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Juliana Paré-Blagoev

Publications and source records attributed to Juliana Paré-Blagoev.

2 recordsLinked to original sources

Reproducibility of activation in Broca's area during covert generation of single words at high field: a single trial FMRI study at 4 T.

Although functional magnetic resonance imaging (FMRI) has arguably become the most ubiquitously used imaging modality, questions remain about the reproducibility of the observed patterns of activation and the acquisition time required to achieve statistically significant and reproducible maps. In the current study, we investigated the reliability of activation in Broca's area, on both a voxel-wise and region of interest level, in response to the covert generation of a single word at 4 T. We also assessed the effects of different parametric (P < 0.01; P < 0.005; P < 0.001) and spatial thresholds (25%, 50% and 75%) on the reproducibility of activation within our region of interest and other randomly selected areas of the brain. We report that the inter-trial consistency of activation within Broca's area for a single trial design using multi-echo EPI is roughly equivalent to previous studies that averaged across a much larger number of trials. However, reliability estimates varied dramatically (approximately 55%) depending on the different parametric and spatial criteria thresholds that were applied to the data. These results show that increased sensitivity at high field strength can be used to reduce the time needed to localize functional activation patterns, which is beneficial for clinical studies such as pre-surgical mapping. Additional benefits of single trial designs, such as the ability to immediately assess for extraneous cognitive processes, are also discussed.

Adult↗

Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging data.

Recently, we described a Bayesian inference approach to the MEG/EEG inverse problem that used numerical techniques to estimate the full posterior probability distributions of likely solutions upon which all inferences were based [Schmidt, D.M., George, J.S., Wood, C.C., 1999. Bayesian inference applied to the electromagnetic inverse problem. Human Brain Mapping 7, 195; Schmidt, D.M., George, J.S., Ranken, D.M., Wood, C.C., 2001. Spatial-temporal bayesian inference for MEG/EEG. In: Nenonen, J., Ilmoniemi, R. J., Katila, T. (Eds.), Biomag 2000: 12th International Conference on Biomagnetism. Espoo, Norway, p. 671]. Schmidt et al. (1999) focused on the analysis of data at a single point in time employing an extended region source model. They subsequently extended their work to a spatiotemporal Bayesian inference analysis of the full spatiotemporal MEG/EEG data set. Here, we formulate spatiotemporal Bayesian inference analysis using a multi-dipole model of neural activity. This approach is faster than the extended region model, does not require use of the subject's anatomical information, does not require prior determination of the number of dipoles, and yields quantitative probabilistic inferences. In addition, we have incorporated the ability to handle much more complex and realistic estimates of the background noise, which may be represented as a sum of Kronecker products of temporal and spatial noise covariance components. This reduces the effects of undermodeling noise. In order to reduce the rigidity of the multi-dipole formulation which commonly causes problems due to multiple local minima, we treat the given covariance of the background as uncertain and marginalize over it in the analysis. Markov Chain Monte Carlo (MCMC) was used to sample the many possible likely solutions. The spatiotemporal Bayesian dipole analysis is demonstrated using simulated and empirical whole-head MEG data.

Algorithms↗