Biomedical subjects

Nelson J Trujillo-Barreto

Publications and source records attributed to Nelson J Trujillo-Barreto.

6 recordsLinked to original sources

PubMed2007

Realistically coupled neural mass models can generate EEG rhythms.

We study the generation of EEG rhythms by means of realistically coupled neural mass models. Previous neural mass models were used to model cortical voxels and the thalamus. Interactions between voxels of the same and other cortical areas and with the thalamus were taken into account. Voxels within the same cortical area were coupled (short-range connections) with both excitatory and inhibitory connections, while coupling between areas (long-range connections) was considered to be excitatory only. Short-range connection strengths were modeled by using a connectivity function depending on the distance between voxels. Coupling strength parameters between areas were defined from empirical anatomical data employing the information obtained from probabilistic paths, which were tracked by water diffusion imaging techniques and used to quantify white matter tracts in the brain. Each cortical voxel was then described by a set of 16 random differential equations, while the thalamus was described by a set of 12 random differential equations. Thus, for analyzing the neuronal dynamics emerging from the interaction of several areas, a large system of differential equations needs to be solved. The sparseness of the estimated anatomical connectivity matrix reduces the number of connection parameters substantially, making the solution of this system faster. Simulations of human brain rhythms were carried out in order to test the model. Physiologically plausible results were obtained based on this anatomically constrained neural mass model.

Brain Mapping↗

PubMed2006

Mechanisms for detecting auditory temporal and spectral deviations operate over similar time windows but are divided differently between the two hemispheres.

In order to keep track of potentially relevant information in the acoustic environment, the human brain processes sounds to a high extent even when they are not attended: it extracts basic features, encodes regularities, and detects deviances. Here, we deliver evidence that the initial 300 ms of a sound contribute more to this preattentive processing than the sound's later parts. We directly compared the influence of the temporal distance relative to sound onset on the processing of the sound's duration and frequency information. The mismatch negativity (MMN), an event-related potential indicator for preattentive feature encoding and deviance detection, was measured for infrequent duration deviants and frequency modulation deviants. The onset of either deviancy was at 100, 200, 300, or 400 ms relative to sound onset. MMN was only elicited for deviations occurring within the first 300 ms after sound onset for both types of deviants. Its neural sources were localized in supra-temporal cortices with source current density analyses (SCD) and variable resolution electromagnetic tomography (VARETA), revealing a right-hemispheric preponderance for frequency modulations but not for duration shortenings. This suggests that preattentive deviance detection is based upon partly diverging functional memory registers for temporal and dynamic spectral information. The influence of temporal distance on MMN in both conditions supports the view that temporal and spectral sound properties are integrated into an auditory object representation prior to preattentive deviance detection. Importantly, the decline of MMN to unattended sounds with larger temporal distance suggests that parts beyond 300 ms are less important for preattentive auditory object representation.

Acoustic Stimulation↗

PubMed2006

Repetition suppression of induced gamma band responses is eliminated by task switching.

The formation of cortical object representations requires the activation of cell assemblies, correlated by induced oscillatory bursts of activity > 20 Hz (induced gamma band responses; iGBRs). One marker of the functional dynamics within such cell assemblies is the suppression of iGBRs elicited by repeated stimuli. This effect is commonly interpreted as a signature of 'sharpening' processes within cell-assemblies, which are behaviourally mirrored in repetition priming effects. The present study investigates whether the sharpening of primed objects is an automatic consequence of repeated stimulus processing, or whether it depends on task demands. Participants performed either a 'living/non-living' or a 'bigger/smaller than a shoebox' classification on repeated pictures of everyday objects. We contrasted repetition-related iGBR effects after the same task was used for initial and repeated presentations (no-switch condition) with repetitions after a task-switch occurred (switch condition). Furthermore, we complemented iGBR analysis by examining other brain responses known to be modulated by repetition-related memory processes (evoked gamma oscillations and event-related potentials; ERPs). The results obtained for the 'no-switch' condition replicated previous findings of repetition suppression of iGBRs at 200-300 ms after stimulus onset. Source modelling showed that this effect was distributed over widespread cortical areas. By contrast, after a task-switch no iGBR suppression was found. We concluded that iGBRs reflect the sharpening of a cell assembly only within the same task. After a task switch the complete object representation is reactivated. The ERP (220-380 ms) revealed suppression effects independent of task demands in bilateral posterior areas and might indicate correlates of repetition priming in perceptual structures.

Adult↗

PubMed2005

Brain electrical tomography (BET) analysis of induced gamma band responses during a simple object recognition task.

The formation of cortical object representations requires the activation of cell assemblies, correlated by induced oscillatory bursts above 20 Hz (gamma band), which are characterized by trial-by-trial latency fluctuations around a mean of approximately 300 ms after stimulus onset. The present electroencephalogram (EEG) study was intended to uncover to the generators of induced gamma band responses (GBRs) and to analyze phase-synchronization between these sources. A standard object recognition task was used to elicit gamma activity. At the scalp surface (electrode space), we found an augmentation of induced GBRs after the presentation of meaningful (familiar) as opposed to meaningless (unfamiliar) stimuli, which was accompanied by a dense pattern of significant phase-locking values between distant recording sites. Subsequently, intracranial current density distributions compatible with the observed scalp voltage topographies were estimated by means of VARETA (Variable Resolution Electromagnetic Tomography). In source space brain electrical tomographies (BETs) revealed widespread generators of induced GBRs at temporal, parietal, posterior, and frontal areas. Phase-locking analysis was calculated between re-constructed electrode signals based on separate forward solutions of the observed generators, thereby eliminating the possibly confounding influence of activity from areas not under observation. The results support the view that induced GBRs signify synchronous neuronal activity in a broadly distributed network during object recognition. The localization of the generators of event-related potentials (ERPs), evoked gamma activity, and induced alpha activity revealed different sources as compared to the induced GBR and, thus, seem to mirror complementary functions during the present task as compared to induced high-frequency brain dynamics.

Adult↗

PubMed2005

Bayesian fMRI time series analysis with spatial priors.

We describe a Bayesian estimation and inference procedure for fMRI time series based on the use of General Linear Models (GLMs). Importantly, we use a spatial prior on regression coefficients which embodies our prior knowledge that evoked responses are spatially contiguous and locally homogeneous. Further, using a computationally efficient Variational Bayes framework, we are able to let the data determine the optimal amount of smoothing. We assume an arbitrary order Auto-Regressive (AR) model for the errors. Our model generalizes earlier work on voxel-wise estimation of GLM-AR models and inference in GLMs using Posterior Probability Maps (PPMs). Results are shown on simulated data and on data from an event-related fMRI experiment.

Bayes Theorem↗

PubMed2004

Bayesian model averaging in EEG/MEG imaging.

In this paper, the Bayesian Theory is used to formulate the Inverse Problem (IP) of the EEG/MEG. This formulation offers a comparison framework for the wide range of inverse methods available and allows us to address the problem of model uncertainty that arises when dealing with different solutions for a single data. In this case, each model is defined by the set of assumptions of the inverse method used, as well as by the functional dependence between the data and the Primary Current Density (PCD) inside the brain. The key point is that the Bayesian Theory not only provides for posterior estimates of the parameters of interest (the PCD) for a given model, but also gives the possibility of finding posterior expected utilities unconditional on the models assumed. In the present work, this is achieved by considering a third level of inference that has been systematically omitted by previous Bayesian formulations of the IP. This level is known as Bayesian model averaging (BMA). The new approach is illustrated in the case of considering different anatomical constraints for solving the IP of the EEG in the frequency domain. This methodology allows us to address two of the main problems that affect linear inverse solutions (LIS): (a) the existence of ghost sources and (b) the tendency to underestimate deep activity. Both simulated and real experimental data are used to demonstrate the capabilities of the BMA approach, and some of the results are compared with the solutions obtained using the popular low-resolution electromagnetic tomography (LORETA) and its anatomically constraint version (cLORETA).

Artifacts↗