Real-Time Resting-State Functional Magnetic Resonance Imaging Using Averaged Sliding Windows with Partial Correlations and Regression of Confounding Signals

Background: Migraine, a neurological disorder under perpetual investigation, has an elusive etiology. An potential association with Right-to-Left Shunt (RLS) exists, yet the precise nature of this connection remains unclear.This study employs the resting-state functional magnetic resonance imaging (rs-fMRI) technique to examine brain functional differences between the migraine patients with and without RLS, aiming at exploring RLS associated alterations in functional segregation and integration. Methods: This study included 32 migraine patients (14 patients with RLS and 18 without RLS), each undergoing rs-fMRI data acquisition. The amplitude of low-frequency fluctuation (ALFF) was employed to investigate functional segregation. Functional connectivity (FC) analysis was conducted to explore the functional integration across distinct brain regions. Graph theory-based network analysis was utilized to assess functional networks in migraine patients with RLS. Pearson correlation analysis further explored the relationship between RLS severity and various functional metrics.. Results: Compared with migraine patients without RLS, migraine patients with RLS exhibited a significant increase in the ALFF in the left middle occipital and superior occipital gyrus; As to FC, the reduced connectivity between the left rolandic operculum and the right middle cingulate gyrus was observed in migraine patients with RLS; Based on the brain networks analysis, migraine patients with RLS displayed higher values of the normalized clustering coefficient and greater betweenness centrality in specific regions, including the left precuneus, right insula, and right inferior temporal gyrus. Further, the study found positive correlations between ALFF values in the temporal lobes, thalamus, left middle occipital, and superior occipital gyrus and RLS severity. Conversely, negative correlations emerged between ALFF values in the right inferior frontal gyrus, middle frontal gyrus, and insula and RLS grading. Finally, the study identified a positive correlation between angular gyrus betweenness centrality and RLS severity. Conclusion: RLS-associated brain functional alterations in migraine consisted of local brain regions, connectivity, and networks involved in pain conduction and regulation did exist in migraine with RLS.

Methamphetamine (MA) can cause brain structural and functional impairment, but there are few studies on whether this difference will sustain on MA abstainers. The purpose of this study is to investigate the correlation of brain networks in MA abstainers. In this study, 47 people detoxified for at least 14 months and 44 normal people took a resting-state functional magnetic resonance imaging (RS-fMRI) scan. A dynamic (i.e., time-varying) functional connectivity (FC) is obtained by applying sliding windows in the time courses on the independent components (ICs). The windowed correlation data for each IC were then clustered by k-means. The number of subjects in each cluster was used as a new feature for individual identification. The results show that the classifier achieved satisfactory performance (82.3% accuracy, 77.7% specificity, and 85.7% sensitivity). We find that there are significant differences in the brain networks of MA abstainers and normal people in the time domain, but the spatial differences are not obvious. Most of the altered functional connections (time-varying) are identified to be located at dorsal default mode network. These results have shown that changes in the correlation of the time domain may play an important role in identifying MA abstainers. Therefore, our findings provide valuable insights in the identification of MA and elucidate the pathological mechanism of MA from a resting-state functional integration point of view.

IntroductionFunctional magnetic resonance imaging (fMRI) often involves long scanning durations to ensure the associated brain activity can be detected. However, excessive experimentation can lead to many undesirable effects, such as from learning and/or fatigue effects, as well as undue discomfort for the subject, which can lead to motion artifact and loss of sustained attention on task. Overly long experimentation ironically can thus have a detrimental effect on signal quality and accurate voxel activation detection. Here, we propose a method of dynamic experimentation with real-time fMRI using a novel statistically-driven approach to fMRI analytics. This new approach to experimental design invokes early stopping when sufficient statistical evidence for assessing the task-related activation is observed.MethodsVoxel-level sequential probability ratio test (SPRT) statistics based on general linear models (GLM) were implemented on fMRI scans of a mathematical 1-back task from 25 subjects, 14 healthy controls and 11 subjects born extremely preterm. This approach is based on likelihood ratios and allows for systematic early stopping based on statistical error thresholds being satisfied. We explored voxel-level serial covariance estimation in real-time using the “sandwich” estimator. We adopted a two-stage estimation approach that allows for the hypothesis tests to be formulated in terms of t-statistic scale, which enhances interpretability. Scan data was collected using a dynamic feedback system that allowed for adaptive experimentation. Numerical parallelization was employed to facilitate completion of computations involving a new scan within every repetition time (TR).ResultsSPRT analytics demonstrate the feasibility and efficiency gains of automated early stopping, while performing comparably in activation detection with full protocols analyzed through standard fMRI software. Dynamic stopping of stimulus administration was achieved in all subjects with typical time savings between 33 - 66% (4 – 8 minutes on a 12 minute scan).ConclusionA systematic statistical approach for early stopping with real-time fMRI experimentation has been implemented. This allows for great savings in scan times, while still eliciting comparable activation patterns as full protocols. This dynamic approach has promise for reducing subject burden and fatigue effects.