Further characterisation of late somatosensory evoked potentials using electroencephalogram and magnetoencephalogram source imaging.

Beside the well-documented involvement of secondary somatosensory area, the cortical network underlying late somatosensory evoked potentials (P60/N60 and P100/N100) is still unknown. Electroencephalogram and magnetoencephalogram source imaging were performed to further investigate the origin of the brain cortical areas involved in late somatosensory evoked potentials, using sensory inputs of different strengths and by testing the correlation between cortical sources. Simultaneous high-density electroencephalograms and magnetoencephalograms were performed in 19 participants, and electrical stimulation was applied to the median nerve (wrist level) at intensity between 1.5 and 9 times the perceptual threshold. Source imaging was undertaken to map the stimulus-induced brain cortical activity according to each individual brain magnetic resonance imaging, during three windows of analysis covering early and late somatosensory evoked potentials. Results for P60/N60 and P100/N100 were compared with those for P20/N20 (early response). According to literature, maximal activity during P20/N20 was found in central sulcus contralateral to stimulation site. During P60/N60 and P100/N100, activity was observed in contralateral primary sensorimotor area, secondary somatosensory area (on both hemispheres) and premotor and multisensory associative cortices. Late responses exhibited similar characteristics but different from P20/N20, and no significant correlation was found between early and late generated activities. Specific clusters of cortical activities were activated with specific input/output relationships underlying early and late somatosensory evoked potentials. Cortical networks, partly common to and distinct from early somatosensory responses, contribute to late responses, all participating in the complex somatosensory brain processing.

Functional Connectivity's Degenerate View of Brain Computation.

Brain computation relies on effective interactions between ensembles of neurons. In neuroimaging, measures of functional connectivity (FC) aim at statistically quantifying such interactions, often to study normal or pathological cognition. Their capacity to reflect a meaningful variety of patterns as expected from neural computation in relation to cognitive processes remains debated. The relative weights of time-varying local neurophysiological dynamics versus static structural connectivity (SC) in the generation of FC as measured remains unsettled. Empirical evidence features mixed results: from little to significant FC variability and correlation with cognitive functions, within and between participants. We used a unified approach combining multivariate analysis, bootstrap and computational modeling to characterize the potential variety of patterns of FC and SC both qualitatively and quantitatively. Empirical data and simulations from generative models with different dynamical behaviors demonstrated, largely irrespective of FC metrics, that a linear subspace with dimension one or two could explain much of the variability across patterns of FC. On the contrary, the variability across BOLD time-courses could not be reduced to such a small subspace. FC appeared to strongly reflect SC and to be partly governed by a Gaussian process. The main differences between simulated and empirical data related to limitations of DWI-based SC estimation (and SC itself could then be estimated from FC). Above and beyond the limited dynamical range of the BOLD signal itself, measures of FC may offer a degenerate representation of brain interactions, with limited access to the underlying complexity. They feature an invariant common core, reflecting the channel capacity of the network as conditioned by SC, with a limited, though perhaps meaningful residual variability.

Predicting functional connectivity from structural connectivity via computational models using MRI: an extensive comparison study.

The relationship between structural connectivity (SC) and functional connectivity (FC) in the human brain can be studied using magnetic resonance imaging (MRI). However many of the underlying physiological mechanisms and parameters cannot be directly observed with MRI. This limitation has motivated the recent use of various computational models meant to bridge the gap. However their absolute and relative explanatory power and the properties that actually drive that power remain insufficiently characterized. We performed an extensive comparison of seven mainstream computational models predicting FC from SC. We investigated the extent to which simulated FC could predict empirical FC. We also applied graph theory to the entire set of simulated and empirical FCs in order to further characterize the relationships between the models and the MRI data. The comparison was performed at three different spatial scales. We found that (i) there were significant effects of scale and model on predictive power; (ii) among all models, the simplest model, the simultaneous autoregressive (SAR) model, was found to consistently perform better than the other models; (iii) the SAR also appeared more 'central' from a graph theory perspective; and (iv) empirical FC only appeared weakly correlated with simulated FCs, and was featured as 'peripheral' in the graph analysis. We conclude that the substantial differences existing between these computational models have little impact on their predictive power for FC and that their capacity to predict FC from SC appears to be both moderate and essentially underlined by a simple core linear process embodied by the SAR model.

Relating structure and function in the human brain: relative contributions of anatomy, stationary dynamics, and non-stationarities.

Investigating the relationship between brain structure and function is a central endeavor for neuroscience research. Yet, the mechanisms shaping this relationship largely remain to be elucidated and are highly debated. In particular, the existence and relative contributions of anatomical constraints and dynamical physiological mechanisms of different types remain to be established. We addressed this issue by systematically comparing functional connectivity (FC) from resting-state functional magnetic resonance imaging data with simulations from increasingly complex computational models, and by manipulating anatomical connectivity obtained from fiber tractography based on diffusion-weighted imaging. We hypothesized that FC reflects the interplay of at least three types of components: (i) a backbone of anatomical connectivity, (ii) a stationary dynamical regime directly driven by the underlying anatomy, and (iii) other stationary and non-stationary dynamics not directly related to the anatomy. We showed that anatomical connectivity alone accounts for up to 15% of FC variance; that there is a stationary regime accounting for up to an additional 20% of variance and that this regime can be associated to a stationary FC; that a simple stationary model of FC better explains FC than more complex models; and that there is a large remaining variance (around 65%), which must contain the non-stationarities of FC evidenced in the literature. We also show that homotopic connections across cerebral hemispheres, which are typically improperly estimated, play a strong role in shaping all aspects of FC, notably indirect connections and the topographic organization of brain networks.

Hierarchical clustering of brain activity during human nonrapid eye movement sleep.

Consciousness is reduced during nonrapid eye movement (NREM) sleep due to changes in brain function that are still poorly understood. Here, we tested the hypothesis that impaired consciousness during NREM sleep is associated with an increased modularity of brain activity. Cerebral connectivity was quantified in resting-state functional magnetic resonance imaging times series acquired in 13 healthy volunteers during wakefulness and NREM sleep. The analysis revealed a modification of the hierarchical organization of large-scale networks into smaller independent modules during NREM sleep, independently from EEG markers of the slow oscillation. Such modifications in brain connectivity, possibly driven by sleep ultraslow oscillations, could hinder the brain's ability to integrate information and account for decreased consciousness during NREM sleep.

Off-line consolidation of motor sequence learning results in greater integration within a cortico-striatal functional network.

The consolidation of motor sequence learning is known to depend on sleep. Work in our laboratory and others have shown that the striatum is associated with this off-line consolidation process. In this study, we aimed to quantify the sleep-dependent dynamic changes occurring at the network level using a measure of functional integration. We directly compared changes in connectivity before and after sleep or the simple passage of daytime. As predicted, the results revealed greater integration within the cortico-striatal network after sleep, but not an equivalent daytime period. Importantly, a similar pattern of results was also observed using a data-driven approach; the increase in integration being specific to a cortico-striatal network, but not to other known functional networks. These findings reveal, for the first time, a new signature of motor sequence consolidation: a greater between-regions interaction within the cortico-striatal system.

Multilevel testing of constraints induced by structural equation modeling in fMRI effective connectivity analysis: A proof of concept.

In functional MRI (fMRI), effective connectivity analysis aims at inferring the causal influences that brain regions exert on one another. A common method for this type of analysis is structural equation modeling (SEM). We here propose a novel method to test the validity of a given model of structural equation. Given a structural model in the form of a directed graph, the method extracts the set of all constraints of conditional independence induced by the absence of links between pairs of regions in the model and tests for their validity in a Bayesian framework, either individually (constraint by constraint), jointly (e.g., by gathering all constraints associated with a given missing link), or globally (i.e., all constraints associated with the structural model). This approach has two main advantages. First, it only tests what is testable from observational data and does allow for false causal interpretation. Second, it makes it possible to test each constraint (or group of constraints) separately and, therefore, quantify in what measure each constraint (or, e..g., missing link) is respected in the data. We validate our approach using a simulation study and illustrate its potential benefits through the reanalysis of published data.

Time-Frequency Analysis of Event-Related Brain Recordings: Connecting Power of Evoked Potential and Inter-Trial Coherence.

OBJECTIVE: In neuroscience, time-frequency analysis has been used to get insight into brain rhythms from brain recordings. In event-related protocols, one applies it to investigate how the brain responds to a stimulation repeated over many trials. In this framework, three measures have been considered: the amplitude of the transform for each single trial averaged across trials, avgAMP; inter-trial phase coherence, ITC; and the power of the evoked potential transform, POWavg. These three measures are sensitive to different aspects of event-related responses, ITC and POWavg sharing a common sensitivity to phase resetting phenomena. METHODS: In the present manuscript, we further investigated the connection between ITC and POWavg using theoretical calculations, a simulation study and analysis of experimental data. RESULTS: We derived exact expressions for the relationship between POWavg and ITC in the particular case of the S-transform of an oscillatory signal. In the more general case, we showed that POWavg and ITC are connected through a relationship that roughly reads POWavg ≈ avgAMP2 × ITC2. This result was confirmed on simulations. We finally compared the theoretical prediction with results from real data. CONCLUSION: We showed that POWavg and ITC are related through an approximate, simple relationship that also involves avgAMP. SIGNIFICANCE: The presented relationship between POWavg, ITC, and avgAMP confirms previous empirical evidence and provides a novel perspective to investigate evoked brain rhythms. It may provide a significant refinement to the neuroscientific toolbox for studying evoked oscillations.

Brain functional integration decreases during propofol-induced loss of consciousness.

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.

Automated Extraction of Mutual Independence Patterns Using Bayesian Comparison of Partition Models.

Mutual independence is a key concept in statistics that characterizes the structural relationships between variables. Existing methods to investigate mutual independence rely on the definition of two competing models, one being nested into the other and used to generate a null distribution for a statistic of interest, usually under the asymptotic assumption of large sample size. As such, these methods have a very restricted scope of application. In this article, we propose to change the investigation of mutual independence from a hypothesis-driven task that can only be applied in very specific cases to a blind and automated search within patterns of mutual independence. To this end, we treat the issue as one of model comparison that we solve in a Bayesian framework. We show the relationship between such an approach and existing methods in the case of multivariate normal distributions as well as cross-classified multinomial distributions. We propose a general Markov chain Monte Carlo (MCMC) algorithm to numerically approximate the posterior distribution on the space of all patterns of mutual independence. The relevance of the method is demonstrated on synthetic data as well as two real datasets, showing the unique insight provided by this approach.

Dynamics of motor-related functional integration during motor sequence learning.

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.

An enhanced voxel-based morphometry method to investigate structural changes: application to Alzheimer's disease.

INTRODUCTION: When characterizing regional cerebral gray matter differences in structural magnetic resonance images (sMRI) by voxel-based morphometry (VBM), one faces a known drawback of VBM, namely that histogram unequalization in the intensity images introduces false-positive results. METHODS: To overcome this limitation, we propose to improve VBM by a new approach (called eVBM for enhanced VBM) that takes the histogram distribution of the sMRI into account by adding a histogram equalization step within the VBM procedure. Combining this technique with two most widely used VBM software packages (FSL and SPM), we studied GM variability in a group of 62 patients with Alzheimer's disease compared to 73 age-matched elderly controls. RESULTS: The results show that eVBM can reduce the number of false-positive differences in gray matter concentration. CONCLUSION: Because it takes advantage of the properties of VBM while improving sMRI histogram distribution at the same time, the proposed method is a powerful approach for analyzing gray matter differences in sMRI and may be of value in the investigation of sMRI gray and white matter abnormalities in a variety of brain diseases.

The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis.

Recent research has shown that intrinsic brain activity as observed by functional magnetic resonance imaging (fMRI) manifest itself as coherent signal changes in networks encompassing brain regions that span long-range neuronal pathways. One of these networks, the so called default mode network, has become the primary target in recent investigations to link intrinsic activity to cognition and how intrinsic signal changes may be altered in disease. In this study we assessed functional connectivity within the default mode network during both rest and a continuous working memory task on a region-by-region basis using partial correlation analysis, a data-driven method that provides insight into effective connectivity within neuronal networks. Prominent features of functional connectivity within the default mode network included an overall strong level of interaction between the precuneus/posterior cingulate region and the rest of the default mode network, as well as a high degree of interaction between the left and right medial temporal lobes combined with weak interactions between the medial temporal lobes and the rest of the default mode network. Additionally, we found support for strong interactions between the precuneus/posterior cingulate cortex and the left inferior parietal lobe as well as between the dorsal and ventral sections of the medial prefrontal cortex. The suggested pivotal role of the precuneus/posterior cingulate cortex in the default mode network is discussed.

Abnormal cortical brain integration of somatosensory afferents in ALS.

OBJECTIVES: Infraclinical sensory alterations have been reported at early stages of amyotrophic lateral sclerosis (ALS). While previous studies mainly focused on early somatosensory evoked potentials (SEPs), late SEPs, which reflect on cortical pathways involved in cognitive-motor functions, are relatively underinvestigated. Early and late SEPs were compared to assess their alterations in ALS. METHODS: Median and ulnar nerves were electrically stimulated at the wrist, at 9 times the perceptual threshold, in 21 ALS patients without clinical evidence of sensory deficits, and 21 age- and gender-matched controls. SEPs were recorded at the Erb point using surface electrodes and using a needle inserted in the scalp, in front of the primary somatosensory area (with reference electrode on the ear lobe). RESULTS: Compared to controls, ALS patients showed comparable peripheral (N9) and early cortical component (N20, P25, N30) reductions, while the late cortical components (N60, P100) were more depressed than the early ones. CONCLUSIONS: The peripheral sensory alteration likely contributed to late SEP depression to a lesser extent than that of early SEPs. SIGNIFICANCE: Late SEPs may provide new insights on abnormal cortical excitability affecting brain areas involved in cognitive-motor functions.

Using partial correlation to enhance structural equation modeling of functional MRI data.

In functional magnetic resonance imaging (fMRI) data analysis, effective connectivity investigates the influence that brain regions exert on one another. Structural equation modeling (SEM) has been the main approach to examine effective connectivity. In this paper, we propose a method that, given a set of regions, performs partial correlation analysis. This method provides an approach to effective connectivity that is data driven, in the sense that it does not require any prior information regarding the anatomical or functional connections. To demonstrate the practical relevance of partial correlation analysis for effective connectivity investigation, we reanalyzed data previously published [Bullmore, Horwitz, Honey, Brammer, Williams, Sharma, 2000. How good is good enough in path analysis of fMRI data? NeuroImage 11, 289-301]. Specifically, we show that partial correlation analysis can serve several purposes. In a pre-processing step, it can hint at which effective connections are structuring the interactions and which have little influence on the pattern of connectivity. As a post-processing step, it can be used both as a simple and visual way to check the validity of SEM optimization algorithms and to show which assumptions made by the model are valid, and which ones should be further modified to better fit the data.

Increase of posterior connectivity in aging within the Ventral Attention Network: A functional connectivity analysis using independent component analysis.

Multiple studies have found neurofunctional changes in normal aging in a context of selective attention. Furthermore, many articles report intrahemispheric alteration in functional networks. However, little is known about age-related changes within the Ventral Attention Network (VAN), which underlies selective attention. The aim of this study is to examine age-related changes within the VAN, focusing on connectivity between its regions. Here we report our findings on the analysis of 27 participants' (13 younger and 14 older healthy adults) BOLD signals as well as their performance on a letter-matching task. We identified the VAN independently for both groups using spatial independent component analysis. Three main findings emerged: First, younger adults were faster and more accurate on the task. Second, older adults had greater connectivity among posterior regions (right temporoparietal junction, right superior parietal lobule, right middle temporal gyrus and left cerebellum crus I) than younger adults but lower connectivity among anterior regions (right anterior insula, right medial superior frontal gyrus and right middle frontal gyrus). Older adults also had more connectivity between anterior and posterior regions than younger adults. Finally, correlations between connectivity and response time on the task showed a trend toward connectivity in posterior regions for the older group and in anterior regions for the younger group. Thus, this study shows that intrahemispheric neurofunctional changes in aging also affect the VAN. The results suggest that, in contexts of selective attention, posterior regions increased in importance for older adults, while anterior regions had reduced centrality.

Exploring large-scale brain networks in functional MRI.

Increasing emphasis has been recently put on large-scale network processing of brain functions. To explore these networks, many approaches have been proposed in functional magnetic resonance imaging (fMRI). Their objective is to answer the following two questions: (1) what brain regions are involved in the functional process under investigation? and (2) how do these regions interact? We review some of the key concepts and corresponding methods to cope with both issues.

A Bayesian Alternative to Mutual Information for the Hierarchical Clustering of Dependent Random Variables.

The use of mutual information as a similarity measure in agglomerative hierarchical clustering (AHC) raises an important issue: some correction needs to be applied for the dimensionality of variables. In this work, we formulate the decision of merging dependent multivariate normal variables in an AHC procedure as a Bayesian model comparison. We found that the Bayesian formulation naturally shrinks the empirical covariance matrix towards a matrix set a priori (e.g., the identity), provides an automated stopping rule, and corrects for dimensionality using a term that scales up the measure as a function of the dimensionality of the variables. Also, the resulting log Bayes factor is asymptotically proportional to the plug-in estimate of mutual information, with an additive correction for dimensionality in agreement with the Bayesian information criterion. We investigated the behavior of these Bayesian alternatives (in exact and asymptotic forms) to mutual information on simulated and real data. An encouraging result was first derived on simulations: the hierarchical clustering based on the log Bayes factor outperformed off-the-shelf clustering techniques as well as raw and normalized mutual information in terms of classification accuracy. On a toy example, we found that the Bayesian approaches led to results that were similar to those of mutual information clustering techniques, with the advantage of an automated thresholding. On real functional magnetic resonance imaging (fMRI) datasets measuring brain activity, it identified clusters consistent with the established outcome of standard procedures. On this application, normalized mutual information had a highly atypical behavior, in the sense that it systematically favored very large clusters. These initial experiments suggest that the proposed Bayesian alternatives to mutual information are a useful new tool for hierarchical clustering.

Relating structural and functional connectivity in MRI: a simple model for a complex brain.

Advances in magnetic resonance imaging (MRI) allow to gain critical insight into the structure of neural networks and their functional dynamics. To relate structural connectivity [as quantified by diffusion-weighted imaging (DWI) tractography] and functional connectivity [as obtained from functional MRI (fMRI)], increasing emphasis has been put on computational models of brain activity. In the present study, we use structural equation modeling (SEM) with structural connectivity to predict functional connectivity. The resulting model takes the simple form of a spatial simultaneous autoregressive model (sSAR), whose parameters can be estimated in a Bayesian framework. On synthetic data, results showed very good accuracy and reliability of the inference process. On real data, we found that the sSAR performed significantly better than two other reference models as well as than structural connectivity alone, but that the Bayesian procedure did not bring significant improvement in fit compared to two simpler approaches. Nonetheless, we also found that the values of the region-specific parameters inferred using Bayesian inference differed significantly across resting-state networks. These results demonstrate 1) that a simple abstract model is able to perform better that more complex models based on more realistic assumptions, 2) that the parameters of the sSAR can be estimated and can potentially be used as biomarkers, but also 3) that the sSAR, while being the best-performing model, is at best still a very crude model of the relationship between structure and function in MRI.

Estimation of the hemodynamic response in event-related functional MRI: Bayesian networks as a framework for efficient Bayesian modeling and inference.

A convenient way to analyze blood-oxygen-level-dependent functional magnetic resonance imaging data consists of modeling the whole brain as a stationary, linear system characterized by its transfer function: the hemodynamic response function (HRF). HRF estimation, though of the greatest interest, is still under investigation, for the problem is ill-conditioned. In this paper, we recall the most general Bayesian model for HRF estimation and show how it can beneficially be translated in terms of Bayesian graphical models, leading to 1) a clear and efficient representation of all structural and functional relationships entailed by the model, and 2) a straightforward numerical scheme to approximate the joint posterior distribution, allowing for estimation of the HRF, as well as all other model parameters. We finally apply this novel technique on both simulations and real data.