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.

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.

Patients with schizophrenia have a smaller volume of cortex than healthy controls. Nevertheless, the substrate of such deficit is not well understood A progressive loss of cortical GM in schizophrenia seemed supported by early studies with magnetic resonance imaging (MRI) in which patients received typical drugs between the baseline and final scans. However, recent MRI results challenge this notion and suggest that structural changes may depend, at least in part, on the type of treatment received. These data may be relevant for a correct interpretation of the substrate of cortical volume deficit in schizophrenia. If that deficit can be even reversed by treatment, as suggested by recent studies, a neuronal substrate seems unlikely. Several lines of evidence instead support that glia cells may have a role in cortical structural and functional deficits in schizophrenia, which would be also in agreement with recent longitudinal results with MRI in patients treated with atypical antipsychotics. These evidences are reviewed in this paper.