A fully adaptive numerical approximation for a two-dimensional epidemic model with nonlinear cross-diffusion

An epidemic model is formulated by a reactionâeuro"diffusion system where the spatial pattern formation is driven by cross-diffusion. The reaction terms describe the local dynamics of susceptible and infected species, whereas the diffusion terms account for the spatial distribution dynamics. For both self-diffusion and cross-diffusion, nonlinear constitutive assumptions are suggested. To simulate the pattern formation two finite volume formulations are proposed, which employ a conservative and a non-conservative discretization, respectively. An efficient simulation is obtained by a fully adaptive multiresolution strategy. Numerical examples illustrate the impact of the cross-diffusion on the pattern formation.

Manual reaction times and brain dynamics after 'awake surgery' of slow-growing tumors invading the parietal area. A case report

Primary objectives: Awake surgeries of slow-growing tumours invading the brain and guided by direct electrical stimulation induce major brain reorganizations accompanied with slight impairments post-operatively. In most cases, these deficits are so slight after a few days that they are often not detectable on classical neuropsychological evaluations. Consequently, this study investigated whether simple visuo-manual reaction time paradigms would sign some level of functional asymmetries between both hemispheres. Importantly, the visual stimulus was located in the saggital plane in order to limit attentional biases and to focus mainly on the inter-hemispheric asymmetry. Methods and procedures: Three patients (aged 41, 59 and 59 years) after resections in parietal regions and a control group (age¼44, SD¼6.9) were compared during simple uni- and bimanual reaction times (RTs). Main outcomes and results: Longer RTs were observed for the contralesional compared to the ipsilesional hand in the unimanual condition. This asymmetry was reversed for the bimanual condition despite longer RTs. Conclusion and clinical implications: Reaction time paradigms are useful in these patients to monitor more precisely their functional deficits, especially their level of functional asymmetry, and to understand brain (re)organization following slowgrowing lesions.

Analysis of a finite volume method for a cross-diffusion model in population dynamics

The main goal of this paper is to propose a convergent finite volume method for a reactionâeuro"diffusion system with cross-diffusion. First, we sketch an existence proof for a class of cross-diffusion systems. Then the standard two-point finite volume fluxes are used in combination with a nonlinear positivity-preserving approximation of the cross-diffusion coefficients. Existence and uniqueness of the approximate solution are addressed, and it is also shown that the scheme converges to the corresponding weak solution for the studied model. Furthermore, we provide a stability analysis to study pattern-formation phenomena, and we perform two-dimensional numerical examples which exhibit formation of nonuniform spatial patterns. From the simulations it is also found that experimental rates of convergence are slightly below second order. The convergence proof uses two ingredients of interest for various applications, namely the discrete Sobolev embedding inequalities with general boundary conditions and a space-time $L^1$ compactness argument that mimics the compactness lemma due to Kruzhkov. The proofs of these results are given in the Appendix.

Effects of anxiety on visuospatial pattern discrimination and decision-making processes: what can we learn from brain dynamics ?

Detection and discrimination of visuospatial input involve at least extracting, selecting and encoding relevant information and decision-making processes allowing selecting a response. These two operations are altered, respectively, by attentional mechanisms that change discrimination capacities, and by beliefs concerning the likelihood of uncertain events. Information processing is tuned by the attentional level that acts like a filter on perception, while decision-making processes are weighed by subjective probability of risk. In addition, it has been shown that anxiety could affect the detection of unexpected events through the modification of the level of arousal. Consequently, purpose of this study concerns whether and how decision-making and brain dynamics are affected by anxiety. To investigate these questions, the performance of women with either a high (12) or a low (12) STAI-T (State-Trait Anxiety Inventory, Spielberger, 1983) was examined in a decision-making visuospatial task where subjects have to recognize a target visual pattern from non-target patterns. The target pattern was a schematic image of furniture arranged in such a way as to give the impression of a living room. Non-target patterns were created by either the compression or the dilatation of the distances between objects. Target and non-target patterns were always presented in the same configuration. Preliminary behavioral results show no group difference in reaction time. In addition, visuo-spatial abilities were analyzed trough the signal detection theory for quantifying perceptual decisions in the presence of uncertainty (Green and Swets, 1966). This theory treats detection of a stimulus as a decision-making process determined by the nature of the stimulus and cognitive factors. Astonishingly, no difference in d' (corresponding to the distance between means of the distributions) and c (corresponds to the likelihood ratio) indexes was observed. Comparison of Event-related potentials (ERP) reveals that brain dynamics differ according to anxiety. It shows differences in component latencies, particularly a delay in anxious subjects over posterior electrode sites. However, these differences are compensated during later components by shorter latencies in anxious subjects compared to non-anxious one. These inverted effects seem indicate that the absence of difference in reaction time rely on a compensation of attentional level that tunes cortical activation in anxious subjects, but they have to hammer away to maintain performance.

The spatio-temporal brain dynamics of processing and integrating sound localization cues in humans.

Interaural intensity and time differences (IID and ITD) are two binaural auditory cues for localizing sounds in space. This study investigated the spatio-temporal brain mechanisms for processing and integrating IID and ITD cues in humans. Auditory-evoked potentials were recorded, while subjects passively listened to noise bursts lateralized with IID, ITD or both cues simultaneously, as well as a more frequent centrally presented noise. In a separate psychophysical experiment, subjects actively discriminated lateralized from centrally presented stimuli. IID and ITD cues elicited different electric field topographies starting at approximately 75 ms post-stimulus onset, indicative of the engagement of distinct cortical networks. By contrast, no performance differences were observed between IID and ITD cues during the psychophysical experiment. Subjects did, however, respond significantly faster and more accurately when both cues were presented simultaneously. This performance facilitation exceeded predictions from probability summation, suggestive of interactions in neural processing of IID and ITD cues. Supra-additive neural response interactions as well as topographic modulations were indeed observed approximately 200 ms post-stimulus for the comparison of responses to the simultaneous presentation of both cues with the mean of those to separate IID and ITD cues. Source estimations revealed differential processing of IID and ITD cues initially within superior temporal cortices and also at later stages within temporo-parietal and inferior frontal cortices. Differences were principally in terms of hemispheric lateralization. The collective psychophysical and electrophysiological results support the hypothesis that IID and ITD cues are processed by distinct, but interacting, cortical networks that can in turn facilitate auditory localization.

How local excitation-inhibition ratio impacts the whole brain dynamics.

The spontaneous activity of the brain shows different features at different scales. On one hand, neuroimaging studies show that long-range correlations are highly structured in spatiotemporal patterns, known as resting-state networks, on the other hand, neurophysiological reports show that short-range correlations between neighboring neurons are low, despite a large amount of shared presynaptic inputs. Different dynamical mechanisms of local decorrelation have been proposed, among which is feedback inhibition. Here, we investigated the effect of locally regulating the feedback inhibition on the global dynamics of a large-scale brain model, in which the long-range connections are given by diffusion imaging data of human subjects. We used simulations and analytical methods to show that locally constraining the feedback inhibition to compensate for the excess of long-range excitatory connectivity, to preserve the asynchronous state, crucially changes the characteristics of the emergent resting and evoked activity. First, it significantly improves the model's prediction of the empirical human functional connectivity. Second, relaxing this constraint leads to an unrealistic network evoked activity, with systematic coactivation of cortical areas which are components of the default-mode network, whereas regulation of feedback inhibition prevents this. Finally, information theoretic analysis shows that regulation of the local feedback inhibition increases both the entropy and the Fisher information of the network evoked responses. Hence, it enhances the information capacity and the discrimination accuracy of the global network. In conclusion, the local excitation-inhibition ratio impacts the structure of the spontaneous activity and the information transmission at the large-scale brain level.

Integrating receptor interactions and dynamics and Interference with endocrine disruptors in the mechanisms of action of PPAR nuclear receptors

Abstract Peroxisome Proliferator-Activated Receptors (PPARs) form a family of three nuclear receptors regulating important cellular and metabolic functions. PPARs control gene expression by directly binding to target promoters as heterodimers with the Retinoid X Receptor (RXR), and their transcriptional activity is enhanced upon activation by natural or pharmacological ligands. The binding of PPAR/RXR heterodimers on target promoters allows the anchoring of a series of coactivators and corepressors involved in promoter remodeling and the recruitment of the transcription machinery. The transcriptional output finally depends on a complex interplay between (i) the respective expression levels of PPARs, RXRs and of other nuclear receptors competing for DNA binding and RXR recruitment, (ii) the availability and the nature of PPAR and RXR ligands, (iii) the expression levels and the nature of the different coactivators and corepressors and (iv) the sequence and the epigenetic status of the promoter. Understanding how all these factors and signals integrate and fine-tune transcription remains a challenge but is necessary to understand the specificity of the physiological functions regulated by PPARs. The work presented herein focuses on the molecular mechanisms of PPAR action and aims at understanding how the interactions and mobility of the receptor modulate transcription in the physiological context of a living cell: Such observations in vivo rely on the use of engineered fluorescent protein chimeras and require the development and the application of complementary imaging techniques such as Fluorescence Recovery After Photobleaching (FRAP), Fluorescence Resonance Energy Transfer (FRET) and Fluorescence Correlation Spectroscopy (FCS). Using such techniques, PPARs are shown to reside solely in the nucleus where they are constitutively associated with RXR but transcriptional activation by ligand binding -does not promote the formation of sub-nuclear structures as observed with other nuclear receptors. In addition, the engagement of unliganded PPARs in large complexes of cofactors in living cells provides a molecular basis for their ligand-independent activity. Ligand binding reduces receptor diffusion by promoting the recruitment of coactivators which further enlarge the size of PPAR complexes to acquire full transcriptional competence. Using these molecular approaches, we deciphered the molecular mechanisms through which phthalates, a class of pollutants from the plastic industry, interfere with PPARγ signaling. Mono-ethyl-hexyl-phthalate (MEHP) binding induces the recruitment of a specific subset of cofactors and translates into the expression of a specific subset of target genes, the transcriptional output being strongly conditioned by the differentiation status of the cell. This selective PPARγ modulation induces limited adipogenic effects in cellular models while exposure to phthalates in animal models leads to protective effects on glucose tolerance and diet-induced obesity. These results demonstrate that phthalates influence lipid and carbohydrate metabolism through complex mechanisms which most likely involve PPARγ but also probably PPARα and PPARß, Altogether, the molecular and physiological demonstration of the interference of pollutants with PPAR action outlines an important role of chemical exposure in metabolic regulations. Résumé Les PPARs (Peroxisome Proliferator-Activated Receptors) forment une famille de récepteurs nucléaires qui régulent des fonctions cellulaires et métaboliques importantes. Les PPARs contrôlent l'expression des gènes en se liant directement à leurs promoteurs sous forme d'hétérodimères avec les récepteurs RXR (Retinoid X Receptor), et leur activité transcriptionnelle est stimulée par la liaison de ligands naturels ou pharmacologiques. L'association des hétérodimères PPAR/RXR avec les promoteurs des gènes cibles permet le recrutement de coactivateurs et de corépresseurs qui vont permettre le remodelage de la chromatine et le recrutement de la machinerie transcriptionnelle. Les actions transcriptionnelles du récepteur dépendent toutefois d'interactions complexes qui sont régulées par (i) le niveau d'expression des PPARs, des RXRs et d'autres récepteurs nucléaires entrant en compétition pour la liaison à l'ADN et l'association avec RXR, (ii) la disponibilité et la nature de ligands de PPAR et de RXR, (iii) les niveaux d'expression et la nature des différents coactivateurs et corépresseurs et (iv) la séquence et le marquage épigénétique des promoteurs. La compréhension des mécanismes qui permettent d'intégrer ces aspects pour assurer une régulation fine de l'activité transcriptionnelle est un défi qu'il est nécessaire de relever pour comprendre la spécificité des fonctions physiologiques régulées par les PPARs. Ce travail concerne l'étude des mécanismes d'action moléculaire des PPARs et vise à mieux comprendre comment les interactions du récepteur avec d'autres protéines ainsi que la mobilité de ce dernier régulent son activité transcriptionnelle dans le contexte physiologique des cellules vivantes. De telles observations reposent sur l'emploi de protéines fusionnées à des protéines fluorescentes ainsi que sur le développement et l'utilisation de techniques d'imagerie complémentaires telles que le FRAP (Fluorescence Recovery After Photobleaching), le FRET (Fluorescence Resonance Energy Transfer) ou la FCS (Fluorescence Corrélation Spectroscopy). En appliquant ces méthodes, nous avons pu montrer que les PPARs résident toujours dans le noyau où ils sont associés de manière constitutive à RXR, mais que l'ajout de ligand n'induit pas la formation de structures sub-nucléaires comme cela a pu être décrit pour d'autres récepteurs nucléaires. De plus, les PPARs sont engagés dans de larges complexes protéiques de cofacteurs en absence de ligand, ce qui procure une explication moléculaire à leur activité ligand-indépendante. La liaison du ligand réduit la vitesse de diffusion du récepteur en induisant le recrutement de coactivateurs qui augmente encore plus la taille des complexes afin d'acquérir un potentiel d'activation maximal. En utilisant ces approches moléculaires, nous avons pu caractériser les mécanismes permettant aux phtalates, une classe de polluants provenant de l'industrie plastique, d'interférer avec PPARγ. La liaison du mono-ethyl-hexyl-phtalate (NERF) à PPARγ induit un recrutement sélectif de cofacteurs, se traduisant par l'induction spécifique d'un sous-ensemble de gènes qui varie en fonction du niveau de différentiation cellulaire. La modulation sélective de PPARγ par le MEHP provoque une adipogenèse modérée dans des modèles cellulaires alors que l'exposition de modèles animaux aux phtalates induit des effets bénéfiques sur la tolérance au glucose et sur le développement de l'obésité. Toutefois, les phtalates ont une action complexe sur le métabolisme glucido-lipidique en faisant intervenir PPARγ mais aussi probablement PPARα et PPARß. Cette démonstration moléculaire et physiologique de l'interférence des polluants avec les récepteurs nucléaires PPAR souligne un rôle important de l'exposition à de tels composés dans les régulations métaboliques.

Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings.

Mathematical methods combined with measurements of single-cell dynamics provide a means to reconstruct intracellular processes that are only partly or indirectly accessible experimentally. To obtain reliable reconstructions, the pooling of measurements from several cells of a clonal population is mandatory. However, cell-to-cell variability originating from diverse sources poses computational challenges for such process reconstruction. We introduce a scalable Bayesian inference framework that properly accounts for population heterogeneity. The method allows inference of inaccessible molecular states and kinetic parameters; computation of Bayes factors for model selection; and dissection of intrinsic, extrinsic and technical noise. We show how additional single-cell readouts such as morphological features can be included in the analysis. We use the method to reconstruct the expression dynamics of a gene under an inducible promoter in yeast from time-lapse microscopy data.

Evaporation from a shallow water table: Diurnal dynamics of measured and simulated water and heat regime at the vicinity of a drying sand surface

Accurate estimates of water losses by evaporation from shallow water tables are important for hydrological, agricultural, and climatic purposes. An experiment was conducted in a weighing lysimeter to characterize the diurnal dynamics of evaporation under natural conditions. Sampling revealed a completely dry surface sand layer after 5 days of evaporation. Its thickness was <1 cm early in the morning, increasing to reach 4?5 cm in the evening. This evidence points out fundamental limitations of the approaches that assume hydraulic connectivity from the water table up to the surface, as well as those that suppose monotonic drying when unsteady conditions prevail. The computed vapor phase diffusion rates from the apparent drying front based on Fick's law failed to reproduce the measured cumulative evaporation during the sampling day. We propose that two processes rule natural evaporation resulting from daily fluctuations of climatic variables: (i) evaporation of water, stored during nighttime due to redistribution and vapor condensation, directly into the atmosphere from the soil surface during the early morning hours, that could be simulated using a mass transfer approach and (ii) subsurface evaporation limited by Fickian diffusion, afterward. For the conditions prevailing during the sampling day, the amount of water stored at the vicinity of the soil surface was 0.3 mm and was depleted before 11:00. Combining evaporation from the surface before 11:00 and subsurface evaporation limited by Fickian diffusion after that time, the agreement between the estimated and measured cumulative evaporation was significantly improved.

How T cell receptors interact with peptide-MHCs: A multiple steered molecular dynamics study.

The T-cell receptor (TCR) interaction with antigenic peptides (p) presented by the major histocompatibility complex (MHC) molecule is a key determinant of immune response. In addition, TCR-pMHC interactions offer examples of features more generally pertaining to protein-protein recognition: subtle specificity and cross-reactivity. Despite their importance, molecular details determining the TCR-pMHC binding remain unsolved. However, molecular simulation provides the opportunity to investigate some of these aspects. In this study, we perform extensive equilibrium and steered molecular dynamics simulations to study the unbinding of three TCR-pMHC complexes. As a function of the dissociation reaction coordinate, we are able to obtain converged H-bond counts and energy decompositions at different levels of detail, ranging from the full proteins, to separate residues and water molecules, down to single atoms at the interface. Many observed features do not support a previously proposed two-step model for TCR recognition. Our results also provide keys to interpret experimental point-mutation results. We highlight the role of water both in terms of interface resolvation and of water molecules trapped in the bound complex. Importantly, we illustrate how two TCRs with similar reactivity and structures can have essentially different binding strategies. Proteins 2011; © 2011 Wiley-Liss, Inc.

Brownian dynamics simulation of DNA condensation.

DNA condensation observed in vitro with the addition of polyvalent counterions is due to intermolecular attractive forces. We introduce a quantitative model of these forces in a Brownian dynamics simulation in addition to a standard mean-field Poisson-Boltzmann repulsion. The comparison of a theoretical value of the effective diameter calculated from the second virial coefficient in cylindrical geometry with some experimental results allows a quantitative evaluation of the one-parameter attractive potential. We show afterward that with a sufficient concentration of divalent salt (typically approximately 20 mM MgCl(2)), supercoiled DNA adopts a collapsed form where opposing segments of interwound regions present zones of lateral contact. However, under the same conditions the same plasmid without torsional stress does not collapse. The condensed molecules present coexisting open and collapsed plectonemic regions. Furthermore, simulations show that circular DNA in 50% methanol solutions with 20 mM MgCl(2) aggregates without the requirement of torsional energy. This confirms known experimental results. Finally, a simulated DNA molecule confined in a box of variable size also presents some local collapsed zones in 20 mM MgCl(2) above a critical concentration of the DNA. Conformational entropy reduction obtained either by supercoiling or by confinement seems thus to play a crucial role in all forms of condensation of DNA.

The effects of physiologically plausible connectivity structure on local and global dynamics in large scale brain models.

Functionally relevant large scale brain dynamics operates within the framework imposed by anatomical connectivity and time delays due to finite transmission speeds. To gain insight on the reliability and comparability of large scale brain network simulations, we investigate the effects of variations in the anatomical connectivity. Two different sets of detailed global connectivity structures are explored, the first extracted from the CoCoMac database and rescaled to the spatial extent of the human brain, the second derived from white-matter tractography applied to diffusion spectrum imaging (DSI) for a human subject. We use the combination of graph theoretical measures of the connection matrices and numerical simulations to explicate the importance of both connectivity strength and delays in shaping dynamic behaviour. Our results demonstrate that the brain dynamics derived from the CoCoMac database are more complex and biologically more realistic than the one based on the DSI database. We propose that the reason for this difference is the absence of directed weights in the DSI connectivity matrix.

Information sharing and social network dynamics : a coevolutionary approach

The capacity to interact socially and share information underlies the success of many animal species, humans included. Researchers of many fields have emphasized the evo¬lutionary significance of how patterns of connections between individuals, or the social networks, and learning abilities affect the information obtained by animal societies. To date, studies have focused on the dynamics either of social networks, or of the spread of information. The present work aims to study them together. We make use of mathematical and computational models to study the dynamics of networks, where social learning and information sharing affect the structure of the population the individuals belong to. The number and strength of the relationships between individuals, in turn, impact the accessibility and the diffusion of the shared information. Moreover, we inves¬tigate how different strategies in the evaluation and choice of interacting partners impact the processes of knowledge acquisition and social structure rearrangement. First, we look at how different evaluations of social interactions affect the availability of the information and the network topology. We compare a first case, where individuals evaluate social exchanges by the amount of information that can be shared by the partner, with a second case, where they evaluate interactions by considering their partners' social status. We show that, even if both strategies take into account the knowledge endowments of the partners, they have very different effects on the system. In particular, we find that the first case generally enables individuals to accumulate higher amounts of information, thanks to the more efficient patterns of social connections they are able to build. Then, we study the effects that homophily, or the tendency to interact with similar partners, has on knowledge accumulation and social structure. We compare the case where individuals who know the same information are more likely to learn socially from each other, to the opposite case, where individuals who know different information are instead more likely to learn socially from each other. We find that it is not trivial to claim which strategy is better than the other. Depending on the possibility of forgetting information, the way new social partners can be chosen, and the population size, we delineate the conditions for which each strategy allows accumulating more information, or in a faster way For these conditions, we also discuss the topological characteristics of the resulting social structure, relating them to the information dynamics outcome. In conclusion, this work paves the road for modeling the joint dynamics of the spread of information among individuals and their social interactions. It also provides a formal framework to study jointly the effects of different strategies in the choice of partners on social structure, and how they favor the accumulation of knowledge in the population. - La capacité d'interagir socialement et de partager des informations est à la base de la réussite de nombreuses espèces animales, y compris les humains. Les chercheurs de nombreux domaines ont souligné l'importance évolutive de la façon dont les modes de connexions entre individus, ou réseaux sociaux et les capacités d'apprentissage affectent les informations obtenues par les sociétés animales. À ce jour, les études se sont concentrées sur la dynamique soit des réseaux sociaux, soit de la diffusion de l'information. Le présent travail a pour but de les étudier ensemble. Nous utilisons des modèles mathématiques et informatiques pour étudier la dynamique des réseaux, où l'apprentissage social et le partage d'information affectent la structure de la population à laquelle les individus appartiennent. Le nombre et la solidité des relations entre les individus ont à leurs tours un impact sur l'accessibilité et la diffusion de l'informa¬tion partagée. Par ailleurs, nous étudions comment les différentes stratégies d'évaluation et de choix des partenaires d'interaction ont une incidence sur les processus d'acquisition des connaissances ainsi que le réarrangement de la structure sociale. Tout d'abord, nous examinons comment des évaluations différentes des interactions sociales influent sur la disponibilité de l'information ainsi que sur la topologie du réseau. Nous comparons un premier cas, où les individus évaluent les échanges sociaux par la quantité d'information qui peut être partagée par le partenaire, avec un second cas, où ils évaluent les interactions en tenant compte du statut social de leurs partenaires. Nous montrons que, même si les deux stratégies prennent en compte le montant de connaissances des partenaires, elles ont des effets très différents sur le système. En particulier, nous constatons que le premier cas permet généralement aux individus d'accumuler de plus grandes quantités d'information, grâce à des modèles de connexions sociales plus efficaces qu'ils sont capables de construire. Ensuite, nous étudions les effets que l'homophilie, ou la tendance à interagir avec des partenaires similaires, a sur l'accumulation des connaissances et la structure sociale. Nous comparons le cas où des personnes qui connaissent les mêmes informations sont plus sus¬ceptibles d'apprendre socialement l'une de l'autre, au cas où les individus qui connaissent des informations différentes sont au contraire plus susceptibles d'apprendre socialement l'un de l'autre. Nous constatons qu'il n'est pas trivial de déterminer quelle stratégie est meilleure que l'autre. En fonction de la possibilité d'oublier l'information, la façon dont les nouveaux partenaires sociaux peuvent être choisis, et la taille de la population, nous déterminons les conditions pour lesquelles chaque stratégie permet d'accumuler plus d'in¬formations, ou d'une manière plus rapide. Pour ces conditions, nous discutons également les caractéristiques topologiques de la structure sociale qui en résulte, les reliant au résultat de la dynamique de l'information. En conclusion, ce travail ouvre la route pour la modélisation de la dynamique conjointe de la diffusion de l'information entre les individus et leurs interactions sociales. Il fournit également un cadre formel pour étudier conjointement les effets de différentes stratégies de choix des partenaires sur la structure sociale et comment elles favorisent l'accumulation de connaissances dans la population.

Correlations between negative-induced cerebral dynamics and idiopathic intellectual disability: an EEG study

Intellectual disability has long been associated with deficits in socio-emotional processing. However, studies investigating brain dynamics of maladaptive socio-emotional skills associated with intellectual disability are scarce. Here, we compared differences in brain activity between low intelligence quotient (I.Q.<75, N=13) and normal controls (N=15) while evaluating their subjective emotions. Positive (P) and negative (N) valenced pictures were presented one at a time to participants of both groups, at a rate of ¾. The task required that each participant evaluate their subjective emotion and press a predefined push-button when done, alternatively P and N. Electroencephalographic (EEG) signals were continuously recorded, and the 1000ms time window following each picture was analyzed offline for power in frequency domain. Alpha low (8-10Hz) and upper (10-13Hz) frequency bands were then compared for both groups and for both P and N emotions in 12 distributed scalp electrodes. The qualitative evaluation of emotions was similar between both groups, with constant longer reaction times for the low IQ participants. The EEG signal comparison shows marked power decrease in upper alpha frequency range for N emotions in low intelligence group. Otherwise no significant difference was noticed between low and normal IQ. Main findings of the present study are (1) results do not support the hypothesis that impairment in developmental intelligence roots in maladaptive emotional processing; (2) the strong alpha power suppression during negative-induced emotions suggests the involvement of an extended neural network and more effortful inhibition processes than positive ones. We call for further studies with a larger sample.