Serenella Salinari

Comparison of different cortical connectivity estimators for high-resolution EEG recordings

The aim of this work is to characterize quantitatively the performance of a body of techniques in the frequency domain for the estimation of cortical connectivity from high‐resolution EEG recordings in different operative conditions commonly encountered in practice. Connectivity pattern estimators investigated are the Directed Transfer Function (DTF), its modification known as direct DTF (dDTF) and the Partial Directed Coherence (PDC). Predefined patterns of cortical connectivity were simulated and then retrieved by the application of the DTF, dDTF, and PDC methods. Signal‐to‐noise ratio (SNR) and length (LENGTH) of EEG epochs were studied as factors affecting the reconstruction of the imposed connectivity patterns. Reconstruction quality and error rate in estimated connectivity patterns were evaluated by means of some indexes of quality for the reconstructed connectivity pattern. The error functions were statistically analyzed with analysis of variance (ANOVA). The whole methodology was then applied to high‐resolution EEG data recorded during the well‐known Stroop paradigm. Simulations indicated that all three methods correctly estimated the simulated connectivity patterns under reasonable conditions. However, performance of the methods differed somewhat as a function of SNR and LENGTH factors. The methods were generally equivalent when applied to the Stroop data. In general, the amount of available EEG affected the accuracy of connectivity pattern estimations. Analysis of 27 s of nonconsecutive recordings with an SNR of 3 or more ensured that the connectivity pattern could be accurately recovered with an error below 7% for the PDC and 5% for the DTF. In conclusion, functional connectivity patterns of cortical activity can be effectively estimated under general conditions met in most EEG recordings by combining high‐resolution EEG techniques, linear inverse estimation of the cortical activity, and frequency domain multivariate methods such as PDC, DTF, and dDTF. Hum. Brain Mapp, 2007.

Mapping distributed sources of cortical rhythms in mild Alzheimer's disease. A multicentric EEG study

The study aimed at mapping (i) the distributed electroencephalographic (EEG) sources specific for mild Alzheimers disease (AD) compared to vascular dementia (VaD) or normal elderly people (Nold) and (ii) the distributed EEG sources sensitive to the mild AD at different stages of severity. Resting EEG (10-20 electrode montage) was recorded from 48 mild AD, 20 VaD, and 38 Nold subjects. Both AD and VaD patients had 24-17 of mini mental state examination (MMSE). EEG rhythms were delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), and beta 2 (20-30 Hz). Cortical EEG sources were modeled by low resolution brain electromagnetic tomography (LORETA). Regarding issue i, there was a decline of central, parietal, temporal, and limbic alpha 1 (low alpha) sources specific for mild AD group with respect to Nold and VaD groups. Furthermore, occipital alpha 1 sources showed a strong decline in mild AD compared to VaD group. Finally, distributed theta sources were largely abnormal in VaD but not in mild AD group. Regarding issue ii, there was a lower power of occipital alpha 1 sources in mild AD subgroup having more severe disease. Compared to previous field studies, this was the first investigation that illustrated the power spectrum profiles at the level of cortical (macroregions) EEG sources in mild AD patients having different severity of the disease with respect to VaD and normal subjects. Future studies should evaluate the clinical usefulness of this approach in early differential diagnosis, disease staging, and therapy monitoring.

First-Phase Insulin Secretion Restoration and Differential Response to Glucose Load Depending on the Route of Administration in Type 2 Diabetic Subjects After Bariatric Surgery

OBJECTIVE—The purpose of this study was to elucidate the mechanisms of diabetes reversibility after malabsorptive bariatric surgery. RESEARCH DESIGN AND METHODS—Peripheral insulin sensitivity and β-cell function after either intravenous (IVGTT) or oral glucose tolerance (OGTT) tests and minimal model analysis were assessed in nine obese, type 2 diabetic subjects before and 1 month after biliopancreatic diversion and compared with those in six normal-weight control subjects. Insulin-dependent whole-body glucose disposal was measured by the euglycemic clamp, and glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) were also measured. RESULTS—The first phase of insulin secretion after the IVGTT was fully normalized after the operation. The disposition index from OGTT data was increased about 10-fold and became similar to the values found in control subjects, and the disposition index from IVGTT data increased about 3.5-fold, similarly to what happened after the euglycemic clamp. The area under the curve (AUC) for GIP decreased about four times (from 3,000 ± 816 to 577 ± 155 pmol · l−1 · min, P < 0.05). On the contrary, the AUC for GLP1 almost tripled (from 150.4 ± 24.4 to 424.4 ± 64.3 pmol · l−1 · min, P < 0.001). No significant correlation was found between GIP or GLP1 percent changes and modification of the sensitivity indexes independently of the route of glucose administration. CONCLUSIONS—Restoration of the first-phase insulin secretion and normalization of insulin sensitivity in type 2 diabetic subjects after malabsorptive bariatric surgery seem to be related to the reduction of the effect of some intestinal factor(s) resulting from intestinal bypass.

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.

Abnormal fronto-parietal coupling of brain rhythms in mild Alzheimer's disease: a multicentric EEG study

Cholinergic deafferentation/recovery in rats mainly impinges on the fronto‐parietal coupling of brain rhythms [D. P. Holschneider et al. (1999) Exp. Brain Res., 126, 270–280]. Is this reflected by the functional coupling of fronto‐parietal cortical rhythms at an early stage of Alzheimers disease (mild AD)? Resting electroencephalographic (EEG) rhythms were studied in 82 patients with mild AD and in control subjects, such as 41 normal elderly (Nold) subjects and 25 patients with vascular dementia (VaD). Patients with AD and VaD had similar mini‐mental state evaluation scores of 17–24. The functional coupling was estimated by means of the synchronization likelihood (SL) of the EEG data at electrode pairs, accounting for linear and non‐linear components of that coupling. Cortical rhythms of interest were delta (2–4 Hz), theta (4–8 Hz), alpha (1 8–10.5 Hz), alpha 2 (10.5–13 Hz), beta 1 (13–20 Hz), beta 2 (20–30 Hz) and gamma (30–40 Hz). A preliminary data analysis (Nold) showed that surface Laplacian transformation of the EEG data reduced the values of SL, possibly because of the reduction of influences due to head volume conduction. Therefore, the final analysis was performed on Laplacian‐transformed EEG data. The SL was dominant at alpha 1 band in all groups. Compared with the Nold subjects, patients with VaD and mild AD presented a marked reduction of SL at both fronto‐parietal (delta–alpha) and inter‐hemispherical (delta–beta) electrode pairs. The feature distinguishing the patients with mild AD with respect to patients with VaD groups was a more prominent reduction of fronto‐parietal alpha 1 SL. These results suggest that mild AD is characterized by an abnormal fronto‐parietal coupling of the dominant human cortical rhythm at 8–10.5 Hz.

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

Cortical Functional Connectivity Networks in Normal and Spinal Cord Injured Patients: Evaluation by Graph Analysis

The present work aims at analyzing the structure of cortical connectivity during the attempt to move a paralyzed limb by a group of spinal cord injured (SCI) patients. Connectivity patterns were obtained by means of the Directed Transfer Function applied to the cortical signals estimated from high resolution EEG recordings. Electrical activity were estimated in normals (Healthy) and SCI patients on twelve regions of interest (ROIs) coincident with Brodmann areas. Degree distributions showed the presence of few cortical regions with a lot of outgoing connections in all the cortical networks estimated irrespectively of the frequency band investigated. For both of the groups (SCI and Healthy), bilateral cingulate motor area (CMA) acts as hub transmitting information flows. The efficiency index, allowed to assert the ordered properties of such estimated cortical networks in both populations. The comparison of such estimated networks with those obtained from random networks, elicited significant differences (P < 0.05, Bonferroni‐corrected for multiple comparisons). A statistical comparison (ANOVA) between SCI patients and healthy subjects showed a significant difference (P < 0.05) between the local efficiency of their respective networks. For three frequency bands (theta 4–7 Hz, alpha 8–12 Hz, and beta 13–29 Hz) the higher value observed in the spinal cord injured population entails a larger level of internal organization and fault tolerance. This fact suggests a sort of compensative mechanism as local response to the alteration in their MIF areas, which is probably due to the indirect effects of the spinal injury. Hum Brain Mapp, 2007.

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.

Neuroelectrical hyperscanning measures simultaneous brain activity in humans.

In this study we illustrate a methodology able to follow and study concurrent and simultaneous brain processes during cooperation between individuals, with non invasive EEG methodologies. We collected data from fourteen pairs of subjects while they were playing a card game with EEG. Data collection was made simultaneously on all the subjects during the card game. An extension of the Granger-causality approach allows us to estimate the functional connection between signals estimated from different Regions of Interest (ROIs) in different brains during the analyzed task. Finally, with the use of graph theory, we contrast the functional connectivity patterns of the two players belonging to the same team. Statistically significant functional connectivities were obtained from signals estimated in the ROIs modeling the anterior cingulate cortex (ACC) and the prefrontal areas described by the Brodmann areas 8 with the signals estimated in all the other modelled cortical areas. Results presented suggested the existence of Granger-sense causal relations between the EEG activity estimated in the prefrontal areas 8 and 9/46 of one player with the EEG activity estimated in the ACC of their companion. We illustrated the feasibility of functional connectivity methodology on the EEG hyperscannings performed on a group of subjects. These functional connectivity estimated from the couple of brains could suggest, in statistical and mathematical terms, the modelled cortical areas that are correlated in Granger-sense during the solution of a particular task. EEG hyperscannings could be used to investigate experimental paradigms where the knowledge of the simultaneous interactions between the subjects have a value.

Directionality of EEG synchronization in Alzheimer's disease subjects

Is directionality of electroencephalographic (EEG) synchronization abnormal in amnesic mild cognitive impairment (MCI) and Alzheimers disease (AD)? EEG data were recorded in 64 normal elderly (Nold), 69 amnesic MCI, and 73 mild AD subjects at rest condition (closed eyes). Direction of information flux within EEG functional coupling at electrode pairs was performed by directed transfer function (DTF) at delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10 Hz), alpha 2 (10-12 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-40 Hz). Parietal to frontal direction of the information flux within EEG functional coupling was stronger in Nold than in MCI and/or AD subjects, namely for alpha and beta rhythms. In contrast, the directional flow within inter-hemispheric EEG functional coupling did not discriminate among the three groups. These results suggest that directionality of parieto-to-frontal EEG synchronization is abnormal not only in AD but also in amnesic MCI.