Biomedical subjects

Eric Maris

Publications and source records attributed to Eric Maris.

5 recordsLinked to original sources

PubMed2006

Theta and gamma oscillations predict encoding and retrieval of declarative memory.

Although studies in animals and patients have demonstrated that brain oscillations play a role in declarative memory encoding and retrieval, little has been done to investigate the temporal dynamics and sources of brain activity in healthy human subjects performing such tasks. In a magnetoencephalography study using pictorial stimuli, we have now identified oscillatory activity in the gamma (60-90 Hz) and theta (4.5-8.5 Hz) band during declarative memory operations in healthy participants. Both theta and gamma activity was stronger for the later remembered compared with the later forgotten items (the "subsequent memory effect"). In the retrieval session, theta and gamma activity was stronger for recognized items compared with correctly rejected new items (the "old/new effect"). The gamma activity was also stronger for recognized compared with forgotten old items (the "recognition effect"). The effects in the theta band were observed over right parietotemporal areas, whereas the sources of the effects in the gamma band were identified in Brodmann area 18/19. We propose that the theta activity is directly engaged in mnemonic operations. The increase in neuronal synchronization in the gamma band in occipital areas may result in a stronger drive to subsequent areas, thus facilitating both memory encoding and retrieval. Alternatively, the gamma synchronization might reflect representations being reinforced by top-down activity from higher-level memory areas. Our results provide additional insight on human declarative memory operations and oscillatory brain activity that complements previous electrophysiological and brain imaging studies.

Adult↗

PubMed2005

Starting and stopping mechanisms of absence epileptic seizures are revealed by hazard functions.

We show that the hazard function provides useful information about the starting and the stopping mechanisms of absence epileptic seizures. The hazard function quantifies changes in the probability that an event (respectively, the starting and the stopping of a seizure) occurs in some small time interval given that it has not occurred yet. It informs us about changes in the concentration of endogenous substances that modulate the neuronal signalling properties of (parts of) the brain. In a pharmacological experiment, we used the hazard function to study the effect of a GABA-transaminase inhibitor (vigabatrin) on the starting and the stopping mechanisms of absence epileptic seizures in a genetic rat model of absence epilepsy (the WAG/Rij rat). This experiment showed that a high GABA level changed the stopping mechanism of the absence epileptic seizures, creating much better conditions for very long seizures to develop. With respect to the starting mechanism, it was found that both with a high and a low GABA level, there was evidence for a recovery mechanism that decreases the probability that a new seizure starts. Initially, this probability is larger with a high GABA level, but gradually it converges to the same constant baseline probability as in the condition with a low GABA level.

Algorithms↗

PubMed2004

Randomization tests for ERP topographies and whole spatiotemporal data matrices.

In ERP studies, the comparison of topographies (multichannel measurements) or whole spatiotemporal data matrices (multichannel time series of measurements), the classical statistical tests very often cannot be used. It is argued that, for these comparisons, randomization tests are an excellent alternative. It is also argued that the randomization test is superior to another resampling method, the bootstrap, because exact probability statements (e.g., p values) can be made. A review is given of the literature on randomization tests designed for electrophysiological data. New randomization tests are presented and applied to two data sets, one coming from a psychopharmacological experiment and the other from an ERP experiment in visual word recognition.

Animals↗

PubMed2003

A resampling method for estimating the signal subspace of spatio-temporal EEG/MEG data.

Source localization using spatio-temporal electroencephalography (EEG) and magnetoencephalography (MEG) data is usually performed by means of signal subspace methods. The first step of these methods is the estimation of a set of vectors that spans a subspace containing as well as possible the signal of interest. This estimation is usually performed by means of a singular value decomposition (SVD) of the data matrix: The rank of the signal subspace (denoted by r) is estimated from a plot in which the singular values are plotted against their rank order, and the signal subspace itself is estimated by the first r singular vectors. The main problem with this method is that it is strongly affected by spatial covariance in the noise. Therefore, two methods are proposed that are much less affected by this spatial covariance, and old and a new method. The old method involves prewhitening of the data matrix, making use of an estimate of the spatial noise covariance matrix. The new method is based on the matrix product of two average data matrices, resulting from a random partition of a set of stochastically independent replications of the spatio-temporal data matrix. The estimated signal subspace is obtained by first filtering out the asymmetric and negative definite components of this matrix product and then retaining the eigenvectors that correspond to the r largest eigenvalues of this filtered data matrix. The main advantages of the partition-based eigen decomposition over prewhited SVD is that 1) it does not require an estimate of the spatial noise covariance matrix and 2b) that it allows one to make use of a resampling distribution (the so-called partitioning distribution) as a natural quantification of the uncertainty in the estimated rank. The performance of three methods (SVD with and without prewhitening, and the partition-based method) is compared in a simulation study. From this study, it could be concluded that prewhited SVD and the partition-based eigen decomposition perform equally well when the amplitude time series are constant, but that the partition-based method performs better when the amplitude time series are variable.

Algorithms↗

PubMed2002

The role of orthographic and phonological codes in the word and the pseudoword superiority effect: an analysis by means of multinomial processing tree models.

Central to the current accounts of the word and the pseudoword superiority effect (WSE and PWSE, respectively) is the concept of a unitized code that is less susceptible to masking than single-letter codes. Current explanations of the WSE and PWSE assume that this unitized code is orthographic, explaining these phenomena by the assumption of dual read-out from unitized and single-letter codes. In this article, orthographic dual read-out models are compared with a phonological dual read-out model (which is based on the assumption that the 1st unitized code is phonological). From this phonological code, an orthographic code is derived, through either lexical access or assembly. Comparison of the orthographic and phonological dual read-out models was performed by formulating both models as multinomial processing tree models. From an application of these models to the data of 2 letter identification experiments, it was clear that the orthographic dual read-out models are insufficient as an explanation of the PWSE, whereas the phonological dual read-out model is sufficient.

Cognition↗