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F Carducci

Publications and source records attributed to F Carducci.

At least 19 recordsLinked to original sources
PubMed2005

Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function.

Nowadays, several types of brain imaging device are available to provide images of the functional activity of the cerebral cortex based on hemodynamic, metabolic, or electromagnetic measurements. However, static images of brain regions activated during particular tasks do not convey the information of how these regions communicate with each other. In this study, advanced methods for the estimation of cortical connectivity from combined high-resolution electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data are presented. These methods include a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from individual magnetic resonance images, multidipole source model, and regularized linear inverse source estimates of cortical current density. 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) fMRI. We estimate functional cortical connectivity by computing the directed transfer function (DTF) on the estimated cortical current density waveforms in regions of interest (ROIs) on the modeled cortical mantle. The proposed method was able to unveil the direction of the information flow between the cortical regions of interest, as it is directional in nature. Furthermore, this method allows to detect changes in the time course of information flow between cortical regions in different frequency bands. The reliability of these techniques was further demonstrated by elaboration of high-resolution EEG and fMRI signals collected during visually triggered finger movements in four healthy subjects. Connectivity patterns estimated for this task reveal an involvement of right parietal and bilateral premotor and prefrontal cortical areas. This cortical region involvement resembles that revealed in previous studies where visually triggered finger movements were analyzed with the use of separate EEG or fMRI measurements.

Adult↗
PubMed2004

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.

Analysis of Variance↗
PubMed2004

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.

Brain Mapping↗
PubMed2004

Coupling between "hand" primary sensorimotor cortex and lower limb muscles after ulnar nerve surgical transfer in paraplegia.

Previous neuroimaging evidence revealed an "invasion" of "hand" over "lower limb" primary sensorimotor cortex in paraplegic subjects, with the exception of a rare patient who received a surgical motor reinnervation of hip-thigh muscles by the ulnar nerve. Here, the authors show that a functional reorganization of cortico-muscular and cortico-cortical oscillatory coupling was related to the recovery of the rare patient, as a paradigmatic case of long-term plasticity in human sensorimotor cortex after motor reinnervation of paraplegic muscles. This conclusion was based on electroencephalographic and electromyographic data collected while the patient and normal control subjects performed isometric muscle contraction of the left hand or lower limb. Cortico-muscular and cortico-cortical coupling was estimated by electroencephalographic-electromyographic coherence and directed transfer function of a multivariate autoregressive model.

Adult↗
PubMed2004

Independent component analysis compared to Laplacian filtering as "deblurring" techniques for event related desynchronization/synchronization.

OBJECTIVES: The aim of the work was to compare two different approaches - one model-dependent, the other data-dependent - for "deblurring" EEG data, in order to improve the estimation of Event-Related Desynchronization/Synchronization. METHODS: Realistic Surface Laplacian filtering (SL) and Infomax Independent Component Analysis (ICA) were applied on multivariate scalp EEG signals (SL: 128 electrodes with MRI-based realistic modeling; ICA: a subset of 19 electrodes, no MRI) prior to beta Event Related Synchronization (ERS) estimation after finger movement in 8 normal subjects. ERS estimation was performed using standard band-pass filtering. ERS peak amplitudes and latencies in the most responsive channel were calculated and the effect of the two methods above was evaluated by one-way analysis of variance (ANOVA) and Sheffe's test. RESULTS: Both methods and their combination significantly improved ERS estimation (greater ERS peak amplitude, p <0.05). The results obtained after ICA on 19 electrodes were not significantly different than the ones obtained with Realistic SL using 128 electrodes and MRI for scalp modeling (p >0.89). CONCLUSIONS: The "low cost" of ICA (19 electrodes, no MRI) imposes such method as a valid alternative to SL filtering. The employ of ICA after SL filtering suggests that the "ideal EEG deblurring method" would unify the two approaches, depending on both the scalp model and the data.

Artifacts↗
PubMed2004

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.

Aged↗
PubMed2003

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.

Algorithms↗
PubMed2003

Multimodal integration of high-resolution EEG and functional magnetic resonance imaging data: a simulation study.

Previous simulation studies have stressed the importance of the use of fMRI priors in the estimation of cortical current density. However, no systematic variations of signal-to-noise ratio (SNR) and number of electrodes were explicitly taken into account in the estimation process. In this simulation study we considered the utility of including information as estimated from fMRI. This was done by using as the dependent variable both the correlation coefficient and the relative error between the imposed and the estimated waveforms at the level of cortical region of interests (ROI). A realistic head and cortical surface model was used. Factors used in the simulations were the different values of SNR of the scalp-generated data, the different inverse operators used to estimated the cortical source activity, the strengths of the fMRI priors in the fMRI-based inverse operators, and the number of scalp electrodes used in the analysis. Analysis of variance results suggested that all the considered factors significantly afflict the correlation and the relative error between the estimated and the simulated cortical activity. For the ROIs analyzed with simulated fMRI hot spots, it was observed that the best estimation of cortical source currents was performed with the inverse operators that used fMRI information. When the ROIs analyzed do not present fMRI hot spots, both standard (i.e., minimum norm) and fMRI-based inverse operators returned statistically equivalent correlation and relative error values.

Analysis of Variance↗
PubMed2002

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.

Biomedical Engineering↗
PubMed2002

Cortical source estimate of combined high resolution EEG and fMRI data related to voluntary movements.

OBJECTIVES: In this paper, we employed advanced methods for the modeling of human cortical activity related to voluntary right one-digit movements from combined high-resolution electroencepholography (EEG) and functional magnetic resonance imaging (fMRI). METHODS: Multimodal integration between EEG and fMRI data was performed by using realistic head models, a large number of scalp electrodes (128) and the estimation of current density strengths by linear inverse estimation. RESULTS: Increasing of spatial details of the estimated cortical density distributions has been detected by using the proposed integration method with respect to the estimation using EEG data alone. CONCLUSION: The proposed method of multimodal EEG-fMRI data is useful to increase spatial resolution of movement-related potentials and can also be applied to other kinds of event-related potentials.

Brain Mapping↗
PubMed2001

Mapping of early and late human somatosensory evoked brain potentials to phasic galvanic painful stimulation.

In the present study, we modeled the spatiotemporal evolution of human somatosensory evoked cortical potentials (SEPs) to brief median-nerve galvanic painful stimulation. SEPs were recorded (-50 to +250 ms) from 12 healthy subjects following nonpainful (reference), slight painful, and moderate painful stimulations (subjective scale). Laplacian transformation of scalp SEPs reduced head volume conduction effects and annulled electric reference influence. Typical SEP components to the galvanic nonpainful stimulation were contralateral frontal P20-N30-N60-N120-P170, central P22-P40, and parietal N20-P30-P60-P120 (N = negativity, P = positivity, number = latency in ms). These components were observed also with the painful stimulations, the N60, N120, P170 having a longer latency with the painful than nonpainful stimulations. Additional SEP components elicited by the painful stimulations were parietomedian P80 as well as central N125, P170 (cP170), and P200. These additional SEP components included the typical vertex negative-positive complex following transient painful stimulations. Latency of the SEP components exclusively elicited by painful stimulation is highly compatible with the involvement of A delta myelinated fibers/spinothalamic pathway. The topography of these components is in line with the response of both nociceptive medial and lateral systems including bilateral primary sensorimotor and anterior cingulate cortical areas. The role of attentive, affective, and motor aspects in the modulation of the reported SEP components merits investigation in future experiments.

Adult↗
PubMed2001

Linear inverse source estimate of combined EEG and MEG data related to voluntary movements.

A method for the modeling of human movement-related cortical activity from combined electroencephalography (EEG) and magnetoencephalography (MEG) data is proposed. This method includes a subject's multi-compartment head model (scalp, skull, dura mater, cortex) constructed from magnetic resonance images, multi-dipole source model, and a regularized linear inverse source estimate based on boundary element mathematics. Linear inverse source estimates of cortical activity were regularized by taking into account the covariance of background EG and MEG sensor noise. EEG (121 sensors) and MEG (43 sensors) data were recorded in separate sessions whereas normal subjects executed voluntary right one-digit movements. Linear inverse source solution of EEG, MEG, and EEG-MEG data were quantitatively evaluated by using three performance indexes. The first two indexes (Dipole Localization Error [DLE] and Spatial Dispersion [SDis]) were used to compute the localization power for the source solutions obtained. Such indexes were based on the information provided by the column of the resolution matrix (i.e., impulse response). Ideal DLE values tend to zero (the source current was correctly retrieved by the procedure). In contrast, high DLE values suggest severe mislocalization in the source reconstruction. A high value of SDis at a source space point mean that such a source will be retrieved by a large area with the linear inverse source estimation. The remaining performance index assessed the quality of the source solution based on the information provided by the rows of the resolution matrix R, i.e., resolution kernels. The i-th resolution kernels of the matrix R describe how the estimation of the i-th source is distorted by the concomitant activity of all other sources. A statistically significant lower dipole localization error was observed and lower spatial dispersion in source solutions produced by combined EEG-MEG data than from EEG and MEG data considered separately (P < 0.05). These effects were not due to an increased number of sensors in the combined EEG-MEG solutions. They result from the independence of source information conveyed by the multimodal measurements. From a physiological point of view, the linear inverse source solution of EEG-MEG data suggested a contralaterally preponderant bilateral activation of primary sensorimotor cortex from the preparation to the execution of the movement. This activation was associated with that of the supplementary motor area. The activation of bilateral primary sensorimotor cortical areas was greater during the processing of afferent information related to the ongoing movement than in the preparation for the motor act. In conclusion, the linear inverse source estimate of combined MEG and EEG data improves the estimate of movement-related cortical activity.

Dura Mater↗
PubMed2001

Automatic alignment of EEG/MEG and MRI data sets.

OBJECTIVES: We developed a new technique of fully automatic alignment of brain data acquired with scalp sensors (e.g. electroencephalography/evoked potential (EP) electrodes, magnetoencephalography sensors) with a magnetic resonance imaging (MRI) volume of the head. METHODS: The method uses geometrical features (two sets of head points: digitized from the subject and extracted from MRI) to guide the alignment. It combines matching on 3 dimensional (3D) geometrical moments that perform the initial alignment, and 3D distance-based alignment that provides the final tuning. To reduce errors of the initial guessed computation resulting from digitization of the head surface points we introduced weights to compute geometrical moments, and a procedure to remove outliers to eliminate incorrectly digitized points. RESULTS: The method was tested on simulated (Monte Carlo trials) and on real data sets. The simulations demonstrated that for the number of test points within the range of 0.1-1% of the total number of head surface points and for the digitization error in the range of -2-2 mm the average map error was between 0.7 and 2.1 mm. The average distance error was less than 1 mm. Tests on real data gave the average distance error between 2.1 and 2.5 mm. CONCLUSIONS: The developed technique is fast, robust and comfortable for the patient and for medical personnel. It registers scalp sensor positions with MRI head volume with accuracy that is satisfactory for localization of biological processes examined with a commonly used number of scalp sensors (32, 64, or 128).

Electroencephalography↗
PubMed2001

Prefrontal [correction of Prefontal] cortex in long-term memory: an "interference" approach using magnetic stimulation.

Neuroimaging has consistently shown engagement of the prefrontal cortex during episodic memory tasks, but the functional relevance of this metabolic/hemodynamic activation in memory processing is still to be determined. We used repetitive transcranial magnetic stimulation (rTMS) to transiently interfere with either left or right prefrontal brain activity during the encoding or retrieval of pictures showing complex scenes. We found that the right dorsolateral prefrontal cortex (DLPFC) was crucial for the retrieval of the encoded pictorial information, whereas the left DLPFC was involved in encoding operations. This 'interference' approach allowed us to establish whether a cortical area activated by a memory task actually contributes to behavioral performance.

Adult↗
PubMed2000

Comparison between human and artificial neural network detection of Laplacian-derived electroencephalographic activity related to unilateral voluntary movements.

A back-propagation artificial neural network (ANN) was tested to verify its capacity to select different classes of single trials (STs) based on the spatial information content of electroencephalographic activity related to voluntary unilateral finger movements. The rationale was that ipsilateral and contralateral primary sensorimotor cortex can be involved in a nonstationary way in the control of unilateral voluntary movements. The movement-related potentials were surface Laplacian-transformed (SL) to reduce head volume conductor effects and to model the response of the primary sensorimotor cortex. The ANN sampled the SL from four or two central channels overlying the primary motor area of both sides in the period of 80 ms preceding the electromyographic response onset in the active muscle. The performance of the ANN was evaluated statistically by calculating the percentage value of agreement between the STs classified by the ANN and those of two investigators (used as a reference). The results showed that both investigator and ANN were capable of selecting STs with the SL maximum in the central area contralateral to the movement (contralateral STs, about 25%), STs with considerable SL values also in the ipsilateral central area (bilateral STs, about 50%), and STs with neither the contralateral nor bilateral pattern ("spatially incoherent" single trials; about 25%). The maximum agreement (64-84%) between the ANN and the investigator was obtained when the ANN used four spatial inputs (P < 0.0000001). Importantly, the common means of all single trials showed a weak or absent ipsilateral response. These results may suggest that a back-propagation ANN could select EEG single trials showing stationary and nonstationary responses of the primary sensorimotor cortex, based on the same spatial criteria as the experimenter.

Electroencephalography↗
PubMed2000

Movement-related electroencephalographic reactivity in Alzheimer disease.

Event-related desynchronization/synchronization (ERD/ERS) of alpha and beta electroencephalographic (EEG) rhythms was investigated in normal subjects and mild Alzheimer Disease patients (AD), performing unilateral right finger movements (about 10 s intermovement interval). Electroencephalographic data were sampled based on 10-20 system electrode montage. Surface Laplacian estimate of the potential reduced the head-volume conductor effects and annulled electrode reference variations. Results showed that EEG reactivity (i.e., ERD/ERS) of modeled contralateral rolandic cortex and motor performance were preserved in mild to moderate AD. In contrast, modeled activity (i.e., ERD/ERS) of frontolateral, centromedial, and ipsilateral rolandic areas was abnormal. Furthermore, interrelatedness of cortical response and movement timing was abnormal in AD patients. These results would support the working hypothesis that mild to moderate AD is a global brain network disease, including processing of sensorimotor information (despite no overt movement disorder). Further investigations will ascertain the clinical relevance of these results.

Aged↗