Cerebrovascular Reactivity Following Spinal Cord Injury.

Background: Spinal cord injuries (SCI) often result in cardiovascular issues, increasing the risk of stroke and cognitive deficits. Objectives: This study assessed cerebrovascular reactivity (CVR) using functional magnetic resonance imaging (fMRI) during a hypercapnic challenge in SCI participants compared to noninjured controls. Methods: Fourteen participants were analyzed (n = 8 with SCI [unless otherwise noted], median age = 44 years; n = 6 controls, median age = 33 years). CVR was calculated through fMRI signal changes. Results: The results showed a longer CVR component (tau) in the grey matter of SCI participants (n = 7) compared to controls (median difference = 3.0 s; p < .05). Time since injury (TSI) correlated negatively with steady-state CVR in the grey matter and brainstem of SCI participants (RS = -0.81, p = .014; RS = -0.84, p = .009, respectively). Lower steady-state CVR in the brainstem of the SCI group (n = 7) correlated with lower diastolic blood pressure (RS = 0.76, p = .046). Higher frequency of hypotensive episodes (n = 7) was linked to lower CVR outcomes in the grey matter (RS = -0.86, p = .014) and brainstem (RS = -0.89, p = .007). Conclusion: Preliminary findings suggest a difference in the dynamic CVR component, tau, between the SCI and noninjured control groups, potentially explaining the higher cerebrovascular health burden in SCI individuals. Exploratory associations indicate that longer TSI, lower diastolic blood pressure, and more hypotensive episodes may lead to poorer CVR outcomes. However, further research is necessary to establish causality and support these observations.

Monofractal analysis of functional magnetic resonance imaging: An introductory review.

The following review will aid readers in providing an overview of scale-free dynamics and monofractal analysis, as well as its applications and potential in functional magnetic resonance imaging (fMRI) neuroscience and clinical research. Like natural phenomena such as the growth of a tree or crashing ocean waves, the brain expresses scale-invariant, or fractal, patterns in neural signals that can be measured. While neural phenomena may represent both monofractal and multifractal processes and can be quantified with many different interrelated parameters, this review will focus on monofractal analysis using the Hurst exponent (H). Monofractal analysis of fMRI data is an advanced analysis technique that measures the complexity of brain signaling by quantifying its degree of scale-invariance. As such, the H value of the blood oxygenation level-dependent (BOLD) signal specifies how the degree of correlation in the signal may mediate brain functions. This review presents a brief overview of the theory of fMRI monofractal analysis followed by notable findings in the field. Through highlighting the advantages and challenges of the technique, the article provides insight into how to best conduct fMRI fractal analysis and properly interpret the findings with physiological relevance. Furthermore, we identify the future directions necessary for its progression towards impactful functional neuroscience discoveries and widespread clinical use. Ultimately, this presenting review aims to build a foundation of knowledge among readers to facilitate greater understanding, discussion, and use of this unique yet powerful imaging analysis technique.