Time-frequency analysis of event-related brain recordings: Effect of noise on power.

In neuroscience, time-frequency analysis is widely used to investigate brain rhythms in brain recordings. In event-related protocols, it is applied to quantify how the brain responds to a stimulation repeated over many trials. We here focus on two common measures: the power of the transform for each single trial averaged across trials, avgPOW; and the power of the transform of the average evoked potential, POWavg.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Combining spatial independent component analysis with regression to identify the subcortical components of resting-state FMRI functional networks.

Functional brain networks are sets of cortical, subcortical, and cerebellar regions whose neuronal activities are synchronous over multiple time scales. Spatial independent component analysis (sICA) is a widespread approach that is used to identify functional networks in the human brain from functional magnetic resonance imaging (fMRI) resting-state data, and there is now a general agreement regarding the cortical regions involved in each network. It is well known that these cortical regions are preferentially connected with specific subcortical functional territories; however, subcortical components (SC) have not been observed whether in a robust or in a reproducible manner using sICA.

Resting state networks' corticotopy: the dual intertwined rings architecture.

How does the brain integrate multiple sources of information to support normal sensorimotor and cognitive functions? To investigate this question we present an overall brain architecture (called "the dual intertwined rings architecture") that relates the functional specialization of cortical networks to their spatial distribution over the cerebral cortex (or "corticotopy"). Recent results suggest that the resting state networks (RSNs) are organized into two large families: 1) a sensorimotor family that includes visual, somatic, and auditory areas and 2) a large association family that comprises parietal, temporal, and frontal regions and also includes the default mode network. We used two large databases of resting state fMRI data, from which we extracted 32 robust RSNs.

Functional connectivity changes in second language vocabulary learning.

Functional connectivity changes in the language network (Price, 2010), and in a control network involved in second language (L2) processing (Abutalebi & Green, 2007) were examined in a group of Persian (L1) speakers learning French (L2) words. Measures of network integration that characterize the global integrative state of a network (Marrelec, Bellec et al., 2008) were gathered, in the shallow and consolidation phases of L2 vocabulary learning.

Default-mode network functional connectivity in aphasia: therapy-induced neuroplasticity.

Previous research on participants with aphasia has mainly been based on standard functional neuroimaging analysis. Recent studies have shown that functional connectivity analysis can detect compensatory activity, not revealed by standard analysis. Little is known, however, about the default-mode network in aphasia.

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.

Impact of meditation training on the default mode network during a restful state.

Mindfulness meditation has been shown to promote emotional stability. Moreover, during the processing of aversive and self-referential stimuli, mindful awareness is associated with reduced medial prefrontal cortex (MPFC) activity, a central default mode network (DMN) component. However, it remains unclear whether mindfulness practice influences functional connectivity between DMN regions and, if so, whether such impact persists beyond a state of meditation.

Assessing the influence of different ROI selection strategies on functional connectivity analyses of fMRI data acquired during steady-state conditions.

In blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI), assessing functional connectivity between and within brain networks from datasets acquired during steady-state conditions has become increasingly common. However, in contrast to connectivity analyses based on task-evoked signal changes, selecting the optimal spatial location of the regions of interest (ROIs) whose timecourses will be extracted and used in subsequent analyses is not straightforward. Moreover, it is also unknown how different choices of the precise anatomical locations within given brain regions influence the estimates of functional connectivity under steady-state conditions.

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.

A nonlinear identification method to study effective connectivity in functional MRI.

In this paper we propose a novel approach for characterizing effective connectivity in functional magnetic resonance imaging (fMRI) data. Unlike most other methods, our approach is nonlinear and does not rely on a priori specification of a model that contains structural information of neuronal populations. Instead, it relies on a nonlinear autoregressive exogenous model and nonlinear system identification theory; the model's nonlinear connectivities are determined using a least squares method.

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.

A theoretical investigation of the relationship between structural equation modeling and partial correlation in functional MRI effective connectivity.

An important field of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) is the investigation of effective connectivity, that is, the actions that a given set of regions exert on one another. We recently proposed a data-driven method based on the partial correlation matrix that could provide some insight regarding the pattern of functional interaction between brain regions as represented by structural equation modeling (SEM). So far, the efficiency of this approach was mostly based on empirical evidence.

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.