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Validating the association between brain activity, as measured in functional MRI, with a combination or a contrast of tasks is usually performed by replicating an experiment in a small group of subjects, and by assessing the presence of a statistically significant average effect across subjects (random effects analyses). While many efforts have been made to control the rate of false detections, statistical characteristics of the data have rarely been studied, and the reliability of the results (supra-thresholds areas that are considered as activated regions) has rarely been assessed. In this work, we take advantage of the large cohort of subjects who underwent the Localizer experiment to study the statistical nature of group data, propose some measures of the reliability of group studies, and address simple methodological questions as : is there, from the point of view of reliability, an optimal statistical threshold for activity maps ? How many subjects should be included in group studies ? What method should be preferred for inference ? Our results suggest that i) optimal thresholds can indeed be found, and are rather lower than usual corrected for multiple comparison thresholds ii) 20 subjects or more should be included in functional neuroimaging studies in order to have sufficient reliability, iii) non-parametric significance assessment should be preferred to parametric methods iv) cluster-level thresholding is more reliable than voxel-based thresholding v) mixed effects tests are much more reliable than random effects tests. Moreover, our study shows that inter-subject variability plays a prominent role in the relatively low sensitivity and reliability of group studies.

Abstract. Over the last five years, new "voxel-based" approaches have allowed important progress in multimodal image registration, notably due to the increasing use of information-theoretic similarity measures. Their wide success has led to the progressive abandon of measures using standard image statistics (mean and variance). Until now, such measures have essentially been based on heuristics. In this paper, we address the determination of a new measure based on standard statistics from a theoretical point of view. We show that it naturally leads to a known concept of probability theory, the correlation ratio. In our derivation, we take as the hypothesis the functional dependence between the image intensities. Although such a hypothesis is not as general as possible, it enables us to model the image smoothness prior very easily. We also demonstrate results of multimodal rigid registration involving Magnetic Resonance (MR.), Computed Tomography (CT), and Positron Emission Tomography (PET) images. These results suggest that the correlation ratio provides a good trade-off between accuracy and robustness.

It has been shown that the difference of squares cost function used by standard realignment packages (SPM and AIR) can lead to the detection of spurious activations, because the motion parameter estimations are biased by the activated areas. Therefore, this paper describes several experiments aiming at selecting a better similarity measure to drive functional magnetic resonance image registration. The behaviors of the Geman-McClure (GM) estimator, of the correlation ratio, and of the mutual information (MI) relative to activated areas are studied using simulated time series and actual data stemming from a 3T magnet. It is shown that these methods are more robust than the usual difference of squares measure. The results suggest also that the measures built from robust metrics like the GM estimator may be the best choice, while MI is also an interesting solution. Some more work, however, is required to compare the various robust metrics proposed in the literature.

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.o what extent is the human species predisposed to acquire language? This question is generally debated by comparing the complexity of the speech input with infants' limited processing resources. Infants face the complex problem of being confronted with a continuous auditory signal that they should learn to segment into phonemes, syllables, words, and constituents and combine to understand and produce new information. Because language acquisition is fast, proceeds through a series of reproducible stages, and exhibits a systematicity that seems to go beyond what could possibly be learned from surrounding speech inputs, some researchers have postulated a special-purpose language acquisition device (1). However, infants' capacity of statistical learning (2) combined with the observation that speech possess numerous regularities have strengthened a constructivist view according to which the infant brain progressively extracts regularities in its environmental inputs (3).In most adults, speech processing relies on a hierarchy of well defined areas centered around the left sylvian fissure. Why does language processing systematically call on those regions? Do they possess special properties that can explain language emergence in humans? Examination of their initial functional organization in the first year of life may ultimately clarify how infants take advantage of their environment to achieve the linguistic sophistication of adults. Thanks to the development of noninvasive brain imaging, we can begin to decipher the cerebral resources at infants' disposal to process s...

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Abstract. In order to improve the robustness of rigid registration algorithms in various medical imaging problems, we propose in this article a general framework built on block matching strategies. This framework combines two stages in a multi-scale hierarchy. The first stage consists in finding for each block (or subregion) of the first image, the most similar subregion in the other image, using a similarity criterion which depends on the nature of the images. The second stage consists in finding the global rigid transformation which best explains most of these local correspondances. This is done with a robust procedure which allows up to 50% of false matches. We show that this approach, besides its simplicity, provides a robust and efficient way to rigidly register images in various situations. This includes for instance the alignment of 2D histological sections for the 3D reconstructions of trimmed organs and tissues, the automatic computation of the mid-sagittal plane in multimodal 3D images of the brain, and the multimodal registration of 3D CT and MR images of the brain. A quantitative evaluation of the results is provided for this last example, as well as a comparison with the classical approaches involving the minimization of a global measure of similarity based on Mutual Information or the Correlation Ratio. This shows a significant improvement of the robustness, for a comparable final accuracy. Although slightly more expensive in terms of computational requirements, the proposed approach can easily be implemented on a parallel architecture, which opens potentialities for real time applications using a large number of processors.

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.