Dense deformation field estimation for atlas-based segmentation of pathological MR brain images.

Atlas registration is a recognized paradigm for the automatic segmentation of normal MR brain images. Unfortunately, atlas-based segmentation has been of limited use in presence of large space-occupying lesions. In fact, brain deformations induced by such lesions are added to normal anatomical variability and they may dramatically shift and deform anatomically or functionally important brain structures. In this work, we chose to focus on the problem of inter-subject registration of MR images with large tumors, inducing a significant shift of surrounding anatomical structures. First, a brief survey of the existing methods that have been proposed to deal with this problem is presented. This introduces the discussion about the requirements and desirable properties that we consider necessary to be fulfilled by a registration method in this context: To have a dense and smooth deformation field and a model of lesion growth, to model different deformability for some structures, to introduce more prior knowledge, and to use voxel-based features with a similarity measure robust to intensity differences. In a second part of this work, we propose a new approach that overcomes some of the main limitations of the existing techniques while complying with most of the desired requirements above. Our algorithm combines the mathematical framework for computing a variational flow proposed by Hermosillo et al. [G. Hermosillo, C. Chefd'Hotel, O. Faugeras, A variational approach to multi-modal image matching, Tech. Rep., INRIA (February 2001).] with the radial lesion growth pattern presented by Bach et al. [M. Bach Cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J.-G. Villemure, J.-Ph. Thiran, Atlas-based segmentation of pathological MR brain images using a model of lesion growth, IEEE Trans. Med. Imag. 23 (10) (2004) 1301-1314.]. Results on patients with a meningioma are visually assessed and compared to those obtained with the most similar method from the state-of-the-art.

Investing in Iowa’s Future, November 2, 2010

The Iowa Transportation Commission (Commission) and Iowa Department of Transportation (Iowa DOT) develop Iowa’s Five-Year Transportation Improvement Program (Five-Year Program) to inform Iowans of planned investments in our state’s multi-modal transportation system. The Five-Year Program is typically updated and approved each year in June. The Five-Year Program encompasses investments in aviation, transit, railroads, trails, and highways. This brochure describes the programming process used by the Commission and Iowa DOT to develop the highway section of the Five-Year Program.

Investing in Iowa’s Highways, June 15, 2011

The Iowa Transportation Commission (Commission) and Iowa Department of Transportation (Iowa DOT) develop Iowa’s Five-Year Transportation Improvement Program (Five-Year Program) to inform Iowans of planned investments in our state’s multi-modal transportation system. The Five-Year Program is typically updated and approved each year in June. The Five-Year Program encompasses investments in aviation, transit, railroads, trails, and highways. This brochure describes the programming process used by the Commission and Iowa DOT to develop the highway section of the Five-Year Program. Each day Iowans are affected by some facet of highway transportation, whether it is to get to work or a medical appointment, receive mail, allow groceries and other goods to be stocked on local shelves, or the many other ways highways keep people, goods and services moving in our state. Iowa’s interstate and primary highways managed by the Iowa DOT are an important part of our personal mobility and state’s economy. They also provide essential connections to Iowa’s secondary roads and city streets. The process of making the critical decisions about what investments will be made to preserve and expand the state-managed highway network is complex. It involves input from a wide range of individuals and organizations, and is based on an expansive programming process.

The connectome viewer toolkit: an open source framework to manage, analyze, and visualize connectomes.

Advanced neuroinformatics tools are required for methods of connectome mapping, analysis, and visualization. The inherent multi-modality of connectome datasets poses new challenges for data organization, integration, and sharing. We have designed and implemented the Connectome Viewer Toolkit - a set of free and extensible open source neuroimaging tools written in Python. The key components of the toolkit are as follows: (1) The Connectome File Format is an XML-based container format to standardize multi-modal data integration and structured metadata annotation. (2) The Connectome File Format Library enables management and sharing of connectome files. (3) The Connectome Viewer is an integrated research and development environment for visualization and analysis of multi-modal connectome data. The Connectome Viewer's plugin architecture supports extensions with network analysis packages and an interactive scripting shell, to enable easy development and community contributions. Integration with tools from the scientific Python community allows the leveraging of numerous existing libraries for powerful connectome data mining, exploration, and comparison. We demonstrate the applicability of the Connectome Viewer Toolkit using Diffusion MRI datasets processed by the Connectome Mapper. The Connectome Viewer Toolkit is available from http://www.cmtk.org/

Generative FDG-PET and MRI model of aging and disease progression in Alzheimer's disease.

The failure of current strategies to provide an explanation for controversial findings on the pattern of pathophysiological changes in Alzheimer's Disease (AD) motivates the necessity to develop new integrative approaches based on multi-modal neuroimaging data that captures various aspects of disease pathology. Previous studies using [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) and structural magnetic resonance imaging (sMRI) report controversial results about time-line, spatial extent and magnitude of glucose hypometabolism and atrophy in AD that depend on clinical and demographic characteristics of the studied populations. Here, we provide and validate at a group level a generative anatomical model of glucose hypo-metabolism and atrophy progression in AD based on FDG-PET and sMRI data of 80 patients and 79 healthy controls to describe expected age and symptom severity related changes in AD relative to a baseline provided by healthy aging. We demonstrate a high level of anatomical accuracy for both modalities yielding strongly age- and symptom-severity- dependant glucose hypometabolism in temporal, parietal and precuneal regions and a more extensive network of atrophy in hippocampal, temporal, parietal, occipital and posterior caudate regions. The model suggests greater and more consistent changes in FDG-PET compared to sMRI at earlier and the inversion of this pattern at more advanced AD stages. Our model describes, integrates and predicts characteristic patterns of AD related pathology, uncontaminated by normal age effects, derived from multi-modal data. It further provides an integrative explanation for findings suggesting a dissociation between early- and late-onset AD. The generative model offers a basis for further development of individualized biomarkers allowing accurate early diagnosis and treatment evaluation.

Systems medicine and integrated care to combat chronic noncommunicable diseases

We propose an innovative, integrated, cost-effective health system to combat major non-communicable diseases (NCDs), including cardiovascular, chronic respiratory, metabolic, rheumatologic and neurologic disorders and cancers, which together are the predominant health problem of the 21st century. This proposed holistic strategy involves comprehensive patient-centered integrated care and multi-scale, multi-modal and multi-level systems approaches to tackle NCDs as a common group of diseases. Rather than studying each disease individually, it will take into account their intertwined gene-environment, socio-economic interactions and co-morbidities that lead to individual-specific complex phenotypes. It will implement a road map for predictive, preventive, personalized and participatory (P4) medicine based on a robust and extensive knowledge management infrastructure that contains individual patient information. It will be supported by strategic partnerships involving all stakeholders, including general practitioners associated with patient-centered care. This systems medicine strategy, which will take a holistic approach to disease, is designed to allow the results to be used globally, taking into account the needs and specificities of local economies and health systems.

Systems Medicine and Integrated Care to Combat Chronic Noncommunicable Diseases.

We propose an innovative, integrated, cost-effective health system to combat major non-communicable diseases (NCDs), including cardiovascular, chronic respiratory, metabolic, rheumatologic and neurologic disorders and cancers, which together are the predominant health problem of the 21st century. This proposed holistic strategy involves comprehensive patient-centered integrated care and multi-scale, multi-modal and multi-level systems approaches to tackle NCDs as a common group of diseases. Rather than studying each disease individually, it will take into account their intertwined gene-environment, socio-economic interactions and co-morbidities that lead to individual-specific complex phenotypes. It will implement a road map for predictive, preventive, personalized and participatory (P4) medicine based on a robust and extensive knowledge management infrastructure that contains individual patient information. It will be supported by strategic partnerships involving all stakeholders, including general practitioners associated with patient-centered care. This systems medicine strategy, which will take a holistic approach to disease, is designed to allow the results to be used globally, taking into account the needs and specificities of local economies and health systems.

Bowing the violin : a case study for auditory-motor pattern modelling in the context of music performance

This project addresses methodological and technological challenges in the development of multi-modal data acquisition and analysis methods for the representation of instrumental playing technique in music performance through auditory-motor patterning models. The case study is violin playing: a multi-modal database of violin performances has been constructed by recording different musicians while playing short exercises on different violins. The exercise set and recording protocol have been designed to sample the space defined by dynamics (from piano to forte) and tone (from sul tasto to sul ponticello), for each bow stroke type being played on each of the four strings (three different pitches per string) at two different tempi. The data, containing audio, video, and motion capture streams, has been processed and segmented to facilitate upcoming analyses. From the acquired motion data, the positions of the instrument string ends and the bow hair ribbon ends are tracked and processed to obtain a number of bowing descriptors suited for a detailed description and analysis of the bow motion patterns taking place during performance. Likewise, a number of sound perceptual attributes are computed from the audio streams. Besides the methodology and the implementation of a number of data acquisition tools, this project introduces preliminary results from analyzing bowing technique on a multi-modal violin performance database that is unique in its class. A further contribution of this project is the data itself, which will be made available to the scientific community through the repovizz platform.

Evaluation of a treatment program for individuals with an intellectual disability who have high risk behaviours

In the past two decades numerous programs have emerged to treat individuals with developmental disabilities who have sexual offending behaviours. There has, however been very few studies that systematically examine the effectiveness of long term treatment with this population. The present research examines the therapeutic outcomes of a multi-modal behaviour approach with six individuals with intellectual disabilities previously charged with sexual assault. The participants also exhibited severe behavioural challenges that included verbal aggression, physical aggression, destruction and self-injury. These six participants (5 males, 1 female) were admitted to a Long Term Residential Treatment Program (LTRTP), due to the severity of their behaviours and due to their lack of treatment success in other programs. Individualized treatment plans focused on the reduction of maladaptive behaviours and the enhancing of skills such as positive coping strategies, socio-sexual knowledge, life skills, recreation and leisure skills. The treatment program also included psychiatric, psychological, medical, behavioural and educational interventions. The participants remained in the Long Term Residential Treatment Program (LTRTP) program from 181 to 932 days (average of 1.5 years). Pre and post treatment evaluations were conducted using the following tools: frequency of target behaviours, Psychopathology Inventory for Mentally Retarded Adults (PIMRA), Emotional Problems Scale (EPS), Socio-Sexual Knowledge and Attitudes Assessment Tool (SSKAAT-R) and Quality of Life Questionnaire (QOL-Q). Recidivism rates and the need for re-hospitalization were also noted for each participant. By offering high levels of individualized interventions, all six participants showed a 37 % rate of reduction in maladaptive behaviours with zero to low rates of inappropriate sexualbehaviour, there were no psychiatric hospitalizations, and there was no recidivism for 5 of 6 participants. In addition, medication was reduced. Mental health scores on the PIMRA were reduced across all participants by 25 % and scores on the Quality of Life Questionnaire increased for all participants by an average of 72 %. These findings add to and build upon the existing literature on long term treatment benefits for individuals with a intellectual disability who sexually offend. By utilizing an individualized and multimodal treatment approach to reduce severe behavioural challenges, not only can the maladaptive behaviours be reduced, but adaptive behaviours can be increased, mental health concerns can be managed, and overall quality of life can be improved.

Using fuction-based CBT with parent involvement to treat OCD in two school-age children with high-functioning autism

Obsessive Compulsive Disorder (OCD) involves excessive worry coupled with engaging in rituals that are believed to help alleviate the worry. Pervasive Developmental Disorders (PODs) are characterized by impairments in social interaction, communication, and the presence of repetitive and/or restrictive behaviours (American Psychiatric Association, 2000). Research suggests that as many as 81% of children with a POD also meet criteria for a diagnosis ofOCD. Currently, only a handful of studies have investigated the use of Cognitive Behavioural Therapy (CBT) in treating OCD in children with autism (Reaven & Hepburn, 2003 ; Sze & Wood, 2007; Lehmkuhl, Storch, Bodtish & Geflken, 2008). In these case studies. the use of a multi-modal CBT treatment package was successful in alleviating OCD behaviours. The current study used function-based CBT with parent involvement and behavioural supplements to treat 2 children with POD and OCD. Using a multiple baseline design across behaviours and participants, parents reported that their child 's anxiety was alleviated and these gains were maintained at 6-month follow-up. According to results of the Children 's Yale-Brown Obsessive Compulsive Scale (Goodman, Price, Rasmussen, Riddle, & Rapoport, 1986) from preto post-test, OCD behaviours of the children decreased II"om the severe to the mild range. In addition, the parents rated the family's level of interference related to their child 's OCD as substantially lower. Last, the CBT treatment received high ratings of consumer satisfaction.

Improving sampling, optimization and feature extraction in Boltzmann machines

L’apprentissage supervisé de réseaux hiérarchiques à grande échelle connaît présentement un succès fulgurant. Malgré cette effervescence, l’apprentissage non-supervisé représente toujours, selon plusieurs chercheurs, un élément clé de l’Intelligence Artificielle, où les agents doivent apprendre à partir d’un nombre potentiellement limité de données. Cette thèse s’inscrit dans cette pensée et aborde divers sujets de recherche liés au problème d’estimation de densité par l’entremise des machines de Boltzmann (BM), modèles graphiques probabilistes au coeur de l’apprentissage profond. Nos contributions touchent les domaines de l’échantillonnage, l’estimation de fonctions de partition, l’optimisation ainsi que l’apprentissage de représentations invariantes. Cette thèse débute par l’exposition d’un nouvel algorithme d'échantillonnage adaptatif, qui ajuste (de fa ̧con automatique) la température des chaînes de Markov sous simulation, afin de maintenir une vitesse de convergence élevée tout au long de l’apprentissage. Lorsqu’utilisé dans le contexte de l’apprentissage par maximum de vraisemblance stochastique (SML), notre algorithme engendre une robustesse accrue face à la sélection du taux d’apprentissage, ainsi qu’une meilleure vitesse de convergence. Nos résultats sont présent ́es dans le domaine des BMs, mais la méthode est générale et applicable à l’apprentissage de tout modèle probabiliste exploitant l’échantillonnage par chaînes de Markov. Tandis que le gradient du maximum de vraisemblance peut-être approximé par échantillonnage, l’évaluation de la log-vraisemblance nécessite un estimé de la fonction de partition. Contrairement aux approches traditionnelles qui considèrent un modèle donné comme une boîte noire, nous proposons plutôt d’exploiter la dynamique de l’apprentissage en estimant les changements successifs de log-partition encourus à chaque mise à jour des paramètres. Le problème d’estimation est reformulé comme un problème d’inférence similaire au filtre de Kalman, mais sur un graphe bi-dimensionnel, où les dimensions correspondent aux axes du temps et au paramètre de température. Sur le thème de l’optimisation, nous présentons également un algorithme permettant d’appliquer, de manière efficace, le gradient naturel à des machines de Boltzmann comportant des milliers d’unités. Jusqu’à présent, son adoption était limitée par son haut coût computationel ainsi que sa demande en mémoire. Notre algorithme, Metric-Free Natural Gradient (MFNG), permet d’éviter le calcul explicite de la matrice d’information de Fisher (et son inverse) en exploitant un solveur linéaire combiné à un produit matrice-vecteur efficace. L’algorithme est prometteur: en terme du nombre d’évaluations de fonctions, MFNG converge plus rapidement que SML. Son implémentation demeure malheureusement inefficace en temps de calcul. Ces travaux explorent également les mécanismes sous-jacents à l’apprentissage de représentations invariantes. À cette fin, nous utilisons la famille de machines de Boltzmann restreintes “spike & slab” (ssRBM), que nous modifions afin de pouvoir modéliser des distributions binaires et parcimonieuses. Les variables latentes binaires de la ssRBM peuvent être rendues invariantes à un sous-espace vectoriel, en associant à chacune d’elles, un vecteur de variables latentes continues (dénommées “slabs”). Ceci se traduit par une invariance accrue au niveau de la représentation et un meilleur taux de classification lorsque peu de données étiquetées sont disponibles. Nous terminons cette thèse sur un sujet ambitieux: l’apprentissage de représentations pouvant séparer les facteurs de variations présents dans le signal d’entrée. Nous proposons une solution à base de ssRBM bilinéaire (avec deux groupes de facteurs latents) et formulons le problème comme l’un de “pooling” dans des sous-espaces vectoriels complémentaires.

Efectividad del tratamiento farmacológico de la agresividad en pacientes con trastorno límite de la personalidad. Revisión sistemática

Introducción: el Trastorno Límite de la Personalidad afecta del 2% al 6% de los adultos en Estados Unidos. Es una condición de alta relevancia dentro de las patologías psiquiátricas debido a características como impulsividad, inestabilidad en las relaciones interpersonales, disregulación en el estado de ánimo y comportamiento agresivo. Esto determina un impacto negativo en la funcionalidad del individuo siendo la agresividad contra sí mismo o contra otras personas uno de sus componentes claves. Métodos: Revisión sistemática de la literatura de artículos de bases de datos y búsqueda manual de revistas relacionadas que aportaran la mejor evidencia con el fin de encontrar estudios que evaluaran, con instrumentos objetivos, los tratamientos farmacológicos disponibles para el manejo de la agresividad en el TLP .Se evaluó calidad metodológica y los estudios se organizaron en tablas de evidencia. Resultados: La búsqueda arrojo 1081 artículos de los cuales se seleccionaron 52 como potenciales y cinco fueron incluidos en esta revisión. Se clasificaron como nivel de evidencia Ib. El topiramato, el aripiprazol, el divalproato y la fluoxetina mostraron mejores resultados que el placebo especialmente en agresividad e impulsividad. El topiramato fue asociado con pérdida de peso. Los medicamentos fueron seguros y bien tolerados. Discusión: Los medicamentos evaluados mostraron ser mejores que placebo. La diversidad en las escalas utilizadas genera complejidad en la interpretación de resultados. Conclusión: La evidencia sugiere que el tratamiento farmacológico es efectivo en síntomas como agresividad e impulsividad comparado con placebo. Deben considerarse estudios que evalúen combinaciones de fármacos y psicoterapia.

Evaluación de la validez de la escala llanto para el dolor en neonatos y menores de cinco años

Introducción: La escala LLANTO para dolor es una escala que hasta la fecha ha sido solo validada en población infantil española, actualmente no se conocen datos en población colombiana. Se pretende validar la escala de dolor LLANTO en pacientes neonatos y menores de 5 años, a través de su aplicación en pacientes atendidos en una de tres instituciones, además comparándola con las escalas FLACC y PIPP dependiendo de edad del paciente. Metodología: Se incluyeron niños con cualquier tipo de dolor, clasificándolos en dos grupos por edad: 1) neonatos y 2) niños entre 1 mes y 5 años de edad, que asistieron a la Fundación Cardioinfantil, Clínica Infantil Colsubsidio o al Hospital Universitario Mayor. Las escalas fueron aplicadas por dos residentes de pediatría y una enfermera especializada en el cuidado de población infantil. Para la prueba piloto se diseñó un cuestionario determinar dificultades en la aplicación de la escala LLANTO. Una vez corregidos los problemas identificados se procederá a la validación de la escala. Resultados: Se presentan los datos de la prueba piloto. Se incluyeron 8 neonatos y 8 niños entre 1 mes y 5 años, esta muestra fue obtenida en un periodo de un mes, con la encuesta se evaluó la aceptación y entendimiento de la escala LLANTO por parte de los evaluadores. La prueba piloto mostró resultados favorables en el 100% de los encuestados. Discusión: Se considera que la escala LLANTO no requiere cambios para continuar con su validación.

Experimental method for teaching specific language structures to hearing-impaired children

This paper presents an experimental Receptive-Expressive Multi-Modal Learning Program of Patterned Language Practice for hearing-impaired children.

In-street wind direction variability in the vicinity of a busy intersection in central London

We present results from fast-response wind measurements within and above a busy intersection between two street canyons (Marylebone Road and Gloucester Place) in Westminster, London taken as part of the DAPPLE (Dispersion of Air Pollution and Penetration into the Local Environment; www.dapple.org.uk) 2007 field campaign. The data reported here were collected using ultrasonic anemometers on the roof-top of a building adjacent to the intersection and at two heights on a pair of lamp-posts on opposite sides of the intersection. Site characteristics, data analysis and the variation of intersection flow with the above-roof wind direction (θref) are discussed. Evidence of both flow channelling and recirculation was identified within the canyon, only a few metres from the intersection for along-street and across-street roof-top winds respectively. Results also indicate that for oblique rooftop flows, the intersection flow is a complex combination of bifurcated channelled flows, recirculation and corner vortices. Asymmetries in local building geometry around the intersection and small changes in the background wind direction (changes in 15-min mean θref of 5–10 degrees) were also observed to have profound influences on the behaviour of intersection flow patterns. Consequently, short time-scale variability in the background flow direction can lead to highly scattered in-street mean flow angles masking the true multi-modal features of the flow and thus further complicating modelling challenges.