Functional segregation of cortical language areas by sentence repetition

Dehaene‐Lambertz, Ghislaine; Dehaene, Stanislas; Anton, Jean-Luc; Campagne, Aurélie; Ciuciu, Philippe; Dehaene, Guillaume; Denghien, Isabelle; Jobert, Antoinette; LeBihan, D.; Sigman, Mariano; Pallier, Christophe; Poline, Jean‐Baptiste

doi:10.1002/hbm.20250

Cited by 115 publications

(126 citation statements)

References 56 publications

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“…After averaging these quantities over the five parcels, we obtained congruent results (T peak = 6.1 s. and T undershoot = 13.2 s. around P He vs. T peak = 6.5 s. and T undershoot = 13.5 s. around P Br ) meaning that the region embedding Hesch's gyrus elicits brain activations faster than the region including Broca's area. This confirms more directly what has been already derived in (Dehaene-Lambertz et al, 2006) although no statistical test is provided to assess the significance of this result. The next question concerns of course the putative reasons of these earlier responses in Hesch's gyrus.…”

supporting

confidence: 87%

“…15(f) confirms the presence of a significant habituation effect in P Br . Our quantitative analysis (see Table 4, right col.) also shows the same trend as outlined by the strong discrepancies between the mean and standard deviations of S 1 and S 2 .In (Dehaene-Lambertz et al, 2006), a sine-wave GLM was designed and fitted to study the speed of habituation. It allowed one to exhibit a temporal organisation of the temporal lobe with fastest responses located in Heschl's gyrus and slowest ones in temporal poles.…”

mentioning

confidence: 53%

“…In (Dehaene-Lambertz et al, 2006), a sine-wave GLM was designed and fitted to study the speed of habituation. It allowed one to exhibit a temporal organisation of the temporal lobe with fastest responses located in Heschl's gyrus and slowest ones in temporal poles.…”

Section: Habituation and Temporal Organisationmentioning

confidence: 99%

“…A highresolution (1 × 1 × 1.2 mm 3 ) anatomical image was also acquired for each subject using a 3-D gradient-echo inversion-recovery sequence. Experimental paradigm and contrast selection The reader may refer to (Dehaene-Lambertz et al, 2006) for details about this fMRI experiment. In short, the motivation of this study was to measure the reduction in the neural activity subserving a cognitive representation when this representation is accessed twice (the socalled "repetition suppression" effect), resulting in a detectable adaptation of the measurable signal in fMRI (Grill-Spector and Malach, 2001;Naccache and Dehaene, 2001).…”

mentioning

confidence: 99%

“…There is a large evidence in these datasets for supporting faster neurodynamics as the main origin of these results instead of faster hemodynamics. This can be checked for instance using complementary analysis like a phase analysis conducted in (Dehaene-Lambertz et al, 2006). …”

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A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI

Idier²,

et al. 2008

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Within-subject analysis in fMRI essentially addresses two problems, i.e., the detection of activated brain regions in response to an experimental task and the estimation of the underlying dynamics, also known as the characterisation of Hemodynamic response function (HRF). So far, both issues have been treated sequentially while it is known that the HRF model has a dramatic impact on the localisation of activations and that the HRF shape may vary from one region to another. In this paper, we conciliate both issues in a region-based joint detection-estimation framework that we develop in the Bayesian formalism. Instead of considering function basis to account for spatial variability, spatially adaptive General Linear Models are built upon region-based non-parametric estimation of brain dynamics. Regions are first identified as functionally homogeneous parcels in the mask of the grey matter using a specific procedure [Thirion, B., Flandin, G., Pinel, P., Roche, A., Ciuciu, P., Poline, J.-B., August 2006. Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRI datasets. Hum. Brain Mapp. 27 (8), 678-693.]. Then, in each parcel, prior information is embedded to constrain this estimation. Detection is achieved by modelling activating, deactivating and nonactivating voxels through mixture models within each parcel. From the posterior distribution, we infer upon the model parameters using Markov Chain Monte Carlo (MCMC) techniques. Bayesian model comparison allows us to emphasize on artificial datasets first that inhomogeneous gamma-Gaussian mixture models outperform Gaussian mixtures in terms of sensitivity/specificity trade-off and second that it is worthwhile modelling serial correlation through an AR(1) noise process at low signal-to-noise (SNR) ratio. Our approach is then validated on an fMRI experiment that studies habituation to auditory sentence repetition. This phenomenon is clearly recovered as well as the hierarchical temporal organisation of the superior temporal sulcus, which is directly derived from the parcel-based HRF estimates. © 2008 Elsevier Inc. All rights reserved.Keywords: Bayesian modelling; fMRI; Gamma-Gaussian mixture model; Detection-estimation; Markov Chain Monte Carlo methods; Bayes factor; Model comparison IntroductionSince the first report of the BOLD effect in human (Ogawa et al., 1990), functional magnetic resonance imaging (fMRI) has represented a powerful tool to non-invasively study the relation between cognitive stimulus and the hemodynamic (BOLD) response. Within-subject analysis in fMRI is usually addressed using a hypothesis-driven approach that actually postulates a model for the HRF and enable voxelwise inference in the General Linear Model (GLM) framework. In this formulation, the modelling of the BOLD response i.e., the definition of the design matrix is crucial. In its simplest form, this matrix relies on a spatially invariant temporal model of the BOLD signal across the brain meaning that the expected response to each stimulus is modelled by ...

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supporting

confidence: 87%

mentioning

confidence: 53%

Section: Habituation and Temporal Organisationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI

Idier²,

et al. 2008

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Two approaches to repetition suppression

Noppeney¹,

Penny²

2006

Human Brain Mapping

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Repetition suppression refers to the phenomenon that prior processing of stimuli (or stimulus attributes) decreases activation elicited by processing subsequent stimuli with identical attributes. We present two complementary approaches to identify regions that show repetition suppression for subsequent sentences with either identical: (1) sentence forms or (2) speakers. The first categorical approach simply compares sentences that are presented in Same and Different blocks. The second factorial approach operationally defines repetition suppression as decreased activation for the subsequent Same stimulus relative to its preceding sentence. To account for nonspecific time confounds, this approach tests for a repetition x condition (Same or Different) interaction. Surprisingly, the two approaches revealed different results: Only the categorical analysis detected sentence repetition effects in multiple regions within a bilateral frontotemporal system that has previously been implicated in sentence processing. These discrepancies might be due to the different efficiencies with which the particular contrasts were estimated or spurious differences in stimuli or attentional set that could not be entirely controlled within a single subject. Finally, we combined the two approaches in a [global null] conjunction analysis.

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Exploring predictive and reproducible modeling with the single‐subject FIAC dataset

Chen

Pereira

Lee

et al. 2006

Human Brain Mapping

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Predictive modeling of functional magnetic resonance imaging (fMRI) has the potential to expand the amount of information extracted and to enhance our understanding of brain systems by predicting brain states, rather than emphasizing the standard spatial mapping. Based on the block datasets of Functional Imaging Analysis Contest (FIAC) Subject 3, we demonstrate the potential and pitfalls of predictive modeling in fMRI analysis by investigating the performance of five models (linear discriminant analysis, logistic regression, linear support vector machine, Gaussian naive Bayes, and a variant) as a function of preprocessing steps and feature selection methods. We found that: (1) independent of the model, temporal detrending and feature selection assisted in building a more accurate predictive model; (2) the linear support vector machine and logistic regression often performed better than either of the Gaussian naive Bayes models in terms of the optimal prediction accuracy; and (3) the optimal prediction accuracy obtained in a feature space using principal components was typically lower than that obtained in a voxel space, given the same model and same preprocessing. We show that due to the existence of artifacts from different sources, high prediction accuracy alone does not guarantee that a classifier is learning a pattern of brain activity that might be usefully visualized, although cross-validation methods do provide fairly unbiased estimates of true prediction accuracy. The trade-off between the prediction accuracy and the reproducibility of the spatial pattern should be carefully considered in predictive modeling of fMRI. We suggest that unless the experimental goal is brain-state classification of new scans on well-defined spatial features, prediction alone should not be used as an optimization procedure in fMRI data analysis.

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Functional segregation of cortical language areas by sentence repetition

Cited by 115 publications

References 56 publications

A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI

A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI

Two approaches to repetition suppression

Exploring predictive and reproducible modeling with the single‐subject FIAC dataset

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