Heart Rate Variability Parameters During Psychogenic Non-epileptic Seizures: Comparison Between Patients With Pure PNES and Comorbid Epilepsy

Romigi, Andrea; Rizzo, G.; Izzi, Francesca; Guerrisi, Maria; Caccamo, Marco; Testa, Federica; Centonze, Diego; Mercuri, Nicola Biagio; Toschi, Nicola

doi:10.3389/fneur.2020.00713

Cited by 16 publications

(14 citation statements)

References 51 publications

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“…Beat detection and correction routines [47] were reimplemented in-house to extract the inter-beat interval series (RR) which was interpolated using third degree splines. In accordance with previous papers [48,49], after making the signal analytic through Hilbert transformation, for each RR series, a time-frequency representation was estimated by computing its Smoothed Pseudo-Wigner-Ville distribution using the time-frequency toolbox documented in [50]. The instantaneous spectral power of the heart high-frequency (HF) band in the interval [0.15-0.4 Hz], which is a proxy for the parasympathetic activity, was then obtained through frequency-averaging and successively convolved with a standard double gamma Haemodynamic response function (α 1 = 6, α 2 = 16, β 1 = 1, β 2 = 2, c = 0.17) [51].…”

Section: (Ii) Brain-heart Interactionssupporting

confidence: 93%

Echo state network models for nonlinear Granger causality

Duggento

Guerrisi

Toschi

2021

Phil. Trans. R. Soc. A.

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While Granger causality (GC) has been often employed in network neuroscience, most GC applications are based on linear multivariate autoregressive (MVAR) models. However, real-life systems like biological networks exhibit notable nonlinear behaviour, hence undermining the validity of MVAR-based GC (MVAR-GC). Most nonlinear GC estimators only cater for additive nonlinearities or, alternatively, are based on recurrent neural networks or long short-term memory networks, which present considerable training difficulties and tailoring needs. We reformulate the GC framework in terms of echo-state networks-based models for arbitrarily complex networks, and characterize its ability to capture nonlinear causal relations in a network of noisy Duffing oscillators, showing a net advantage of echo state GC (ES-GC) in detecting nonlinear, causal links. We then explore the structure of ES-GC networks in the human brain employing functional MRI data from 1003 healthy subjects drawn from the human connectome project, demonstrating the existence of previously unknown directed within-brain interactions. In addition, we examine joint brain-heart signals in 15 subjects where we explore directed interaction between brain networks and central vagal cardiac control in order to investigate the so-called central autonomic network in a causal manner. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

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Section: (Ii) Brain-heart Interactionssupporting

confidence: 93%

Echo state network models for nonlinear Granger causality

Duggento

Guerrisi

Toschi

2021

Phil. Trans. R. Soc. A.

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“…Besides epilepsy, a substantial number of the patients admitted to EMUs present with PNES, and a smaller group displays an association of both pathologies (35,36). It is shown that PNES patients manifest functional, anatomical, and autonomic brain changes compared with healthy subjects and epilepsy patients without PNES (37)(38)(39)(40). On the other hand, it is suggested that a wide range of psychopathologies may be the basis of PNES and that their treatment could improve clinical outcomes, avoiding the perpetuation of ongoing psychogenic seizures (35).…”

Section: Discussionmentioning

confidence: 99%

Mood Disturbances, Anxiety, and Impact on Quality of Life in Patients Admitted to Epilepsy Monitoring Units

et al. 2021

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Introduction: The overall combined prevalence of anxiety and depression in patients with epilepsy has been estimated at 20.2 and 22.9%, respectively, and is considered more severe in drug-refractory epilepsy. Patients admitted to epilepsy monitoring units constitute a particular group. Also, patients with psychogenic non-epileptic seizures can reach more than 20% of all admissions. This study aims to characterize these symptoms in a large cohort of patients admitted for evaluation in a tertiary epilepsy center.Materials and Methods: The study was conducted among 493 consecutive patients (age: 38.78 ± 12.7, 57% females) admitted for long-term video EEG from January 2013 to February 2021. Demographic, clinical, and mood disorder patients' data were collected. Anxiety and depression symptoms were assessed through the Hospital Anxiety Depression Scale (HADS-A and HADS-D), the State Trait Anxiety Inventory (STAI), and Beck Depression Inventory (BDI-II). Quality of life was determined using the QOLIE-10. Patients were divided into three groups: patients with epilepsy (n = 395), psychogenic non-epileptic seizures (PNES) (n = 56), and combined (n = 33). A univariate and multivariate regression analysis was performed for variables associated with quality of life.Results: Of 493 patients, 45.0% had structural etiology, and considering epilepsy classification, 43.6% were of temporal lobe origin. In addition, 32.45% of patients had a previous psychiatric history, 49.9% of patients had depressive symptoms in BDI, and 30.9% according to HADS-D; 56.42 and 52.63% of patients presented pathological anxiety scores in STAI-T and STAI-S, respectively; and 44.78% according to HADS-A. PNES and combined groups revealed a higher incidence of pathologic BDI scores (64.29 and 78.79%, p < 0.001) as well as pathologic HADS-A scores (p = 0.001). Anxiety and depression pathologic results are more prevalent in females, HADS-A (females = 50.7%, males = 36.8%; p = 0.0027) and BDI > 13 (females = 56.6%, males = 41.0%; p = 0.0006). QOLIE-10 showed that 71% of the patients had their quality of life affected with significantly higher scores in the combined group than in the epilepsy and PNES groups (p = 0.0015).Conclusions: Subjective anxiety, depression, and reduced quality of life are highly prevalent in patients with refractory epilepsy. These symptoms are more evident when PNES are associated with epilepsy and more severe among female patients. Most of the cases were not previously diagnosed. These factors should be considered in everyday clinical practice, and specific approaches might be adapted depending on the patient's profile.

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“…Autonomic changes described in patients with PNES are starting to show possible contributors such as heart rate variability during PNES. 12 Prospective studies in this population may be able to answer these questions in the future.…”

Section: Commentarymentioning

confidence: 98%