Multimodal Mri-based Study In Patients With Spg4 Mutations.

Mutations in the SPG4 gene (SPG4-HSP) are the most frequent cause of hereditary spastic paraplegia, but the extent of the neurodegeneration related to the disease is not yet known. Therefore, our objective is to identify regions of the central nervous system damaged in patients with SPG4-HSP using a multi-modal neuroimaging approach. In addition, we aimed to identify possible clinical correlates of such damage. Eleven patients (mean age 46.0 ± 15.0 years, 8 men) with molecular confirmation of hereditary spastic paraplegia, and 23 matched healthy controls (mean age 51.4 ± 14.1years, 17 men) underwent MRI scans in a 3T scanner. We used 3D T1 images to perform volumetric measurements of the brain and spinal cord. We then performed tract-based spatial statistics and tractography analyses of diffusion tensor images to assess microstructural integrity of white matter tracts. Disease severity was quantified with the Spastic Paraplegia Rating Scale. Correlations were then carried out between MRI metrics and clinical data. Volumetric analyses did not identify macroscopic abnormalities in the brain of hereditary spastic paraplegia patients. In contrast, we found extensive fractional anisotropy reduction in the corticospinal tracts, cingulate gyri and splenium of the corpus callosum. Spinal cord morphometry identified atrophy without flattening in the group of patients with hereditary spastic paraplegia. Fractional anisotropy of the corpus callosum and pyramidal tracts did correlate with disease severity. Hereditary spastic paraplegia is characterized by relative sparing of the cortical mantle and remarkable damage to the distal portions of the corticospinal tracts, extending into the spinal cord.

Multimodal passive vibration control of sandwich beams with shunted shear piezoelectric materials

This work presents a performance analysis of multimodal passive vibration control of a sandwich beam using shear piezoelectric materials, embedded in a sandwich beam core, connected to independent resistive shunt circuits. Shear piezoelectric actuators were recently shown to be more interesting for higher frequencies and stiffer structures. In particular, for shunted damping, it was shown that equivalent material loss factors of up to 31% can be achieved by optimizing the shunt circuit. In the present work, special attention is given to the design of multimodal vibration control through independent shunted shear piezoelectric sensors. In particular, a parametric analysis is performed to evaluate optimal configurations for a set of modes to be damped. Then, a methodology to evaluate the modal damping resulting from each shunted piezoelectric sensor is presented using the modal strain energy method. Results show that modal damping factors of 1%-2% can be obtained for three selected vibration modes.

Interface Multimodal com Predição de Movimentos para Uso em Reabilitação de Membros Inferiores

Esta dissertação apresenta o desenvolvimento de uma plataforma multimodal de aquisição e processamento de sinais. O projeto proposto insere-se no contexto do desenvolvimento de interfaces multimodais para aplicação em dispositivos robóticos cujo propósito é a reabilitação motora adaptando o controle destes dispositivos de acordo com a intenção do usuário. A interface desenvolvida adquire, sincroniza e processa sinais eletroencefalográficos (EEG), eletromiográficos (EMG) e sinais provenientes de sensores inerciais (IMUs). A aquisição dos dados é feita em experimentos realizados com sujeitos saudáveis que executam tarefas motoras de membros inferiores. O objetivo é analisar a intenção de movimento, a ativação muscular e o início efetivo dos movimentos realizados, respectivamente, através dos sinais de EEG, EMG e IMUs. Para este fim, uma análise offline foi realizada. Nessa análise, são utilizadas técnicas de processamento dos sinais biológicos e técnicas para processar sinais provenientes de sensores inerciais. A partir destes, os ângulos da articulação do joelho também são aferidos ao longo dos movimentos. Um protocolo experimental de testes foi proposto para as tarefas realizadas. Os resultados demonstraram que o sistema proposto foi capaz de adquirir, sincronizar, processar e classificar os sinais combinadamente. Análises acerca da acurácia dos classificadores utilizados mostraram que a interface foi capaz de identificar intenção de movimento em 76, 0 ± 18, 2% dos movimentos. A maior média de tempo de antecipação ao movimento foi obtida através da análise do sinal de EEG e foi de 716, 0±546, 1 milisegundos. A partir da análise apenas do sinal de EMG, este valor foi de 88, 34 ± 67, 28 milisegundos. Os resultados das etapas de processamento dos sinais biológicos, a medição dos ângulos da articulação, bem como os valores de acurácia e tempo de antecipação ao movimento se mostraram em conformidade com a literatura atual relacionada.

Robust temporal alignment of multimodal cardiac sequences

Given the dynamic nature of cardiac function, correct temporal alignment of pre-operative models and intraoperative images is crucial for augmented reality in cardiac image-guided interventions. As such, the current study focuses on the development of an image-based strategy for temporal alignment of multimodal cardiac imaging sequences, such as cine Magnetic Resonance Imaging (MRI) or 3D Ultrasound (US). First, we derive a robust, modality-independent signal from the image sequences, estimated by computing the normalized crosscorrelation between each frame in the temporal sequence and the end-diastolic frame. This signal is a resembler for the left-ventricle (LV) volume curve over time, whose variation indicates di erent temporal landmarks of the cardiac cycle. We then perform the temporal alignment of these surrogate signals derived from MRI and US sequences of the same patient through Dynamic Time Warping (DTW), allowing to synchronize both sequences. The proposed framework was evaluated in 98 patients, which have undergone both 3D+t MRI and US scans. The end-systolic frame could be accurately estimated as the minimum of the image-derived surrogate signal, presenting a relative error of 1:6 1:9% and 4:0 4:2% for the MRI and US sequences, respectively, thus supporting its association with key temporal instants of the cardiac cycle. The use of DTW reduces the desynchronization of the cardiac events in MRI and US sequences, allowing to temporally align multimodal cardiac imaging sequences. Overall, a generic, fast and accurate method for temporal synchronization of MRI and US sequences of the same patient was introduced. This approach could be straightforwardly used for the correct temporal alignment of pre-operative MRI information and intra-operative US images.

Theoretical Analysis of Multimodal Four-Wave Mixing in Optical Microwires

Optical fiber microwires (OFMs) are nonlinear optical waveguides that support several spatial modes. The multimodal generalized nonlinear Schrodinger equation (MM-GNLSE) is deduced taking into account the linear and nonlinear modal coupling. A detailed theoretical description of four-wave mixing (FWM) considering the modal coupling is developed. Both, the intramode and the intermode phase-matching conditions is calculated for an optical microwire in a strong guiding regime. Finally, the FWM dynamics is studied and the amplitude evolution of the pump beams, the signal and the idler are analyzed.

Multimodal species abundance distributions: a deconstruction approach reveals the processes behind the pattern

Copyright © 2014 The Authors. Oikos © 2014 Nordic Society Oikos.

Classification and staging of chronic liver disease from multimodal data

Chronic liver disease (CLD) is most of the time an asymptomatic, progressive, and ultimately potentially fatal disease. In this study, an automatic hierarchical procedure to stage CLD using ultrasound images, laboratory tests, and clinical records are described. The first stage of the proposed method, called clinical based classifier (CBC), discriminates healthy from pathologic conditions. When nonhealthy conditions are detected, the method refines the results in three exclusive pathologies in a hierarchical basis: 1) chronic hepatitis; 2) compensated cirrhosis; and 3) decompensated cirrhosis. The features used as well as the classifiers (Bayes, Parzen, support vector machine, and k-nearest neighbor) are optimally selected for each stage. A large multimodal feature database was specifically built for this study containing 30 chronic hepatitis cases, 34 compensated cirrhosis cases, and 36 decompensated cirrhosis cases, all validated after histopathologic analysis by liver biopsy. The CBC classification scheme outperformed the nonhierachical one against all scheme, achieving an overall accuracy of 98.67% for the normal detector, 87.45% for the chronic hepatitis detector, and 95.71% for the cirrhosis detector.

Constructing and Using Multimodal Narratives to Research in Science Education: Contributions Based on Practical Classroom

This study deals with the problem of how to collect genuine and useful data about science classroom practices, and preserving the complex and holistic nature of teaching and learning. Additionally, we were looking for an instrument that would allow comparability and verifiability for teaching and research purposes. Given the multimodality of teaching and learning processes, we developed the multimodal narrative (MN), which describes what happens during a task and incorporates data such as examples of students’ work.

Enhancing Multimodal Silent Speech Interfaces With Feature Selection

In research on Silent Speech Interfaces (SSI), different sources of information (modalities) have been combined, aiming at obtaining better performance than the individual modalities. However, when combining these modalities, the dimensionality of the feature space rapidly increases, yielding the well-known "curse of dimensionality". As a consequence, in order to extract useful information from this data, one has to resort to feature selection (FS) techniques to lower the dimensionality of the learning space. In this paper, we assess the impact of FS techniques for silent speech data, in a dataset with 4 non-invasive and promising modalities, namely: video, depth, ultrasonic Doppler sensing, and surface electromyography. We consider two supervised (mutual information and Fisher's ratio) and two unsupervised (meanmedian and arithmetic mean geometric mean) FS filters. The evaluation was made by assessing the classification accuracy (word recognition error) of three well-known classifiers (knearest neighbors, support vector machines, and dynamic time warping). The key results of this study show that both unsupervised and supervised FS techniques improve on the classification accuracy on both individual and combined modalities. For instance, on the video component, we attain relative performance gains of 36.2% in error rates. FS is also useful as pre-processing for feature fusion. Copyright © 2014 ISCA.

Sistema multimodal para captura e anotação de vídeo

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Personalization platform for multimodal ubiquitous computing applications

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Fatores de prognóstico para os resultados de sucesso da fisioterapia multimodal em utentes com dor cervical crónica

RESUMO: Introdução: O conhecimento acerca da influência das características dos indivíduos com dor cervical crónica (DCC) no prognóstico dos resultados alcançados com a Fisioterapia é ainda inconsistente, sendo escassos os estudos desenvolvidos neste âmbito. Objetivo: Este relatório pretende determinar se um modelo baseado em fatores de prognóstico é capaz de prever os resultados de sucesso da Fisioterapia, a curto prazo, em utentes com DCC, ao nível da incapacidade funcional, intensidade da dor e perceção global de melhoria. Metodologia: Trata-se de estudo de coorte prospetivo com 112 participantes. Os utentes foram avaliados na primeira semana de tratamento e sete semanas após o início da intervenção. Os instrumentos utilizados foram o Neck Disability Index–Versão Portuguesa (NDI-PT) e a Escala Numérica da Dor (END) nos dois momentos de avaliação, um Questionário de Caracterização Sociodemográfica e Clínica da Amostra na baseline e a Patient Global Impression Change Scale–Versão Portuguesa (PGIC-PT) no follow-up. As características sociodemográficas e clínicas foram incluídas como potenciais fatores de prognóstico e estes foram definidos com base nas diferenças mínimas clinicamente importantes (DMCI) dos instrumentos NDIPT (DMCI≥6) e END (DMCI≥2) e no critério de pontuação ≥5 na PGIC-PT. A análise dos dados foi realizada através do método de regressão logística (backward conditional procedure) para identificar as associações entre os indicadores e as variáveis de resultado (p<0.05). Resultados: Dos 112 participantes incluídos no estudo, 108 completaram o follow-up (média de idade: 51.76±10.19). No modelo multivariado relativo à incapacidade funcional, os resultados de sucesso encontram-se associados a elevados níveis de incapacidade na baseline (OR=1.123; 95% IC 1.056–1.194) e a duração da dor inferior a 12 meses (OR=2.704; 95% IC 1.138–6.424). Este modelo explica 30.0% da variância da melhoria da funcionalidade e classifica corretamente 74.1% dos utentes (sensibilidade: 75.9%; especificidade: 72.0%). O modelo relativo à intensidade da dor identificou apenas a associação do outcome com níveis elevados de intensidade da dor na baseline (OR=1.321; 95% IC 1.047–1.668), explicando 7.5% da variância da redução da mesma e classificando corretamente 68.2% dos utentes (sensibilidade: 94.4%; especificidade: 16.7%). O modelo final referente à perceção global de melhoria apresentou uma associação com a intensidade da dor na baseline (OR=0.621; 95% IC 0.465–0.829), com a presença de cefaleias e/ou tonturas (OR=2.538; 95% IC 0.987–6.526) e com a duração da dor superior a 12 meses (OR=0.279; 95% IC 0.109–0.719). Este modelo explica 27.5% da variância dos resultados de sucesso para este outcome e classifica corretamente 73.1% dos utentes (sensibilidade: 81.8%; especificidade: 59.5%). Conclusões: Utentes com DCC com elevada incapacidade na baseline e queixas de dor há menos de 12 meses apresentam maior probabilidade de obter melhorias ao nível da incapacidade funcional. Elevados níveis de intensidade da dor na baseline predizem resultados de sucesso na redução da dor após sete semanas de tratamento. Utentes com DCC com baixos níveis de dor na baseline, com cefaleias e/ou tonturas e com queixas de dor há mais de 12 meses apresentam maior probabilidade de obter uma melhor perceção de melhoria.--------------- ABSTRACT:Introduction: The influence of the characteristics of individuals with chronic neck pain (CNP) on the prognosis of physiotherapy outcomes is still inconsistent, there being few studies developed in this context. Aim: This study seeks to determine whether a model based on prognostic factors can predict the short-term physiotherapy successful outcomes in CNP patients, regarding functional disability, pain intensity and perceived recovery. Methodology: This is a prospective cohort study with 112 participants. Patients were assessed during the first week of treatment and seven weeks after the start of the intervention. The instruments used were the Neck Disability Index–Portuguese Version (NDI-PT) and the Numerical Rating Scale (NRS) at both moments of assessment, a Sample Sociodemographic and Clinical Characterization Questionnaire at baseline and Patient Global Impression Change Scale–Portuguese Version (PGIC-PT) at the follow-up. The sociodemographic and clinical characteristics were included as potential predictors of successful outcomes, and these were defined on the basis of minimal clinically important differences (MCID) of NDI-PT (MCID≥6) and END (MCID≥2) and the criteria score ≥5 on the PGIC-PT. Data analysis was performed using logistic regression (backward conditional procedure) to identify associations between predictors and outcomes (p<0.05). Results: Of the 112 participants included in the study, 108 completed the follow-up (mean age: 51.76±10.19). In the multivariate model of functional disability, the successful outcomes are associated with high levels of disability at baseline (OR = 1.123; 95% CI 1.056-1.194), and pain duration shorter than 12 months (OR=2.704; 95% CI 1.138–6.424). This model explains 30.0% of the variance of improved functional capacity and correctly classifies 74.1% of the patients (sensitivity: 75.9%, specificity: 72.0%). The model for pain intensity solely identified an outcome association with high pain intensity at baseline (OR=1.321; 95% CI 1.047-1.668), explaining 7.5% of the variance of pain reduction and correctly classifying 68.2% of the patients (sensitivity: 94.4%, specificity: 16.7%). The final model of perceived recovery showed an association with pain intensity at baseline (OR=0.621; 95% CI 0465-0829), with the presence of headache and/or dizziness (OR=2.538; 95% CI 0.987-6.526) and the duration of pain over 12 months (OR=0.279; 95% CI 0.109-0.719). This model explains 27.5% of the variance of successful outcomes and correctly classifies 73.1% of the patients (sensitivity: 81.8%, specificity: 59.5%). Conclusions: Patients with CNP with high disability at baseline and complaints of pain for less than 12 months are more likely to obtain improvements in functional disability. High levels of pain intensity at baseline predict successful outcomes in pain reduction after seven weeks of treatment. Patients with CNP with low levels of pain at baseline, with headache and/or dizziness and with pain complaints for more than 12 months are more likely to get a better perceived recovery.

Interação multimodal de dispositivos robóticos em ambiente simulado

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

A network approach to brain aging through multimodal neuroimaging

Tese de Doutoramento em Ciências da Saúde.