A pharmaco-EEG study on antipsychotic drugs in healthy volunteers

RATIONALE: Both psychotropic drugs and mental disorders have typical signatures in quantitative electroencephalography (EEG). Previous studies found that some psychotropic drugs had EEG effects opposite to the EEG effects of the mental disorders treated with these drugs (key-lock principle). OBJECTIVES: We performed a placebo-controlled pharmaco-EEG study on two conventional antipsychotics (chlorpromazine and haloperidol) and four atypical antipsychotics (olanzapine, perospirone, quetiapine, and risperidone) in healthy volunteers. We investigated differences between conventional and atypical drug effects and whether the drug effects were compatible with the key-lock principle. METHODS: Fourteen subjects underwent seven EEG recording sessions, one for each drug (dosage equivalent of 1 mg haloperidol). In a time-domain analysis, we quantified the EEG by identifying clusters of transiently stable EEG topographies (microstates). Frequency-domain analysis used absolute power across electrodes and the location of the center of gravity (centroid) of the spatial distribution of power in different frequency bands. RESULTS: Perospirone increased duration of a microstate class typically shortened in schizophrenics. Haloperidol increased mean microstate duration of all classes, increased alpha 1 and beta 1 power, and tended to shift the beta 1 centroid posterior. Quetiapine decreased alpha 1 power and shifted the centroid anterior in both alpha bands. Olanzapine shifted the centroid anterior in alpha 2 and beta 1. CONCLUSIONS: The increased microstate duration under perospirone and haloperidol was opposite to effects previously reported in schizophrenic patients, suggesting a key-lock mechanism. The opposite centroid changes induced by olanzapine and quetiapine compared to haloperidol might characterize the difference between conventional and atypical antipsychotics.

From EEG dependency multichannel matching pursuit to sparse topographic EEG decomposition

In this work, we present a multichannel EEG decomposition model based on an adaptive topographic time-frequency approximation technique. It is an extension of the Matching Pursuit algorithm and called dependency multichannel matching pursuit (DMMP). It takes the physiologically explainable and statistically observable topographic dependencies between the channels into account, namely the spatial smoothness of neighboring electrodes that is implied by the electric leadfield. DMMP decomposes a multichannel signal as a weighted sum of atoms from a given dictionary where the single channels are represented from exactly the same subset of a complete dictionary. The decomposition is illustrated on topographical EEG data during different physiological conditions using a complete Gabor dictionary. Further the extension of the single-channel time-frequency distribution to a multichannel time-frequency distribution is given. This can be used for the visualization of the decomposition structure of multichannel EEG. A clustering procedure applied to the topographies, the vectors of the corresponding contribution of an atom to the signal in each channel produced by DMMP, leads to an extremely sparse topographic decomposition of the EEG.

How does spatial extent of fMRI datasets affect independent component analysis decomposition?

Spatial independent component analysis (sICA) of functional magnetic resonance imaging (fMRI) time series can generate meaningful activation maps and associated descriptive signals, which are useful to evaluate datasets of the entire brain or selected portions of it. Besides computational implications, variations in the input dataset combined with the multivariate nature of ICA may lead to different spatial or temporal readouts of brain activation phenomena. By reducing and increasing a volume of interest (VOI), we applied sICA to different datasets from real activation experiments with multislice acquisition and single or multiple sensory-motor task-induced blood oxygenation level-dependent (BOLD) signal sources with different spatial and temporal structure. Using receiver operating characteristics (ROC) methodology for accuracy evaluation and multiple regression analysis as benchmark, we compared sICA decompositions of reduced and increased VOI fMRI time-series containing auditory, motor and hemifield visual activation occurring separately or simultaneously in time. Both approaches yielded valid results; however, the results of the increased VOI approach were spatially more accurate compared to the results of the decreased VOI approach. This is consistent with the capability of sICA to take advantage of extended samples of statistical observations and suggests that sICA is more powerful with extended rather than reduced VOI datasets to delineate brain activity.

A BAYESIAN HIERARCHICAL FRAMEWORK FOR SPATIAL MODELING OF fMRI DATA

Functional neuroimaging techniques enable investigations into the neural basis of human cognition, emotions, and behaviors. In practice, applications of functional magnetic resonance imaging (fMRI) have provided novel insights into the neuropathophysiology of major psychiatric,neurological, and substance abuse disorders, as well as into the neural responses to their treatments. Modern activation studies often compare localized task-induced changes in brain activity between experimental groups. One may also extend voxel-level analyses by simultaneously considering the ensemble of voxels constituting an anatomically defined region of interest (ROI) or by considering means or quantiles of the ROI. In this work we present a Bayesian extension of voxel-level analyses that offers several notable benefits. First, it combines whole-brain voxel-by-voxel modeling and ROI analyses within a unified framework. Secondly, an unstructured variance/covariance for regional mean parameters allows for the study of inter-regional functional connectivity, provided enough subjects are available to allow for accurate estimation. Finally, an exchangeable correlation structure within regions allows for the consideration of intra-regional functional connectivity. We perform estimation for our model using Markov Chain Monte Carlo (MCMC) techniques implemented via Gibbs sampling which, despite the high throughput nature of the data, can be executed quickly (less than 30 minutes). We apply our Bayesian hierarchical model to two novel fMRI data sets: one considering inhibitory control in cocaine-dependent men and the second considering verbal memory in subjects at high risk for Alzheimer’s disease. The unifying hierarchical model presented in this manuscript is shown to enhance the interpretation content of these data sets.

MODIFIED TEST STATISTICS BY INTER-VOXEL VARIANCE SHRINKAGE WITH AN APPLICATION TO fMRI

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique which is commonly used to quantify changes in blood oxygenation and flow coupled to neuronal activation. One of the primary goals of fMRI studies is to identify localized brain regions where neuronal activation levels vary between groups. Single voxel t-tests have been commonly used to determine whether activation related to the protocol differs across groups. Due to the generally limited number of subjects within each study, accurate estimation of variance at each voxel is difficult. Thus, combining information across voxels in the statistical analysis of fMRI data is desirable in order to improve efficiency. Here we construct a hierarchical model and apply an Empirical Bayes framework on the analysis of group fMRI data, employing techniques used in high throughput genomic studies. The key idea is to shrink residual variances by combining information across voxels, and subsequently to construct an improved test statistic in lieu of the classical t-statistic. This hierarchical model results in a shrinkage of voxel-wise residual sample variances towards a common value. The shrunken estimator for voxelspecific variance components on the group analyses outperforms the classical residual error estimator in terms of mean squared error. Moreover, the shrunken test-statistic decreases false positive rate when testing differences in brain contrast maps across a wide range of simulation studies. This methodology was also applied to experimental data regarding a cognitive activation task.

Reduced neuronal efficacy in progressive mild cognitive impairment: a prospective fMRI study on visuospatial processing

Mild cognitive impairment (MCI) often refers to the preclinical stage of dementia, where the majority develop Alzheimer's disease (AD). Given that neurodegenerative burden and compensatory mechanisms might exist before accepted clinical symptoms of AD are noticeable, the current prospective study aimed to investigate the functioning of brain regions in the visuospatial networks responsible for preclinical symptoms in AD using event-related functional magnetic resonance imaging (fMRI). Eighteen MCI patients were evaluated and clinically followed for approximately 3 years. Five progressed to AD (PMCI) and eight remained stable (SMCI). Thirteen age-, gender- and education-matched controls also participated. An angle discrimination task with varying task demands was used. Brain activation patterns as well as task demand-dependent and -independent signal changes between the groups were investigated by using an extended general linear model including individual performance (reaction time [RT]) of each single trial. Similar behavioral (RT and accuracy) responses were observed between MCI patients and controls. A network of bilateral activations, e.g. dorsal pathway, which increased linearly with increasing task demand, was engaged in all subjects. Compared with SMCI patients and controls, PMCI patients showed a stronger relation between task demand and brain activity in left superior parietal lobules (SPL) as well as a general task demand-independent increased activation in left precuneus. Altered brain function can be detected at a group level in individuals that progress to AD before changes occur at the behavioral level. Increased parietal activation in PMCI could reflect a reduced neuronal efficacy due to accumulating AD pathology and might predict future clinical decline in patients with MCI.

Reduced hippocampal anisotropy related to anteriorization of alpha EEG in schizophrenia

Dysfunctions of the hippocampus have been suggested to be related to schizophrenia, and reduced connectivity with other brain regions may be a key for the pathophysiology. The aim of this study was to investigate the effect of white matter anomalies in the hippocampus, as a sign of altered connectivity, on the brain electrical activity. We investigated seven first episode schizophrenic patients and seven age, gender and education-matched controls with diffusion tensor imaging and resting EEG. Fractional anisotropy was computed based on diffusion tensor imaging data for the right and left hippocampus for both groups. No group differences were found in hippocampal fractional anisotropy, EEG spectral power and topography. However a significant correlation was found between more anterior alpha activity and lower fractional anisotropy of both hippocampi in schizophrenics, but not in controls. More anterior alpha activity has been described in schizophrenia. We conclude that this feature might depict a group of schizophrenic patients with reduced hippocampal connectivity.

An EEG approach to the neurodevelopmental hypothesis of schizophrenia studying schizophrenics, normal controls and adolescents

Based on an integrative brain model which focuses on memory-driven and EEG state-dependent information processing for the organisation of behaviour, we used the developmental changes of the awake EEG to further investigate the hypothesis that neurodevelopmental abnormalities (deviations in organisation and reorganisation of cortico-cortical connectivity during development) are involved in the pathogenesis of schizophrenia. First-episode, neuroleptic-naive schizophrenics and their matched controls and three age groups of normal adolescents were studied (total: 70 subjects). 19-channel EEG delta-theta, alpha and beta spectral band centroid frequencies during resting (baseline) and after verbal stimuli were used as measure of the level of attained complexity and momentary excitability of the neuronal network (working memory). Schizophrenics compared with all control groups showed lower delta-theta activity centroids and higher alpha and beta activity centroids. Reactivity centroids (centroid after stimulus minus centroid during resting) were used as measure of update of working memory. Schizophrenics showed partial similarities in delta-theta and beta reactivity centroids with the 11-year olds and in alpha reactivity centroids with the 13-year olds. Within the framework of our model, the results suggest multifactorially elicited imbalances in the level of excitability of neuronal networks in schizophrenia, resulting in network activation at dissociated complexity levels, partially regressed and partially prematurely developed. It is hypothesised that activation of age- and/or state-inadequate representations for coping with realities becomes manifest as productive schizophrenic symptoms. Thus, the results support some aspects of the neurodevelopmental hypothesis.

EEG reactivity and EEG activity in never-treated acute schizophrenics, measured with spectral parameters and dimensional complexity

Our approaches to the use of EEG studies for the understanding of the pathogenesis of schizophrenic symptoms are presented. The basic assumptions of a heuristic and multifactorial model of the psychobiological brain mechanisms underlying the organization of normal behavior is described and used in order to formulate and test hypotheses about the pathogenesis of schizophrenic behavior using EEG measures. Results from our studies on EEG activity and EEG reactivity (= EEG components of a memory-driven, adaptive, non-unitary orienting response) as analyzed with spectral parameters and "chaotic" dimensionality (correlation dimension) are summarized. Both analysis procedures showed a deviant brain functional organization in never-treated first-episode schizophrenia which, within the framework of the model, suggests as common denominator for the pathogenesis of the symptoms a deviation of working memory, the nature of which is functional and not structural.

Differences in the EEG profiles of early and late responders to antipsychotic treatment in first-episode, drug-naive psychotic patients

The aim of this study was to search for differences in the EEG of first-episode, drug-naive patients having a schizophrenic syndrome which presented different time courses in response to antipsychotic treatment. Thirteen patients who fulfilled DSM-IV diagnosis for schizophrenia or schizophreniform disorder participated in this study. Before beginning antipsychotic treatment, the EEG was recorded. On the same day psychopathological ratings were assessed using the ADMDP system, and again after 7 and 28 days of treatment. The resting EEG (19 leads) was subject to spectral analysis involving power values for six frequency bands. The score for the schizophrenic syndrome was used to divide the patients into two groups: those who displayed a clinically meaningful improvement of this syndrome (reduction of more than 30%) after 7 days of treatment (early responders, ER) and those who showed this improvement after 28 days (late responders. LR). Analysis of variance for repeated measures between ER, LR and their matched controls with the 19 EEG leads yielded highly significant differences for the factor group in the alpha2 and beta2 frequency band. No difference was found between the slow-wave frequency bands. Compared to controls the LR group showed significantly higher alpha2 and beta2 power and, in comparison to the ER group, significantly higher alpha2 power. There were no significant differences between the ER and the control group. These findings point to differences in brain physiology between ER and LR. The implications for diagnosis and treatment are discussed.

Native EEG and treatment effects in neuroleptic-naïve schizophrenic patients: time and frequency domain approaches

Time domain analysis of electroencephalography (EEG) can identify subsecond periods of quasi-stable brain states. These so-called microstates assumingly correspond to basic units of cognition and emotion. On the other hand, Global Field Synchronization (GFS) is a frequency domain measure to estimate functional synchronization of brain processes on a global level for each EEG frequency band [Koenig, T., Lehmann, D., Saito, N., Kuginuki, T., Kinoshita, T., Koukkou, M., 2001. Decreased functional connectivity of EEG theta-frequency activity in first-episode, neuroleptic-naive patients with schizophrenia: preliminary results. Schizophr Res. 50, 55-60.]. Using these time and frequency domain analyzes, several previous studies reported shortened microstate duration in specific microstate classes and decreased GFS in theta band in drug naïve schizophrenia compared to controls. The purpose of this study was to investigate changes of these EEG parameters after drug treatment in drug naïve schizophrenia. EEG analysis was performed in 21 drug-naive patients and 21 healthy controls. 14 patients were reevaluated 2-8 weeks (mean 4.3) after the initiation of drug administration. The results extended findings of treatment effect on brain functions in schizophrenia, and imply that shortened duration of specific microstate classes seems a state marker especially in patients with later neuroleptic responsive, while lower theta GFS seems a state-related phenomenon and that higher gamma GFS is a trait like phenomenon.

An fMRI-study of locally oriented perception in autism: altered early visual processing of the block design test

Autism has been associated with enhanced local processing on visual tasks. Originally, this was based on findings that individuals with autism exhibited peak performance on the block design test (BDT) from the Wechsler Intelligence Scales. In autism, the neurofunctional correlates of local bias on this test have not yet been established, although there is evidence of alterations in the early visual cortex. Functional MRI was used to analyze hemodynamic responses in the striate and extrastriate visual cortex during BDT performance and a color counting control task in subjects with autism compared to healthy controls. In autism, BDT processing was accompanied by low blood oxygenation level-dependent signal changes in the right ventral quadrant of V2. Findings indicate that, in autism, locally oriented processing of the BDT is associated with altered responses of angle and grating-selective neurons, that contribute to shape representation, figure-ground, and gestalt organization. The findings favor a low-level explanation of BDT performance in autism.

Decreased EEG synchronization in Alzheimer's disease and mild cognitive impairment

The hypothesis of a functional disconnection of neuro-cognitive networks in patients with mild cognitive impairment (MCI) and Alzheimer Dementia was investigated using baseline resting EEG data. EEG databases from New York (264 subjects) and Stockholm (155 subjects), including healthy controls and patients with varying degrees of cognitive decline or Alzheimer Dementia were analyzed using Global Field Synchronization (GFS), a novel measure of global EEG synchronization. GFS reflects the global amount of phase-locked activity at a given frequency by a single number; it is independent of the recording reference and of implicit source models. Patients showed decreased GFS values in Alpha, Beta, and Gamma frequency bands, and increased GFS values in the Delta band, confirming the hypothesized disconnection syndrome. The results are discussed within the framework of current knowledge about the functional significance of the affected frequency bands.

The experimental combination of rTMS and fMRI reveals the functional relevance of parietal cortex for visuospatial functions

We combined repetitive transcranial magnetic stimulation (rTMS) and functional magnetic resonance imaging (fMRI) to investigate the functional relevance of parietal cortex activation during the performance of visuospatial tasks. fMRI provides information about local transient changes in neuronal activation during behavioural or cognitive tasks. Information on the functional relevance of this activation was obtained by using rTMS to induce temporary regional deactivations. We thereby turned the physiological parameter of brain activity into an independent variable controlled and manipulated by the experimenter and investigated its effect on the performance of the cognitive tasks within a controlled experimental design. We investigated cognitive tasks that were performed on the same visual material but differed in the demand on visuospatial functions. For the visuospatial tasks we found a selective enhancement of fMRI signal in the superior parietal lobule (SPL) and a selective impairment of performance after rTMS to this region in comparison to a control group. We could thus show that the parietal cortex is functionally important for the execution of spatial judgements on visually presented material and that TMS as an experimental tool has the potential to interfere with higher cognitive functions such as visuospatial information processing.