Seizure semiology: an overview of the 'inverse problem'.

In clinical practice, a classification of seizures based on clinical signs and symptoms leads to an improved understanding of epilepsy-related issues and therefore strongly contributes to a better patient care. The inverse problem involves inferring the anatomical brain localization of a seizure from the scalp surface EEG, a concept we apply here to correlate seizure origin with seizure semiology. The spheres of sensorium, motor features, consciousness changes and autonomic alterations during ictal and postictal manifestations are reviewed, including several subdivisions used to better categorize particular features. Particular attention is given to behavioral features, as well as to features occurring in idiopathic generalized epileptic syndromes and psychogenic nonepileptic spells.

A test of Jessor's problem behavior theory in a Eurasian and a Western European developmental context.

PURPOSE: The current study tested the applicability of Jessor's problem behavior theory (PBT) in national probability samples from Georgia and Switzerland. Comparisons focused on (1) the applicability of the problem behavior syndrome (PBS) in both developmental contexts, and (2) on the applicability of employing a set of theory-driven risk and protective factors in the prediction of problem behaviors. METHODS: School-based questionnaire data were collected from n = 18,239 adolescents in Georgia (n = 9499) and Switzerland (n = 8740) following the same protocol. Participants rated five measures of problem behaviors (alcohol and drug use, problems because of alcohol and drug use, and deviance), three risk factors (future uncertainty, depression, and stress), and three protective factors (family, peer, and school attachment). Final study samples included n = 9043 Georgian youth (mean age = 15.57; 58.8% females) and n = 8348 Swiss youth (mean age = 17.95; 48.5% females). Data analyses were completed using structural equation modeling, path analyses, and post hoc z-tests for comparisons of regression coefficients. RESULTS: Findings indicated that the PBS replicated in both samples, and that theory-driven risk and protective factors accounted for 13% and 10% in Georgian and Swiss samples, respectively in the PBS, net the effects by demographic variables. Follow-up z-tests provided evidence of some differences in the magnitude, but not direction, in five of six individual paths by country. CONCLUSION: PBT and the PBS find empirical support in these Eurasian and Western European samples; thus, Jessor's theory holds value and promise in understanding the etiology of adolescent problem behaviors outside of the United States.

Graph theory reveals dysconnected hubs in 22q11DS and altered nodal efficiency in patients with hallucinations.

Schizophrenia is postulated to be the prototypical dysconnection disorder, in which hallucinations are the core symptom. Due to high heterogeneity in methodology across studies and the clinical phenotype, it remains unclear whether the structural brain dysconnection is global or focal and if clinical symptoms result from this dysconnection. In the present work, we attempt to clarify this issue by studying a population considered as a homogeneous genetic sub-type of schizophrenia, namely the 22q11.2 deletion syndrome (22q11.2DS). Cerebral MRIs were acquired for 46 patients and 48 age and gender matched controls (aged 6-26, respectively mean age = 15.20 ± 4.53 and 15.28 ± 4.35 years old). Using the Connectome mapper pipeline (connectomics.org) that combines structural and diffusion MRI, we created a whole brain network for each individual. Graph theory was used to quantify the global and local properties of the brain network organization for each participant. A global degree loss of 6% was found in patients' networks along with an increased Characteristic Path Length. After identifying and comparing hubs, a significant loss of degree in patients' hubs was found in 58% of the hubs. Based on Allen's brain network model for hallucinations, we explored the association between local efficiency and symptom severity. Negative correlations were found in the Broca's area (p < 0.004), the Wernicke area (p < 0.023) and a positive correlation was found in the dorsolateral prefrontal cortex (DLPFC) (p < 0.014). In line with the dysconnection findings in schizophrenia, our results provide preliminary evidence for a targeted alteration in the brain network hubs' organization in individuals with a genetic risk for schizophrenia. The study of specific disorganization in language, speech and thought regulation networks sharing similar network properties may help to understand their role in the hallucination mechanism.

A Monte Carlo approach to the inverse problem of diffuse pollution risk in agricultural catchments

The hydrological and biogeochemical processes that operate in catchments influence the ecological quality of freshwater systems through delivery of fine sediment, nutrients and organic matter. Most models that seek to characterise the delivery of diffuse pollutants from land to water are reductionist. The multitude of processes that are parameterised in such models to ensure generic applicability make them complex and difficult to test on available data. Here, we outline an alternative - data-driven - inverse approach. We apply SCIMAP, a parsimonious risk based model that has an explicit treatment of hydrological connectivity. we take a Bayesian approach to the inverse problem of determining the risk that must be assigned to different land uses in a catchment in order to explain the spatial patterns of measured in-stream nutrient concentrations. We apply the model to identify the key sources of nitrogen (N) and phosphorus (P) diffuse pollution risk in eleven UK catchments covering a range of landscapes. The model results show that: 1) some land use generates a consistently high or low risk of diffuse nutrient pollution; but 2) the risks associated with different land uses vary both between catchments and between nutrients; and 3) that the dominant sources of P and N risk in the catchment are often a function of the spatial configuration of land uses. Taken on a case-by-case basis, this type of inverse approach may be used to help prioritise the focus of interventions to reduce diffuse pollution risk for freshwater ecosystems. (C) 2012 Elsevier B.V. All rights reserved.

Estimation of the correlation structure of crustal velocity heterogeneity from seismic reflection data

Numerous sources of evidence point to the fact that heterogeneity within the Earth's deep crystalline crust is complex and hence may be best described through stochastic rather than deterministic approaches. As seismic reflection imaging arguably offers the best means of sampling deep crustal rocks in situ, much interest has been expressed in using such data to characterize the stochastic nature of crustal heterogeneity. Previous work on this problem has shown that the spatial statistics of seismic reflection data are indeed related to those of the underlying heterogeneous seismic velocity distribution. As of yet, however, the nature of this relationship has remained elusive due to the fact that most of the work was either strictly empirical or based on incorrect methodological approaches. Here, we introduce a conceptual model, based on the assumption of weak scattering, that allows us to quantitatively link the second-order statistics of a 2-D seismic velocity distribution with those of the corresponding processed and depth-migrated seismic reflection image. We then perform a sensitivity study in order to investigate what information regarding the stochastic model parameters describing crustal velocity heterogeneity might potentially be recovered from the statistics of a seismic reflection image using this model. Finally, we present a Monte Carlo inversion strategy to estimate these parameters and we show examples of its application at two different source frequencies and using two different sets of prior information. Our results indicate that the inverse problem is inherently non-unique and that many different combinations of the vertical and lateral correlation lengths describing the velocity heterogeneity can yield seismic images with the same 2-D autocorrelation structure. The ratio of all of these possible combinations of vertical and lateral correlation lengths, however, remains roughly constant which indicates that, without additional prior information, the aspect ratio is the only parameter describing the stochastic seismic velocity structure that can be reliably recovered.

Implementing statistical learning methods through Bayesian networks. Part 1: a guide to Bayesian parameter estimation using forensic science data

As a thorough aggregation of probability and graph theory, Bayesian networks currently enjoy widespread interest as a means for studying factors that affect the coherent evaluation of scientific evidence in forensic science. Paper I of this series of papers intends to contribute to the discussion of Bayesian networks as a framework that is helpful for both illustrating and implementing statistical procedures that are commonly employed for the study of uncertainties (e.g. the estimation of unknown quantities). While the respective statistical procedures are widely described in literature, the primary aim of this paper is to offer an essentially non-technical introduction on how interested readers may use these analytical approaches - with the help of Bayesian networks - for processing their own forensic science data. Attention is mainly drawn to the structure and underlying rationale of a series of basic and context-independent network fragments that users may incorporate as building blocs while constructing larger inference models. As an example of how this may be done, the proposed concepts will be used in a second paper (Part II) for specifying graphical probability networks whose purpose is to assist forensic scientists in the evaluation of scientific evidence encountered in the context of forensic document examination (i.e. results of the analysis of black toners present on printed or copied documents).

Geological realism in hydrogeological and geophysical inverse modeling: A review

Scientific curiosity, exploration of georesources and environmental concerns are pushing the geoscientific research community toward subsurface investigations of ever-increasing complexity. This review explores various approaches to formulate and solve inverse problems in ways that effectively integrate geological concepts with geophysical and hydrogeological data. Modern geostatistical simulation algorithms can produce multiple subsurface realizations that are in agreement with conceptual geological models and statistical rock physics can be used to map these realizations into physical properties that are sensed by the geophysical or hydrogeological data. The inverse problem consists of finding one or an ensemble of such subsurface realizations that are in agreement with the data. The most general inversion frameworks are presently often computationally intractable when applied to large-scale problems and it is necessary to better understand the implications of simplifying (1) the conceptual geological model (e.g., using model compression); (2) the physical forward problem (e.g., using proxy models); and (3) the algorithm used to solve the inverse problem (e.g., Markov chain Monte Carlo or local optimization methods) to reach practical and robust solutions given today's computer resources and knowledge. We also highlight the need to not only use geophysical and hydrogeological data for parameter estimation purposes, but also to use them to falsify or corroborate alternative geological scenarios.

Source wavelet estimation for waveform inversion of crosshole georadar data

AbstractFor a wide range of environmental, hydrological, and engineering applications there is a fast growing need for high-resolution imaging. In this context, waveform tomographic imaging of crosshole georadar data is a powerful method able to provide images of pertinent electrical properties in near-surface environments with unprecedented spatial resolution. In contrast, conventional ray-based tomographic methods, which consider only a very limited part of the recorded signal (first-arrival traveltimes and maximum first-cycle amplitudes), suffer from inherent limitations in resolution and may prove to be inadequate in complex environments. For a typical crosshole georadar survey the potential improvement in resolution when using waveform-based approaches instead of ray-based approaches is in the range of one order-of- magnitude. Moreover, the spatial resolution of waveform-based inversions is comparable to that of common logging methods. While in exploration seismology waveform tomographic imaging has become well established over the past two decades, it is comparably still underdeveloped in the georadar domain despite corresponding needs. Recently, different groups have presented finite-difference time-domain waveform inversion schemes for crosshole georadar data, which are adaptations and extensions of Tarantola's seminal nonlinear generalized least-squares approach developed for the seismic case. First applications of these new crosshole georadar waveform inversion schemes on synthetic and field data have shown promising results. However, there is little known about the limits and performance of such schemes in complex environments. To this end, the general motivation of my thesis is the evaluation of the robustness and limitations of waveform inversion algorithms for crosshole georadar data in order to apply such schemes to a wide range of real world problems.One crucial issue to making applicable and effective any waveform scheme to real-world crosshole georadar problems is the accurate estimation of the source wavelet, which is unknown in reality. Waveform inversion schemes for crosshole georadar data require forward simulations of the wavefield in order to iteratively solve the inverse problem. Therefore, accurate knowledge of the source wavelet is critically important for successful application of such schemes. Relatively small differences in the estimated source wavelet shape can lead to large differences in the resulting tomograms. In the first part of my thesis, I explore the viability and robustness of a relatively simple iterative deconvolution technique that incorporates the estimation of the source wavelet into the waveform inversion procedure rather than adding additional model parameters into the inversion problem. Extensive tests indicate that this source wavelet estimation technique is simple yet effective, and is able to provide remarkably accurate and robust estimates of the source wavelet in the presence of strong heterogeneity in both the dielectric permittivity and electrical conductivity as well as significant ambient noise in the recorded data. Furthermore, our tests also indicate that the approach is insensitive to the phase characteristics of the starting wavelet, which is not the case when directly incorporating the wavelet estimation into the inverse problem.Another critical issue with crosshole georadar waveform inversion schemes which clearly needs to be investigated is the consequence of the common assumption of frequency- independent electromagnetic constitutive parameters. This is crucial since in reality, these parameters are known to be frequency-dependent and complex and thus recorded georadar data may show significant dispersive behaviour. In particular, in the presence of water, there is a wide body of evidence showing that the dielectric permittivity can be significantly frequency dependent over the GPR frequency range, due to a variety of relaxation processes. The second part of my thesis is therefore dedicated to the evaluation of the reconstruction limits of a non-dispersive crosshole georadar waveform inversion scheme in the presence of varying degrees of dielectric dispersion. I show that the inversion algorithm, combined with the iterative deconvolution-based source wavelet estimation procedure that is partially able to account for the frequency-dependent effects through an "effective" wavelet, performs remarkably well in weakly to moderately dispersive environments and has the ability to provide adequate tomographic reconstructions.

Intégration de méthodes de data mining dans le renseignement criminel : analyse par des structures issues de la théorie des graphes dans le profilage des stupéfiants

Data mining can be defined as the extraction of previously unknown and potentially useful information from large datasets. The main principle is to devise computer programs that run through databases and automatically seek deterministic patterns. It is applied in different fields of application, e.g., remote sensing, biometry, speech recognition, but has seldom been applied to forensic case data. The intrinsic difficulty related to the use of such data lies in its heterogeneity, which comes from the many different sources of information. The aim of this study is to highlight potential uses of pattern recognition that would provide relevant results from a criminal intelligence point of view. The role of data mining within a global crime analysis methodology is to detect all types of structures in a dataset. Once filtered and interpreted, those structures can point to previously unseen criminal activities. The interpretation of patterns for intelligence purposes is the final stage of the process. It allows the researcher to validate the whole methodology and to refine each step if necessary. An application to cutting agents found in illicit drug seizures was performed. A combinatorial approach was done, using the presence and the absence of products. Methods coming from the graph theory field were used to extract patterns in data constituted by links between products and place and date of seizure. A data mining process completed using graphing techniques is called ``graph mining''. Patterns were detected that had to be interpreted and compared with preliminary knowledge to establish their relevancy. The illicit drug profiling process is actually an intelligence process that uses preliminary illicit drug classes to classify new samples. Methods proposed in this study could be used \textit{a priori} to compare structures from preliminary and post-detection patterns. This new knowledge of a repeated structure may provide valuable complementary information to profiling and become a source of intelligence.

Efficient total variation algorithm for fetal brain MRI reconstruction.

Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy, Total Variation (TV)- based energies and more recently non-local means. Although TV energies are quite attractive because of their ability in edge preservation, standard explicit steepest gradient techniques have been applied to optimize fetal-based TV energies. The main contribution of this work lies in the introduction of a well-posed TV algorithm from the point of view of convex optimization. Specifically, our proposed TV optimization algorithm or fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n2) and O(1/√ε), while existing techniques are in O(1/n2) and O(1/√ε). We apply our algorithm to (1) clinical newborn data, considered as ground truth, and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy.

The Hydrophobic interaction: modeling hydrophobic interactions and aggregation of non-polar particles in aqueous solutions

Malgré son importance dans notre vie de tous les jours, certaines propriétés de l?eau restent inexpliquées. L'étude des interactions entre l'eau et les particules organiques occupe des groupes de recherche dans le monde entier et est loin d'être finie. Dans mon travail j'ai essayé de comprendre, au niveau moléculaire, ces interactions importantes pour la vie. J'ai utilisé pour cela un modèle simple de l'eau pour décrire des solutions aqueuses de différentes particules. Récemment, l?eau liquide a été décrite comme une structure formée d?un réseau aléatoire de liaisons hydrogènes. En introduisant une particule hydrophobe dans cette structure à basse température, certaines liaisons hydrogènes sont détruites ce qui est énergétiquement défavorable. Les molécules d?eau s?arrangent alors autour de cette particule en formant une cage qui permet de récupérer des liaisons hydrogènes (entre molécules d?eau) encore plus fortes : les particules sont alors solubles dans l?eau. A des températures plus élevées, l?agitation thermique des molécules devient importante et brise les liaisons hydrogènes. Maintenant, la dissolution des particules devient énergétiquement défavorable, et les particules se séparent de l?eau en formant des agrégats qui minimisent leur surface exposée à l?eau. Pourtant, à très haute température, les effets entropiques deviennent tellement forts que les particules se mélangent de nouveau avec les molécules d?eau. En utilisant un modèle basé sur ces changements de structure formée par des liaisons hydrogènes j?ai pu reproduire les phénomènes principaux liés à l?hydrophobicité. J?ai trouvé une région de coexistence de deux phases entre les températures critiques inférieure et supérieure de solubilité, dans laquelle les particules hydrophobes s?agrègent. En dehors de cette région, les particules sont dissoutes dans l?eau. J?ai démontré que l?interaction hydrophobe est décrite par un modèle qui prend uniquement en compte les changements de structure de l?eau liquide en présence d?une particule hydrophobe, plutôt que les interactions directes entre les particules. Encouragée par ces résultats prometteurs, j?ai étudié des solutions aqueuses de particules hydrophobes en présence de co-solvants cosmotropiques et chaotropiques. Ce sont des substances qui stabilisent ou déstabilisent les agrégats de particules hydrophobes. La présence de ces substances peut être incluse dans le modèle en décrivant leur effet sur la structure de l?eau. J?ai pu reproduire la concentration élevée de co-solvants chaotropiques dans le voisinage immédiat de la particule, et l?effet inverse dans le cas de co-solvants cosmotropiques. Ce changement de concentration du co-solvant à proximité de particules hydrophobes est la cause principale de son effet sur la solubilité des particules hydrophobes. J?ai démontré que le modèle adapté prédit correctement les effets implicites des co-solvants sur les interactions de plusieurs corps entre les particules hydrophobes. En outre, j?ai étendu le modèle à la description de particules amphiphiles comme des lipides. J?ai trouvé la formation de différents types de micelles en fonction de la distribution des regions hydrophobes à la surface des particules. L?hydrophobicité reste également un sujet controversé en science des protéines. J?ai défini une nouvelle échelle d?hydrophobicité pour les acides aminés qui forment des protéines, basée sur leurs surfaces exposées à l?eau dans des protéines natives. Cette échelle permet une comparaison meilleure entre les expériences et les résultats théoriques. Ainsi, le modèle développé dans mon travail contribue à mieux comprendre les solutions aqueuses de particules hydrophobes. Je pense que les résultats analytiques et numériques obtenus éclaircissent en partie les processus physiques qui sont à la base de l?interaction hydrophobe.<br/><br/>Despite the importance of water in our daily lives, some of its properties remain unexplained. Indeed, the interactions of water with organic particles are investigated in research groups all over the world, but controversy still surrounds many aspects of their description. In my work I have tried to understand these interactions on a molecular level using both analytical and numerical methods. Recent investigations describe liquid water as random network formed by hydrogen bonds. The insertion of a hydrophobic particle at low temperature breaks some of the hydrogen bonds, which is energetically unfavorable. The water molecules, however, rearrange in a cage-like structure around the solute particle. Even stronger hydrogen bonds are formed between water molecules, and thus the solute particles are soluble. At higher temperatures, this strict ordering is disrupted by thermal movements, and the solution of particles becomes unfavorable. They minimize their exposed surface to water by aggregating. At even higher temperatures, entropy effects become dominant and water and solute particles mix again. Using a model based on these changes in water structure I have reproduced the essential phenomena connected to hydrophobicity. These include an upper and a lower critical solution temperature, which define temperature and density ranges in which aggregation occurs. Outside of this region the solute particles are soluble in water. Because I was able to demonstrate that the simple mixture model contains implicitly many-body interactions between the solute molecules, I feel that the study contributes to an important advance in the qualitative understanding of the hydrophobic effect. I have also studied the aggregation of hydrophobic particles in aqueous solutions in the presence of cosolvents. Here I have demonstrated that the important features of the destabilizing effect of chaotropic cosolvents on hydrophobic aggregates may be described within the same two-state model, with adaptations to focus on the ability of such substances to alter the structure of water. The relevant phenomena include a significant enhancement of the solubility of non-polar solute particles and preferential binding of chaotropic substances to solute molecules. In a similar fashion, I have analyzed the stabilizing effect of kosmotropic cosolvents in these solutions. Including the ability of kosmotropic substances to enhance the structure of liquid water, leads to reduced solubility, larger aggregation regime and the preferential exclusion of the cosolvent from the hydration shell of hydrophobic solute particles. I have further adapted the MLG model to include the solvation of amphiphilic solute particles in water, by allowing different distributions of hydrophobic regions at the molecular surface, I have found aggregation of the amphiphiles, and formation of various types of micelle as a function of the hydrophobicity pattern. I have demonstrated that certain features of micelle formation may be reproduced by the adapted model to describe alterations of water structure near different surface regions of the dissolved amphiphiles. Hydrophobicity remains a controversial quantity also in protein science. Based on the surface exposure of the 20 amino-acids in native proteins I have defined the a new hydrophobicity scale, which may lead to an improvement in the comparison of experimental data with the results from theoretical HP models. Overall, I have shown that the primary features of the hydrophobic interaction in aqueous solutions may be captured within a model which focuses on alterations in water structure around non-polar solute particles. The results obtained within this model may illuminate the processes underlying the hydrophobic interaction.<br/><br/>La vie sur notre planète a commencé dans l'eau et ne pourrait pas exister en son absence : les cellules des animaux et des plantes contiennent jusqu'à 95% d'eau. Malgré son importance dans notre vie de tous les jours, certaines propriétés de l?eau restent inexpliquées. En particulier, l'étude des interactions entre l'eau et les particules organiques occupe des groupes de recherche dans le monde entier et est loin d'être finie. Dans mon travail j'ai essayé de comprendre, au niveau moléculaire, ces interactions importantes pour la vie. J'ai utilisé pour cela un modèle simple de l'eau pour décrire des solutions aqueuses de différentes particules. Bien que l?eau soit généralement un bon solvant, un grand groupe de molécules, appelées molécules hydrophobes (du grecque "hydro"="eau" et "phobia"="peur"), n'est pas facilement soluble dans l'eau. Ces particules hydrophobes essayent d'éviter le contact avec l'eau, et forment donc un agrégat pour minimiser leur surface exposée à l'eau. Cette force entre les particules est appelée interaction hydrophobe, et les mécanismes physiques qui conduisent à ces interactions ne sont pas bien compris à l'heure actuelle. Dans mon étude j'ai décrit l'effet des particules hydrophobes sur l'eau liquide. L'objectif était d'éclaircir le mécanisme de l'interaction hydrophobe qui est fondamentale pour la formation des membranes et le fonctionnement des processus biologiques dans notre corps. Récemment, l'eau liquide a été décrite comme un réseau aléatoire formé par des liaisons hydrogènes. En introduisant une particule hydrophobe dans cette structure, certaines liaisons hydrogènes sont détruites tandis que les molécules d'eau s'arrangent autour de cette particule en formant une cage qui permet de récupérer des liaisons hydrogènes (entre molécules d?eau) encore plus fortes : les particules sont alors solubles dans l'eau. A des températures plus élevées, l?agitation thermique des molécules devient importante et brise la structure de cage autour des particules hydrophobes. Maintenant, la dissolution des particules devient défavorable, et les particules se séparent de l'eau en formant deux phases. A très haute température, les mouvements thermiques dans le système deviennent tellement forts que les particules se mélangent de nouveau avec les molécules d'eau. A l'aide d'un modèle qui décrit le système en termes de restructuration dans l'eau liquide, j'ai réussi à reproduire les phénomènes physiques liés à l?hydrophobicité. J'ai démontré que les interactions hydrophobes entre plusieurs particules peuvent être exprimées dans un modèle qui prend uniquement en compte les liaisons hydrogènes entre les molécules d'eau. Encouragée par ces résultats prometteurs, j'ai inclus dans mon modèle des substances fréquemment utilisées pour stabiliser ou déstabiliser des solutions aqueuses de particules hydrophobes. J'ai réussi à reproduire les effets dûs à la présence de ces substances. De plus, j'ai pu décrire la formation de micelles par des particules amphiphiles comme des lipides dont la surface est partiellement hydrophobe et partiellement hydrophile ("hydro-phile"="aime l'eau"), ainsi que le repliement des protéines dû à l'hydrophobicité, qui garantit le fonctionnement correct des processus biologiques de notre corps. Dans mes études futures je poursuivrai l'étude des solutions aqueuses de différentes particules en utilisant les techniques acquises pendant mon travail de thèse, et en essayant de comprendre les propriétés physiques du liquide le plus important pour notre vie : l'eau.

An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization.

Although fetal anatomy can be adequately viewed in new multi-slice MR images, many critical limitations remain for quantitative data analysis. To this end, several research groups have recently developed advanced image processing methods, often denoted by super-resolution (SR) techniques, to reconstruct from a set of clinical low-resolution (LR) images, a high-resolution (HR) motion-free volume. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has been quite attracted by Total Variation energies because of their ability in edge preserving but only standard explicit steepest gradient techniques have been applied for optimization. In a preliminary work, it has been shown that novel fast convex optimization techniques could be successfully applied to design an efficient Total Variation optimization algorithm for the super-resolution problem. In this work, two major contributions are presented. Firstly, we will briefly review the Bayesian and Variational dual formulations of current state-of-the-art methods dedicated to fetal MRI reconstruction. Secondly, we present an extensive quantitative evaluation of our SR algorithm previously introduced on both simulated fetal and real clinical data (with both normal and pathological subjects). Specifically, we study the robustness of regularization terms in front of residual registration errors and we also present a novel strategy for automatically select the weight of the regularization as regards the data fidelity term. Our results show that our TV implementation is highly robust in front of motion artifacts and that it offers the best trade-off between speed and accuracy for fetal MRI recovery as in comparison with state-of-the art methods.

EEG-based functional brain networks: does the network size matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.