Multivariate Granger causality unveils directed parietal to prefrontal cortex connectivity during task-free MRI.

While a large body of research has focused on the study of functional brain "connectivity", few investigators have focused on directionality of brain-brain interactions which, in spite of the mostly bidirectional anatomical substrates, cannot be assumed to be symmetrical. We employ a multivariate Granger Causality-based approach to estimating directed in-network interactions and quantify its advantages using extensive realistic synthetic BOLD data simulations to match Human Connectome Project (HCP) data specification. We then apply our framework to resting state functional MRI (rs-fMRI) data provided by the HCP to estimate the directed connectome of the human brain. We show that the functional interactions between parietal and prefrontal cortices commonly observed in rs-fMRI studies are not symmetrical, but consists of directional connectivity from parietal areas to prefrontal cortices rather than vice versa. These effects are localized within the same hemisphere and do not generalize to cross-hemispheric functional interactions. Our data are consistent with neurophysiological evidence that posterior parietal cortices involved in processing and integration of multi-sensory information modulate the function of more anterior prefrontal regions implicated in action control and goal-directed behaviour. The directionality of functional connectivity can provide an additional layer of information in interpreting rs-fMRI studies both in health and disease

Assessment of spontaneous cardiovascular oscillations in Parkinson's disease

Parkinson's disease (PD) has been reported to involve postganglionic sympathetic failure and a wide spectrum of autonomic dysfunctions including cardiovascular, sexual, bladder, gastrointestinal and sudo-motor abnormalities. While these symptoms may have a significant impact on daily activities, as well as quality of life, the evaluation of autonomic nervous system (ANS) dysfunctions relies on a large and expensive battery of autonomic tests only accessible in highly specialized laboratories. In this paper we aim to devise a comprehensive computational assessment of disease-related heartbeat dynamics based on instantaneous, time-varying estimates of spontaneous (resting state) cardiovascular oscillations in PD. To this end, we combine standard ANS-related heart rate variability (HRV) metrics with measures of instantaneous complexity (dominant Lyapunov exponent and entropy) and higher-order statistics (bispectra). Such measures are computed over 600-s recordings acquired at rest in 29 healthy subjects and 30 PD patients. The only significant group-wise differences were found in the variability of the dominant Lyapunov exponent. Also, the best PD vs. healthy controls classification performance (balanced accuracy: 73.47%) was achieved only when retaining the time-varying, non-stationary structure of the dynamical features, whereas classification performance dropped significantly (balanced accuracy: 61.91%) when excluding variability-related features. Additionally, both linear and nonlinear model features correlated with both clinical and neuropsychological assessments of the considered patient population. Our results demonstrate the added value and potential of instantaneous measures of heartbeat dynamics and its variability in characterizing PD-related disabilities in motor and cognitive domains

A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations.

High-frequency neuroelectric signals like electroencephalography (EEG) or magnetoencephalography (MEG) provide a unique opportunity to infer causal relationships between local activity of brain areas. While causal inference is commonly performed through classical Granger causality (GC) based on multivariate autoregressive models, this method may encounter important limitations (e.g., data paucity) in the case of high dimensional data from densely connected systems like the brain. Additionally, physiological signals often present long-range dependencies which commonly require high autoregressive model orders/number of parameters. We present a generalization of autoregressive models for GC estimation based on Wiener-Volterra decompositions with Laguerre polynomials as basis functions. In this basis, the introduction of only one additional global parameter allows to capture arbitrary long dependencies without increasing model order, hence retaining model simplicity, linearity and ease of parameters estimation. We validate our method in synthetic data generated from families of complex, densely connected networks and demonstrate superior performance as compared to classical GC. Additionally, we apply our framework to studying the directed human brain connectome through MEG data from 89 subjects drawn from the Human Connectome Project (HCP) database, showing that it is able to reproduce current knowledge as well as to uncover previously unknown directed influences between cortical and limbic brain regions.MR

Reconstructing multivariate causal structure between functional brain networks through a Laguerre-Volterra based Granger causality approach

Classical multivariate approaches based on Granger causality (GC) which estimate functional connectivity in the brain are almost exclusively based on autoregressive models. Nevertheless, information available from past samples is limited due to both signal autocorrelation and necessarily low model orders. Consequently, multiple time-scales interactions are usually unaccounted for. To overcome these limitations, in this study we propose the use of discrete-time orthogonal Laguerre basis functions within a Wiener-Volterra decomposition of the BOLD signals to perform effective GC assessments of brain functional connectivity. We validate our method in synthetic noisy oscillator networks, and analyze experimental fMRI data from 30 healthy subjects publicly available within the Human Connectome Project (HCP). Synthetic results demonstrate that our Laguerre-Volterra based GC estimates outperform classical approaches in terms of accuracy in detecting true causal links while rejecting false causal links in complex nonlinear networks. Human data analysis shows for the first time that the default mode network modulates both the salience network as well as fronto-temporal circuits in a causal fashion

Increased instability of heartbeat dynamics in Parkinson's disease

Parkinson's disease (PD) has been reported to involve postganglionic sympathetic failure and, in 25% of patients, autonomic failure. In this work we investigate autonomic dynamics in PD using a novel methodology able to provide instantaneous estimates of the Lyapunov spectrum within a point process framework. © 2013 CCAL

A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations

High-frequency neuroelectric signals like electroencephalography (EEG) or magnetoencephalography (MEG) provide a unique opportunity to infer causal relationships between local activity of brain areas. While causal inference is commonly performed through classical Granger causality (GC) based on multivariate autoregressive models, this method may encounter important limitations (e.g., data paucity) in the case of high dimensional data from densely connected systems like the brain. Additionally, physiological signals often present long-range dependencies which commonly require high autoregressive model orders/number of parameters. We present a generalization of autoregressive models for GC estimation based on Wiener–Volterra decompositions with Laguerre polynomials as basis functions. In this basis, the introduction of only one additional global parameter allows to capture arbitrary long dependencies without increasing model order, hence retaining model simplicity, linearity and ease of parameters estimation. We validate our method in synthetic data generated from families of complex, densely connected networks and demonstrate superior performance as compared to classical GC. Additionally, we apply our framework to studying the directed human brain connectome through MEG data from 89 subjects drawn from the Human Connectome Project (HCP) database, showing that it is able to reproduce current knowledge as well as to uncover previously unknown directed influences between cortical and limbic brain regions

Increased instability of heartbeat dynamics in Parkinson's disease

Parkinson's disease (PD) has been reported to involve postganglionic sympathetic failure and, in 25% of patients, autonomic failure. In this work we investigate autonomic dynamics in PD using a novel methodology able to provide instantaneous estimates of the Lyapunov spectrum within a point process framework. © 2013 CCAL

Lower instantaneous entropy of heartbeat dynamics during seizures in untreated temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is associated with dysfunction of the autonomic nervous system. While it is known that heart rate variability (HRV) changes in epilepsy comprise both ictal (CRI) and interictal (INT) autonomic cardiac effects, the mechanisms leading to these alterations are not well understood. In this paper we investigate the alterations in instantaneous autonomic complexity during CRI in untreated TLE using bipolar ECG recordings from 10 patients with at least one seizure originating from temporal regions as recorded by video-EEG monitoring. We isolated artifact-free INT and CRI periods and computed mean values of instantaneous point-process Approximate and Sample Entropy (ipApEn and ipSampEn, respectively). ipApEn was significantly lower (p<0.02) and ipSampEn was lower (p<0.065) in CRI vs. INT. The variability (median absolute deviation) of ipApEn was also significantly lower (p<0.03) in CRI vs INT. Our results suggest that ictal events in untreated TLE are associated with a decrease in heartbeat complexity and its variability, possibly pointing toward subtle autonomic changes which may accompany or precede seizures, and can only be detected using an instantaneous, time resolved approach to quantifying autonomic complexity

Assessment of Oral Microbiome Changes in Healthy and COVID-19-Affected Pregnant Women: A Narrative Review

During pregnancy, there are several metabolic changes and an alteration in the composition of microorganisms that inhabit the oral cavity, with an increase in pathogenic bacteria that promote the onset of gingival diseases. This review is based on research in reference to the PICO model (Problem/Intervention/Comparison/Outcome), related to changes in the oral microbiome of pregnant women and possible oral consequences in patients with COVID-19. The results showed a growth of some pathogenic bacteria in pregnant women, including Aggregatibacter actinomycetemcomitans and Fusobacterium nucleatum, and the selective growth of the Prevotella intermedia, Porphyromonas gingivalis and Tannerella species, probably due to the fact that these bacteria use progesterone as a source of nutrition. These same bacteria are implicated in the development of periodontal disease. Periodontal pockets have bidirectional interactions between the oral cavity and the systemic circulatory system through the peripheral gingival blood vessels. The affinity of the SARS-CoV-2 virus to specific membrane receptors is now clear, and could involve the internal and external epithelial lining or the fibroblasts of the periodontal ligament. According to the results of the present review, the control of oral microbiome changes during pregnancy would be welcomed. The use of probiotics could help clinicians manage pregnant patients, reducing inflammatory indexes. Future studies should focus not only on changes in the level of the oral microbiome in pregnancy or the correlation between periodontal disease and COVID-19, but also on oral changes induced by both clinical situations