The sensitivity of instantaneous coherence for considering elementary comparison processing. Part II: Similarities and differences between EEG and MEG coherences

Coherent Activity in Bilateral Parieto-Occipital Cortices during P300-BCI Operation

The visual P300 brain–computer interface (BCI), a popular system for electroencephalography (EEG)-based BCI, uses the P300 event-related potential to select an icon arranged in a flicker matrix. In earlier studies, we used green/blue (GB) luminance and chromatic changes in the P300-BCI system and reported that this luminance and chromatic flicker matrix was associated with better performance and greater subject comfort compared with the conventional white/gray (WG) luminance flicker matrix. To highlight areas involved in improved P300-BCI performance, we used simultaneous EEG–fMRI recordings and showed enhanced activities in bilateral and right lateralized parieto-occipital areas. Here, to capture coherent activities of the areas during P300-BCI, we collected whole-head 306-channel magnetoencephalography data. When comparing functional connectivity between the right and left parieto-occipital channels, significantly greater functional connectivity in the alpha band was observed under the GB flicker matrix condition than under the WG flicker matrix condition. Current sources were estimated with a narrow-band adaptive spatial filter, and mean imaginary coherence was computed in the alpha band. Significantly greater coherence was observed in the right posterior parietal cortex under the GB than under the WG condition. Re-analysis of previous EEG-based P300-BCI data showed significant correlations between the power of the coherence of the bilateral parieto-occipital cortices and their performance accuracy. These results suggest that coherent activity in the bilateral parieto-occipital cortices plays a significant role in effectively driving the P300-BCI.

Essential tremor and tremor in Parkinson's disease are associated with distinct 'tremor clusters' in the ventral thalamus

Different tremor entities such as Essential Tremor (ET) or tremor in Parkinson's disease (PD) can be ameliorated by the implantation of electrodes in the ventral thalamus for Deep Brain Stimulation (DBS). The exact neural mechanisms underlying this treatment, as well as the specific pathophysiology of the tremor in both diseases to date remain elusive. Since tremor-related local field potentials (LFP) have been shown to cluster with a somatotopic representation in the subthalamic nucleus, we here investigated the neurophysiological correlates of tremor in the ventral thalamus in ET and PD using power and coherence analysis. Local field potentials (LFPs) at different recording depths and surface electromyographic signals (EMGs) from the extensor and flexor muscles of the contralateral forearm were recorded simultaneously in twelve ET and five PD patients. Data analysis revealed individual electrophysiological patterns of LFP-EMG coherence at single and double tremor frequency for each patient. Patterns observed varied in their spatial distribution within the Ventral lateral posterior nucleus of the thalamus (VLp), revealing a specific topography of 'tremor clusters' for PD and ET. The data strongly suggest that within VLp individual tremor-related electrophysiological signatures exist in ET and PD tremor.

Characterisation of tremor-associated local field potentials in the subthalamic nucleus in Parkinson's disease

We simultaneously recorded local field potentials (LFPs) in the subthalamic nucleus (STN) and surface electromyographic signals (EMGs) from the extensor and flexor muscles of the contralateral forearm in eight patients with idiopathic tremor-dominant Parkinson's disease (resting tremor) during the bilateral implantation of deep brain stimulation electrodes. Recordings were made at different heights (in 0.5- to 2.0-mm steps beginning outside the STN) using up to five concentrically configured macroelectrodes (2 mm apart). The patients were instructed to relax their contralateral forearm (rest condition). We analysed the coherence between tremor EMGs and STN LFPs, which showed significant tremor-associated coupling at single tremor and double tremor frequencies. Moreover, the EMG-LFP coherences were characterised by differences between antagonistic muscles (flexor, extensor) and by the spatial distribution of LFPs within the STN. Coherence at single and double tremor frequencies occurred significantly more frequently within STN than above STN (in the zona incerta). In this study, we were able to show that, within STN, tremor-associated LFP activity varied with spatial distribution and with the contralateral antagonistic forearm muscles. These findings suggest the existence of distribution- and muscle-specific tremor-associated LFP activity at different tremor frequencies and an organisation of tremor-related subloops within the STN.

Assessment of brain interactivity in the motor cortex from the concept of functional connectivity and spectral analysis of fMRI data

Functional magnetic resonance imaging (fMRI) was used to assess the contributions of movement preparation and execution of a visuomotor task in a cerebral motor network. The functional connectivity of the voxel time series between brain regions in the frequency space was investigated by performing spectral analysis of fMRI time series. The regional interactivities between the two portions of the supplementary motor area (pre-SMA and SMA-proper) and the primary motor cortex (M1), defined as a seed region, were evaluated. The spectral parameter of coherence was used to describe a correlation structure in the frequency domain between two voxel-based time series and to infer the strength of the functional interaction within our presumed motor network of connections. The results showed meaningful differences of the functional interactions between the two portions of the SMA and the M1 area depending on the task conditions. This approach demonstrated the existence of a functional dissociation between the pre-SMA and SMA-proper subregions. We therefore conclude that spectral analysis is useful for identifying functional interactions of brain regions and might provide a powerful tool to quantify changes in connectivity profiles associated with various components of an experimental task.

Effects of alcohol on spontaneous neuronal oscillations: a combined magnetoencephalography and electroencephalography study

Electroencephalography (EEG) and magnetoencephalography (MEG) can detect different aspects of alcohol effects on auditory processing measured with event-related potentials and magnetic fields. The present study aimed to detect alcohol-induced changes in spontaneous neuronal oscillations with combined EEG and MEG techniques. The effects of alcohol on spontaneous neuronal rhythms were studied in 12 healthy subjects after 0.8 g/kg alcohol or juice in a double-blind, placebo-controlled, cross-over design using simultaneous high-resolution MEG and EEG in eyes-open and eyes-closed conditions. The data were analyzed with a power spectral density analysis. MEG recording showed that alcohol significantly increased the relative power of alpha rhythm (8-10 Hz) and reduced the relative power of beta activity (17-25 Hz) in both left and right hemispheres, but only in the eyes-closed condition. These effects did not depend on gender. No analogous statistically significant changes were observed in EEG rhythms. However, the power of alpha and beta rhythms was positively correlated in MEG and EEG recordings, indicating that MEG and EEG reflect similar processes. A distinct sensitivity of MEG and EEG to the sources of cortical oscillations, a better signal-to-noise ratio of MEG, as well as strong spatial blurring of potentials in EEG are most likely the reasons for the observed differences in the effects of alcohol on spontaneous oscillations as detected with two methods.

Integrated technology for evaluation of brain function and neural plasticity

The study of neural plasticity has expanded rapidly in the past decades and has shown the remarkable ability of the developing, adult, and aging brain to be shaped by environmental inputs in health and after a lesion. Robust experimental evidence supports the hypothesis that neuronal aggregates adjacent to a lesion in the sensorimotor brain areas can take over progressively the function previously played by the damaged neurons. It definitely is accepted that such a reorganization modifies sensibly the interhemispheric differences in somatotopic organization of the sensorimotor cortices. This reorganization largely subtends clinical recovery of motor performances and sensorimotor integration after a stroke. Brain functional imaging studies show that recovery from hemiplegic strokes is associated with a marked reorganization of the activation patterns of specific brain structures. To regain hand motor control, the recovery process tends over time to bring the bilateral motor network activation toward a more normal intensity/extent, while overrecruiting simultaneously new areas, perhaps to sustain this process. Considerable intersubject variability exists in activation/hyperactivation pattern changes over time. Some patients display late-appearing dorsolateral prefrontal cortex activation, suggesting the development of "executive" strategies to compensate for the lost function. The AH in stroke often undergoes a significant "remodeling" of sensory and motor hand somatotopy outside the "normal" areas, or enlargement of the hand representation. The UH also undergoes reorganization, although to a lesser degree. Although absolute values of the investigated parameters fluctuate across subjects, secondary to individual anatomic variability, variation is minimal with regards to interhemispheric differences, due to the fact that individual morphometric characters are mirrored in the two hemispheres. Excessive interhemispheric asymmetry of the sensorimotor hand areas seems to be the parameter with highest sensitivity in describing brain reorganization after a monohemispheric lesion, and mapping motor and somatosensory cortical areas through focal TMS, fMRI, PET, EEG, and MEG is useful in studying hand representation and interhemispheric asymmetries in normal and pathologic conditions. TMS and MEG allow the detection of sensorimotor areas reshaping, as a result of either neuronal reorganization or recovery of the previously damaged neural network. These techniques have the advantage of high temporal resolution but also have limitations. TMS provides only bidimensional scalp maps, whereas MEG, even if giving three-dimensional mapping of generator sources, does so by means of inverse procedures that rely on the choice of a mathematical model of the head and the sources. These techniques do not test movement execution and sensorimotor integration as used in everyday life. fMRI and PET may provide the ideal means to integrate the findings obtained with the other two techniques. This multitechnology combined approach is at present the best way to test the presence and amount of plasticity phenomena underlying partial or total recovery of several functions, sensorimotor above all. Dynamic patterns of recovery are emerging progressively from the relevant literature. Enhanced recruitment of the affected cortex, be it spared perilesional tissue, as in the case of cortical stroke, or intact but deafferented cortex, as in subcortical strokes, seems to be the rule, a mechanism especially important in early postinsult stages. The transfer over time of preferential activation toward contralesional cortices, as observed in some cases, seems, however, to reflect a less efficient type of plastic reorganization, with some aspects of maladaptive plasticity. Reinforcing the use of the affected side can cause activation to increase again in the affected side with a corresponding enhancement of clinical function. Activation of the UH MI may represent recruitment of direct (uncrossed) corticospinal tracts and relate more to mirror movements, but it more likely reflects activity redistribution within preexisting bilateral, large-scale motor networks. Finally, activation of areas not normally engaged in the dysfunctional tasks, such as the dorsolateral prefrontal cortex or the superior parietal cortex in motor paralysis, might reflect the implication of compensatory cognitive strategies. An integrated approach with technologies able to investigate functional brain imaging is of considerable value in providing information on the excitability, extension, localization, and functional hierarchy of cortical brain areas. Deepening knowledge of the mechanisms regulating the long-term recovery (even if partial), observed for most neurologic sequelae after neural damage, might prompt newer and more efficacious therapeutic and rehabilitative strategies for neurologic diseases.

Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data

Understanding functional connectivity within the brain is crucial to understanding neural function; even the simplest cognitive operations are supported by highly distributed neural circuits. We developed a novel method to measure task-related functional interactions between neural regions by applying coherence and partial coherence analyses to functional magnetic resonance imaging (fMRI) data. Coherence and partial coherence are spectral measures that estimate the linear time-invariant (LTI) relationship between time series. They can be used to generate maps of task-specific connectivity associated with seed regions of interest (ROIs). These maps may then be compared across tasks, revealing nodes with task-related changes of connectivity to the seed ROI. To validate the method, we applied it to an event-related fMRI data set acquired while subjects performed two sequence tapping tasks, one of which required more bimanual coordination. Areas showing increased functional connectivity with both tasks were the same as those showing increased activity. Furthermore, though there were no significant differences in mean activity between the two tasks, significant increases in interhemispheric coherence were found between the primary motor (M1) and premotor (PM) regions for the task requiring more bimanual coordination. This increase in interhemispheric connectivity is supported by other brain imaging techniques as well as patient studies.

Phase-coupling of theta-gamma EEG rhythms during short-term memory processing

Because of the importance of oscillations as a general phenomenon of neuronal activity the use of EEG spectral analysis is among the most important approaches for studying human information processing. Usually, oscillations at different frequencies occur simultaneously during information processing. Thus, the question for synchronisation of different frequencies by phase coupling and its possible functional significance is of primary importance. An answer may be given by bispectral analysis. Estimation of the (cross-) bispectrum allows to identify synchronised frequencies and possibly, the existence of non-linear phase coupling of different oscillators. Previous studies have demonstrated the simultaneous occurrence of slow (4-7 Hz) and fast (20-30 Hz) oscillations at frontal and prefrontal electrode positions during memory processing. However, interrelations between these rhythms have not been investigated up to now. In order to test short-term memory, the Sternberg task with random figures and number words was carried out on 10 female subjects. During the task EEG was recorded. Power and bispectral analyses from frontal, prefrontal and frontopolar regions were performed off-line. Increased power was found in both the theta and the gamma bands. Strong phase-coupling between theta at Fz and gamma at F3 and at Fp1, respectively, was shown for memorising number words by means of cross-bicoherence. A possible reason for this is an amplitude modulation of gamma frequencies by slow oscillations. The correspondent coherence analysis between the envelope of gamma frequencies at Fp1 and the raw EEG at Fz supports this presumption. This finding is interpreted as an EEG aspect of the functional linking between the prefrontal areas and the G.cinguli (as part of the limbic system), which are both extremely important for memory functions.

Cortical connectivity in high frequency beta-rhythm in schizophrenics with positive and negative symptoms

During the last decade the role of high frequency EEG activity in the 'binding phenomenon' was discovered. It was supposed that this phenomenon provided the integration between different brain structures underlying higher nervous functions and possibly even consciousness [Proc. Natl. Acad. Sci. 90 (1993) 2078; Annu. Rev. Neurosci. 18 (1995) 555; J. Neurosci. V 16 (1996) 4240; Am. Physiol. Soc. (1998) 1567; Induced Rhythms in the Brain (1992) 425; NeuroReport 8 (1997) 531; Proc. Natl. Acad. Sci. USA 94 (1997) 12198]. Schizophrenia is considered as a disorder of the integration between different brain regions [Review of Psychiatry 18 (1999a) 29; Conceptual Advances in Russian Neuroscience: Complex Brain Functions (1999) 151; Brain Res. Rev. 31 (2000) 301], and in the present work we have studied cortical connectivity, focusing on those connections which are maintained by high frequency EEG-rhythm (20-40 Hz). The results showed a high degree of biopotential synchronisation between definite cortical areas during cognitive processes in normal subjects and have evidenced significant functional connectivity disturbances in schizophrenia in this EEG frequency domain.

Cortical connectivity during word association search

Cortical connectivity was studied in tasks of generating the use of words in comparison with reading aloud the same words. These tasks were used earlier in PET and high density ERP recordings studies (Posner and Raichle, 1997; Abdullaev and Posner, 1998), in which both the functional anatomy and the time course of cortical areas involved in word processing were described. The wavelet transforms of ERP records and the calculation of correlations between wavelet curves were used to reveal connections between cortical areas. Three stages of intracortical communications while task performance were found. These were: (1) the connections between right and left frontal and central areas which preceded stimulus delivery and persisted up to 180 ms after it; (2) the network connecting right and left frontal with left posterior temporal-parietal junction at 280-450 ms; and (3) communications between left and right temporal zones in 550-800 ms. The data are in good agreement with results of previous PET and ERP studies and supply the earlier findings with circuitry of cortical information transfer.

Instantaneous multivariate EEG coherence analysis by means of adaptive high-dimensional autoregressive models

This study presents an efficient algorithm for the fitting of multivariate autoregressive models (MVAR) with time-dependent parameters to multidimensional signals. Thereby, the dimension of the model may be chosen to equal the number of signal channels. The autoregressive (AR) parameter matrices are estimated by an extension of the recursive least squares (RLS) algorithm with forgetting factor. The estimation procedure includes a single trial as well as an ensemble mean approach. The latter approach allows the simultaneous fit of one mean MVAR model to a set of single trials, each of them representing the measurement of the same task. A particular advantage of this ensemble mean approach is that it requires only a low computation effort in comparison to well known procedures applied to single trials. Furthermore, the ensemble mean approach is linked with a high adaptation capability. The properties of the estimator are investigated using simulated time series. It can be demonstrated that the adaptation capability of the estimation (measured by its adaptation speed and variance) does not depend on the model dimension. The mean MVAR fit is applied to 19-dimensional EEG data, recorded during an elementary comparison procedure. The calculation of ordinary and multiple coherence is discussed. The sensitivity of the multiple instantaneous EEG coherence will be demonstrated.

Instantaneous EEG coherence analysis during the Stroop task

OBJECTIVE

In the present study the Stroop effect is analyzed by means of EEG coherence analysis in addition to traditional analysis of behavioral data (reaction time) and ERP analysis. Data from 10 normal subjects are examined.

METHODS

In particular, a special dynamic approach for a continuous coherence estimation is applied to investigate the procedural evolution of functional cortical relationships during the Stroop task.

RESULTS

The frequency band of 13-20 Hz is found to be sensitive to the discrimination between the congruent and the incongruent task conditions on the basis of instantaneous coherence analysis. The magnitude of coherence values within the time interval of late potentials and the maximal coherence values are used to assess the strength of interaction between distinct areas of the cortex. Higher coherences are observed within the left frontal and left parietal areas, as well as between them for the incongruent situation in comparison with the congruent situation. Furthermore, the time-points of maximal coherence allows a procedural discrimination between both situations. The peak synchrony described by the time-points of maximal coherence correlates strongly with the reaction times mainly within the frontal area and between fronto-parietal areas in the incongruent case, whereas this correlation is restricted to the right hemisphere in the congruent case.

The sensitivity of instantaneous coherence for considering elementary comparison processing. Part II: Similarities and differences between EEG and MEG coherences

The EEG (electroencephalogram) coherence depends on EEG deviation type. A high level of sensitivity of instantaneous coherence for investigating elementary cognitive tasks could be shown in the case of unipolar reference (ear lobe reference). In order to validate of this result the same investigations were performed for MEG (magnetoencephalogram) coherence, where EEG and MEG were measured simultaneously. A strong correlation between time intervals with high EEG and MEG coherence could be shown. The equivalence of the sensitivity of EEG and MEG coherence for the description of the dynamic behaviour of information processing and the distinction between different elementary cognitive tasks is proven statistically.