Schizophrenia with auditory hallucinations: a voxel-based morphometry study.

Many studies have shown widespread but subtle pathological changes in gray matter in patients with schizophrenia. Some of these studies have related specific alterations to the genesis of auditory hallucinations, particularly in the left superior temporal gyrus, but none has analysed the relationship between morphometric data and a specific scale for auditory hallucinations. The present study aims to define the presence and characteristics of structural abnormalities in relation with the intensity and phenomenology of auditory hallucinations by means of magnetic resonance voxel-based morphometry (MR-VBM) method applied on a highly homogeneous group of 18 persistent hallucinatory patients meeting DSM-IV criteria for schizophrenia compared to 19 healthy matched controls. Patients were evaluated using the PSYRATS scale for auditory hallucinations. Reductions of gray matter concentration in patients to controls were observed in bilateral insula, bilateral superior temporal gyri and left amygdala. In addition, specific relationships between left inferior frontal and right postcentral gyri reductions and the severity of auditory hallucinations were observed. All these areas might be implicated in the genesis and/or persistence of auditory hallucinations through specific mechanisms. Precise morphological abnormalities may help to define reliable MR-VBM biomarkers for the genesis and persistence of auditory hallucinations.

Emotional words induce enhanced brain activity in schizophrenic patients with auditory hallucinations.

Neuroimaging studies of emotional response in schizophrenia have mainly used visual (faces) paradigms and shown globally reduced brain activity. None of these studies have used an auditory paradigm. Our principal aim is to evaluate the emotional response of patients with schizophrenia to neutral and emotional words. An auditory emotional paradigm based on the most frequent words heard by psychotic patients with auditory hallucinations was designed. This paradigm was applied to evaluate cerebral activation with functional magnetic resonance imaging (fMRI) in 11 patients with schizophrenia with persistent hallucinations and 10 healthy subjects. We found a clear enhanced activity of the frontal lobe, temporal cortex, insula, cingulate, and amygdala (mainly right side) in patients when hearing emotional words in comparison with controls. Our findings are consistent with other studies suggesting a relevant role for emotional response in the pathogenesis and treatment of auditory hallucinations.

A nonparametric MRI inhomogeneity correction method.

Magnetic resonance images are commonly affected by intensity inhomogeneities which make it difficult to obtain any quantitative measures from them. We present a new method of automatically correcting this artifact using a nonparametric coarse to fine approach which allows bias fields to be modeled with different frequency ranges without user supervision. We also propose a new entropy-related cost function based on the combination of intensity and gradient image features for more robust homogeneity measurement. The proposed methodology has been evaluated for both synthetic and real data and compared with state of the art methods, showing the best results in the comparison. The proposed method is fully automatic and has no input parameters, making it very easy to use in a clinical environment.

Multicomponent MR Image Denoising.

Magnetic Resonance images are normally corrupted by random noise from the measurement process complicating the automatic feature extraction and analysis of clinical data. It is because of this reason that denoising methods have been traditionally applied to improve MR image quality. Many of these methods use the information of a single image without taking into consideration the intrinsic multicomponent nature of MR images. In this paper we propose a new filter to reduce random noise in multicomponent MR images by spatially averaging similar pixels using information from all available image components to perform the denoising process. The proposed algorithm also uses a local Principal Component Analysis decomposition as a postprocessing step to remove more noise by using information not only in the spatial domain but also in the intercomponent domain dealing in a higher noise reduction without significantly affecting the original image resolution. The proposed method has been compared with similar state-of-art methods over synthetic and real clinical multicomponent MR images showing an improved performance in all cases analyzed.

MRI denoising using non-local means.

Magnetic Resonance (MR) images are affected by random noise which limits the accuracy of any quantitative measurements from the data. In the present work, a recently proposed filter for random noise removal is analyzed and adapted to reduce this noise in MR magnitude images. This parametric filter, named Non-Local Means (NLM), is highly dependent on the setting of its parameters. The aim of this paper is to find the optimal parameter selection for MR magnitude image denoising. For this purpose, experiments have been conducted to find the optimum parameters for different noise levels. Besides, the filter has been adapted to fit with specific characteristics of the noise in MR image magnitude images (i.e. Rician noise). From the results over synthetic and real images we can conclude that this filter can be successfully used for automatic MR denoising.

Robust MRI brain tissue parameter estimation by multistage outlier rejection.

This article addresses the problem of the tissue type parameter estimation in brain MRI in the presence of partial volume effects. Automatic MRI brain tissue classification is hampered by partial volume effects that are caused by the finite resolution of the acquisition process. Due to this effect intensity distributions in brain MRI cannot be well modeled by a simple mixture of Gaussians and therefore more complex models have been developed. Unfortunately, these models do not seem to be robust enough for clinical conditions, as the quality of the tissue classification decreases rapidly with the image quality. Also, the application of these methods for pathological images with unmodeled intensities (e.g. MS plaques, tumors, etc.) remains uncertain. In the present work a new robust method for brain tissue characterization is presented, treating the partial volume affected voxels as outliers of the pure tissue distributions. The proposed method estimates the tissue characteristics from a reduced set of intensities belonging to a particular pure tissue class. This reduced set is selected by using a trimming procedure based on local gradient information and distributional data. This feature makes the method highly tolerant of a large amount of unexpected intensities without degrading its performance. The proposed method has been evaluated using both synthetic and real MR data and compared with state-of-the-art methods showing the best results in the comparative.