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 five 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 linear inverse estimation and directed transfer function: simulations and application to real data.

OBJECTIVE: To test a technique called Directed Transfer Function (DTF) for the estimation of human cortical connectivity, by means of simulation study and human study, using high resolution EEG recordings related to finger movements. METHODS: The method of the Directed Transfer Function (DTF) is a frequency-domain approach, based on a multivariate autoregressive modeling of time series and on the concept of Granger causality. Since the spreading of the potential from the cortex to the sensors makes it difficult to infer the relation between the spatial patterns on the sensor space and those on the cortical sites, we propose the use of the DTF method on cortical signals estimated from high resolution EEG recordings, which exhibit a higher spatial resolution than conventional cerebral electromagnetic measures. The simulation study was followed by an analysis of variance (ANOVA) of the results obtained for different levels of Signal to Noise Ratio (SNR) and temporal length, as they have been systematically imposed on simulated signals. The whole methodology was then applied to high resolution EEG data recorded during a visually paced finger movement. RESULTS: The statistical analysis performed returns that during simulations, DTF is able to estimate correctly the imposed connectivity patterns under reasonable operative conditions, i.e. when data exhibit a SNR of at least 3 and a length of at least 75 s of non-consecutive recordings at 64 Hz of sampling rate, equivalent, more generally, to 4800 data samples. CONCLUSIONS: Functional connectivity patterns of cortical activity can be effectively estimated under general conditions met in any practical EEG recordings, by combining high resolution EEG techniques, linear inverse estimation and the DTF method. SIGNIFICANCE: The estimation of cortical connectivity can be performed not only with hemodynamic measurements, by using functional MRI recordings, but also with modern EEG recordings treated with advanced computational techniques.

Pharmacokinetic analysis of azelaic acid disodium salt. A proposed substrate for total parenteral nutrition.

Azelaic acid was the first dicarboxylic acid proposed as an alternative energy substrate in total parenteral nutrition. In this study, the pharmacokinetics of azelaic acid were investigated in 12 healthy volunteers, 7 receiving a constant infusion (10g over 90 min) and 5 a bolus dose (1g). The 24h urinary excretion and plasma concentration in blood samples taken at regular intervals were assayed by gas-liquid chromatography. Experimental data were analysed by a 2-compartment nonlinear model that describes both tubular secretion and cellular uptake in Michaelis-Menten terms. A high value of urinary excretion (mean 76.9% of infused dose) and a mean clearance of 8.42 L/h were found, suggesting the presence of tubular secretion. Estimating the population mean of the pharmacokinetic model parameters gave a maximal cellular uptake of 0.657 g/h. The model predicts that 90% of the maximal uptake should be reached in the plateau phase of a constant infusion of 2.2 g/h. The presence of extensive and rapid losses through urinary excretion, and the low estimated value of the maximal cellular uptake, indicate that azelaic acid is not suitable as an energy substrate for total parenteral nutrition.

Peristaltic transport of a solid bolus.

An analysis of the peristaltic propulsion of a solid spherical bolus enclosed in a contractile membrane is presented. The model is based on in vitro preparations of intestinal segments, and utilizes a simplified representation of the mechanical properties of the muscular coats of the wall. The sequence of deformed configurations of the membrane and the displacement of the bolus are obtained by numerical solution of the model equations. The analysis presented in this paper could be useful for other studies in biomechanics (e.g. uterine contraction and motion of red blood cells in narrow capillaries).

Pharmacokinetic analysis of dodecanedioic acid in humans from bolus data.

BACKGROUND: Excretion and tissue uptake of dodecanedioic acid (C12), a proposed alternative fuel substrate, was investigated in humans by bolus experiments. METHODS: Seven overnight-fasting healthy male volunteers received i.v. a bolus (1 g) of C12. Blood samples were collected after C12 administration at intervals of 15 minutes, and C12 serum concentration was measured by high-performance liquid chromatography. C12 excretion in 24-hour urine was measured. Binding of C12 in human serum was determined in separate equilibrium dialysis experiments by means of an isotopic compound (disodic salt of (1,12)14C-dodecanedioic acid). A two-compartment model was used for describing C12 kinetics. RESULTS: The excreted amount of C12 in 24-hour urine was found to be, on the average, 1.62% of administered dose. The apparent number of binding sites per albumin molecule was 3.1 +/- 0.2 (estimate +/- SE) with an affinity constant of 6.4 +/- 1.8 mM-1. The distribution volume of central compartment was 5.56 +/- 3.13 L and that of peripheral compartment was 87.4 +/- 30.4 L. The rate constant of exchange between compartments was 4.60 +/- 3.50 L/min, that or urinary excretion 25.6 +2- 15.5 mL/min, and that of tissue uptake 2.17 +/- 0.86 L/min. CONCLUSIONS: These results are promising for C12 utilization in parenteral nutrition, because C12 elimination in urine is low whereas tissue uptake appears to be rather efficient.

Statistical analysis of topographic maps of short-latency somatosensory evoked potentials in normal and parkinsonian subjects.

This work had the following objectives: i) to integrate temporal analysis (N30 peak) with power-spectrum topographic mapping of short-latency somatosensory evoked potentials (SEP's) recorded in parkinsonian and normal control subjects; and ii) to analyze with a new statistical approach the between-group topographical differences in both the time and frequency domains. The principal aim was to better determine the topography of the scalp frontal areas where the amplitude of the N30 wave was previously found to be significantly reduced in parkinsonians. The statistical procedure was based on the combined use of descriptive data analysis (DDA) and multivariate analysis. In the context of DDA, an improved version of significance probability mapping (SPM) was used by which it is possible to evaluate homo- and nonhomoscedastic data with parametric tests. The statistical evaluation of between-group differences was performed with the multivariate Hotelling's T2 test and the associated post hoc test. With this statistical procedure, it was possible to determine that the between-group statistical differences in both the temporal and power spectrum distributions were localized only in midline and contiguous contralateral frontal areas of the scalp.

Computerized processing of EEG-EOG-EMG artifacts for multi-centric studies in EEG oscillations and event-related potentials.

The aim of this study is to present a package including standard software for the electroencephalographic (EEG), electro-oculographic (EOG) and electromyographic (EMG) preliminary data analysis, which may be suitable to standardize the results of many EEG research centers studies (i.e. multi-centric studies) especially focused on event-related potentials. In particular, our software package includes (semi)automatic procedures for (i) EOG artifact detection and correction, (ii) EMG analysis, (iii) EEG artifact analysis, (iv) optimization of the ratio between artifact-free EEG channels and trials to be rejected. The performances of the software package on EOG-EEG-EMG data related to cognitive-motor tasks were evaluated with respect to the preliminary data analysis performed by two expert electroencephalographists (gold standard). Due to its extreme importance for multi-centric EEG studies, we compared the performances of two representative "regression" methods for the EOG correction in time and frequency domains. The aim was the selection of the most suitable method in the perspective of a multi-centric EEG study. The results showed an acceptable agreement of approximately 95% between the human and software behaviors, for the detection of vertical and horizontal EOG artifacts, the measurement of hand EMG responses for a cognitive-motor paradigm, the detection of involuntary mirror movements, and the detection of EEG artifacts. Furthermore, our results indicated a particular reliability of a 'regression' EOG correction method operating in time domain (i.e. ordinary least squares). These results suggest that such a software package could be used for multi-centric EEG studies.

Pharmacokinetics of sebacic acid in rats.

The pharmacokinetics of disodium sebacate (Sb) was studied in Wistar rats of both sexes. Sebacate was administered either as intra-peritoneal (i.p.) bolus (six doses ranging from 10 mg to 320 mg) or as oral bolus (two doses: 80 and 160 mg). Plasma and urinary concentrations of Sb and urinary concentrations of Sb and its products of beta-oxidation (suberic and adipic acids) were measured by an improved method using gas-liquid chromatography/mass-spectrometry. A single compartment with two linear elimination routes was selected after no increase in significance was shown by an additional compartment and after a saturable mechanism was found to be unsuitable. Both renal and non-renal elimination parameters were obtained by Marquardt non linear fitting of plasma concentrations together with urinary elimination. The data reported are calculated from the analysis on the whole population of rats and referred to an average body weight (bw) of 100 g. The Sb half-time was 31.5 min. The tissue elimination rate was 0.0122 min-1. The overall volume of distribution was found to be 26.817 ml/100 g bw. The renal clearance was 0.291 ml/min/100 g of bw, which is much less than the value of GFR reported in literature (about 1 ml/min/100 g bw), suggesting the presence of Sb reabsorption from the ultrafiltrate. The value of Sb renal clearance was found to be a concentration-independent function, suggesting the presence of a passive back-diffusion. The relative bioavailability of the oral form compared to the i.p. form was 69.09%, showing a good absorption of the drug.

Introducing BF++: AC++ framework for cognitive bio-feedback systems design.

OBJECTIVE: This paper addressed the issue of building-up a framework for the realization of several cognitive bio-feedback (CBF) systems. It minimizes the programming effort and maximizes the efficiency and the cross-platform portability so that it can be used with many platforms (either software or hardware). METHODS: A generic CBF system was decomposed into six modules: acquisition, kernel, feedback rule, patient feedback, operator user interface and persistent storage. The way in which these modules interact was defined by immutable software interfaces in a way that allows to completely substitute a module without the need to modify the others. RESULTS: Three Brain Computer Interface engines were developed with less than 40 lines of C++ code each. They can also be used under virtually any platform that supports an ANSI C++ compiler. CONCLUSION: A framework for the implementation of a wide range of CBF systems was developed. Compared to the other approaches that are described in the literature, the proposed one is the most efficient, the most protable across different platforms, the most generic and the one that allows the realization of the cheapest final systems.

EEG deblurring techniques in a clinical context.

OBJECTIVES: EEG scalp potential distributions recorded in humans are affected by low spatial resolution and by the dependence on the electrical reference used. High resolution EEG technologies are available to drastically increase the spatial resolution of the raw EEG. Such technologies include the computation of surface Laplacian (SL) of the recorded potentials, as well as the use of realistic head models to estimate the cortical sources via linear inverse procedure (low resolution brain electromagnetic tomography, LORETA). However, these deblurring procedures are generally used in conjunction with EEG recordings with 64-128 scalp electrodes and with realistic head models obtained via sequential magnetic resonance images (MRIs) of the subjects. Such recording setup it is not often available in the clinical context, due to both the unavailability of these technologies and the scarce compliance of the patients with them. In this study we addressed the use of SL and LORETA deblurring techniques to analyze data from a standard 10-20 system (19 electrodes) in a group of Alzheimer disease (AD) patients. METHODS: EEG data related to unilateral finger movements were gathered from 10 patients affected by AD. SL and LORETA techniques were applied for source estimation of EEG data. The use of MRIs for the construction of head models was avoided by using the quasi-realistic head model of the Brain Imaging Neurology Institute of Montreal. RESULTS: A similar cortical activity estimated by the SL and LORETA techniques was observed during an identical time period of the acquired EEG data in the examined population. CONCLUSIONS: The results of the present study suggest that both SL and LORETA approaches can be usefully applied in the clinical context, by using quasi-realistic head modeling and a standard 10-20 system as electrode montage (19 electrodes). These results represent a reciprocal cross-validation of the two mathematically independent techniques in a clinical environment.

Classification of EEG mental patterns by using two scalp electrodes and Mahalanobis distance-based classifiers.

OBJECTIVES: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes. METHODS: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used. RESULTS: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes. CONCLUSIONS: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

Solving the neuroimaging puzzle: the multimodal integration of neuroelectromagnetic and functional magnetic resonance recordings.

In this chapter, advanced methods for the modeling of human cortical activity from combined high-resolution electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data are reviewed. These methods include a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from magnetic resonance images, multidipole source model, and regularized linear inverse source estimates. Determination of the priors in the resolution of the linear inverse problem was performed with the use of information from the hemodynamic responses of the cortical areas as revealed by block-designed (strength of activated voxels) and event-related (coupling of activated voxels) fMRI. As an example, these methods were applied to EEG (128 electrodes) and fMRI data, which were recorded in separate sessions while normal subjects executed voluntary right one-digit movements.

Recognition of imagined hand movements with low resolution surface Laplacian and linear classifiers.

EEG-based Brain Computer Interfaces (BCIs) require on-line detection of mental states from spontaneous EEG signals. In this framework, it was suggested that EEG patterns can be better detected with EEG data transformed with Surface Laplacian computation (SL) than with the unprocessed raw potentials. However, accurate SL estimates require the use of many EEG electrodes, when local estimation methods are used. Since BCI devices have to use a limited number of electrodes for practical reasons, we investigated the performances of spline methods for SL estimates using a limited number of electrodes (low resolution SL). Recognition of mental activity was attempted on both raw and SL-transformed EEG data from five healthy people performing two mental tasks, namely imagined right and left hand movements. Linear classifiers were used including Signal Space Projection (SSP) and Fisher's linear discriminant. Results showed an acceptable average correlation between the waveforms obtained with the low resolution SL and these obtained with the SL computed from 26 electrodes (full resolution SL). More importantly, satisfactorily recognition scores for mental EEG-patterns were obtained with the low-resolution surface Laplacian transformation of the recorded potentials when compared with those obtained by using full resolution SL (82%). These results demonstrated also the utility of linear classifiers for the detection of mental patterns in the BCI field.

Influence of P300 latency jitter on event related potential-based brain-computer interface performance.

OBJECTIVE: Several ERP-based brain-computer interfaces (BCIs) that can be controlled even without eye movements (covert attention) have been recently proposed. However, when compared to similar systems based on overt attention, they displayed significantly lower accuracy. In the current interpretation, this is ascribed to the absence of the contribution of short-latency visual evoked potentials (VEPs) in the tasks performed in the covert attention modality. This study aims to investigate if this decrement (i) is fully explained by the lack of VEP contribution to the classification accuracy; (ii) correlates with lower temporal stability of the single-trial P300 potentials elicited in the covert attention modality. APPROACH: We evaluated the latency jitter of P300 evoked potentials in three BCI interfaces exploiting either overt or covert attention modalities in 20 healthy subjects. The effect of attention modality on the P300 jitter, and the relative contribution of VEPs and P300 jitter to the classification accuracy have been analyzed. MAIN RESULTS: The P300 jitter is higher when the BCI is controlled in covert attention. Classification accuracy negatively correlates with jitter. Even disregarding short-latency VEPs, overt-attention BCI yields better accuracy than covert. When the latency jitter is compensated offline, the difference between accuracies is not significant. SIGNIFICANCE: The lower temporal stability of the P300 evoked potential generated during the tasks performed in covert attention modality should be regarded as the main contributing explanation of lower accuracy of covert-attention ERP-based BCIs.

Self-calibration algorithm in an asynchronous P300-based brain-computer interface.

OBJECTIVE: Reliability is a desirable characteristic of brain-computer interface (BCI) systems when they are intended to be used under non-experimental operating conditions. In addition, their overall usability is influenced by the complex and frequent procedures that are required for configuration and calibration. Earlier studies examined the issue of asynchronous control in P300-based BCIs, introducing dynamic stopping and automatic control suspension features. This report proposes and evaluates an algorithm for the automatic recalibration of the classifier's parameters using unsupervised data. APPROACH: Ten healthy subjects participated in five P300-based BCI sessions throughout a single day. First, we examined whether continuous adaptation of control parameters improved the accuracy of the asynchronous system over time. Then, we assessed the performance of the self-calibration algorithm with respect to the no-recalibration and supervised calibration conditions with regard to system accuracy and communication efficiency. MAIN RESULTS: Offline tests demonstrated that continuous adaptation of the control parameters significantly increased the communication efficiency of asynchronous P300-based BCIs. The self-calibration algorithm correctly assigned labels to unsupervised data with 95% accuracy, effecting communication efficiency that was comparable with that of supervised repeated calibration. SIGNIFICANCE: Although additional online tests that involve end-users under non-experimental conditions are needed, these preliminary results are encouraging, from which we conclude that the self-calibration algorithm is a promising solution to improve P300-based BCI usability and reliability.

Investigating statistical differences in connectivity patterns properties at single subject level: a new resampling approach.

Methods based on the multivariate autoregressive (MVAR) approach are commonly used for effective connectivity estimation as they allow to include all available sources into a unique model. To ensure high levels of accuracy for high model dimensions, all the observations are used to provide a unique estimation of the model, and thus of the network and its properties. The unavailability of a distribution of connectivity values for a single experimental condition prevents to perform statistical comparisons between different conditions at a single subject level. This is a major limitation, especially when dealing with the heterogeneity of clinical conditions presented by patients. In the present paper we proposed a novel approach to the construction of a distribution of connectivity in a single subject case. The proposed approach is based on small perturbations of the networks properties and allows to assess significant changes in brain connectivity indexes derived from graph theory. Its feasibility and applicability were investigated by means of a simulation study and an application to real EEG data.

A new descriptor of neuroelectrical activity during BCI-assisted motor imagery-based training in stroke patients.

In BCI applications for stroke rehabilitation, BCI systems are used with the aim of providing patients with an instrument that is capable of monitoring and reinforcing EEG patterns generated by motor imagery (MI). In this study we proposed an offline analysis on data acquired from stroke patients subjected to a BCI-assisted MI training in order to define an index for the evaluation of MI-BCI training session which is independent from the settings adopted for the online control and which is able to describe the properties of neuroelectrical activations across sessions. Results suggest that such index can be adopted to sort the trails within a session according to the adherence to the task.

Individual cortical connectivity changes after stroke: a resampling approach to enable statistical assessment at single-subject level.

One of the main limitations commonly encountered when dealing with the estimation of brain connectivity is the difficulty to perform a statistical assessment of significant changes in brain networks at a single-subject level. This is mainly due to the lack of information about the distribution of the connectivity estimators at different conditions. While group analysis is commonly adopted to perform a statistical comparison between conditions, it may impose major limitations when dealing with the heterogeneity expressed by a given clinical condition in patients. This holds true particularly for stroke when seeking for quantitative measurements of the efficacy of any rehabilitative intervention promoting recovery of function. The need is then evident of an assessment which may account for individual pathological network configuration associated with different level of patients' response to treatment; such network configuration is highly related to the effect that a given brain lesion has on neural networks. In this study we propose a resampling-based approach to the assessment of statistically significant changes in cortical connectivity networks at a single subject level. First, we provide the results of a simulation study testing the performances of the proposed approach under different conditions. Then, to show the sensitivity of the method, we describe its application to electroencephalographic (EEG) data recorded from two post-stroke patients who showed different clinical recovery after a rehabilitative intervention.

Investigating the effects of a sensorimotor rhythm-based BCI training on the cortical activity elicited by mental imagery.

OBJECTIVE: It is well known that to acquire sensorimotor (SMR)-based brain-computer interface (BCI) control requires a training period before users can achieve their best possible performances. Nevertheless, the effect of this training procedure on the cortical activity related to the mental imagery ability still requires investigation to be fully elucidated. The aim of this study was to gain insights into the effects of SMR-based BCI training on the cortical spectral activity associated with the performance of different mental imagery tasks. APPROACH: Linear cortical estimation and statistical brain mapping techniques were applied on high-density EEG data acquired from 18 healthy participants performing three different mental imagery tasks. Subjects were divided in two groups, one of BCI trained subjects, according to their previous exposure (at least six months before this study) to motor imagery-based BCI training, and one of subjects who were naive to any BCI paradigms. MAIN RESULTS: Cortical activation maps obtained for trained and naive subjects indicated different spectral and spatial activity patterns in response to the mental imagery tasks. Long-term effects of the previous SMR-based BCI training were observed on the motor cortical spectral activity specific to the BCI trained motor imagery task (simple hand movements) and partially generalized to more complex motor imagery task (playing tennis). Differently, mental imagery with spatial attention and memory content could elicit recognizable cortical spectral activity even in subjects completely naive to (BCI) training. SIGNIFICANCE: The present findings contribute to our understanding of BCI technology usage and might be of relevance in those clinical conditions when training to master a BCI application is challenging or even not possible.

A new statistical approach for the extraction of adjacency matrix from effective connectivity networks.

Graph theory is a powerful mathematical tool recently introduced in neuroscience field for quantitatively describing the main properties of investigated connectivity networks. Despite the technical advancements provided in the last few years, further investigations are needed for overcoming actual limitations in the field. In fact, the absence of a common procedure currently applied for the extraction of the adjacency matrix from a connectivity pattern has been leading to low consistency and reliability of ghaph indexes among the investigated population. In this paper we proposed a new approach for adjacency matrix extraction based on a statistical threshold as valid alternative to empirical approaches, extensively used in Neuroscience field (i.e. fixing the edge density). In particular we performed a simulation study for investigating the effects of the two different extraction approaches on the topological properties of the investigated networks. In particular, the comparison was performed on two different datasets, one composed by uncorrelated random signals (null-model) and the other one by signals acquired on a mannequin head used as a phantom (EEG null-model). The results highlighted the importance to use a statistical threshold for the adjacency matrix extraction in order to describe the real existing topological properties of the investigated networks. The use of an empirical threshold led to an erroneous definition of small-world properties for the considered connectivity patterns.