Autonomic symptoms in Parkinson\'s disease result from variable involvement of the central and peripheral systems, but many aspects remain unclear. The analysis of functional connectivity has shown promising results in assessing the pathophysiology of Parkinson\'s disease. This study aims to investigate the association between autonomic symptoms and cortical functional connectivity in early Parkinson\'s disease patients using high-density EEG. 53 early Parkinson\'s disease patients (F/M 18/35) and 49 controls (F/M 20/29) were included. Autonomic symptoms were evaluated using the Scales for Outcomes in Parkinson\'s disease-Autonomic Dysfunction score. Data were recorded with a 64-channel EEG system. We analyzed cortical functional connectivity, based on weighted phase-lag index, in θ-α-β-low-γ bands. A network-based statistic was used to perform linear regression between Scales for Outcomes in Parkinson\'s disease-Autonomic Dysfunction score and functional connectivity in Parkinson\'s disease patients. We observed a positive relation between the Scales for Outcomes in Parkinson\'s disease-Autonomic Dysfunction score and α-functional connectivity (network τ = 2.8, P = 0.038). Regions with higher degrees were insula and limbic lobe. Moreover, we found positive correlations between the mean connectivity of this network and the gastrointestinal, cardiovascular, and thermoregulatory domains of Scales for Outcomes in Parkinson\'s disease-Autonomic Dysfunction. Our results revealed abnormal functional connectivity in specific areas in Parkinson\'s disease patients with greater autonomic symptoms. Insula and limbic areas play a significant role in the regulation of the autonomic system. Increased functional connectivity in these regions might represent the central compensatory mechanism of peripheral autonomic dysfunction in Parkinson\'s disease.

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.
This study focuses on brain-heart interplay (BHI) during pre- and postictal periods surrounding seizures. Employing multichannel EEG and heart rate variability data from subjects with focal epilepsy, our analysis reveals a disrupted brain-heart axis dynamic, particularly in the heart-to-brain direction. Notably, sympathovagal activity alterations during preictal to postictal transitions underscore the autonomic nervous system’s pivotal role in epilepsy dynamics. While brain-to-heart information flow targeting low-frequency band cardiac oscillations remains stable, significant changes occur in cortical oscillations, predominantly in central regions, influencing high-frequeny-band heartbeat oscillations, that is, vagal activity. Viewing seizures as states of hyperexcitability and confirming focal epilepsy as a network disease affecting both central and peripheral neural dynamics, our study enhances understanding of BHI in epilepsy. These findings offer potential for advanced diagnostic and therapeutic approaches grounded in bodily neural activity for individuals with epilepsy.

Pupillary changes can be an important semiologic feature in focal epilepsy. Though the subcortical networks involving pupillomotor function have been described, cortical generators of pupillary dilation and constriction in humans are not well known. In this report, we describe a case of pupillary constriction occurring during seizures in a patient with drug resistant focal epilepsy. On stereoelectroencephalography, onset was noted within the posterior segment of the right intraparietal sulcus and direct cortical electrical stimulation of these electrode contacts reproduced pupillary constriction associated with habitual seizures. This is the first case report to describe ictal pupillary constriction during SEEG with confirmation of the cortical localization by direct cortical electrical stimulation. The posterior segment of the right intraparietal sulcus localization of pupillary constriction may aid in surgical evaluation patients with drug resistant focal epilepsy.

Stroke can lead to cardiac complications such as arrhythmia, myocardial injury, and cardiac dysfunction, collectively termed stroke-heart syndrome (SHS). These cardiac alterations typically peak within 72 h of stroke onset and can have long-term effects on cardiac function. Post-stroke cardiac complications seriously affect prognosis and are the second most frequent cause of death in patients with stroke. Although traditional vascular risk factors contribute to SHS, other potential mechanisms indirectly induced by stroke have also been recognized. Accumulating clinical and experimental evidence has emphasized the role of central autonomic network disorders and inflammation as key pathophysiological mechanisms of SHS. Therefore, an assessment of post-stroke cardiac dysautonomia is necessary. Currently, the development of treatment strategies for SHS is a vital but challenging task. Identifying potential key mediators and signaling pathways of SHS is essential for developing therapeutic targets. Therapies targeting pathophysiological mechanisms may be promising. Remote ischemic conditioning exerts protective effects through humoral, nerve, and immune-inflammatory regulatory mechanisms, potentially preventing the development of SHS. In the future, well-designed trials are required to verify its clinical efficacy. This comprehensive review provides valuable insights for future research.

Higher order regulation of autonomic function is maintained by the coordinated activity of specific cortical and subcortical brain regions, collectively referred to as the central autonomic network (CAN). Autonomic changes are frequently observed in Alzheimer\'s disease (AD) and dementia, but no studies to date have investigated whether plasma AD biomarkers are associated with CAN functional connectivity changes in at risk older adults.
Independently living older adults (N = 122) without major neurological or psychiatric disorder were recruited from the community. Participants underwent resting-state brain fMRI and a CAN network derived from a voxel-based meta-analysis was applied for overall, sympathetic, and parasympathetic CAN connectivity using the CONN Functional Toolbox. Sensorimotor network connectivity was studied as a negative control. Plasma levels of amyloid (Aβ42, Aβ40), neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) were assessed using digital immunoassay. The relationship between plasma AD biomarkers and within-network functional connectivity was studied using multiple linear regression adjusted for demographic covariates and Apolipoprotein E (APOE) genotype. Interactive effects with APOE4 carrier status were also assessed.
All autonomic networks were positively associated with Aβ42/40 ratio and remained so after adjustment for age, sex, and APOE4 carrier status. Overall and parasympathetic networks were negatively associated with GFAP. The relationship between the parasympathetic CAN and GFAP was moderated by APOE4 carrier status, wherein APOE4 carriers with low parasympathetic CAN connectivity displayed the highest plasma GFAP concentrations (B = 910.00, P = .004). Sensorimotor connectivity was not associated with any plasma AD biomarkers, as expected.
The present study findings suggest that CAN function is associated with plasma AD biomarker levels. Specifically, lower CAN functional connectivity is associated with decreased plasma Aβ42/40, indicative of cerebral amyloidosis, and increased plasma GFAP in APOE4 carriers at risk for AD. These findings could suggest higher order autonomic and parasympathetic dysfunction in very early-stage AD, which may have clinical implications.

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Abnormalities within the hypothalamic-pituitary-adrenal (HPA) axis and autonomic nervous system have been implicated in depression. Studies have reported glucocorticoid insensitivity and reduced heart rate variability (HRV) in depressive disorders. However, little is known about the effects of cortisol on HRV and resting-state functional connectivity (rsFC) of the central autonomic network (CAN) in depression. We collected resting-state fMRI and cardiac data for women with different depression histories (n = 61) after administration of cortisol and placebo using a double-blind crossover design. We computed rsFC for R-amygdala and L-amygdala seeds and assessed the change in HRV after cortisol (cortisol-placebo). Analyses examined the effects of acute cortisol administration on HRV and rsFC of the R-amygdala and L-amygdala. There was a significant interaction between HRV and treatment for rsFC between the amygdala and CAN regions. We found lower rsFC between the L-amygdala and putamen for those with a greater decrease in HRV after cortisol. There was also reduced rsFC between the R-amygdala and dorsomedial prefrontal cortex, putamen, middle cingulate cortex, insula, and cerebellum in those with lower HRV after cortisol. These results remained significant after adjusting for depression symptoms, age, and race. Our findings suggest that the effect of cortisol on CAN connectivity is related to its effects on HRV. Overall, these results could inform transdiagnostic interventions targeting HRV and the stress response systems across clinical and non-clinical populations.

BACKGROUND: Despite accumulation of a substantial body of literature supporting the role of exercise on frontal lobe functioning, relatively less is understood of the interconnectivity of ventromedial prefrontal cortical (vmPFC) regions that underpin cardio-autonomic regulation predict cardiac chronotropic competence (CC) in response to sub-maximal exercise.
METHODS: Eligibility of 161 adults (mean age = 48.6, SD = 18.3, 68% female) was based upon completion of resting state brain scan and sub-maximal bike test. Sliding window analysis of the resting state signal was conducted over 45-s windows, with 50% overlap, to assess how changes in photoplethysmography-derived HRV relate to vmPFC functional connectivity with the whole brain. CC was assessed based upon heart rate (HR) changes during submaximal exercise (HR change /HRmax (206-0.88 × age) - HRrest).
RESULTS: During states of elevated HRV the vmPFC showed greater rsFC with an 83-voxel region of the hypothalamus (p < 0.001, uncorrected). Beta estimates of vmPFC connectivity extracted from a 6-mm sphere around this region emerged as the strongest predictor of CC (b = 0.283, p <.001) than age, BMI, and resting HRV F(8,144) = 6.30, p <.001.
CONCLUSIONS: Extensive glutamatergic innervation of the hypothalamus by the vmPFC allows for top-down control of the hypothalamus and its various autonomic efferents which facilitate chronotropic response during sub-maximal exercise.

UNASSIGNED: Patients with epilepsy frequently experience autonomic dysfunction, closely related to sudden unexplained death in epilepsy (SUDEP). SUDEP occurs most often at night or during sleep, and frequent nocturnal seizures are an established risk factor. This study investigated the influence of nocturnal seizures on autonomic dysfunction in epilepsy.
UNASSIGNED: This retrospective study enrolled frontal lobe epilepsy (FLE) patients who performed 24-hour EEG monitoring. All participants were divided into nocturnal FLE (NFLE, > 90% of seizures occurring during sleep) or diurnal FLE (DFLE) groups. EEG and ECG signals were simultaneously obtained during waking and sleep stages. EEG current density source and connectivity analysis of the autonomic network were performed. ECG was analyzed across time and frequency domains heart rate variability (HRV) analysis method was used. The obtained parameters were compared between the NFLE and DFLE groups.
UNASSIGNED: Fifteen NFLE and 16 DFLE patients were enrolled with no significant difference in age, sex, disease duration, seizure frequency, or the number of anti-seizure medications between the two groups. During sleep, a decrease in HRV parameters and an increase of the beta-1 (13-22 Hz) current source density power in the bilateral paracentral lobule (BA4,5,6), precuneus (BA7), and cingulate (BA31) were observed in the NFLE group compared to DFLE group. The NFLE group also showed hyperconnectivity in the central autonomic (12 edges distributed over 10 nodes), sympathetic (2 edges distributed over 3 nodes), and parasympathetic (4 edges distributed over 6 nodes) beta-1 frequency band networks during sleep. During wakefulness, central and cardiac autonomic variables were not significantly different between the NFLE and DFLE groups.
UNASSIGNED: Interictal cardiac and central autonomic dysfunction occurred simultaneously and can be attributed to the brain-heart autonomic axis. Our findings suggest that nocturnal seizures may contribute to interictal autonomic dysfunction during sleep in people with epilepsy.

Entropy-based and fractal-based metrics derived from heart rate variability (HRV) have enriched the way cardiovascular dynamics can be described in terms of complexity. The most commonly used multifractal testing, a method using q moments to explore a range of fractal scaling in small-sized and large-sized fluctuations, is based on detrended fluctuation analysis, which examines the power-law relationship of standard deviation with the timescale in the measured signal. A more direct testing of a multifractal structure exists based on the Shannon entropy of bin (signal subparts) proportion. This work aims to reanalyze HRV during cognitive tasks to obtain new markers of HRV complexity provided by entropy-based multifractal spectra using the method proposed by Chhabra and Jensen in 1989. Inter-beat interval durations (RR) time series were obtained in 28 students comparatively in baseline (viewing a video) and during three cognitive tasks: Stroop color and word task, stop-signal, and go/no-go. The new HRV estimators were extracted from the f/α singularity spectrum of the RR magnitude increment series, established from q-weighted stable (log-log linear) power laws, namely: (i) the whole spectrum width (MF) calculated as αmax - αmin; the specific width representing large-sized fluctuations (MFlarge) calculated as α0 - αq+; and small-sized fluctuations (MFsmall) calculated as αq- - α0. As the main results, cardiovascular dynamics during Stroop had a specific MF signature while MFlarge was rather specific to go/no-go. The way these new HRV markers could represent different aspects of a complete picture of the cognitive-autonomic interplay is discussed, based on previously used entropy- and fractal-based markers, and the introduction of distribution entropy (DistEn), as a marker recently associated specifically with complexity in the cardiovascular control.