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We explored the cortical dynamics during movements of an unaffected body part in tetraplegic subjects with chronic spinal cord injury (SCI). The aims were to find out whether the intact movements were associated with a physiological time‐varying pattern of activity in the motor‐related cortical areas and whether the primary motor area (MI) activation followed a somatotopic distribution. Event‐related potentials to self‐initiated lip movements were analyzed by means of cortical source imaging of EEG recorded from seven tetraplegic subjects and seven control subjects. Regions of interest (ROIs) were selected on individual MRI and the time‐varying electrophysiologic activity (cortical current density, CCD) was estimated on these ROIs and subjected to across‐subject analysis. A significant, bilateral movement‐related pattern of MI activation was detected during motor task execution in SCI patients as well as in controls. The site of local maxima activation displayed a symmetrical discrete distribution within MI, consistently with a putative somatotopic lip representation, in all the subjects. The supplementary motor area proper (SMAp) was always coactivated with MI and coactivation was characterized by a time course with typical premotion and motion phases over both motor areas. A clear‐cut temporal delay between the SMAp and MI activation did not occur either in SCI patients or in controls. These findings obtained with noninvasive neuroelectrical source imaging document that in chronic SCI subjects “executive” motor areas are engaged with a preserved temporal and spatial pattern during preparation and execution of intact movements. Hum Brain Mapp, 2005. © 2005 Wiley‐Liss, Inc.

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. Hum. Brain Mapping 14:197–209, 2001. © 2001 Wiley‐Liss, Inc.

Magnetoencephalography (MEG) includes fast and comfortable recording procedures very suitable for the neurophysiological study of cognitive functions in aged people. In this exploratory MEG study in normal young adults, we tested whether very simple short‐term memory (STM) demands induce visible changes in amplitude and latency of surface α rhythms. Two delayed response tasks were used. In the STM condition, a simple cue stimulus (one bit) was memorized along a brief delay period (3.5–5.5 s). In the control (no short‐term memory; NSTM) condition, the cue stimulus remained available along the delay period. To make extremely simple the tasks, the explicit demand was visuospatial but the retention could be also based on phonological and somatomotor coding. Compared to the control condition, the amplitude of the α 1 (6–8 Hz) ERD decreased in the left hemisphere, whereas the amplitude of the α 2 (8–10 Hz) and α 3 (10–12 Hz) event‐related desynchronization (ERD) increased in right and left parietal areas, respectively. Furthermore, the latency of the α ERD peak was slightly but significantly (P < 0.05) later in STM compared to control condition. In conclusion, whole‐head MEG technology and very simple STM demands revealed significant changes of human neuromagnetic α rhythms in normal young adults. Hum. Brain Mapp 24:184–192, 2005. © 2004 Wiley‐Liss, Inc.

Previous simulation studies have stressed the importance of the multimodal integration of electroencephalography (EEG) and magnetoencephalography (MEG) data in the estimation of cortical current density. In such studies, no systematic variations of the signal‐to‐noise ratio (SNR) and of the number of sensors were explicitly taken into account in the estimation process. We investigated effects of variable SNR and number of sensors on the accuracy of current density estimate by using multimodal EEG and MEG data. This was done by using as the dependent variable both the correlation coefficient (CC) and the relative error (RE) between imposed and 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: (1) the SNR of the simulated scalp data (with seven levels: infinite, 30, 20, 10, 5, 3, 1); (2) the particular inverse operator used to estimate the cortical source activity from the simulated scalp data (INVERSE, with two levels, including minimum norm and weighted minimum norm); and (3) the number of EEG or MEG sensors employed in the analysis (SENSORS, with three levels: 128, 61, 29 for EEG and 153, 61, or 38 in MEG). Analysis of variance demonstrated that all the considered factors significantly affect the CC and the RE indexes. Combined EEG–MEG data produced statistically significant lower RE and higher CC in source current density reconstructions compared to that estimated by the EEG and MEG data considered separately. These observations hold for the range of SNR values presented by the analyzed data. The superiority of current density estimation by multimodal integration of EEG and MEG was not due to differences in number of sensors between unimodal (EEG, MEG) and combined (EEG–MEG) inverse estimates. In fact, the current density estimate relative to the EEG–MEG multimodal integration involved 61 EEG plus 63 MEG sensors, whereas estimations carried out with the single modalities alone involved 128 sensors for EEG and 153 sensors for MEG. The results of the simulations also suggest that the use of simultaneous 29 EEG sensors during the MEG measurements carried out with full sensor arrangements (153 sensors) returned an accuracy of the cortical source estimate statistically similar to that obtained by combining 64 EEG and 153 MEG sensors. Hum. Brain Mapp. 22:54–64, 2004. © 2004 Wiley‐Liss, Inc.

The main aims of this study were 1) a fine spatial analysis of electroencephalographic (EEG) oscillations after galvanic painful stimulation (nonpainful stimulation as a reference) and 2) a comparative evaluation of phase‐ and nonphase‐locked component of these EEG oscillations. Preliminary surface Laplacian transformation of EEG data (31 channels) reduced head volume conductor effects. EEG phase values were computed by FFT analysis and the statistical evaluation of these values was performed by Rayleigh test (P < 0.05). About 50% of the EEG single trials presented statistically the same FFT phase value of the evoked EEG oscillations (phase‐locked single trials), indicating a preponderant phase‐locked compared to nonphase‐locked component. The remaining single trials showed random FFT phase values (nonphase‐locked single trials), indicating a preponderant nonphase‐locked compared to phase‐locked component. Compared to nonpainful stimulation, painful stimulation increased phase‐locked theta to gamma band responses in the contralateral hemisphere and decreased the phase‐locked beta band response in the ipsilateral hemisphere. Furthermore, nonphase‐locked alpha band response decreased in the ipsilateral fronto‐central area. In conclusion, both decreased and increased EEG oscillatory responses to galvanic painful stimulation would occur in parallel in different cortical regions and in the phase‐ and nonphase‐locked EEG data sets. This enriches the actual debate on the mapping of event‐related oscillatory activity of human brain. Hum. Brain Mapping 15:112–123, 2002. © 2002 Wiley‐Liss, Inc.

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. Hum. Brain Mapping 12:168–179, 2001. © 2001 Wiley‐Liss, Inc.