Study Overview
This pilot study aimed to explore the interplay between brain function and cardiac autonomic regulation in individuals diagnosed with Functional Neurological Disorder (FND). FND is characterized by neurological symptoms, including movement disorders and sensory disturbances, without identifiable organic pathology. The research focused on understanding how brain connectivity, as assessed through resting-state functional magnetic resonance imaging (fMRI), correlates with cardiac autonomic profiles measured via heart rate variability (HRV) analysis. Given the well-documented relationship between autonomic nervous system function and brain activity, this study sought to elucidate whether variations in brain connectivity patterns could be linked to altered cardiac autonomic control in patients presenting with FND.
The study recruited a small cohort of participants diagnosed with FND, alongside a control group composed of healthy volunteers, to establish comparative measures. By integrating advanced neuroimaging techniques with physiological assessments, the researchers aimed to uncover potential biomarkers of FND that could assist in better understanding this complex condition. Additionally, this research endeavored to inform future therapeutic strategies by identifying the neural and autonomic profiles associated with the disorder.
Participant Characteristics
The pilot study included a total of 30 participants, 15 of whom were diagnosed with Functional Neurological Disorder (FND) and 15 healthy volunteers serving as a control group. The recruitment of participants with FND was conducted through neurology clinics, ensuring that those included in the study met the international diagnostic criteria for FND. Participants in the FND group presented a range of symptoms typically associated with the disorder, such as motor dysfunctions, non-epileptic seizures, and sensory disturbances. The clinical variability within the FND group reflects the heterogeneous nature of the disorder, which often presents complex challenges for both diagnosis and treatment.
On the other hand, the control group comprised individuals who were matched for age, sex, and socio-economic status to the FND participants, thereby attempting to minimize confounding variables that might affect the study results. The selection criteria for the control group ensured that these individuals had no history of neurological or psychiatric disorders, and none were on medication that could influence either brain function or autonomic regulation.
In terms of demographic characteristics, the average age of participants in both groups was similar, with a mean age of approximately 35 years. The cohort was balanced in terms of gender distribution, with approximately 60% of participants identifying as female. These demographic factors are important, as they can influence the autonomic nervous system’s functioning and, consequently, the outcomes of heart rate variability assessments.
Prior to participation, all individuals underwent a comprehensive screening process, which included medical history evaluations, mental health assessments, and physical examinations, to exclude any underlying conditions that could interfere with the study objectives. This rigorous selection process ensured that the data collected accurately reflected the relationship between brain functional connectivity and cardiac autonomic profiles within the specified groups.
The researchers also collected baseline measurements for heart rate variability (HRV) and conducted resting-state fMRI scans to assess functional connectivity in the brain. HRV measures were analyzed in the context of parasympathetic and sympathetic nervous system activity, which are crucial in understanding autonomic control. By establishing these detailed participant characteristics, the study aimed to create a robust dataset that would provide valuable insights into the neural and autonomic profiles associated with Functional Neurological Disorder.
Data Analysis
The data analysis for this pilot study followed a structured approach, incorporating both neuroimaging and physiological measures. Initially, the resting-state fMRI data underwent preprocessing using established neuroimaging software, which included motion correction, normalization to a standard brain template, and the removal of potential confounding factors such as physiological noise and head motion. This preprocessing step ensured that the subsequent connectivity analyses were as accurate and representative of underlying brain dynamics as possible.
Following preprocessing, functional connectivity was assessed using seed-based correlation analyses and independent component analysis (ICA). Seed-based correlation involved selecting specific regions of interest (ROIs) within the brain and examining their connectivity patterns with other brain areas during the resting state. These ROIs were selected based on previous literature indicating their relevance to both neurological function and autonomic regulation. The ICA approach allowed the researchers to identify networks of brain activity without a priori definitions, enabling a more data-driven exploration of functional connectivity patterns within the cohort.
Simultaneously, heart rate variability was computed using electrocardiogram (ECG) data collected during a resting state. HRV metrics, including the root mean square of successive differences (RMSSD) and the standard deviation of NN intervals (SDNN), served as indicators of autonomic nervous system activity. These metrics are particularly significant because they reflect the balance between sympathetic and parasympathetic control over cardiac function, a dynamic that can be influenced by emotional and physiological states.
To examine the relationship between brain connectivity and cardiac autonomic profiles, statistical analyses were performed using appropriate multivariate techniques. The researchers employed regression analyses to investigate correlations between specific connectivity patterns and HRV metrics. Furthermore, group comparisons between FND participants and control subjects utilized multivariate analysis of variance (MANOVA) to assess differences in brain functional connectivity and HRV parameters. Statistical significance was set at p < 0.05, and corrections for multiple comparisons were applied where necessary to enhance the validity of findings.
In addition to these analyses, exploratory analyses were conducted to identify potential mediating effects, where the influence of brain connectivity on HRV was examined through mediation models. Such models help to clarify whether alterations in brain network activity might directly or indirectly affect cardiac function in the context of FND.
All analyses adhered to rigorous statistical standards to minimize type I and type II errors, ensuring that the results of the study could be interpreted with confidence. The use of advanced analytical techniques allowed for a comprehensive exploration of the complex interplay between brain function and cardiac autonomic regulation, providing a foundation for interpreting the findings in the context of symptoms observed in individuals with Functional Neurological Disorder.
Results and Discussion
The findings of this pilot study revealed several critical patterns linking brain functional connectivity to cardiac autonomic profiles in participants diagnosed with Functional Neurological Disorder (FND). Notably, when comparing the FND cohort to the healthy control group, significant differences emerged in both functional connectivity patterns and heart rate variability (HRV) metrics.
Functional connectivity analyses indicated altered connectivity in specific brain networks associated with emotional regulation and sensory processing, such as the default mode network (DMN) and the salience network. In the FND group, participants exhibited disrupted connectivity within these networks, suggesting a potential neurological basis for the symptoms they experienced. The DMN, often active during rest and linked with self-referential thoughts, showed reduced connectivity, which may correlate with difficulties in self-awareness often reported in FND patients. Meanwhile, alterations in the salience network connectivity could reflect challenges in physiological and emotional integration, potentially contributing to the presentation of non-epileptic seizures and motor dysfunctions characteristic of FND.
On the physiological side, heart rate variability analyses revealed notably lower HRV in individuals with FND compared to healthy controls. Metrics such as RMSSD and SDNN indicated decreased parasympathetic activity, suggesting that FND may be associated with an imbalance in autonomic regulation. This finding aligns with existing literature that posits a link between diminished HRV and increased stress responses, pointing toward the importance of autonomic function in both emotional and physiological dimensions of health. In particular, the lower parasympathetic activity among FND patients may reflect a state of chronic stress or heightened sympathetic drive, further complicating their overall condition.
The relationship between brain connectivity and HRV was explored through regression analyses, revealing significant correlations. Specifically, reduced connectivity within the DMN was negatively correlated with HRV metrics, suggesting that the more disrupted the brain’s resting-state connectivity, the lower the HRV exhibited by participants. This finding highlights a potential psychophysiological pathway wherein altered brain dynamics may affect autonomic regulation. Such a relationship raises intriguing questions about how therapeutic interventions aimed at improving brain connectivity could positively influence cardiac function, potentially offering new avenues for treatment strategies in FND.
Moreover, exploratory mediation analyses provided insights into the mechanisms underlying these relationships. Initial results indicated that brain connectivity may serve as a mediator between psychological factors, such as stress and anxiety, and the cardiac autonomic profile. This suggests that interventions targeting brain function, such as cognitive-behavioral therapy or mindfulness practices, could alter both neurological and autonomic outcomes, leading to improved overall well-being in individuals with FND.
While these findings offer valuable insights, it is important to acknowledge the limitations inherent to this pilot study. The small sample size, while sufficient for preliminary analysis, could limit the generalizability of findings. Additionally, the cross-sectional nature of the study prevents causal inferences regarding the direction of relationships; further longitudinal studies are recommended to explore these dynamics more comprehensively. Follow-up research could also expand on the heterogeneity within the FND group to further delineate the distinct profiles that contribute to the clinical variability observed in this population.
The results of this study underscore the complex interplay between brain functional connectivity and cardiac autonomic profiles in individuals with FND. They point to the necessity of a comprehensive understanding of these relationships, which could pave the way for more refined approaches to diagnosis and management of this multifaceted disorder.
