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Consciousness has been related to the amount of integrated information that the brain is able to generate. In this paper, we tested the hypothesis that the loss of consciousness caused by propofol anesthesia is associated with a significant reduction in the capacity of the brain to integrate information. To assess the functional structure of the whole brain, functional integration and partial correlations were computed from fMRI data acquired from 18 healthy volunteers during resting wakefulness and propofol-induced deep sedation. Total integration was significantly reduced from wakefulness to deep sedation in the whole brain as well as within and between its constituent networks (or systems). Integration was systematically reduced within each system (i.e., brain or networks), as well as between networks. However, the ventral attentional network maintained interactions with most other networks during deep sedation. Partial correlations further suggested that functional connectivity was particularly affected between parietal areas and frontal or temporal regions during deep sedation. Our findings suggest that the breakdown in brain integration is the neural correlate of the loss of consciousness induced by propofol. They stress the important role played by parietal and frontal areas in the generation of consciousness., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Motor skill learning is associated with profound changes in brain activation patterns over time. Associative and rostral premotor cortical and subcortical regions are mostly recruited during the early phase of explicit motor learning, while sensorimotor regions may increase their activity during the late learning phases. Distinct brain networks are therefore engaged during the early and late phases of motor skill learning. How these regions interact with one another and how information is transferred from one circuit to the other has been less extensively studied. In this study, we used functional MRI (fMRI) at 3T to follow the changes in functional connectivity in the associative/premotor and the sensorimotor networks, during extended practice (4 weeks) of an explicitly known sequence of finger movements. Evolution of functional connectivity was assessed using integration, a measure that quantifies the total amount of interaction within a network. When comparing the integration associated with a complex finger movement sequence to that associated with a simple sequence, we observed two patterns of decrease during the 4 weeks of practice. One was not specific as it was observed for all sequences, whereas a specific decrease was observed only for the execution of the learned sequence. This second decrease was a consequence of a relative decrease in associative/premotor network integration, together with a relative increase in between-network integration. These findings are in line with the hypothesis that information is transferred from the associative/premotor circuit to the sensorimotor circuit during the course of motor learning.

We present a new clinical-radiological correlation method (AnaCOM) that aims at establishing structure-function relationships. We validated AnaCOM by assessing the location of lesions that are associated with altered performances in a well-studied task: the verbal fluency task. We retrospectively reviewed 64 brain-damaged patients who had focal lesions in a variety of cortical sites due to stroke, hemorrhage or tumor surgery. All patients were tested for verbal fluency at the time of the MRI examination. MRI volumes were normalized using a mask covering brain lesions and artifacts. The brain lesions were then segmented using the normalized MRI. In each patient, a verbal fluency score was assigned to each voxel in the segmented area. Subsequently, segmentations were superimposed and voxels were gathered in clusters defined by the overlap of the patients' lesion. For each cluster, the scores were statistically compared to those obtained by controls for the same task. This process allowed the construction of cluster-by-cluster statistical maps of anatomo-clinical correlations. As expected, the statistical map indicated that two regions were significantly associated with a deficit in the fluency task: one located in Broca's area and the other in the preSMA. AnaCOM does not require a priori selection of the location of lesions or task scores. The method complements the functional imaging techniques, as it tells which regions are necessary for a given function and it explores cortical regions as well as the white matter.

In this work, we propose a symmetrical multimodal EEG/fMRI information fusion approach dedicated to the identification of event-related bioelectric and hemodynamic responses. Unlike existing, asymmetrical EEG/fMRI data fusion algorithms, we build a joint EEG/fMRI generative model that explicitly accounts for local coupling/uncoupling of bioelectric and hemodynamic activities, which are supposed to share a common substrate. Under a dedicated assumption of spatio-temporal separability, the spatial profile of the common EEG/fMRI sources is introduced as an unknown hierarchical prior on both markers of cerebral activity. Thereby, a devoted Variational Bayesian (VB) learning scheme is derived to infer common EEG/fMRI sources from a joint EEG/fMRI dataset. This yields an estimate of the common spatial profile, which is built as a trade-off between information extracted from EEG and fMRI datasets. Furthermore, the spatial structure of the EEG/fMRI coupling/uncoupling is learned exclusively from the data. The proposed data generative model and devoted VBEM learning scheme thus provide an un-supervised well-balanced approach for the fusion of EEG/fMRI information. We first demonstrate our approach on synthetic data. Results show that, in contrast to classical EEG/fMRI fusion approach, the method proved efficient and robust regardless of the EEG/fMRI discordance level. We apply the method on EEG/fMRI recordings from a patient with epilepsy, in order to identify brain areas involved during the generation of epileptic spikes. The results are validated using intracranial EEG measurements.

Examination of functional interactions through effective connectivity requires the determination of three distinct levels of information: (1) the regions involved in the process and forming the spatial support of the network, (2) the presence or absence of interactions between each pair of regions, and (3) the directionality of the existing interactions. While many methods exist to select regions (Step 1), very little is available to complete Step 2. The two main methods developed so far, structural equation modeling (SEM) and dynamical causal modeling (DCM), usually require precise prior information to be used, while such information is sometimes lacking. Assuming that Step 1 was successfully completed, we here propose a data-driven method to deal with Step 2 and extract functional interactions from fMRI datasets through partial correlations. Partial correlation is more closely related to effective connectivity than marginal correlation and provides a convenient graphical representation for functional interactions. As an instance of brain interactivity investigation, we consider how simple hand movements are processed by the bihemispheric cortical motor network. In the proposed framework, Bayesian analysis makes it possible to estimate and test the partial statistical dependencies between regions without any prior model on the underlying functional interactions. We demonstrate the interest of this approach on real data.

Cognition is thought to result from interactions within large-scale networks of brain regions. Here, we propose a method to identify these large-scale networks using functional magnetic resonance imaging (fMRI). Regions belonging to such networks are defined as sets of strongly interacting regions, each of which showing a homogeneous temporal activity. Our method of large-scale network identification (LSNI) proceeds by first detecting functionally homogeneous regions. The networks of functional interconnections are then found by comparing the correlations among these regions against a model of the correlations in the noise. To test the LSNI method, we first evaluated its specificity and sensitivity on synthetic data sets. Then, the method was applied to four real data sets with a block-designed motor task. The LSNI method correctly recovered the regions whose temporal activity was locked to the stimulus. In addition, it detected two other main networks highly reproducible across subjects, whose activity was dominated by slow fluctuations (0-0.1 Hz). One was located in medial and dorsal regions, and mostly overlapped the "default" network of the brain at rest [Greicius, M.D., Krasnow, B., Reiss, A.L., Menon, V., 2003. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences of the U.S.A. 100, 253-258]; the other was composed of lateral frontal and posterior parietal regions. The LSNI method we propose allows to detect in an exploratory and systematic way all the regions and large-scale networks activated in the working brain.

The brain plays a central role in sexual motivation. To identify cerebral areas whose activation was correlated with sexual desire, eight healthy male volunteers were studied with functional magnetic resonance imaging (fMRI). Visual stimuli were sexually stimulating photographs (S condition) and emotionally neutral photographs (N condition). Subjective responses pertaining to sexual desire were recorded after each condition. To image the entire brain, separate runs focused on the upper and the lower parts of the brain. Statistical Parametric Mapping was used for data analysis. Subjective ratings confirmed that sexual pictures effectively induced sexual arousal. In the S condition compared to the N condition, a group analysis conducted on the upper part of the brain demonstrated an increased signal in the parietal lobes (superior parietal lobules, left intraparietal sulcus, left inferior parietal lobule, and right postcentral gyrus), the right parietooccipital sulcus, the left superior occipital gyrus, and the precentral gyri. In addition, a decreased signal was recorded in the right posterior cingulate gyrus and the left precuneus. In individual analyses conducted on the lower part of the brain, an increased signal was found in the right and/or left middle occipital gyrus in seven subjects, and in the right and/or left fusiform gyrus in six subjects. In conclusion, fMRI allows to identify brain responses to visual sexual stimuli. Among activated regions in the S condition, parietal areas are known to be involved in attentional processes directed toward motivationally relevant stimuli, while frontal premotor areas have been implicated in motor preparation and motor imagery. Further work is needed to identify those specific features of the neural responses that distinguish sexual desire from other emotional and motivational states.