Altered Relationship Between Heart Rate Variability and fMRI-Based Functional Connectivity in People With Epilepsy

Impaired brain-heart axis in focal epilepsy: Alterations in information flow and implications for seizure dynamics

This study delves into functional brain-heart interplay (BHI) dynamics during interictal periods before and after seizure events in focal epilepsy. Our analysis focuses on elucidating the causal interaction between cortical and autonomic nervous system (ANS) oscillations, employing electroencephalography and heart rate variability series. The dataset for this investigation comprises 47 seizure events from 14 independent subjects, obtained from the publicly available Siena Dataset. Our findings reveal an impaired brain-heart axis especially in the heart-to-brain functional direction. This is particularly evident in bottom-up oscillations originating from sympathovagal activity during the transition between preictal and postictal periods. These results indicate a pivotal role of the ANS in epilepsy dynamics. Notably, the brain-to-heart information flow targeting cardiac oscillations in the low-frequency band does not display significant changes. However, there are noteworthy changes in cortical oscillations, primarily originating in central regions, influencing heartbeat oscillations in the high-frequency band. Our study conceptualizes seizures as a state of hyperexcitability and a network disease affecting both cortical and peripheral neural dynamics. Our results pave the way for a deeper understanding of BHI in epilepsy, which holds promise for the development of advanced diagnostic and therapeutic approaches also based on bodily neural activity for individuals living with epilepsy.

Disrupted Brain Functional Status in Patients with Reversible Cerebral Vasoconstriction Syndrome

OBJECTIVES

The aim of this study was to investigate the functional networks in subjects with reversible cerebral vasoconstriction syndrome (RCVS) using resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS

We prospectively recruited patients with RCVS and healthy controls (HCs) between February 2017 and April 2021. The rs-fMRI data were analyzed using graph theory methods. We compared node-based global and regional topological metrics (Bundle 1) and network-based intranetwork and internetwork connectivity (Bundle 2) between RCVS patients and HCs. We also explored the associations of clinical and vascular (ie, the Lindegaard index, LI) parameters with significant rs-fMRI metrics.

RESULTS

A total of 104 RCVS patients and 93 HCs were included in the final analysis. We identified significantly decreased local efficiency of the left dorsal anterior insula (dAI; p = 0.0005) in RCVS patients within 30 days after disease onset as compared to HCs, which improved 1 month later. RCVS patients also had increased global efficiency (p = 0.009) and decreased average degree centrality (p = 0.045), clustering coefficient (p = 0.033), and assortativity values (p = 0.003) in node-based analysis. In addition, patients with RCVS had increased internetwork connectivity of the default mode network (DMN) with the salience (p = 0.027) and dorsal attention (p = 0.016) networks. Significant correlations between LI and regional local efficiency in left dAI (rs  = -0.418, p = 0.042) was demonstrated.

INTERPRETATION

The significantly lower local efficiency of the left dAI, suggestive of impaired central autonomic modulation, was negatively correlated with vasoconstriction severity, which is highly plausible for the pathogenesis of RCVS. ANN NEUROL 2023;94:772-784.

Exploring the brain with sleep-related injuries, and fixing it

The focus of my research efforts rests with determining dysfunctional neural systems underlying disorders of sleep, and identifying interventions to overcome those disorders. Aberrant central and physiological control during sleep exerts serious consequences, including disruptions in breathing, motor control, blood pressure, mood, and cognition, and plays a major role in sudden infant death syndrome, congenital central hypoventilation, and sudden unexpected death in epilepsy, among other concerns. The disruptions can be traced to brain structural injury, leading to inappropriate outcomes. Identification of failing systems arose from the assessment of single neuron discharge in intact, freely moving and state-changing human and animal preparations within multiple systems, including serotonergic action and motor control sites. Optical imaging of chemosensitive, blood pressure and other breathing regulatory areas, especially during development, were useful to show integration of regional cellular action in modifying neural output. Identification of damaged neural sites in control and afflicted humans through structural and functional magnetic resonance imaging procedures helped to identify the sources of injury, and the nature of interactions between brain sites that compromise physiological systems and lead to failure. Interventions to overcome flawed regulatory processes were developed, and incorporate noninvasive neuromodulatory means to recruit ancient reflexes or provide peripheral sensory stimulation to assist breathing drive to overcome apnea, reduce the frequency of seizures, and support blood pressure in conditions where a failure to perfuse can lead to death.

Altered spontaneous brain activity during dobutamine challenge in healthy young adults: A resting-state functional magnetic resonance imaging study

Introduction

There is a growing interest in exploring brain-heart interactions. However, few studies have investigated the brain-heart interactions in healthy populations, especially in healthy young adults. The aim of this study was to explore the association between cardiovascular and spontaneous brain activities during dobutamine infusion in healthy young adults.

Methods

Forty-eight right-handed healthy participants (43 males and 5 females, range: 22-34 years) underwent vital signs monitoring, cognitive function assessment and brain MRI scans. Cardiovascular function was evaluated using blood pressure and heart rate, while two resting-state functional magnetic resonance imaging (rs-fMRI) methods-regional homogeneity (ReHo) and amplitude of low-frequency fluctuation (ALFF)-were used together to reflect the local neural activity of the brain. Logistic regression was used to model the association between brain and heart.

Results

Results showed that blood pressure and heart rate significantly increased after dobutamine infusion, and the performance in brain functional activity was the decrease in ReHo in the left gyrus rectus and in ALFF in the left frontal superior orbital. The results of logistic regression showed that the difference of diastolic blood pressure (DBP) had significant positive relationship with the degree of change of ReHo, while the difference of systolic blood pressure (SBP) had significant negative impact on the degree of change in ALFF.

Discussion

These findings suggest that the brain-heart interactions exist in healthy young adults under acute cardiovascular alterations, and more attention should be paid to blood pressure changes in young adults and assessment of frontal lobe function to provide them with more effective health protection management.

A multi-measure approach for assessing the performance of fMRI preprocessing strategies in resting-state functional connectivity

It is well established that head motion and physiological processes (e.g. cardiac and breathing activity) should be taken into consideration when analyzing and interpreting results in fMRI studies. However, even though recent studies aimed to evaluate the performance of different preprocessing pipelines there is still no consensus on the optimal strategy. This is partly due to the fact that the quality control (QC) metrics used to evaluate differences in performance across pipelines have often yielded contradictory results. Furthermore, preprocessing techniques based on physiological recordings or data decomposition techniques (e.g. aCompCor) have not been comprehensively examined. Here, to address the aforementioned issues, we propose a framework that summarizes the scores from eight previously proposed and novel QC metrics to a reduced set of two QC metrics that reflect the signal-to-noise ratio and the reduction in motion artifacts and biases in the preprocessed fMRI data. Using this framework, we evaluate the performance of three commonly used practices on the quality of data: 1) Removal of nuisance regressors from fMRI data, 2) discarding motion-contaminated volumes (i.e., scrubbing) before regression, and 3) low-pass filtering the data and the nuisance regressors before their removal. Using resting-state fMRI data from the Human Connectome Project, we show that the scores of the examined QC metrics improve the most when the global signal (GS) and about 17% of principal components from white matter (WM) are removed from the data. Finally, we observe a small further improvement with low-pass filtering at 0.20 Hz and milder variants of WM denoising, but not with scrubbing.