Maria Grazia Marciani

Tracking the Time-Varying Cortical Connectivity Patterns by Adaptive Multivariate Estimators

The directed transfer function (DTF) and the partial directed coherence (PDC) are frequency-domain estimators that are able to describe interactions between cortical areas in terms of the concept of Granger causality. However, the classical estimation of these methods is based on the multivariate autoregressive modelling (MVAR) of time series, which requires the stationarity of the signals. In this way, transient pathways of information transfer remains hidden. The objective of this study is to test a time-varying multivariate method for the estimation of rapidly changing connectivity relationships between cortical areas of the human brain, based on DTF/PDC and on the use of adaptive MVAR modelling (AMVAR) and to apply it to a set of real high resolution EEG data. This approach will allow the observation of rapidly changing influences between the cortical areas during the execution of a task. The simulation results indicated that time-varying DTF and PDC are able to estimate correctly the imposed connectivity patterns under reasonable operative conditions of signal-to-noise ratio (SNR) ad number of trials. An SNR of Ave and a number of trials of at least 20 provide a good accuracy in the estimation. After testing the method by the simulation study, we provide an application to the cortical estimations obtained from high resolution EEG data recorded from a group of healthy subject during a combined foot-lips movement and present the time-varying connectivity patterns resulting from the application of both DTF and PDC. Two different cortical networks were detected with the proposed methods, one constant across the task and the other evolving during the preparation of the joint movement.

Assessing cortical functional connectivity by partial directed coherence: simulations and application to real data

The aim of this paper is to test a technique called partial directed coherence (PDC) and its modification (squared PDC; sPDC) for the estimation of human cortical connectivity by means of simulation study, in which both PDC and sPDC were studied by analysis of variance. The statistical analysis performed returned that both PDC and sPDC are able to estimate correctly the imposed connectivity patterns when data exhibit a signal-to-noise ratio of at least 3 and a length of at least 27 s of nonconsecutive recordings at 250 Hz of sampling rate, equivalent, more generally, to 6750 data samples

High-Resolution EEG Techniques for Brain-Computer Interface Applications

High-resolution electroencephalographic (HREEG) techniques allow estimation of cortical activity based on non-invasive scalp potential measurements, using appropriate models of volume conduction and of neuroelectrical sources. In this study we propose an application of this body of technologies, originally developed to obtain functional images of the brains electrical activity, in the context of brain-computer interfaces (BCI). Our working hypothesis predicted that, since HREEG pre-processing removes spatial correlation introduced by current conduction in the head structures, by providing the BCI with waveforms that are mostly due to the unmixed activity of a small cortical region, a more reliable classification would be obtained, at least when the activity to detect has a limited generator, which is the case in motor related tasks. HREEG techniques employed in this study rely on (i) individual head models derived from anatomical magnetic resonance images, (ii) distributed source model, composed of a layer of current dipoles, geometrically constrained to the cortical mantle, (iii) depth-weighted minimum L(2)-norm constraint and Tikhonov regularization for linear inverse problem solution and (iv) estimation of electrical activity in cortical regions of interest corresponding to relevant Brodmann areas. Six subjects were trained to learn self modulation of sensorimotor EEG rhythms, related to the imagination of limb movements. Off-line EEG data was used to estimate waveforms of cortical activity (cortical current density, CCD) on selected regions of interest. CCD waveforms were fed into the BCI computational pipeline as an alternative to raw EEG signals; spectral features are evaluated through statistical tests (r(2) analysis), to quantify their reliability for BCI control. These results are compared, within subjects, to analogous results obtained without HREEG techniques. The processing procedure was designed in such a way that computations could be split into a setup phase (which includes most of the computational burden) and the actual EEG processing phase, which was limited to a single matrix multiplication. This separation allowed to make the procedure suitable for on-line utilization, and a pilot experiment was performed. Results show that lateralization of electrical activity, which is expected to be contralateral to the imagined movement, is more evident on the estimated CCDs than in the scalp potentials. CCDs produce a pattern of relevant spectral features that is more spatially focused, and has a higher statistical significance (EEG: 0.20+/-0.114 S.D.; CCD: 0.55+/-0.16 S.D.; p=10(-5)). A pilot experiment showed that a trained subject could utilize voluntary modulation of estimated CCDs for accurate (eight targets) on-line control of a cursor. This study showed that it is practically feasible to utilize HREEG techniques for on-line operation of a BCI system; off-line analysis suggests that accuracy of BCI control is enhanced by the proposed method.

Orexinergic system dysregulation, sleep impairment, and cognitive decline in Alzheimer disease.

IMPORTANCEnNocturnal sleep disruption develops in Alzheimer disease (AD) owing to the derangement of the sleep-wake cycle regulation pathways. Orexin contributes to the regulation of the sleep-wake cycle by increasing arousal levels and maintaining wakefulness.nnnOBJECTIVESnTo study cerebrospinal fluid levels of orexin in patients with AD, to evaluate the relationship of orexin cerebrospinal fluid levels with the degree of dementia and the cerebrospinal fluid AD biomarkers (tau proteins and β-amyloid 1-42), and to analyze potentially related sleep architecture changes measured by polysomnography.nnnDESIGN, SETTING, AND PARTICIPANTSnWe conducted a case-control study from August 1, 2012, through May 31, 2013. We included 48 drug-naive AD patients referred to the Neurological Clinic of the University Hospital of Rome Tor Vergata. Based on the Mini-Mental State Examination score, 21 patients were included in mild AD group (score, ≥21), whereas 27 were included in the moderate to severe AD group (score, <21). The control group consisted of 29 nondemented participants of similar age and sex.nnnEXPOSUREnLaboratory assessment of cerebrospinal fluid levels of orexin, tau proteins, and β-amyloid 1-42 and polysomnographic assessment of sleep variables.nnnMAIN OUTCOMES AND MEASURESnLevels of orexin, tau proteins, and β-amyloid 1-42; macrostructural variables of nocturnal sleep (total sleep time, sleep efficiency, sleep onset and rapid eye movement [REM] sleep latencies, non-REM and REM sleep stages, and wakefulness after sleep onset); and Mini-Mental State Examination scores.nnnRESULTSnPatients with moderate to severe AD presented with higher mean (SD) orexin levels compared with controls (154.36 [28.16] vs 131.03 [26.55]; P < .01) and with more impaired nocturnal sleep with respect to controls and patients with mild AD. On the other hand, in the global AD group, orexin levels were positively correlated with total tau protein levels (r = 0.32; P = .03) and strictly related to sleep impairment. Finally, cognitive impairment, as measured by the Mini-Mental State Examination, was correlated with sleep structure deterioration.nnnCONCLUSIONS AND RELEVANCEnOur results demonstrate that, in AD, increased cerebrospinal fluid orexin levels are related to a parallel sleep deterioration, which appears to be associated with cognitive decline. Therefore, the orexinergic system seems to be dysregulated in AD, and its output and function appear to be overexpressed along the progression of the neurodegenerative process. This overexpression may result from an imbalance of the neurotransmitter networks regulating the wake-sleep cycle toward the orexinergic system promoting wakefulness.

Hyperhomocysteinemia in epileptic patients on new antiepileptic drugs.

Purpose:u2002 Older enzyme‐inducing antiepileptic drugs (AEDs) may induce supraphysiologic plasma concentrations of total (t) homocysteine (Hcy). The aim of the present study was to investigate the effect of new AEDs on plasma tHcy levels.

Sleep disorders in adult-onset myotonic dystrophy type 1: a controlled polysomnographic study

Background:u2002 Sleep disturbances and excessive daytime somnolence are common and disabling features in adult‐onset myotonic dystrophy type 1 (DM1).

Hypermethods for EEG hyperscanning

Until now, in EEG studies the activity of the brain during simple or complex tasks have been recorded in a single subject. Often, during such EEG recordings, subjects interacts with the external devices or the researchers in order to reproduce conditions similar to the those usually occurring in the real-life. However, in order to study the concurrent activity in subjects interacting in cooperation or competition activities, the issue of the simultaneous recording of their brain activity became mandatory. The simultaneous recording of hemodynamic or neuroelectric activity of the brain is called hyperscanning. We would like present results obtained by EEG hyperscannings performed on a group of subjects engaged in cooperative games. The EEG hyperscannings have been performed with the simultaneous use of high resolution EEG devices on groups of three and four subjects while they were playing cooperative games. The analysis of such data have been conducted with analysis method that taken into account the particular nature of the data simultaneously gathered from different subjects.. We called these methods hypermethods. In particular, we estimate the concurrent activity in multiple brains of the group and we depicted the causal connections between regions of different brains (hyperconnectivity). The resulting causality patterns will link certain areas of the brain of a subject to the waveforms obtained from the other brain areas of another subject of the same group. Results obtained in a study of several groups recorded by the hyperscanning reveals causal links between prefrontal areas of the different subjects when they are performing cooperative games in different frequency bands. Hypermethods for hyperscanning will open a different area for the study of neuroscience, in which the activity of multiple brains during social cooperation could be investigated. In such area the importance of EEG will be relevant due to its temporal and spatial resolution now obtainable with the high resolution EEG techniques

Cortical Activity and Connectivity of Human Brain during the Prisoner's Dilemma: an EEG Hyperscanning Study

A major limitation of the approaches used in most of the studies performed so far for the characterization of the brain responses during social interaction is that only one of the participating brains is measured each time. The ldquointeractionrdquo between cooperating, competing or communicating brains is thus not measured directly, but inferred by independent observations aggregated by cognitive models and assumptions that link behavior and neural activation. In this paper, we use the simultaneous neuroelectric recording of several subjects engaged in cooperative games (EEG hyperscanning). This EEG hyperscanning allow us to observe and model directly the neural signature of human interactions in order to understand the cerebral processes generating and generated by social cooperation or competition. We used a paradigm called Prisoners Dilemma derived from the game theory. Results collected in a population of 22 subjects suggested that the most consistently activated structure in social interaction paradigms is the medial prefrontal cortex, which is found to be active in all the conflict situations analyzed. The role of the anterior cingulated cortex (ACC) assumes a main character being a discriminant factor for the ldquodefectrdquo attitude of the entire population examined. This observation is compatible with the role that the Theory of Mind assigns to the ACC.

High Resolution EEG Hyperscanning During a Card Game

In order to study the concurrent activity in subjects interacting in cooperation or competition activities, the issue of the simultaneous recording of their brain activity became mandatory. The simultaneous recording of neuroelectric activity of the brain is called EEG hyperscanning. We would like present results obtained by EEG hyperscannings performed on a group of subjects engaged in a card game. The EEG hyperscannings have been performed with the simultaneous use of high resolution EEG devices on groups of four subjects while they were playing a card game. We estimated the concurrent activity in multiple brains of the group and we depicted the causal connections between regions of different brains. Results obtained in a study of several groups recorded by the EEG hyperscanning reveal larger activity in prefrontal and anterior cingulated cortex in different frequency bands for the player that start the game when compared to other players. EEG hyperscannings will open a different area for the study of neuroscience, in which the activity of multiple brains during social cooperation could be investigated.

Brain Network Analysis From High-Resolution EEG Recordings by the Application of Theoretical Graph Indexes

The extraction of the salient characteristics from brain connectivity patterns is an open challenging topic since often the estimated cerebral networks have a relative large size and complex structure. Since a graph is a mathematical representation of a network, which is essentially reduced to nodes and connections between them, the use of a theoretical graph approach would extract significant information from the functional brain networks estimated through different neuroimaging techniques. The present work intends to support the development of the ldquobrain network analysis:rdquo a mathematical tool consisting in a body of indexes based on the graph theory able to improve the comprehension of the complex interactions within the brain. In the present work, we applied for demonstrative purpose some graph indexes to the time-varying networks estimated from a set of high-resolution EEG data in a group of healthy subjects during the performance of a motor task. The comparison with a random benchmark allowed extracting the significant properties of the estimated networks in the representative Alpha (7-12 Hz) band. Altogether, our findings aim at proving how the brain network analysis could reveal important information about the time-frequency dynamics of the functional cortical networks.