Event-related potentials of social comparisons in depression and social anxiety.

Social comparison is central in human life and can be especially challenging in depression and social anxiety. We assessed event-related potentials and emotions using a social comparison task in which participants received feedback on both their own and a co-player's performance, in participants with depression and/or social anxiety (n = 63) and healthy controls (n = 72). Participants reported more negative emotions for downward (being better than the co-player [participant correct, co-player wrong]) and upward (being worse than the co-player [participant wrong, co-player correct]) comparisons versus even outcomes, with these effects being stronger in depression and social anxiety. At the Medial Frontal Negativity, both controls and depressed participants showed a more negative amplitude for upward comparison versus both the participant and co-player performing wrong. Socially anxious subjects showed the opposite effect, possibly due to greater expectations about being worse than others. The P300 decreased for downward and upward comparisons compared to even outcomes, which may relate to the higher levels of conflict of social inequality. Depressed and socially anxious subjects showed a blunted P300 increase over time in response to the task outcomes, suggesting deficits in allocating resources for the attention of incoming social information. The LPP showed increased amplitude for downward and upward comparison versus the even outcomes and no group effect. Emotional findings suggest that social comparisons are more difficult for depressed and socially anxious individuals. Event-related potentials findings may shed light on the neural substrates of these difficulties.

Event-related potentials during the ultimatum game in people with symptoms of depression and/or social anxiety.

Depression and social anxiety are common disorders that have a profound impact on social functioning. The need for studying the neural substrates of social interactions in mental disorders using interactive tasks has been emphasized. The field of neuroeconomics, which combines neuroscience techniques and behavioral economics multiplayer tasks such as the Ultimatum Game (UG), can contribute in this direction. We assessed emotions, behavior, and Event-Related Potentials in participants with depression and/or social anxiety symptoms (MD/SA, n = 63, 57 females) and healthy controls (n = 72, 67 females), while they played the UG. In this task, participants received fair, mid-value, and unfair offers from other players. Mixed linear models were implemented to assess trial level changes in neural activity. The MD/SA group reported higher levels of sadness in response to mid-value and unfair offers compared to controls. In controls, the Medial Frontal Negativity associated with fair offers increased over time, while this dynamic was not observed in the MD/SA group. The MD/SA group showed a decreased P3/LPP in all offers, compared to controls. These results indicate an enhanced negative emotional response to unfairness in the MD/SA group. Neural results reveal a blunted response over time to positive social stimuli in the MD/SA group. Moreover, between-group differences in P3/LPP may relate to a reduced saliency of offers and/or to a reduced availability of resources for processing incoming stimuli in the MD/SA group. Findings may shed light into the neural substrates of social difficulties in these disorders.

DIVA Meets EEG: Model Validation Using Formant-Shift Reflex.

The neurocomputational model 'Directions into Velocities of Articulators' (DIVA) was developed to account for various aspects of normal and disordered speech production and acquisition. The neural substrates of DIVA were established through functional magnetic resonance imaging (fMRI), providing physiological validation of the model. This study introduces DIVA_EEG an extension of DIVA that utilizes electroencephalography (EEG) to leverage the high temporal resolution and broad availability of EEG over fMRI. For the development of DIVA_EEG, EEG-like signals were derived from original equations describing the activity of the different DIVA maps. Synthetic EEG associated with the utterance of syllables was generated when both unperturbed and perturbed auditory feedback (first formant perturbations) were simulated. The cortical activation maps derived from synthetic EEG closely resembled those of the original DIVA model. To validate DIVA_EEG, the EEG of individuals with typical voices (N = 30) was acquired during an altered auditory feedback paradigm. The resulting empirical brain activity maps significantly overlapped with those predicted by DIVA_EEG. In conjunction with other recent model extensions, DIVA_EEG lays the foundations for constructing a complete neurocomputational framework to tackle vocal and speech disorders, which can guide model-driven personalized interventions.

Estimation of auditory steady-state responses based on the averaging of independent EEG epochs.

The amplitude of auditory steady-state responses (ASSRs) generated in the brainstem of rats exponentially decreases over the sequential averaging of EEG epochs. This behavior is partially due to the adaptation of the ASSR induced by the continuous and monotonous stimulation. In this study, we analyzed the potential clinical relevance of the ASSR adaptation. ASSR were elicited in eight anesthetized adult rats by 8-kHz tones, modulated in amplitude at 115 Hz. We called independent epochs to those EEG epochs acquired with sufficiently long inter-stimulus interval, so the ASSR contained in any given epoch is not affected by the previous stimulation. We tested whether the detection of ASSRs is improved when the response is computed by averaging independent EEG epochs, containing only unadapted auditory responses. The improvements in the ASSR detection obtained with standard, weighted and sorted averaging were compared. In the absence of artifacts, when the ASSR was elicited by continuous acoustic stimulation, the computation of the ASSR amplitude relied upon the averaging method. While the adaptive behavior of the ASSR was still evident after the weighting of epochs, the sorted averaging resulted in under-estimations of the ASSR amplitude. In the absence of artifacts, the ASSR amplitudes computed by averaging independent epochs did not depend on the averaging procedure. Averaging independent epochs resulted in higher ASSR amplitudes and halved the number of EEG epochs needed to be acquired to achieve the maximum detection rate of the ASSR. Acquisition protocols based on averaging independent EEG epochs, in combination with appropriate averaging methods for artifact reduction might contribute to develop more accurate hearing assessments based on ASSRs.

Functional Connectivity and Quantitative EEG in Women with Alcohol Use Disorders: A Resting-State Study.

This study was aimed at exploring the electroencephalographic features associated with alcohol use disorders (AUD) during a resting-state condition, by using quantitative EEG and Functional Connectivity analyses. In addition, we explored whether EEG functional connectivity is associated with trait impulsivity. Absolute and relative powers and Synchronization Likelihood (SL) as a measure of functional connectivity were analyzed in 15 AUD women and fifteen controls matched in age, gender and education. Correlation analysis between self-report impulsivity as measured by the Barratt impulsiveness Scale (BIS-11) and SL values of AUD patients were performed. Our results showed increased absolute and relative beta power in AUD patients compared to matched controls, and reduced functional connectivity in AUD patients predominantly in the beta and alpha bands. Impaired connectivity was distributed at fronto-central and occipito-parietal regions in the alpha band, and over the entire scalp in the beta band. We also found that impaired functional connectivity particularly in alpha band at fronto-central areas was negative correlated with non-planning dimension of impulsivity. These findings suggest that functional brain abnormalities are present in AUD patients and a disruption of resting-state EEG functional connectivity is associated with psychopathological traits of addictive behavior.

Habituation of Auditory Steady State Responses Evoked by Amplitude-Modulated Acoustic Signals in Rats.

Generation of the auditory steady state responses (ASSR) is commonly explained by the linear combination of random background noise activity and the stationary response. Based on this model, the decrease of amplitude that occurs over the sequential averaging of epochs of the raw data has been exclusively linked to the cancelation of noise. Nevertheless, this behavior might also reflect the non-stationary response of the ASSR generators. We tested this hypothesis by characterizing the ASSR time course in rats with different auditory maturational stages. ASSR were evoked by 8-kHz tones of different supra-threshold intensities, modulated in amplitude at 115 Hz. Results show that the ASSR amplitude habituated to the sustained stimulation and that dishabituation occurred when deviant stimuli were presented. ASSR habituation increased as animals became adults, suggesting that the ability to filter acoustic stimuli with no-relevant temporal information increased with age. Results are discussed in terms of the current model of the ASSR generation and analysis procedures. They might have implications for audiometric tests designed to assess hearing in subjects who cannot provide reliable results in the psychophysical trials.

Improving electroencephalographic source localization of epileptogenic zones with time-frequency analysis.

The combination of recently developed methods for electroencephalographic (EEG) space-time-frequency analysis can provide noninvasive functional neuroimages necessary for obtaining an accurate localization of the epileptogenic zone. The aim of this study was to determine if time-frequency (TF) analysis, followed by EEG source localization, would improve the detection and identification of epileptogenic and related activity. Seventeen patients with refractory frontal lobe epilepsy (FLE) were studied using video EEG recording. TF analysis identified the first epileptogenic EEG changes. Using the Bayesian model averaging (BMA) approach, we compared brain electromagnetic tomographic (BET) images, constructed from the TF domain, with BET images constructed from the time domain only. We determined if the localization identified by BET images was concordant with the localization from medical history and video EEG recording. TF analysis provided a clear display of subtle EEG features, including EEG lateralization, and more concordant and delimited epileptogenic zones, compared with time-domain source analysis. In conclusion, EEG TF analysis improves source localization. After a thorough validation, this methodology could become a useful noninvasive tool for localizing the epileptogenic zone in clinical practice.

Musical expertise and foreign speech perception.

The aim of this experiment was to investigate the influence of musical expertise on the automatic perception of foreign syllables and harmonic sounds. Participants were Cuban students with high level of expertise in music or in visual arts and with the same level of general education and socio-economic background. We used a multi-feature Mismatch Negativity (MMN) design with sequences of either syllables in Mandarin Chinese or harmonic sounds, both comprising deviants in pitch contour, duration and Voice Onset Time (VOT) or equivalent that were either far from (Large deviants) or close to (Small deviants) the standard. For both Mandarin syllables and harmonic sounds, results were clear-cut in showing larger MMNs to pitch contour deviants in musicians than in visual artists. Results were less clear for duration and VOT deviants, possibly because of the specific characteristics of the stimuli. Results are interpreted as reflecting similar processing of pitch contour in speech and non-speech sounds. The implications of these results for understanding the influence of intense musical training from childhood to adulthood and of genetic predispositions for music on foreign language perception are discussed.

Glucose metabolism during resting state reveals abnormal brain networks organization in the Alzheimer's disease and mild cognitive impairment.

This paper aims to study the abnormal patterns of brain glucose metabolism co-variations in Alzheimer disease (AD) and Mild Cognitive Impairment (MCI) patients compared to Normal healthy controls (NC) using the Alzheimer Disease Neuroimaging Initiative (ADNI) database. The local cerebral metabolic rate for glucose (CMRgl) in a set of 90 structures belonging to the AAL atlas was obtained from Fluro-Deoxyglucose Positron Emission Tomography data in resting state. It is assumed that brain regions whose CMRgl values are significantly correlated are functionally associated; therefore, when metabolism is altered in a single region, the alteration will affect the metabolism of other brain areas with which it interrelates. The glucose metabolism network (represented by the matrix of the CMRgl co-variations among all pairs of structures) was studied using the graph theory framework. The highest concurrent fluctuations in CMRgl were basically identified between homologous cortical regions in all groups. Significant differences in CMRgl co-variations in AD and MCI groups as compared to NC were found. The AD and MCI patients showed aberrant patterns in comparison to NC subjects, as detected by global and local network properties (global and local efficiency, clustering index, and others). MCI network's attributes showed an intermediate position between NC and AD, corroborating it as a transitional stage from normal aging to Alzheimer disease. Our study is an attempt at exploring the complex association between glucose metabolism, CMRgl covariations and the attributes of the brain network organization in AD and MCI.

Computing sources of epileptic discharges using the novel BMA approach: comparison with other distributed inverse solution methods.

Electroencephalography (EEG) source localization in epileptology continues to be a challenge for neuroscientists. A number of inverse solution (IS) methodologies have been proposed to solve this problem, and their advantages and limitations have been described. In the present work, a previously developed IS approach called Bayesian model averaging (BMA) is introduced in clinical practice in order to improve the localization accuracy of epileptic discharge sources. For this study, 31 patients with the diagnosis of partial epilepsies were studied: 14 had benign childhood epilepsy with centrotemporal spikes and 17 had temporal lobe epilepsy (TLE). The underlying epileptic sources were localized using the BMA approach, and the results were compared with those expected from the clinical diagnosis. Additional comparisons with results obtained from 3 of the most commonly used distributed IS methods for these purposes (minimum norm [MN], weighted minimum norm [WMN], and low-resolution electromagnetic tomography [LORETA]) were carried out in terms of source localization accuracy and spatial resolutions. The BMA approach estimated discharge sources that were consistent with the clinical diagnosis, and this method outperformed LORETA, MN, and WMN in terms of both localization accuracy and spatial resolution. The BMA was able to localize deeper generators with high accuracy. In conclusion, the BMA methodology has a great potential for the noninvasive accurate localization of epileptic sources, even those located in deeper structures. Therefore, it could be a promising tool for clinical practice in epileptology, although additional studies in other types of epileptic syndromes are necessary.

Maturational time course of the envelope following response to amplitude-modulated acoustic signals in rats.

OBJECTIVE: The maturation pattern of the envelope following response (EFR) was described using rats as an experimental model. DESIGN: EFRs were recorded in animals at different postnatal ages (15, 20, 25, 35, and 70 postnatal days) in response to broadband noise (BBN) and tones of 8000 and 4000 Hz modulated in amplitude using a continuous sweep of modulation frequencies. Responses were analysed in the 90-190 Hz modulation frequency (MF) range. STUDY SAMPLE: Forty individuals (eight individuals for each age bracket) were included in the present study. RESULTS: During maturation, the MF at which the maximum amplitude was obtained (BMF, best modulation frequency) shifted to higher values when animals were stimulated with tones. At the same time, the amplitude of the response at the BMF increased. For every group of animals, the amplitude of the response continuously decreased for MFs higher than the BMF. However, less steep decreases of amplitude were obtained as animals became adults. CONCLUSIONS: These results provide normative data regarding the maturation of the EFR in rats. They provide information for the development of predictor models to estimate the temporal resolution of the auditory system during maturation.

White matter architecture rather than cortical surface area correlates with the EEG alpha rhythm.

There are few studies on the neuroanatomical determinants of EEG spectral properties that would explain its substantial inter-individual variability in spite of decades of biophysical modeling that predicts this type of relationship. An exception is the negative relation between head size and the spectral position of the alpha peak (P(alpha)) reported in Nunez et al. (1978)-proposed as evidence of the influence of global boundary conditions on slightly damped neocortical waves. Here, we attempt to reexamine this finding by computing the correlations of occipital P(alpha) with various measures of head size and cortical surface area, for 222 subjects from the EEG/MRI database of the Cuban Human Brain Mapping Project. No relation is found (p>0.05). On the other hand, biophysical models also predict that white matter architecture, determining time delays and connectivities, could have an important influence on P(alpha). This led us to explore relations between P(alpha) and DTI fractional anisotropy by means of a multivariate penalized regression. Clusters of voxels with highly significant relations were found. These were positive within the Posterior and Superior Corona Radiata for both hemispheres, supporting biophysical theories predicting that the period of cortico-thalamocortical cycles might be modulating the alpha frequency. Posterior commissural fibers of the Corpus Callosum present the strongest relationships, negative in the inferior part (Splenium), connecting the inferior occipital lobes and positive in the superior part (Isthmus and Tapetum), connecting the superior occipital cortices. We found that white matter architecture rather than neocortical area determines the dynamics of the alpha rhythm.

False discovery rate and permutation test: an evaluation in ERP data analysis.

Current analysis of event-related potentials (ERP) data is usually based on the a priori selection of channels and time windows of interest for studying the differences between experimental conditions in the spatio-temporal domain. In this work we put forward a new strategy designed for situations when there is not a priori information about 'when' and 'where' these differences appear in the spatio-temporal domain, simultaneously testing numerous hypotheses, which increase the risk of false positives. This issue is known as the problem of multiple comparisons and has been managed with methods that control the false discovery rate (FDR), such as permutation test and FDR methods. Although the former has been previously applied, to our knowledge, the FDR methods have not been introduced in the ERP data analysis. Here we compare the performance (on simulated and real data) of permutation test and two FDR methods (Benjamini and Hochberg (BH) and local-fdr, by Efron). All these methods have been shown to be valid for dealing with the problem of multiple comparisons in the ERP analysis, avoiding the ad hoc selection of channels and/or time windows. FDR methods are a good alternative to the common and computationally more expensive permutation test. The BH method for independent tests gave the best overall performance regarding the balance between type I and type II errors. The local-fdr method is preferable for high dimensional (multichannel) problems where most of the tests conform to the empirical null hypothesis. Differences among the methods according to assumptions, null distributions and dimensionality of the problem are also discussed.

EEG source imaging with spatio-temporal tomographic nonnegative independent component analysis.

This article describes a spatio-temporal EEG/MEG source imaging (ESI) that extracts a parsimonious set of "atoms" or components, each the outer product of both a spatial and a temporal signature. The sources estimated are localized as smooth, minimally overlapping patches of cortical activation that are obtained by constraining spatial signatures to be nonnegative (NN), orthogonal, sparse, and smooth-in effect integrating ESI with NN-ICA. This constitutes a generalization of work by this group on the use of multiple penalties for ESI. A multiplicative update algorithm is derived being stable, fast and converging within seconds near the optimal solution. This procedure, spatio-temporal tomographic NN ICA (STTONNICA), is equally able to recover superficial or deep sources without additional weighting constraints as tested with simulations. STTONNICA analysis of ERPs to familiar and unfamiliar faces yields an occipital-fusiform atom activated by all faces and a more frontal atom that only is active with familiar faces. The temporal signatures are at present unconstrained but can be required to be smooth, complex, or following a multivariate autoregressive model.

Hemispheric modulations of alpha-band power reflect the rightward shift in attention induced by enhanced attentional load.

Rightward shifts in attention are a common consequence of brain injury. A growing body of evidence appears to suggest that increases in attentional load, and decreases in alertness can lead to rightward shifts in attention in healthy and patient populations. It is unclear however whether these factors affect spatial biases in attention at the level of preparatory control processes or at the level of stimulus driven expression mechanisms. Whilst such effects cannot easily be dissociated behaviourally, the robust association between changes in alpha-band activity and shifts in visual attention provides a neural marker by which the temporal dynamics of effects of attentional load on spatial processing might be examined. Here we use electroencephalography to examine the relationship between modulations in alpha-band activity and behavioural outcome on a dual task paradigm comprising a detection task (t1), closely followed by a temporal order judgment task (t2). We examine the effects of high (respond to t1 and t2) and low (t2 only) attentional load conditions on spatial bias and changes in lateralization of alpha-band activity over the course of the trial. As anticipated a rightward bias in detecting target onsets was observed in the temporal order judgment task (t2) under conditions of high attentional load. This rightward shift in attention was associated with changes in the lateralization of alpha-band activity that occurred only after the presentation of t2, suggesting that attentional load may primarily influence expression mechanisms.

Inferring multiple maxima in intravoxel white matter fiber distribution.

A new methodology is introduced that characterizes the intravoxel orientation distribution function (ODF) based on a single-fiber model of the diffusion MRI signal. Using a Bayesian framework the probability of finding a fiber in a specific orientation is obtained. The proposed ODF estimation relies on a cigar-like diffusion tensor model, the methodology is thus denominated Bayesian cigar-like diffusion tensor (BCDT). This work makes two major contributions: 1) the study of single-fiber models in detecting fibers with different volume fractions in a voxel, and 2) the introduction of the Nth-root correction to improve the detection of fibers with smaller volume fractions, where N is the number of diffusion MRI measurements. It is demonstrated that the incomplete signal modeling fails to reconstruct the relative fiber volume fractions, especially when the intravoxel diffusion profiles have dissimilar contributions to the diffusion MRI signal. In this situation the fibers with smaller contributions are hardly detectable. The BCDT method proposed here reduces this effect by introducing the Nth-root correction, making multiple fibers estimable. The performance of the new methodology is illustrated using synthetic and real data, as well as the data from a phantom of intersecting capillaries.

Exploring event-related brain dynamics with tests on complex valued time-frequency representations.

A question subject to intense debate is whether scalp-recorded event-related brain potentials are due to phase resetting of the ongoing electroencephalogram (EEG) or rather to the superimposition of time-locked components on background activity. The two hypotheses are usually assessed by means of statistics in the time-frequency domain, for example, through wavelet transformation of multiple EEG trials that yield for each time and frequency a scatter plot of complex values coefficients. Currently, intertrial phase correlation (phase locking or phase coherence) is taken as evidence for phase resetting at a given frequency and latency. Here we present a formal analysis using a complex t-statistic to illustrate that such measures are, in effect, tests for the mean vector of the repeated trials, and as such on their own are inappropriate measures of phase resetting. We also propose simple t-like statistics for testing changes in (i) the mean (presence of an event-related potential), (ii) the amplitude variance (presence of (de)synchronization) and (iii) the concentration of phases (phase locking). The first two statistics are found to be proper measures of the presence of a non-zero mean activity and induced activity, respectively. In the third case, two different tests are introduced: one based on measuring the alignment of coefficients in the complex plane and another derived from the argument that phase locking persists when the mean of the coefficients is removed. Both statistics gave unambiguous evidence of the presence of phase locking suggesting that they constitute promising tools in the analysis of event-related brain dynamics.

Identifying complex brain networks using penalized regression methods.

The recorded electrical activity of complex brain networks through the EEG reflects their intrinsic spatial, temporal and spectral properties. In this work we study the application of new penalized regression methods to i) the spatial characterization of the brain networks associated with the identification of faces and ii) the PARAFAC analysis of resting-state EEG. The use of appropriate constraints through non-convex penalties allowed three types of inverse solutions (Loreta, Lasso Fusion and ENet L) to spatially localize networks in agreement with previous studies with fMRI. Furthermore, we propose a new penalty based in the Information Entropy for the constrained PARAFAC analysis of resting EEG that allowed the identification in time, frequency and space of those brain networks with minimum spectral entropy. This study is an initial attempt to explicitly include complexity descriptors as a constraint in multilinear EEG analysis.