Structural Correlates of Depressive Symptoms in Prodromal Alzheimer’s Disease.

Background/Aims: We conducted a cross-sectional study to investigate the structural magnetic resonance imaging correlates of depressive symptoms at the initial clinical stages of Alzheimer’s disease (AD). Methods: Subjects aged 65 or more were categorized as prodromal AD (n=18), mild AD (n=35), or normal cognition (n=76). Depressive symptoms were measured by means of the 15-item abridged version of the Geriatric Depression Scale. Potential gray matter correlates of depressive symptoms were analyzed using the Statistical Parametric Mapping software package. Results: Significant results were obtained in the prodromal AD group only. In that group, depressive symptoms were related to atrophy in the left precentral gyrus (Brodmann area 6) (p≤0.01, FWE corrected). Conclusion: Our results, added to the existing literature, suggest that dysfunction in left-sided, cognitively and functionally salient, cortical regions along with relative preservation of deficit awareness, provided by the right hemisphere, explain depressive symptoms in the initial clinical stages of AD.

Automatic classffication of structural mri for diagnosis of neurodegenerative diseaess / Clasificación automática de IRM estructural para el diagnóstico de enfermedades neurodegenerativas

This paper presents an automatic approach which classifies structural Magnetic Resonance images into pathological or healthy controls. A classification model was trained to find the boundaries that allow to separate the study groups. The method uses the deformation values from a set of regions, automatically identified as relevant, in a process that selects the statistically significant regions of a t-test under the restriction that this significance must be spatially coherent within a neighborhood of 5 voxels. The proposed method was assessed to distinguish healthy controls from schizophrenia patients. Classification results showed accuracy between 74% and 89%, depending on the stage of the disease and number of training samples.

Este artículo presenta un método automático para la clasificación de individuos en grupos patológicos o controles sanos haciendo uso de imágenes de resonancia magnética. El método propuesto usa los valores de deformación del sujeto analizado a un cerebro plantilla, para entrenar un modelo de clasificación capaz de identificar las fronteras que separan los grupos de estudio en un espacio de características dado. Con el fin de reducir la dimensionalidad del problema, un conjunto de regiones relevantes es automáticamente extraído en un proceso que selecciona las regiones estadísticamente significativas en una prueba t-student, con la restricción de mantener coherencia en dicha significancia en una vecindad de 5 voxeles. El método propuesto fue evaluado en la clasificación de pacientes con esquizofrenia y sujetos sanos. Los resultados mostraron un desempeño entre el 74 y el 89%, el cual depende principalmente del número de muestras empleadas para el entrenamiento del modelo.