Automatic classification of MR scans in Alzheimer's disease.

To be diagnostically useful, structural MRI must reliably distinguish Alzheimer's disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detection of a variety of disease states. The aims of this study were to assess how successfully support vector machines assigned individual diagnoses and to determine whether data-sets combined from multiple scanners and different centres could be used to obtain effective classification of scans. We used linear support vector machines to classify the grey matter segment of T1-weighted MR scans from pathologically proven AD patients and cognitively normal elderly individuals obtained from two centres with different scanning equipment. Because the clinical diagnosis of mild AD is difficult we also tested the ability of support vector machines to differentiate control scans from patients without post-mortem confirmation. Finally we sought to use these methods to differentiate scans between patients suffering from AD from those with frontotemporal lobar degeneration. Up to 96% of pathologically verified AD patients were correctly classified using whole brain images. Data from different centres were successfully combined achieving comparable results from the separate analyses. Importantly, data from one centre could be used to train a support vector machine to accurately differentiate AD and normal ageing scans obtained from another centre with different subjects and different scanner equipment. Patients with mild, clinically probable AD and age/sex matched controls were correctly separated in 89% of cases which is compatible with published diagnosis rates in the best clinical centres. This method correctly assigned 89% of patients with post-mortem confirmed diagnosis of either AD or frontotemporal lobar degeneration to their respective group. Our study leads to three conclusions: Firstly, support vector machines successfully separate patients with AD from healthy aging subjects. Secondly, they perform well in the differential diagnosis of two different forms of dementia. Thirdly, the method is robust and can be generalized across different centres. This suggests an important role for computer based diagnostic image analysis for clinical practice.

Creació d'un programa de rentat industrial amb Visual Basic

Treball de recerca realitzat per alumnes d’ensenyament secundari i guardonat amb un Premi CIRIT per fomentar l'esperit científic del Jovent l’any 2010. L’objectiu del treball ha consistit a desenvolupar un programa informàtic per controlar les fases d'un procés de rentat industrial.

Auto-conceito, Auto-estima e Aprendizagem em crianças e Adolescentes Portadores de Deficiência Visual. A perspectiva dos seus professores.

O presente trabalho monográfico enquadra-se no plano curricular, constituindo um dos requisitos para a obtenção de licenciatura em psicologia, variante de Educação e Desenvolvimento, pela Universidade Jean Piaget de Cabo Verde. Este trabalho intitula-se “Auto conceito Auto estima e aprendizagem das crianças e adolescentes portadores de deficiência visual e as perspectivas dos seus professores”.Procura-se com este trabalho, conhecer o auto conceito e a auto estima das crianças portadoras da deficiência visual que frequentam o Centro de ADVIC São Filipe - Praia; verificar a influência da deficiência visual na aprendizagem; averiguar a relação entre auto conceito auto estima com a aprendizagem das crianças e adolescentes na perspectiva dos seus professores.Participaram deste estudo sete alunos (7), sendo cinco (5) do sexo feminino e dois (2) de sexo masculino e seis (6) professores. Para a recolha de dados utilizamos metodologia de natureza qualitativa. A análise dos dados procedeu-se com análise de conteúdo das entrevistas feitos aos alunos o que nos permitiu analisar as cinco (5) dimensões do auto conceito e auto estima global dos alunos, nas entrevistas aos professores analisamos as suas perspectivas em relação a aprendizagem dos alunos portadores de deficiência visual.Os resultados deste estudo revelam que e o auto conceito auto-estima dessas crianças e adolescentes caracteriza-se pela positiva, os seus comportamentos contentes e alegres faz com que muitas das vezes pareçam divertidos. Parece haver influência da deficiência na aprendizagem das crianças e existe relação entre o auto conceito e auto estima e aprendizagem. Constatou-se ainda, que a maneira positiva de encarar, lidar com a sua deficiência facilita a aprendizagem isso segundo as perspectivas dos seus professores.

Estudio prospectivo de sintomatología visual positiva en fase aguda de ictus: incidencia y características.

Desconocemos los mecanismos fisiopatológicos subyacentes a la aparición de alucinaciones/alucinosis visual en pacientes con ictus, su incidencia, características y valor predictivo topográfico o pronóstico. En este trabajo estudiamos prospectivamente 78 pacientes con ictus isquémico/hemorrágico agudo y ausencia de enfermedad neurodegenerativa/psiquiátrica basal o clínica alucinatoria previa, administrándoles cuestionario estandarizado sobre alucinaciones/alucinosis visual y realizándoles prueba de neuroimagen. Un subgrupo de pacientes también cuenta con EEG y evaluación neuropsicológica. La incidencia de alucinaciones/alucinosis fue del 16,7%, siendo la mayoría imágenes complejas, con presentación precoz y curso autolimitado. Se asoció con lesiones occipitales, defecto campimétrico inicial, y alteraciones del sueño entre otras variables.

Model-based Segmentation and Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning

Computed Tomography (CT) represents the standard imaging modality for tumor volume delineation for radiotherapy treatment planning of retinoblastoma despite some inherent limitations. CT scan is very useful in providing information on physical density for dose calculation and morphological volumetric information but presents a low sensitivity in assessing the tumor viability. On the other hand, 3D ultrasound (US) allows a highly accurate definition of the tumor volume thanks to its high spatial resolution but it is not currently integrated in the treatment planning but used only for diagnosis and follow-up. Our ultimate goal is an automatic segmentation of gross tumor volume (GTV) in the 3D US, the segmentation of the organs at risk (OAR) in the CT and the registration of both modalities. In this paper, we present some preliminary results in this direction. We present 3D active contour-based segmentation of the eye ball and the lens in CT images; the presented approach incorporates the prior knowledge of the anatomy by using a 3D geometrical eye model. The automated segmentation results are validated by comparing with manual segmentations. Then, we present two approaches for the fusion of 3D CT and US images: (i) landmark-based transformation, and (ii) object-based transformation that makes use of eye ball contour information on CT and US images.

Automated analysis of background EEG and reactivity during therapeutic hypothermia in comatose patients after cardiac arrest.

Visual analysis of electroencephalography (EEG) background and reactivity during therapeutic hypothermia provides important outcome information, but is time-consuming and not always consistent between reviewers. Automated EEG analysis may help quantify the brain damage. Forty-six comatose patients in therapeutic hypothermia, after cardiac arrest, were included in the study. EEG background was quantified with burst-suppression ratio (BSR) and approximate entropy, both used to monitor anesthesia. Reactivity was detected through change in the power spectrum of signal before and after stimulation. Automatic results obtained almost perfect agreement (discontinuity) to substantial agreement (background reactivity) with a visual score from EEG-certified neurologists. Burst-suppression ratio was more suited to distinguish continuous EEG background from burst-suppression than approximate entropy in this specific population. Automatic EEG background and reactivity measures were significantly related to good and poor outcome. We conclude that quantitative EEG measurements can provide promising information regarding current state of the patient and clinical outcome, but further work is needed before routine application in a clinical setting.

Cognitive disorganisation in schizotypy is associated with deterioration in visual backward masking.

To understand the causes of schizophrenia, a search for stable markers (endophenotypes) is ongoing. In previous years, we have shown that the shine-through visual backward masking paradigm meets the most important characteristics of an endophenotype. Here, we tested masking performance differences between healthy students with low and high schizotypy scores as determined by the self-report O-Life questionnaire assessing schizotypy along three dimensions, i.e. positive schizotypy (unusual experiences), cognitive disorganisation, and negative schizotypy (introvertive anhedonia). Forty participants performed the shine-through backward masking task and a classical cognitive test, the Wisconsin Card Sorting Task (WCST). We found that visual backward masking was impaired for students scoring high as compared to low on the cognitive disorganisation dimension, whereas the positive and negative schizotypy dimensions showed no link to masking performance. We also found group differences for students scoring high and low on the cognitive disorganisation factor for the WCST. These findings indicate that the shine-through paradigm is sensitive to differences in schizotypy which are closely linked with the pathological expression in schizophrenia.

Feature Extraction from Whole-Sky Ground-Based Images for Cloud-Type Recognition

Several features that can be extracted from digital images of the sky and that can be useful for cloud-type classification of such images are presented. Some features are statistical measurements of image texture, some are based on the Fourier transform of the image and, finally, others are computed from the image where cloudy pixels are distinguished from clear-sky pixels. The use of the most suitable features in an automatic classification algorithm is also shown and discussed. Both the features and the classifier are developed over images taken by two different camera devices, namely, a total sky imager (TSI) and a whole sky imager (WSC), which are placed in two different areas of the world (Toowoomba, Australia; and Girona, Spain, respectively). The performance of the classifier is assessed by comparing its image classification with an a priori classification carried out by visual inspection of more than 200 images from each camera. The index of agreement is 76% when five different sky conditions are considered: clear, low cumuliform clouds, stratiform clouds (overcast), cirriform clouds, and mottled clouds (altocumulus, cirrocumulus). Discussion on the future directions of this research is also presented, regarding both the use of other features and the use of other classification techniques

Voluntary Explicit versus Involuntary Conceptual Memory Are Associated with Dissociable fMRI Responses in Hippocampus, Amygdala, and Parietal Cortex for Emotional and Neutral Word Pairs.

Although functional neuroimaging studies have supported the distinction between explicit and implicit forms of memory, few have matched explicit and implicit tests closely, and most of these tested perceptual rather than conceptual implicit memory. We compared event-related fMRI responses during an intentional test, in which a group of participants used a cue word to recall its associate from a prior study phase, with those in an incidental test, in which a different group of participants used the same cue to produce the first associate that came to mind. Both semantic relative to phonemic processing at study, and emotional relative to neutral word pairs, increased target completions in the intentional test, but not in the incidental test, suggesting that behavioral performance in the incidental test was not contaminated by voluntary explicit retrieval. We isolated the neural correlates of successful retrieval by contrasting fMRI responses to studied versus unstudied cues for which the equivalent "target" associate was produced. By comparing the difference in this repetition-related contrast across the intentional and incidental tests, we could identify the correlates of voluntary explicit retrieval. This contrast revealed increased bilateral hippocampal responses in the intentional test, but decreased hippocampal responses in the incidental test. A similar pattern in the bilateral amygdale was further modulated by the emotionality of the word pairs, although surprisingly only in the incidental test. Parietal regions, however, showed increased repetition-related responses in both tests. These results suggest that the neural correlates of successful voluntary explicit memory differ in directionality, even if not in location, from the neural correlates of successful involuntary implicit (or explicit) memory, even when the incidental test taps conceptual processes.