BET differences among simultaneous evoked frequency band responses during early-stage visual processing distinguish schizophrenia from healthy subjects.

In this work, we attempt to extend to the schizophrenia's research the evidence that different frequency bands may emerge from different sources during early-stage visual processing, in a mental state-specific manner, while subjects are passively viewing a visual stimulus. We applied standard pattern reversal stimulation (checker-board), a task with low cognitive demands, coupled to a dense EEG recording system to estimate the neural correlates of the evoked theta, alpha, beta, beta1, and gamma frequency band responses by means of brain electrical tomography (BET). After filtering the evoked activity using different band-passes, a very different picture about the current sources during P100 will emerge. The results showed notable differences between the two groups. In healthy subjects we localized the significances in the anterior cingulate, caudate nucleus, thalamus, precuneous region, and superior parietal that were more active for gamma band. In patients with schizophrenia differences occupy the hippocampus, parahippocampus, thalamus, midbrain, precuneus, and superior parietal regions. Most areas were more active for gamma band except precuneous and superior parietal region more active for theta and alpha frequency band. These sets of regions, in both groups, reflect events that are parallel to and partly independent of the P100 component, while in the schizophrenia, these regions have been previous linked to the major symptoms of the disease. We concluded that this result provides important evidence indicating that the proposed method is able to differentiate electrophysiological patterns in healthy subjects from those in patients with schizophrenia.

Sources of synchronized induced Gamma-Band responses during a simple object recognition task: a replication study in human MEG.

Natural stimuli are compiled of numerous features, which are cortically represented in dispersed structures. Synchronized oscillations in the Gamma-Band (>30 Hz; induced Gamma-Band Responses, iGBRs), are regarded as a plausible mechanism to re-integrate these regions into a meaningful cortical object representation. Using electroencephalography (EEG) it was demonstrated that the generators of iGBRs can be localized to temporal, parietal, posterior, and frontal areas. The present magnetoencephalogram (MEG) study intended to replicate these findings in order contribute to the ongoing debate regarding the possible functional difference of high-frequency signals as measured by both techniques. During a standard object recognition task we found an augmentation of the iGBR after the presentation of meaningful as opposed to meaningless stimuli at approximately 160-440 ms after stimulus onset. This peak was localized to inferior temporal gyri, superior parietal lobules and the right middle frontal gyrus. Importantly, most of these brain structures were significantly phase-locked to each other. The implications of these results are twofold: (1) they present further evidence for the view that iGBRs signify neuronal activity in a broadly distributed network during object recognition. (2) MEG is well suited to detect induced high-frequency oscillations with a very similar morphology as revealed by EEG recordings, thereby eliminating known problems with electroencephalographical methods (e.g. reference confounds). In contrast to the iGBR, the localization of event-related fields (ERFs) and evoked Gamma-Band Response (eGBRs) revealed generators in focal visual areas, and thus, seem to mirror early sensory processing.

3D statistical parametric mapping of EEG source spectra by means of variable resolution electromagnetic tomography (VARETA).

This article describes a new method for 3D QEEG tomography in the frequency domain. A variant of Statistical Parametric Mapping is presented for source log spectra. Sources are estimated by means of a Discrete Spline EEG inverse solution known as Variable Resolution Electromagnetic Tomography (VARETA). Anatomical constraints are incorporated by the use of the Montreal Neurological Institute (MNI) probabilistic brain atlas. Efficient methods are developed for frequency domain VARETA in order to estimate the source spectra for the set of 10(3)-10(5) voxels that comprise an EEG/MEG inverse solution. High resolution source Z spectra are then defined with respect to the age dependent mean and standard deviations of each voxel, which are summarized as regression equations calculated from the Cuban EEG normative database. The statistical issues involved are addressed by the use of extreme value statistics. Examples are shown that illustrate the potential clinical utility of the methods herein developed.

[Cognitive diagnosis of cerebrovascular disease by event related potentials: anatomical sources that generate P300]. / Diagnóstico cognitivo en la enfermedad cerebrovascular mediante potenciales relacionados con eventos: fuentes anatómicas generadoras de la P300.

INTRODUCTION: Cerebrovascular disease causes different cognitive alterations. There is a need to develop tools that are capable of diagnosing them. One of them could be event related potentials. These provide an indicator of cognitive processing in real time. PATIENTS AND METHODS: A study was conducted of 10 patients with cerebral infarction in the frontal region and 10 paired healthy controls. Evaluation of the patients was performed a week after the stroke. A continuous performance test was applied to both groups together with the recording of the electrical activity in the brain in order to obtain the P300 component. The results were submitted to the non-parametric Student's t test, and the Bayesian model averaging method (BMAM) was employed to calculate the sources generating the electrical activity recorded on the electroencephalogram. RESULTS: Patients displayed significantly poorer performances compared to the healthy controls in the attention test. The BMAM showed that the P300 component was related to the right hand temporal structures in healthy controls, whereas the left temporoparietal regions were also involved in the patients. CONCLUSIONS: These findings indicate the existence of subclinical disorders affecting sustained attention and that they can only be detected by very sensitive tools; furthermore, they also have implications for the brain circuits regulating sustained attention and the P300 component.

DEM: a variational treatment of dynamic systems.

This paper presents a variational treatment of dynamic models that furnishes time-dependent conditional densities on the path or trajectory of a system's states and the time-independent densities of its parameters. These are obtained by maximising a variational action with respect to conditional densities, under a fixed-form assumption about their form. The action or path-integral of free-energy represents a lower bound on the model's log-evidence or marginal likelihood required for model selection and averaging. This approach rests on formulating the optimisation dynamically, in generalised coordinates of motion. The resulting scheme can be used for online Bayesian inversion of nonlinear dynamic causal models and is shown to outperform existing approaches, such as Kalman and particle filtering. Furthermore, it provides for dual and triple inferences on a system's states, parameters and hyperparameters using exactly the same principles. We refer to this approach as dynamic expectation maximisation (DEM).

Diffusion-based spatial priors for imaging.

We describe a Bayesian scheme to analyze images, which uses spatial priors encoded by a diffusion kernel, based on a weighted graph Laplacian. This provides a general framework to formulate a spatial model, whose parameters can be optimized. The application we have in mind is a spatiotemporal model for imaging data. We illustrate the method on a random effects analysis of fMRI contrast images from multiple subjects; this simplifies exposition of the model and enables a clear description of its salient features. Typically, imaging data are smoothed using a fixed Gaussian kernel as a pre-processing step before applying a mass-univariate statistical model (e.g., a general linear model) to provide images of parameter estimates. An alternative is to include smoothness in a multivariate statistical model (Penny, W.D., Trujillo-Barreto, N.J., Friston, K.J., 2005. Bayesian fMRI time series analysis with spatial priors. Neuroimage 24, 350-362). The advantage of the latter is that each parameter field is smoothed automatically, according to a measure of uncertainty, given the data. In this work, we investigate the use of diffusion kernels to encode spatial correlations among parameter estimates. Nonlinear diffusion has a long history in image processing; in particular, flows that depend on local image geometry (Romeny, B.M.T., 1994. Geometry-driven Diffusion in Computer Vision. Kluwer Academic Publishers) can be used as adaptive filters. This can furnish a non-stationary smoothing process that preserves features, which would otherwise be lost with a fixed Gaussian kernel. We describe a Bayesian framework that incorporates non-stationary, adaptive smoothing into a generative model to extract spatial features in parameter estimates. Critically, this means adaptive smoothing becomes an integral part of estimation and inference. We illustrate the method using synthetic and real fMRI data.

Sustained spatial attention to vibration is mediated in primary somatosensory cortex.

Focusing attention to a specific body location has been shown to improve processing of events presented at this body location. One important debate concerns the stage in the somatosensory pathway at which the neural response is modulated when one attends to a tactile stimulus. Previous studies focused on components of the somatosensory evoked potential to transient stimuli, and demonstrated an early cortical attentional modulation. The neural basis of sustained spatial stimulus processing with continuous stimulation remains, however, largely unexplored. A way to approach this topic is to present vibrating stimuli with different frequencies for several seconds simultaneously to different body locations while subjects have to attend to the one or the other location. The amplitude of the somatosensory steady-state evoked potential (SSSEP) elicited by these vibrating stimuli increases with attention. On the basis of 128 electrode recordings, we investigated the topographical distribution and the underlying cortical sources by means of a VARETA approach of this attentional amplitude modulation of the SSSEP. Sustained spatial attention was found to be mediated in primary somatosensory cortex with no differences in SSSEP amplitude topographies between attended and unattended body locations. These result patterns were seen as evidence for a low-level sensory gain control mechanism in tactile spatial attention.

Diagnóstico cognitivo en la enfermedad cerebrovascular mediante potenciales relacionados con eventos: fuentes anatómicas generadoras de la P300 / Cognitive diagnosis of cerebrovascular disease by event-related potentials: anatomical sources that generate p300

Introducción. La enfermedad cerebrovascular causa alteraciones cognitivas disímiles. Es preciso desarrollar herramientas capaces de diagnosticarlas, y una de ellas podría ser los potenciales relacionados con eventos. Éstos brindan un indicador en tiempo real del procesamiento cognitivo. Pacientes y métodos. Se estudiaron 10 pacientes con infarto cerebral en la región frontal y 10 controles sanos pareados. La evaluación de los pacientes se realizó una semana después de presentar el infarto. A ambos grupos se les aplicó un test de ejecución continua asociado al registro de la actividad eléctrica cerebral para la obtención del componente P300. Los resultados obtenidos se sometieron a la prueba no paramétrica t de Student, y el electroencefalograma, al método de promediación de modelos bayesianos (MPMB) para el cálculo de las fuentes generadoras de la actividad eléctrica registrada. Resultados. Los pacientes tuvieron ejecuciones significativamente más pobres que los controles sanos en la prueba de atención. El MPMB mostró que el componente P300 se relaciona con las estructuras temporales derechas en los controles sanos, mientras que en los pacientes se suman las zonas temporoparietales izquierdas. Conclusiones. Estos resultados indican la existencia de alteraciones subclínicas de la atención sostenida, y que sólo herramientas más sensibles pueden detectarlas; además, tienen implicaciones para los circuitos cerebrales reguladores de la atención sostenida y del componente P300 (AU)

Introduction. Cerebrovascular disease causes different cognitive alterations. There is a need to develop tools that are capable of diagnosing them. One of them could be event-related potentials. These provide an indicator of cognitive processing in real time. Patients and methods. A study was conducted of 10 patients with cerebral infarction in the frontal region and 10 paired healthy controls. Evaluation of the patients was performed a week after the stroke. A continuous performance test was applied to both groups together with the recording of the electrical activity in the brain in order to obtain the P300 component. The results were submitted to the non-parametric Student’s t test, and the Bayesian model averaging method (BMAM) was employed to calculate the sources generating the electrical activity recorded on the electroencephalogram. Results. Patients displayed significantly poorer performances compared to the healthy controls in the attention test. The BMAM showed that the P300 component was related to the right-hand temporal structures in healthy controls, whereas the left temporoparietal regions were also involved in the patients. Conclusions. These findings indicate the existence of subclinical disorders affecting sustained attention and that they can only be detected by very sensitive tools; furthermore, they also have implications for the brain circuits regulating sustained attention and the P300 component (AU)