head motion, respiration, and other global imaging artifacts. Others have argued that removing the GS distorts the resulting correlation matrices and inappropriately alters the results of group comparisons and relationships to behavior. At the core of this argument is the assessment of dimensionality in terms of the number of brain networks with uncorrelated time series. If the dimensionality is high, then the distortions due to GS removal could be effectively negligible. In the current paper, we examine the dimensionality of resting-state fMRI data using principal component analyses (PCA) and network clustering analyses. In two independent datasets (Set 1: N?=?62, Set 2: N?=?32), scree plots of the eigenvalues level off at or prior to 10 principal components, with prominent elbows at 3 and 7 components. While network clustering analyses have previously demonstrated that numerous networks can be distinguished with high thresholding of the voxel-wise correlation matrices, lower thresholding reveals a lower-dimensional hierarchical structure, with the first prominent branch at 2 networks (corresponding to the previously described "task-positive"/"task-negative" distinction) and further stable subdivisions at 4, 7 and 17. Since inter-correlated time series within these larger branches do not cancel to zero when averaged, the hierarchical nature of the correlation structure results in low effective dimensionality. Consistent with this, partial correlation analyses revealed that network-specific variance remains present in the GS at each level of the hierarchy, accounting for at least 14-18% of the overall GS variance in each dataset. These results demonstrate that GS regression is expected to remove substantial portions of network-specific brain signals along with artifacts, not simply whole-brain signals corresponding to arousal levels. We highlight alternative means of controlling for residual global artifacts when not removing the GS.
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Project description:Brain function in "resting" state has been extensively studied with functional magnetic resonance imaging (FMRI). However, drawing valid inferences, particularly for group comparisons, is fraught with pitfalls. Differing levels of brain-wide correlations can confound group comparisons. Global signal regression (GSReg) attempts to reduce this confound and is commonly used, even though it differentially biases correlations over brain regions, potentially leading to false group differences. We propose to use average brain-wide correlations as a measure of global correlation (GCOR), and examine the circumstances under which it can be used to identify or correct for differences in global fluctuations. In the process, we show the bias induced by GSReg to be a function only of the data's covariance matrix, and use simulations to compare corrections with GCOR as covariate to GSReg under various scenarios. We find that unlike GSReg, GCOR is a conservative approach that can reduce global variations, while avoiding the introduction of false significant differences, as GSReg can. However, as with GSReg, one cannot escape the interaction effect between the grouping variable and GCOR covariate on effect size. While GCOR is a complementary measure for resting state-FMRI applicable to legacy data, it is a lesser substitute for proper level-I denoising. We also assess the applicability of GCOR to empirical data with motion-based subject grouping and compare group differences to those using GSReg. We find that, while GCOR reduced correlation differences between high and low movers, it is doubtful that motion was the sole driver behind the differences in the first place.
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Project description:Regional homogeneity (ReHo) and the amplitude of low-frequency fluctuation (ALFF) are two approaches to depicting different regional characteristics of resting-state functional magnetic resonance imaging (RS-fMRI) data. Whether they can complementarily reveal brain regional functional abnormalities in attention-deficit/hyperactivity disorder (ADHD) remains unknown. In this study, we applied ReHo and ALFF to 23 medication-naïve boys diagnosed with ADHD and 25 age-matched healthy male controls using whole-brain voxel-wise analysis. Correlation analyses were conducted in the ADHD group to investigate the relationship between the regional spontaneous brain activity measured by the two approaches and the clinical symptoms of ADHD. We found that the ReHo method showed widely-distributed differences between the two groups in the fronto-cingulo-occipito-cerebellar circuitry, while the ALFF method showed a difference only in the right occipital area. When a larger smoothing kernel and a more lenient threshold were used for ALFF, more overlapped regions were found between ALFF and ReHo, and ALFF even found some new regions with group differences. The ADHD symptom scores were correlated with the ReHo values in the right cerebellum, dorsal anterior cingulate cortex and left lingual gyrus in the ADHD group, while no correlation was detected between ALFF and ADHD symptoms. In conclusion, ReHo may be more sensitive to regional abnormalities, at least in boys with ADHD, than ALFF. And ALFF may be complementary to ReHo in measuring local spontaneous activity. Combination of the two may yield a more comprehensive pathophy-siological framework for ADHD.
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