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Alexis Roche

Publications and source records attributed to Alexis Roche.

3 recordsLinked to original sources

Functional organization of perisylvian activation during presentation of sentences in preverbal infants.

We examined the functional organization of cerebral activity in 3-month-old infants when they were listening to their mother language. Short sentences were presented in a slow event-related functional MRI paradigm. We then parsed the infant's network of perisylvian responsive regions into functionally distinct regions based on their speed of activation and sensitivity to sentence repetition. An adult-like structure of functional MRI response delays was observed along the superior temporal regions, suggesting a hierarchical processing scheme. The fastest responses were recorded in the vicinity of Heschl's gyrus, whereas responses became increasingly slower toward the posterior part of the superior temporal gyrus and toward the temporal poles and inferior frontal regions (Broca's area). Activation in the latter region increased when the sentence was repeated after a 14-s delay, suggesting the early involvement of Broca's area in verbal memory. The fact that Broca's area is active in infants before the babbling stage implies that activity in this region is not the consequence of sophisticated motor learning but, on the contrary, that this region may drive, through interactions with the perceptual system, the learning of the complex motor sequences required for future speech production. Our results point to a complex, hierarchical organization of the human brain in the first months of life, which may play a crucial role in language acquisition in our species.

Adult↗

Dealing with the shortcomings of spatial normalization: multi-subject parcellation of fMRI datasets.

The analysis of functional magnetic resonance imaging (fMRI) data recorded on several subjects resorts to the so-called spatial normalization in a common reference space. This normalization is usually carried out on a voxel-by-voxel basis, assuming that after coregistration of the functional images with an anatomical template image in the Talairach reference system, a correct voxel-based inference can be carried out across subjects. Shortcomings of such approaches are often dealt with by spatially smoothing the data to increase the overlap between subject-specific activated regions. This procedure, however, cannot adapt to each anatomo-functional subject configuration. We introduce a novel technique for intra-subject parcellation based on spectral clustering that delineates homogeneous and connected regions. We also propose a hierarchical method to derive group parcels that are spatially coherent across subjects and functionally homogeneous. We show that we can obtain groups (or cliques) of parcels that well summarize inter-subject activations. We also show that the spatial relaxation embedded in our procedure improves the sensitivity of random-effect analysis.

Algorithms↗

Combined permutation test and mixed-effect model for group average analysis in fMRI.

In group average analyses, we generalize the classical one-sample t test to account for heterogeneous within-subject uncertainties associated with the estimated effects. Our test statistic is defined as the maximum likelihood ratio corresponding to a Gaussian mixed-effect model. The test's significance level is calibrated using the same sign permutation framework as in Holmes et al., allowing for exact specificity control under a mild symmetry assumption about the subjects' distribution. Because our likelihood ratio test does not rely on homoscedasticity, it is potentially more sensitive than both the standard t test and its permutation-based version. We present results from the Functional Imaging Analysis Contest 2005 dataset to support this claim.

Algorithms↗