The putative role of the so-called Visual Word Form Area (VWFA) during reading remains under debate. For some authors, this region is specifically involved in a pre-lexical processing of words and pseudowords, whereas such specificity is challenged by others given the VWFA involvement during both non-word reading and word listening. Here, we further investigated this issue, measuring BOLD variations and their lateralization with fMRI during word and non-word reading, in order to evaluate the lexicality effect, and during reading and listening of words, in order to evaluate the impact of stimulus delivery modality on word processing networks. Region of interest (ROI) analysis was first performed in three target areas: 1-VWFA as defined by a meta-analysis of the word reading literature, 2-a middle temporal area (T2) found co-activated by both word reading and listening, 3-an inferior occipital area (OI) belonging to the unimodal visual cortex of the inferior occipital gyrus. VWFA activity was found not different between word and non-word reading but was more leftward lateralized during word reading due to a reduction of activity in the VWFA right counterpart. A similar larger leftward lateralization during word reading was also uncovered in the T2 ROI but was related to a larger left side activity. Such a lexicality effect was not observed in the OI ROI. By contrast, BOLD increases during listening were restricted to the left VWFA and T2 ROIs. Voxel-based analysis (SPM99) showed that semantic areas were more active during word than non-word reading and co-activated by both reading and listening, exhibiting a left lateralized activity in all tasks. These results indicate that the left VWFA would be the place where visual and verbal representations bind under the control of left semantic areas.

The advent of functional neuroimaging has allowed tremendous advances in our understanding of brain-language relationships, in addition to generating substantial empirical data on this subject in the form of thousands of activation peak coordinates reported in a decade of language studies. We performed a large-scale meta-analysis of this literature, aimed at defining the composition of the phonological, semantic, and sentence processing networks in the frontal, temporal, and inferior parietal regions of the left cerebral hemisphere. For each of these language components, activation peaks issued from relevant component-specific contrasts were submitted to a spatial clustering algorithm, which gathered activation peaks on the basis of their relative distance in the MNI space. From a sample of 730 activation peaks extracted from 129 scientific reports selected among 260, we isolated 30 activation clusters, defining the functional fields constituting three distributed networks of frontal and temporal areas and revealing the functional organization of the left hemisphere for language. The functional role of each activation cluster is discussed based on the nature of the tasks in which it was involved. This meta-analysis sheds light on several contemporary issues, notably on the fine-scale functional architecture of the inferior frontal gyrus for phonological and semantic processing, the evidence for an elementary audio-motor loop involved in both comprehension and production of syllables including the primary auditory areas and the motor mouth area, evidence of areas of overlap between phonological and semantic processing, in particular at the location of the selective human voice area that was the seat of partial overlap of the three language components, the evidence of a cortical area in the pars opercularis of the inferior frontal gyrus dedicated to syntactic processing and in the posterior part of the superior temporal gyrus a region selectively activated by sentence and text processing, and the hypothesis that different working memory perception-actions loops are identifiable for the different language components. These results argue for large-scale architecture networks rather than modular organization of language in the left hemisphere.

Subjects were scanned in a single functional MRI (fMRI) experiment that enabled us to localize cortical regions in each subject in the occipital and temporal lobes that responded significantly in a variety of contrasts: faces>objects, body parts>objects, scenes>objects, objects>scrambled objects, and moving>stationary stimuli. The resulting activation maps were co-registered across subjects using spherical surface coordinates [Fischl et al., Hum Brain Mapp 1999;8:272-284] to produce a "percentage overlap map" indicating the percentage of subjects who showed a significant response for each contrast at each point on the surface. Prominent among the overlapping activations in these contrasts were the fusiform face area (FFA), extrastriate body area (EBA), parahippocampal place area (PPA), lateral occipital complex (LOC), and MT+/V5; only a few other areas responded consistently across subjects in these contrasts. Another analysis showed that the spatial profile of the selective response drops off quite sharply outside the standard borders of the FFA and PPA (less so for the EBA and MT+/V5), indicating that these regions are not simply peaks of very broad selectivities spanning centimeters of cortex, but fairly discrete regions of cortex with distinctive functional profiles. The data also yielded a surprise that challenges our understanding of the function of area MT+: a higher response to body parts than to objects. The anatomical consistency of each of our functionally defined regions across subjects and the spatial sharpness of their activation profiles within subjects highlight the fact that these regions constitute replicable and distinctive landmarks in the functional organization of the human brain.

We used magnetoencephalography (MEG) to map the spatiotemporal evolution of cortical activity for visual word recognition. We show that for five-letter words, activity in the left hemisphere (LH) fusiform gyrus expands systematically in both the posterior-anterior and medial-lateral directions over the course of the first 500 ms after stimulus presentation. Contrary to what would be expected from cognitive models and hemodynamic studies, the component of this activity that spatially coincides with the visual word form area (VWFA) is not active until around 200 ms post-stimulus, and critically, this activity is preceded by and co-active with activity in parts of the inferior frontal gyrus (IFG, BA44/6). The spread of activity in the VWFA for words does not appear in isolation but is co-active in parallel with spread of activity in anterior middle temporal gyrus (aMTG, BA 21 and 38), posterior middle temporal gyrus (pMTG, BA37/39), and IFG.

Partial least squares (PLS) analysis has been used to characterize distributed signals measured by neuroimaging methods like positron emission tomography (PET), functional magnetic resonance imaging (fMRI), event-related potentials (ERP) and magnetoencephalography (MEG). In the application to PET, it has been used to extract activity patterns differentiating cognitive tasks, patterns relating distributed activity to behavior, and to describe large-scale interregional interactions or functional connections. This paper reviews the more recent extension of PLS to the analysis of spatiotemporal patterns present in fMRI, ERP, and MEG data. We present a basic mathematical description of PLS and discuss the statistical assessment using permutation testing and bootstrap resampling. These two resampling methods provide complementary information of the statistical strength of the extracted activity patterns (permutation test) and the reliability of regional contributions to the patterns (bootstrap resampling). Simulated ERP data are used to guide the basic interpretation of spatiotemporal PLS results, and examples from empirical ERP and fMRI data sets are used for further illustration. We conclude with a discussion of some caveats in the use of PLS, including nonlinearities, nonorthogonality, and interpretation difficulties. We further discuss its role as an important tool in a pluralistic analytic approach to neuroimaging.

Cohen and Dehaene et al. proposed that the Visual Word Form Area (VWFA) in the left midfusiform gyrus, contrary to its name, is limited to the extraction of an abstract letter string and not involved in proper visual word recognition. We examined this prelexical function of the VWFA by a parametric block design with five levels of written word frequency. The lowest level was represented by pseudowords and the highest level by words of very high frequency. Contrary to the assumed prelexical function of the VWFA, increasing frequency was associated with decreasing brain activation in a large posterior cluster of the left hemisphere including middle and posterior fusiform regions. The same negative relation between frequency and activation was found in several left frontal clusters. The relation of increasing frequency and decreasing activation in occipitotemporal regions corresponds to a similar relation in the same brain regions found by studies which experimentally manipulated object or face familiarity. This convergence suggests that fusiform regions are specialized for extracting and storing abstract patterns when processing visual objects and these patterns serve as recognition units in subsequent encounters with the same objects.

Haxby et al. [Science 293 (2001) 2425] recently argued that category-related responses in the ventral temporal (VT) lobe during visual object identification were overlapping and distributed in topography. This observation contrasts with prevailing views that object codes are focal and localized to specific areas such as the fusiform and parahippocampal gyri. We provide a critical test of Haxby?s hypothesis using a neural network (NN) classifier that can detect more general topographic representations and achieves 83% correct generalization performance on patterns of voxel responses in out-of-sample tests. Using voxel-wise sensitivity analysis we show that substantially the same VT lobe voxels contribute to the classification of all object categories, suggesting the code is combinatorial. Moreover, we found no evidence for local single category representations. The neural network representations of the voxel codes were sensitive to both category and superordinate level features that were only available implicitly in the object categories.

Traditional (univariate) analysis of functional MRI (fMRI) data relies exclusively on the information contained in the time course of individual voxels. Multivariate analyses can take advantage of the information contained in activity patterns across space, from multiple voxels. Such analyses have the potential to greatly expand the amount of information extracted from fMRI data sets. In the present study, multivariate statistical pattern recognition methods, including linear discriminant analysis and support vector machines, were used to classify patterns of fMRI activation evoked by the visual presentation of various categories of objects. Classifiers were trained using data from voxels in predefined regions of interest during a subset of trials for each subject individually. Classification of subsequently collected fMRI data was attempted according to the similarity of activation patterns to prior training examples. Classification was done using only small amounts of data (20 s worth) at a time, so such a technique could, in principle, be used to extract information about a subject?s percept on a near real-time basis. Classifiers trained on data acquired during one session were equally accurate in classifying data collected within the same session and across sessions separated by more than a week, in the same subject. Although the highest classification accuracies were obtained using patterns of activity including lower visual areas as input, classification accuracies well above chance were achieved using regions of interest restricted to higher-order object-selective visual areas. In contrast to typical fMRI data analysis, in which hours of data across many subjects are averaged to reveal slight differences in activation, the use of pattern recognition methods allows a subtle 10-way discrimination to be performed on an essentially trial-by-trial basis within individuals, demonstrating that fMRI data contain far more information than is typically appreciated.