Electroencephalography and clinical neurophysiology最新文献

A series of recent anatomical and functional data has radically changed our view on the organization of the motor cortex in primates. In the present article we present this view and discuss its fundamental principles. The basic principles are the following: (a) the motor cortex, defined as the agranular frontal cortex, is formed by a mosaic of separate areas, each of which contains an independent body movement representation, (b) each motor area plays a specific role in motor control, based on the specificity of its cortical afferents and descending projections, (c) in analogy to the motor cortex, the posterior parietal cortex is formed by a multiplicity of areas, each of which is involved in the analysis of particular aspects of sensory information. There are no such things as multipurpose areas for space or body schema and (d) the parieto-frontal connections form a series of segregated anatomical circuits devoted to specific sensorimotor transformations. These circuits transform sensory information into action. They represent the basic functional units of the motor system. Although these conclusions mostly derive from monkey experiments, anatomical and brain-imaging evidence suggest that the organization of human motor cortex is based on the same principles. Possible homologies between the motor cortices of humans and non-human primates are discussed.

Objectives: The use of latero-orbital (Lo) electrodes is a routine practice in any EEG laboratory to evaluate eye motion, but there are no data about their usefulness in revealing interictal epileptiform abnormalities.

Methods: In 60 consecutive patients (27 men, 33 women, mean age 36 8 years, range 17–72) with complex partial seizures, we prospectively evaluated the utility of Lo electrodes in comparison with anterior temporal (AT) electrodes, for the detection of interictal epileptiform discharges (SW).

Results: No epileptiform abnormality was seen in 4/60 patients. Both AT and Lo electrodes were significantly superior to 10–20 electrodes for detection of both patients and foci. Indeed, the standard 10–20 system alone allowed the detection of only 39 independent epileptiform foci in 35/56 (63%) patients, while AT and Lo electrodes were necessary for detection of 23 epileptiform foci in the remaining 21/56 (37%) patients. Importantly, there was no statistically significant difference in detection between AT and Lo electrodes.

Conclusions: Recordings from Lo electrodes are comparable to those from AT electrodes and are useful for localizing interictal temporal spiking activity. Lo electrodes may be substituted for basal electrodes in the day-to-day evaluation of patients with complex partial seizures.

Objective: To investigate the accuracy of forward and inverse techniques for EEG and MEG dipole localization.

Design and Methods: A human skull phantom was constructed with brain, skull and scalp layers and realistic relative conductivities. Thirty two independent current dipoles were distributed within the `brain' region and EEG and MEG data collected separately for each dipole. The true dipole locations and orientations and the morphology of the brain, skull and scalp layers were extracted from X-ray CT data. The location of each dipole was estimated from the EEG and MEG data using the R-MUSIC inverse method and forward models based on spherical and realistic head geometries. Additional computer simulations were performed to investigate the factors affecting localization accuracy.

Results: Localization errors using the relatively simpler locally fitted sphere approach are only slightly greater than those using a BEM approach. The average localization error over the 32 dipoles was 7–8 mm for EEG and 3 mm for MEG.

Conclusion: The superior performance of MEG over EEG appears to be because the latter is more sensitive to errors in the forward model arising from simplifying assumptions concerning the conductivity of the skull, scalp and brain.

By analyzing simulated neuromagnetic recordings with a 37 channel magnetometer system, it was shown that the accuracy of a dipole source localization is considerably improved if the standard least-squares fit procedure is replaced by a maximum likelihood estimation accounting for the covariances of the noise in the measurement channels. Spatially correlated noise was generated by random dipoles homogeneously distributed in a sphere representing the brain. The study suggests that a maximum likelihood estimation reduces the standard deviations of the estimated dipole parameters by roughly a factor of two.

We compare the localisation of epileptic foci by means of (1) EEG, (2) magnetoencephalography (MEG) and (3) combined EEG/MEG data in a group of patients suffering from pharmaco-resistant focal epilepsy. Individual epileptic events were localised by means of a moving dipole model in a 4-shell spherical head approximation. A patient's epileptic activity was summarised by calculating the spatial density distribution (DD) of all localised events, and the centre of gravity of DD was considered the most likely locus of seizure generation. To verify these loci a subgroup of 6 patients was selected, in which seizures could be related to a clearly identifiable lesion in MRI. On average, the combined EEG/MEG approach resulted in the smallest error (1.8 cm distance between calculated locus and the nearest lesion border); using only MEG yielded the largest error (2.4 cm), while EEG resulted in an intermediate value (2.2 cm). In the individual patients, EEG/MEG would also rank intermediate, but never worst. In summary, combining EEG/MEG appears to be a more robust approach to localisation than using only EEG or only MEG. Finally, we also report on the use of the barbiturate methohexital as a safe method of increasing the number of spike events during an EEG/MEG recording session.

Objectives: A framework for combining bioelectric and biomagnetic data is presented. The data are transformed to signal-to-noise ratios and reconstruction algorithms utilizing a new regularization approach are introduced.

Methods: Extensive simulations are carried out for 19 different EEG and MEG montages with radial and tangential test dipoles at different eccentricities and noise levels. The methods are verified by real SEP/SEF measurements. A common realistic volume conductor is used and the less-well-known in-vivo conductivities are matched by calibration to the magnetic data. Single equivalent dipole fits as well as spatiotemporal source models are presented for single and combined modality evaluations and overlaid to anatomic MR images.

Results: Normalized sensitivity and dipole resolution profiles of these acquisition systems are derived from these synthetic data. The methods are verified by simultaneously measured somasensory data.

Conclusions: Superior spatial resolution of the combined data studies is revealed, which is due to the complementary nature of both modalities and the increased number of sensors. a better understanding of the underlying neironal processes can be acheived, since an improved differentiation between quasi-tangential and quasi-radial sources is possible.

Recent studies have disclosed several oscillations occurring during resting sleep within the frequency range of the classical delta band (0.5–4 Hz). There are at least 3 oscillations with distinct mechanisms and sites of origin: a slow (<1 Hz) cortically-generated oscillation, a clock-like thalamic oscillation (1–4 Hz), and a cortical oscillation (1–4 Hz). The present paper reviews data on these oscillations and the possible mechanisms which coalesce them into the polymorphic waves of slow wave sleep. Data stem from intracellular (over 500 single cell and 50 double impalements) and field potentials recorded from the cortex and thalamus of cats (120 animals) under ketamine and xylazine anesthesia. Other experiments were based on whole night EEG recordings from humans (5 subjects). The frequency of the slow oscillation both in anesthetized animals and in naturally sleeping humans ranged between 0.1 and 1 Hz (89% of the cases being between 0.5 and 0.9 Hz). The slow (<1 Hz) oscillation is reflected in the EEG as rhythmic sequences of surface-negative waves (associated with hyperpolarizations of deeply-lying neurons) and surface-positive K-complexes (representing excitation in large pools of cortical neurons). Through its long-range synchronization, the slow oscillation has the ability to trigger and to group thalamically-generated spindles and two delta (1–4 Hz) oscillations. Finally, it is argued that the analysis of the electroencephalogram should transcend the spectral analyses, by taking into account the shape of the waves and, when possible, the basic mechanisms that generate those waves.