Identifying trajectories of adolescent smoking: An application of latent growth mixture modeling

The goal of the current study was to identify discrete longitudinal patterns of change in adolescent smoking using latent growth mixture modeling. Five distinct longitudinal patterns were identified. A group of early rapid escalators was characterized by early escalation (at age 13) that rapidly increased to heavy smoking. A pattern characterized by occasional puffing up until age 15, at which time smoking escalated to moderate levels was also identified (late moderate escalators). Another group included adolescents who, after age 15, began to escalate slowly in their smoking to light (0.5 cigarettes per month) levels (late slow escalators). Finally, a group of stable light smokers (those who smoked 1-2 cigarettes per month) and a group of stable puffers (those. who smoked only a few puffs per month) were also identified. The stable puffer group was the largest group and represented 25% of smokers.

Computational modeling of prefrontal cortex circuits

Dissertation presented to obtain the Ph.D degree in Biology

The distribution of Biomphalaria spp. in different habitats in relation to physical, biological, water contact and cognitive factors in a rural area in Minas Gerais, Brazil

A total of 256 sites in 11 habitats were surveyed for Biomphalaria in Melquiades rural area (State of Minas Gerais) in August and November 1999 and in March 2000. Of the 1,780 Biomphalaria collected, 1,721 (96.7%) were B. glabrata and 59 (3.3%) B. straminea. Snails were found in all habitats except in wells, with the largest mean numbers in tanks, seepage ponds and canals, and the smallest numbers in springs, rice fields and fishponds. People's knowledge of the occurrence of Biomphalaria at the collection sites and the presence of Biomphalaria ova were strongly correlated with the occurrence of snails, and distance between houses and collection sites, as well as water velocity were inversely correlated with Biomphalaria occurrence (p < 0.001). The strongest predictor o f Biomphalaria occurrence was the presence of tilapia fish in fishponds. Fourteen Biomphalaria (0.8% of all snails) found at 6 sites were infected with Schistosoma mansoni. Suggestions are made for the utilization of local people's knowledge in snail surveys and further studies are recommended on the possible use of tilapia for biological control of Biomphalaria in fishponds, as well as modeling of S. mansoni transmission and reinfection.

Training effects with the observer-rated cognitive errors and the coping action patterns scales

The use of observer-rated scales requires that raters be trained until they have become reliable in using the scales. However, few studies properly report how training in using a given rating scale is conducted or indeed how it should be conducted. This study examined progress in interrater reliability over 6 months of training with two observer-rated scales, the Cognitive Errors Rating Scale and the Coping Action Patterns Rating Scale. The evolution of the intraclass correlation coefficients was modeled using hierarchical linear modeling. Results showed an overall training effect as well as effects of the basic training phase and of the rater calibration phase, the latter being smaller than the former. The results are discussed in terms of implications for rater training in psychotherapy research.

Further insights on the French WISC-IV factor structure through Bayesian structural equation modeling

The interpretation of the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) is based on a 4-factor model, which is only partially compatible with the mainstream Cattell-Horn-Carroll (CHC) model of intelligence measurement. The structure of cognitive batteries is frequently analyzed via exploratory factor analysis and/or confirmatory factor analysis. With classical confirmatory factor analysis, almost all crossloadings between latent variables and measures are fixed to zero in order to allow the model to be identified. However, inappropriate zero cross-loadings can contribute to poor model fit, distorted factors, and biased factor correlations; most important, they do not necessarily faithfully reflect theory. To deal with these methodological and theoretical limitations, we used a new statistical approach, Bayesian structural equation modeling (BSEM), among a sample of 249 French-speaking Swiss children (8-12 years). With BSEM, zero-fixed cross-loadings between latent variables and measures are replaced by approximate zeros, based on informative, small-variance priors. Results indicated that a direct hierarchical CHC-based model with 5 factors plus a general intelligence factor better represented the structure of the WISC-IV than did the 4-factor structure and the higher order models. Because a direct hierarchical CHC model was more adequate, it was concluded that the general factor should be considered as a breadth rather than a superordinate factor. Because it was possible for us to estimate the influence of each of the latent variables on the 15 subtest scores, BSEM allowed improvement of the understanding of the structure of intelligence tests and the clinical interpretation of the subtest scores.

Modeling the impact of lesions in the human brain.

Lesions of anatomical brain networks result in functional disturbances of brain systems and behavior which depend sensitively, often unpredictably, on the lesion site. The availability of whole-brain maps of structural connections within the human cerebrum and our increased understanding of the physiology and large-scale dynamics of cortical networks allow us to investigate the functional consequences of focal brain lesions in a computational model. We simulate the dynamic effects of lesions placed in different regions of the cerebral cortex by recording changes in the pattern of endogenous ("resting-state") neural activity. We find that lesions produce specific patterns of altered functional connectivity among distant regions of cortex, often affecting both cortical hemispheres. The magnitude of these dynamic effects depends on the lesion location and is partly predicted by structural network properties of the lesion site. In the model, lesions along the cortical midline and in the vicinity of the temporo-parietal junction result in large and widely distributed changes in functional connectivity, while lesions of primary sensory or motor regions remain more localized. The model suggests that dynamic lesion effects can be predicted on the basis of specific network measures of structural brain networks and that these effects may be related to known behavioral and cognitive consequences of brain lesions.

MORM--a Petri net based model for assessing OH&S risks in industrial processes: modeling qualitative aspects.

Because of the increase in workplace automation and the diversification of industrial processes, workplaces have become more and more complex. The classical approaches used to address workplace hazard concerns, such as checklists or sequence models, are, therefore, of limited use in such complex systems. Moreover, because of the multifaceted nature of workplaces, the use of single-oriented methods, such as AEA (man oriented), FMEA (system oriented), or HAZOP (process oriented), is not satisfactory. The use of a dynamic modeling approach in order to allow multiple-oriented analyses may constitute an alternative to overcome this limitation. The qualitative modeling aspects of the MORM (man-machine occupational risk modeling) model are discussed in this article. The model, realized on an object-oriented Petri net tool (CO-OPN), has been developed to simulate and analyze industrial processes in an OH&S perspective. The industrial process is modeled as a set of interconnected subnets (state spaces), which describe its constitutive machines. Process-related factors are introduced, in an explicit way, through machine interconnections and flow properties. While man-machine interactions are modeled as triggering events for the state spaces of the machines, the CREAM cognitive behavior model is used in order to establish the relevant triggering events. In the CO-OPN formalism, the model is expressed as a set of interconnected CO-OPN objects defined over data types expressing the measure attached to the flow of entities transiting through the machines. Constraints on the measures assigned to these entities are used to determine the state changes in each machine. Interconnecting machines implies the composition of such flow and consequently the interconnection of the measure constraints. This is reflected by the construction of constraint enrichment hierarchies, which can be used for simulation and analysis optimization in a clear mathematical framework. The use of Petri nets to perform multiple-oriented analysis opens perspectives in the field of industrial risk management. It may significantly reduce the duration of the assessment process. But, most of all, it opens perspectives in the field of risk comparisons and integrated risk management. Moreover, because of the generic nature of the model and tool used, the same concepts and patterns may be used to model a wide range of systems and application fields.

Strategy selection: An introduction to the modeling challenge

Modeling the mechanisms that determine how humans and other agents choose among different behavioral and cognitive processes-be they strategies, routines, actions, or operators-represents a paramount theoretical stumbling block across disciplines, ranging from the cognitive and decision sciences to economics, biology, and machine learning. By using the cognitive and decision sciences as a case study, we provide an introduction to what is also known as the strategy selection problem. First, we explain why many researchers assume humans and other animals to come equipped with a repertoire of behavioral and cognitive processes. Second, we expose three descriptive, predictive, and prescriptive challenges that are common to all disciplines which aim to model the choice among these processes. Third, we give an overview of different approaches to strategy selection. These include cost‐benefit, ecological, learning, memory, unified, connectionist, sequential sampling, and maximization approaches. We conclude by pointing to opportunities for future research and by stressing that the selection problem is far from being resolved.

Computational modeling of resting-state activity demonstrates markers of normalcy in children with prenatal or perinatal stroke.

Children who sustain a prenatal or perinatal brain injury in the form of a stroke develop remarkably normal cognitive functions in certain areas, with a particular strength in language skills. A dominant explanation for this is that brain regions from the contralesional hemisphere "take over" their functions, whereas the damaged areas and other ipsilesional regions play much less of a role. However, it is difficult to tease apart whether changes in neural activity after early brain injury are due to damage caused by the lesion or by processes related to postinjury reorganization. We sought to differentiate between these two causes by investigating the functional connectivity (FC) of brain areas during the resting state in human children with early brain injury using a computational model. We simulated a large-scale network consisting of realistic models of local brain areas coupled through anatomical connectivity information of healthy and injured participants. We then compared the resulting simulated FC values of healthy and injured participants with the empirical ones. We found that the empirical connectivity values, especially of the damaged areas, correlated better with simulated values of a healthy brain than those of an injured brain. This result indicates that the structural damage caused by an early brain injury is unlikely to have an adverse and sustained impact on the functional connections, albeit during the resting state, of damaged areas. Therefore, these areas could continue to play a role in the development of near-normal function in certain domains such as language in these children.

Intra- and inter-hemispheric interactions in somatosensory processing of pain : dynamical causal modeling analysis of fMRI data

La douleur est une expérience perceptive comportant de nombreuses dimensions. Ces dimensions de douleur sont inter-reliées et recrutent des réseaux neuronaux qui traitent les informations correspondantes. L’élucidation de l'architecture fonctionnelle qui supporte les différents aspects perceptifs de l'expérience est donc une étape fondamentale pour notre compréhension du rôle fonctionnel des différentes régions de la matrice cérébrale de la douleur dans les circuits corticaux qui sous tendent l'expérience subjective de la douleur. Parmi les diverses régions du cerveau impliquées dans le traitement de l'information nociceptive, le cortex somatosensoriel primaire et secondaire (S1 et S2) sont les principales régions généralement associées au traitement de l'aspect sensori-discriminatif de la douleur. Toutefois, l'organisation fonctionnelle dans ces régions somato-sensorielles n’est pas complètement claire et relativement peu d'études ont examiné directement l'intégration de l'information entre les régions somatiques sensorielles. Ainsi, plusieurs questions demeurent concernant la relation hiérarchique entre S1 et S2, ainsi que le rôle fonctionnel des connexions inter-hémisphériques des régions somatiques sensorielles homologues. De même, le traitement en série ou en parallèle au sein du système somatosensoriel constitue un autre élément de questionnement qui nécessite un examen plus approfondi. Le but de la présente étude était de tester un certain nombre d'hypothèses sur la causalité dans les interactions fonctionnelle entre S1 et S2, alors que les sujets recevaient des chocs électriques douloureux. Nous avons mis en place une méthode de modélisation de la connectivité, qui utilise une description de causalité de la dynamique du système, afin d'étudier les interactions entre les sites d'activation définie par un ensemble de données provenant d'une étude d'imagerie fonctionnelle. Notre paradigme est constitué de 3 session expérimentales en utilisant des chocs électriques à trois différents niveaux d’intensité, soit modérément douloureux (niveau 3), soit légèrement douloureux (niveau 2), soit complètement non douloureux (niveau 1). Par conséquent, notre paradigme nous a permis d'étudier comment l'intensité du stimulus est codé dans notre réseau d'intérêt, et comment la connectivité des différentes régions est modulée dans les conditions de stimulation différentes. Nos résultats sont en faveur du mode sériel de traitement de l’information somatosensorielle nociceptive avec un apport prédominant de la voie thalamocorticale vers S1 controlatérale au site de stimulation. Nos résultats impliquent que l'information se propage de S1 controlatéral à travers notre réseau d'intérêt composé des cortex S1 bilatéraux et S2. Notre analyse indique que la connexion S1→S2 est renforcée par la douleur, ce qui suggère que S2 est plus élevé dans la hiérarchie du traitement de la douleur que S1, conformément aux conclusions précédentes neurophysiologiques et de magnétoencéphalographie. Enfin, notre analyse fournit des preuves de l'entrée de l'information somatosensorielle dans l'hémisphère controlatéral au côté de stimulation, avec des connexions inter-hémisphériques responsable du transfert de l'information à l'hémisphère ipsilatéral.

A prospective two-year examination of cognitive and behavioral correlates of provoked vestibulodynia outcomes.

Note de l'éditeur : This article may not exactly replicate the final version published in the journal. It is not the copy of record. / Cet article ne constitue pas la version officielle, et peut différer de la version publiée dans la revue.

Molecule modeling of human pentameric alpha(7) neuronal nicotinic acetylcholine receptor and its interaction with its agonist and competitive antagonist

The nicotinic Acetylcholine Receptor (nAChR) is the major class of neurotransmitter receptors that is involved in many neurodegenerative conditions such as schizophrenia, Alzheimer's and Parkinson's diseases. The N-terminal region or Ligand Binding Domain (LBD) of nAChR is located at pre- and post-synaptic nervous system, which mediates synaptic transmission. nAChR acts as the drug target for agonist and competitive antagonist molecules that modulate signal transmission at the nerve terminals. Based on Acetylcholine Binding Protein (AChBP) from Lymnea stagnalis as the structural template, the homology modeling approach was carried out to build three dimensional model of the N-terminal region of human alpha(7)nAChR. This theoretical model is an assembly of five alpha(7) subunits with 5 fold axis symmetry, constituting a channel, with the binding picket present at the interface region of the subunits. alpha-netlrotoxin is a potent nAChR competitive antagonist that readily blocks the channel resulting in paralysis. The molecular interaction of alpha-Bungarotoxin, a long chain alpha-neurotoxin from (Bungarus multicinctus) and human alpha(7)nAChR seas studied. Agonists such as acetylcholine, nicotine, which are used in it diverse array of biological activities, such as enhancements of cognitive performances, were also docked with the theoretical model of human alpha(7)nAChR. These docked complexes were analyzed further for identifying the crucial residues involved in interaction. These results provide the details of interaction of agonists and competitive antagonists with three dimensional model of the N-terminal region of human alpha(7)nAChR and thereby point to the design of novel lead compounds.

Skilloscopy: Bayesian modeling of decision makers' skill

This paper proposes and demonstrates an approach, Skilloscopy, to the assessment of decision makers. In an increasingly sophisticated, connected and information-rich world, decision making is becoming both more important and more difficult. At the same time, modelling decision-making on computers is becoming more feasible and of interest, partly because the information-input to those decisions is increasingly on record. The aims of Skilloscopy are to rate and rank decision makers in a domain relative to each other: the aims do not include an analysis of why a decision is wrong or suboptimal, nor the modelling of the underlying cognitive process of making the decisions. In the proposed method a decision-maker is characterised by a probability distribution of their competence in choosing among quantifiable alternatives. This probability distribution is derived by classic Bayesian inference from a combination of prior belief and the evidence of the decisions. Thus, decision-makers’ skills may be better compared, rated and ranked. The proposed method is applied and evaluated in the gamedomain of Chess. A large set of games by players across a broad range of the World Chess Federation (FIDE) Elo ratings has been used to infer the distribution of players’ rating directly from the moves they play rather than from game outcomes. Demonstration applications address questions frequently asked by the Chess community regarding the stability of the Elo rating scale, the comparison of players of different eras and/or leagues, and controversial incidents possibly involving fraud. The method of Skilloscopy may be applied in any decision domain where the value of the decision-options can be quantified.