Cortical Current Density Research Articles

Evaluation of cortical current density imaging methods using intracranial electrocorticograms and functional MRI

Objective:EEG source imaging provides important information regarding the underlying neural activity from noninvasive electrophysiological measurements. The aim of the present study was to evaluate source reconstruction techniques by means of the intracranial electrocorticograms (ECoGs) and functional MRI. Methods:Five source imaging algorithms, including the minimum norm least square (MNLS), LORETA with Lp-norm (p equal to 1, 1.5 and 2), sLORETA, the minimum Lp-norm (p equal to 1 and 1.5; when p=2, the MNLS method is mathematically equivalent to the minimum Lp-norm) and L1-norm (the linear programming) methods, were evaluated in a group of 10 human subjects, in a paradigm with somatosensory stimulation. Cortical current density (CCD) distributions were estimated from the scalp somatosensory evoked potentials (SEPs), at approximately 30 ms following electrical stimulation of median nerve at the wrist. Realistic geometry boundary element head models were constructed from the MRIs of each subject and used in the CCD analysis. Functional MRI results obtained from a motor task and sensory stimulation in all subjects were used to identify the central sulcus, motor and sensory areas. In three patients undergoing neurosurgical evaluation, ECoGs were recorded in response to the somatosensory stimulation, and were used to help determine the central sulcus and the sensory cortex. Results:The CCD distributions estimated by the Lp-norm and LORETA-Lp methods were smoother when the p values were high. The LORETA based on the L1-norm performed better than the LORETA-L2 method for imaging well localized sources such as the P30 component of the SEP. The mean and standard deviation of the distance between the location of maximum CCD value and the central sulcus, estimated by the minimum Lp-norm (with p equal to 1), L1-norm (the Linear programming) and LORETA-Lp (with p equal to 1) methods, were 4, 7, 7 mm and 3, 4, 2 mm, respectively (after converting into Talairach coordinates). The mean and standard deviation of the aforementioned distance, estimated by the MNLS, LORETA with Lp-norm (p equal to 1.5 and 2.0), sLORETA and the minimum Lp-norm (p equal to 1.5) methods, were over 11 mm and 6 mm, respectively. Conclusions:The present experimental study suggests that L1-norm-based algorithms provide better performance than L2 and L1.5-norm-based algorithms, in the context of CCD imaging of well localized sources induced by somatosensory electrical stimulation of median nerve at the wrist.

EEG dynamics of the frontoparietal network during reaching preparation in humans

Visuomotor transformation processes are essential when accurate reaching movements towards a visual target have to be performed. In contrast, those transformations are not needed for similar, but non-visually guided, arm movements. According to previous studies, these transformations are carried out by neuronal populations located in the parietal and frontal cortical areas (the so-called "dorsal visual stream"). However, it is still debated whether these processes are mediated by the sequential and/or parallel activation of the frontoparietal areas. To investigate this issue, we designed a task where the same visual cue could represent either the target of a reaching/pointing movement or the go-signal for a similar but non-targeting arm movement. By subtracting the event-related potentials (ERPs) recorded from healthy subjects performing the two conditions, we identified the brain processes underlying the visuomotor transformations needed for accurate reaching/pointing movements. We then localized the generators by means of cortical current density (CCD) reconstruction and studied their dynamics from visual cue presentation to movement onset. The results showed simultaneous activation of the parietal and frontal areas from 140 to 260 ms. The results are interpreted as neural correlates of two critical phases of visuomotor integration, namely target selection and movement selection. Our findings suggest that the visuomotor transformation processes required for correct reaching/pointing movements do not rely on a purely sequential activation of the frontoparietal areas, but mainly on a parallel information processing system, where feedback circuits play an important role before movement onset.

Cue-Invariant Networks for Figure and Background Processing in Human Visual Cortex

Lateral occipital cortical areas are involved in the perception of objects, but it is not clear how these areas interact with first tier visual areas. Using synthetic images portraying a simple texture-defined figure and an electrophysiological paradigm that allows us to monitor cortical responses to figure and background regions separately, we found distinct neuronal networks responsible for the processing of each region. The figure region of our displays was tagged with one temporal frequency (3.0 Hz) and the background region with another (3.6 Hz). Spectral analysis was used to separate the responses to the two regions during their simultaneous presentation. Distributed source reconstructions were made by using the minimum norm method, and cortical current density was measured in a set of visual areas defined on retinotopic and functional criteria with the use of functional magnetic resonance imaging. The results of the main experiments, combined with a set of control experiments, indicate that the figure region, but not the background, was routed preferentially to lateral cortex. A separate network extending from first tier through more dorsal areas responded preferentially to the background region. The figure-related responses were mostly invariant with respect to the texture types used to define the figure, did not depend on its spatial location or size, and mostly were unaffected by attentional instructions. Because of the emergent nature of a segmented figure in our displays, feedback from higher cortical areas is a likely candidate for the selection mechanism by which the figure region is routed to lateral occipital cortex.

W17 Single and paired pulse TMS: Comparisons between natural and magnetic activation and between monophasic and biphasic TMS

ized 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) and the Partial Directed Coherence (PDC) on the estimated cortical current density waveforms in regions of interest (ROIs) on the modeled cortical mantle. Furthermore, this method allows to detect changes in the time course of information flow between cortical regions in different frequency bands. Results: The reliability of these techniques was further demonstrated by simulation studies and the elaboration of high-resolution EEG and fMRI signals collected during different cognitive and motor tasks in healthy subjects. Discussion: Multimodal integration of high resolution EEG and fMRI data offers a significant aid for the study of the ‘‘when’’ , ‘‘where’’, and ‘‘how’’ in the cortical brain regions.

Effects of fMRI–EEG mismatches in cortical current density estimation integrating fMRI and EEG: A simulation study

ObjectiveMultimodal functional neuroimaging by combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) has been studied to achieve high-resolution reconstruction of the spatiotemporal cortical current density (CCD) distribution. However, mismatches between these two imaging modalities may occur due to their different underlying mechanisms. The aim of the present study is to investigate the effects of different types of fMRI–EEG mismatches, including fMRI invisible sources, fMRI extra regions and fMRI displacement, on the fMRI-constrained cortical imaging in a computer simulation based on realistic-geometry boundary-element-method (BEM) model. MethodsTwo methods have been adopted to integrate the synthetic fMRI and EEG data for CCD imaging. In addition to the well-known 90% fMRI-constrained Wiener filter approach (Liu AK, Belliveau JW, Dale AM. PNAS 1998;95:8945–8950.), we propose a novel two-step algorithm (referred to as ‘Twomey algorithm’) for fMRI–EEG integration. In the first step, a ‘hard’ spatial prior derived from fMRI is imposed to solve the EEG inverse problem with a reduced source space; in the second step, the fMRI constraint is removed and the source estimate from the first step is re-entered as the initial guess of the desired solution into an EEG least squares fitting procedure with Twomey regularization. Twomey regularization is a modified Tikhonov technique that attempts to simultaneously minimize the distance between the desired solution and the initial estimate, and the residual errors of fitness to EEG data. The performance of the proposed Twomey algorithm has been evaluated both qualitatively and quantitatively along with the lead-field normalized minimum norm (WMN) and the 90% fMRI-weighted Wiener filter approach, under repeated and randomized source configurations. Point spread function (PSF) and localization error (LE) are used to measure the performance of different imaging approaches with or without a variety of fMRI–EEG mismatches. ResultsThe results of the simulation show that the Twomey algorithm can successfully reduce the PSF of fMRI invisible sources compared to the Wiener estimation, without losing the merit of having much lower PSF of fMRI visible sources relative to the WMN solution. In addition, the existence of fMRI extra sources does not significantly affect the accuracy of the fMRI–EEG integrated CCD estimation for both the Wiener filter method and the proposed Twomey algorithm, while the Twomey algorithm may further reduce the chance of occurring spurious sources in the extra fMRI regions. The fMRI displacement away from the electrical source causes enlarged localization error in the imaging results of both the Twomey and Wiener approaches, while Twomey gives smaller LE than Wiener with the fMRI displacement ranging from 1–2cm. With less than 2cm fMRI displacement, the LEs for the Twomey and Wiener approaches are still smaller than in the WMN solution. ConclusionsThe present study suggests that the presence of fMRI invisible sources is the most problematic factor responsible for the error of fMRI–EEG integrated imaging based on the Wiener filter approach, whereas this approach is relatively robust against the fMRI extra regions and small displacement between fMRI activation and electrical current sources. While maintaining the above advantages possessed by the Wiener filter approach, the Twomey algorithm can further effectively alleviate the underestimation of fMRI invisible sources, suppress fMRI spurious sources and improve the robustness against fMRI displacement. Therefore, the Twomey algorithm is expected to improve the reliability of multimodal cortical source imaging against fMRI–EEG mismatches. SignificanceThe proposed method promises to provide a useful alternative for multimodal neuroimaging integrating fMRI and EEG.

Motor-related cortical dynamics to intact movements in tetraplegics as revealed by high-resolution EEG.

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.

Multimodal integration of EEG, MEG and fMRI data for the solution of the neuroimage puzzle

In this paper, advanced methods for the modeling of human cortical activity from combined high-resolution electroencephalography (EEG), magnetoencephalography (MEG) 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 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. Examples of the application of these methods to the estimation of the time varying cortical current density activity in selected region of interest (ROI) are presented for movement-related high-resolution EEG data.

Estimation of the effective and functional human cortical connectivity with structural equation modeling and directed transfer function applied to high-resolution EEG

Different brain imaging devices are presently available to provide images of the human functional cortical activity, 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 are interconnected. The concept of brain connectivity plays a central role in the neuroscience, and different definitions of connectivity, functional and effective, have been adopted in literature. While the functional connectivity is defined as the temporal coherence among the activities of different brain areas, the effective connectivity is defined as the simplest brain circuit that would produce the same temporal relationship as observed experimentally among cortical sites. The structural equation modeling (SEM) is the most used method to estimate effective connectivity in neuroscience, and its typical application is on data related to brain hemodynamic behavior tested by functional magnetic resonance imaging (fMRI), whereas the directed transfer function (DTF) method is a frequency-domain approach based on both a multivariate autoregressive (MVAR) modeling of time series and on the concept of Granger causality. This study presents advanced methods for the estimation of cortical connectivity by applying SEM and DTF on the cortical signals estimated from high-resolution electroencephalography (EEG) recordings, since these signals exhibit a higher spatial resolution than conventional cerebral electromagnetic measures. To estimate correctly the cortical signals, we used a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from individual MRI, a distributed source model and a regularized linear inverse source estimates of cortical current density. Before the application of SEM and DTF methodology to the cortical waveforms estimated from high-resolution EEG data, we performed a simulation study, in which different main factors (signal-to-noise ratio, SNR, and simulated cortical activity duration, LENGTH) were systematically manipulated in the generation of test signals, and the errors in the estimated connectivity were evaluated by the analysis of variance (ANOVA). The statistical analysis returned that during simulations, both SEM and DTF estimators were able to correctly estimate the imposed connectivity patterns under reasonable operative conditions, that is, when data exhibit an SNR of at least 3 and a LENGTH of at least 75 s of nonconsecutive EEG recordings at 64 Hz of sampling rate. Hence, effective and 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 and linear inverse estimation with SEM or DTF methods. We conclude that the estimation of cortical connectivity can be performed not only with hemodynamic measurements, but also with EEG signals treated with advanced computational techniques.

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.

Dynamics of parietofrontal networks underlying visuospatial short-term memory encoding

Brain imaging studies in TEP, functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) have shown that visuospatial short-term memory tasks depend on dorsal parietofrontal networks. Knowing the spatiotemporal dynamics of this network would provide further understanding of the neural bases of the encoding process. We combined magnetoencephalography (MEG) with EEG and fMRI techniques to study this network in a task, in which participants had to judge the symmetry in position of two dots, presented either simultaneously (“immediate comparison”) or successively (“memorization” of a first dot and “delayed comparison”, after 3 s, with a second dot). With EEG, larger amplitude was observed in the parietocentral P3b component (350–500 ms) in the immediate and “delayed comparisons” than in “memorization” condition, where topography at this time was more anterior and right lateralized. MEG provided a more accurate localization and temporal variations of sources, revealing a strong M4 component at 450 ms in the “memorization” condition, with two sources localized in parietal and right premotor regions. These localizations are consistent with both fMRI foci and EEG cortical current source densities (CSD), but only MEG revealed the strong increase in premotor region at 450 ms related to “memorization”. These combined results suggest that EEG P3B and MEG M4 components reflect two different dynamics in parietofrontal networks: the parietocentral P3b indexes a decision mechanism during the immediate and “delayed comparisons”, whereas the MEG M4 component, with a larger right premotor source, reflects the encoding process in visuospatial short-term memory.

Motor imagery classification by means of source analysis for brain–computer interface applications

We report a pilot study of performing classification of motor imagery for brain–computer interface applications, by means of source analysis of scalp-recorded EEGs. Independent component analysis (ICA) was used as a spatio-temporal filter extracting signal components relevant to left or right motor imagery (MI) tasks. Source analysis methods including equivalent dipole analysis and cortical current density imaging were applied to reconstruct equivalent neural sources corresponding to MI, and classification was performed based on the inverse solutions. The classification was considered correct if the equivalent source was found over the motor cortex in the corresponding hemisphere. A classification rate of about 80% was achieved in the human subject studied using both the equivalent dipole analysis and the cortical current density imaging analysis. The present promising results suggest that the source analysis approach could manifest a clearer picture on the cortical activity, and thus facilitate the classification of MI tasks from scalp EEGs.

Multimodal integration of EEG and MEG data: a simulation study with variable signal-to-noise ratio and number of sensors.

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.

Abnormalities of P300 cortical current density in unmedicated depressed patients revealed by LORETA analysis of event-related potentials.

The purpose of the present study was to investigate the neural substrates underlying event-related potential (ERP) abnormalities, with respect to the generators of the ERP components in depressed patients. Using an oddball paradigm, ERP from auditory stimuli were recorded from 22 unmedicated patients with current depressive episodes and compared with those from 22 age- and gender-matched normal controls. Cortical current densities of the N100 and P300 components were analyzed using low-resolution electromagnetic tomography (LORETA). Group differences in cortical current density were mapped on a 3-D cortex model. The results revealed that N100 cortical current densities did not differ between the two groups, while P300 cortical current densities were significantly lower in depressed patients over the bilateral temporal lobes, the left frontal region, and the right temporal-parietal area. Furthermore, the cortical area in which the group difference in P300 current density had been identified was remarkably larger over the right than the left hemisphere, thus supporting the hypothesis of right hemisphere dysfunction in depression.

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.

Abnormalities of auditory P300 cortical current density in patients with schizophrenia using high density recording

While P300 current density analysis has been performed in schizophrenic patients, the event-related potential data have never been obtained using a high density recording, nor have their cortical images been well demonstrated. In this study, the auditory P300 elicited by an oddball paradigm was recorded using a high density recording system of 128 channels. Thirteen male patients who met DSM-IV criteria for schizophrenia were compared with 20 healthy male controls. The cortical current density analysis of low resolution electromagnetic tomography (LORETA) was applied to the P300 component, and this resulted in the values of 6222 current density points over the surface of a cortex model. The inter-group difference of P300 current density was assessed using a point-by-point comparison by t-test. While the normal controls demonstrated the cortical activation of bilateral frontal, temporal and parietal cortex during the oddball paradigm, visual inspection suggested that in the schizophrenic patients these areas were less activated. The inter-group significance of P300 current density was dominant over the left hemisphere, and particularly over the left prefrontal area. It is concluded that the LORETA current density analysis localizes the neural activity from the cortical fronto-temporo-parietal network as the neural substrates of the scalp recorded P300. The dysfunction of such a network, especially over the left hemisphere, possibly subserves the scalp recorded P300 abnormality in schizophrenia.

Estimation of neural dynamics from MEG/EEG cortical current density maps: application to the reconstruction of large-scale cortical synchrony.

There is a growing interest in elucidating the role of specific patterns of neural dynamics--such as transient synchronization between distant cell assemblies--in brain functions. Magnetoencephalography (MEG)/electroencephalography (EEG) recordings consist in the spatial integration of the activity from large and multiple remotely located populations of neurons. Massive diffusive effects and poor signal-to-noise ratio (SNR) preclude the proper estimation of indices related to cortical dynamics from nonaveraged MEG/EEG surface recordings. Source localization from MEG/EEG surface recordings with its excellent time resolution could contribute to a better understanding of the working brain. We propose a robust and original approach to the MEG/EEG distributed inverse problem to better estimate neural dynamics of cortical sources. For this, the surrogate data method is introduced in the MEG/EEG inverse problem framework. We apply this approach on nonaveraged data with poor SNR using the minimum norm estimator and find source localization results weakly sensitive to noise. Surrogates allow the reduction of the source space in order to reconstruct MEG/EEG data with reduced biases in both source localization and time-series dynamics. Monte Carlo simulations and results obtained from real MEG data indicate it is possible to estimate non invasively an important part of cortical source locations and dynamic and, therefore, to reveal brain functional networks.

Cortical current density of ERPs in unmedicated depressed patients

In order to investigate abnormal cortical activities in depressed patients, the cortical current density of the event-related potentials (ERPs) was measured using low-resolution electromagnetic tomography (LORETA). Twenty-two unmedicated depressed patients and 22 normal controls were recruited for this study. Sixteen-channel ERPs were recorded using auditory oddball paradigm. Latency-adjusted grand average ERPs were averaged at the point of maximal global field power of each N100 and P300 in individual target ERPs. 3-D cortical current source density maps for each component were reconstructed using LORETA analysis. LORETA revealed that the cortical sources of N100 were located in the bilateral temporal region in both groups. The cortical sources of P300 were demonstrated in the bilateral temporal and prefrontal regions in both groups. In patient group, however, the current density on the left prefrontal region is weakened with increasing current density on the right prefrontal region as compared to the control group. These results may suggest some hemispheric imbalances in the frontal cortex in depressed patients.

Neuroleptics change P300 generation in schizophrenia: LORETA analysis

Many studies have investigated the relationship between medication and P300 abnormalities in schizophrenics; however, the effects of neuroleptic medication on the P300 generation were still unclear. Event-related potentials (ERPs) were recorded from nine drug-free patients with schizophrenia including five neuroleptic naives; their ERPs were re-recorded after about 3 months of neuroleptic medication. Sixteen channel ERP data were obtained using auditory oddball paradigms. There was no difference in the mean P300 peak latency and amplitude before and after the treatment. On the other hand, the mean P300 latency at the point of maximal GFP was shorter. Latency adjusted grand average at the point of maximal GFP of each individual P300 component. Then, the current source density of the P300 was computed using low-resolution electromagnetic tomography (LORETA) analysis. LORETA revealed that the cortical current density of P300 components increased after treatment, especially in the front–temporal areas. These results suggest that neuroleptic medication changes the P300 generators in schizophrenia.

Anterior cingulate theta activity is associated with degree of treatment response in major depression

Background: The anterior cingulate cortex (ACC), a functionally heterogeneous region, has attracted considerable attention in depression research. Whereas dorsal ACC hypoactivity has been observed in depression, rostral ACC hyperactivity has been associated with better treatment response. Using electroencephalographic (EEG) imaging techniques, we tested whether rostral ACC activity during a depressive episode predicted later treatment response. Since the ACC has been suggested as a possible neuronal generator for theta activity, the effect was hypothesized to emerge only within this band. Methods: Thirty-minute, 28-channel resting EEG data were recorded in 18 unmedicated subjects with major depression and 18 matched, control subjects. For depressed subjects, clinical outcome was assessed 4–6 months later, after nortriptyline treatment. Adopting a median split strategy, pre-treatment EEG data of those subjects with relatively better ( n=9) and worse ( n=9) treatment response were compared using low resolution electromagnetic tomography (LORETA), which computed three-dimensional cortical current density for classical EEG frequency bands using a Talairach brain atlas. Results: Consistent with our predictions, better treatment response was uniquely associated with hyperactivity (higher theta) in the rostral ACC (BA 24/32). Conclusions: The present results replicate prior hemodynamic findings implicating the rostral ACC for treatment response in depression with an electrophysiological imaging technique.

A double-dissociation of English past-tense production revealed by event-related potentials and low-resolution electromagnetic tomography (LORETA)

Objectives: Evidence of systematic double-dissociations of neural activity associated with the generation of regular and irregular past tense in healthy individuals may prove decisive in distinguishing between single- and dual-route models of morphological processing, because the former (connectionist models of morphological processing) have only been able to simulate double-dissociations of past-tense morphology as low-probability phenomena. Methods: Twenty-eight channel event-related potentials (ERPs) were recorded in response to past-tense production and subsequently analyzed using a 3-stage strategy. Results: A data-driven algorithm temporally segmented the ERPs into 16 distinct epochs of stable field configuration (microstates). A space-oriented brain electric field analysis determined that one of those epochs, 288–321 ms after the verb stem presentation, showed significant differences between the regular and irregular verb conditions. As a further test of these results, a novel source localization technique that computes 3-dimensional distribution of cortical current density in the Talairach brain atlas – low-resolution electromagnetic tomography – found in the above microstate more activity for regulars in the right prefrontal and right temporal areas and for irregulars in the left temporal areas and the anterior cingulate cortex, which can be taken as evidence of systematic double-dissociation. Conclusions: The present results achieved with a source localization technique provide evidence of a two-way compartmentalization of neural activity corresponding to regular and irregular past tense, thus corroborating the dual-mechanism character of verb morphology.