We present a multivariate alternative to the voxel-basedmorphometry (VBM) approach called source-based morphometry (SBM), to study gray matter differences between patients and healthy controls. The SBM approach begin...
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We present a multivariate alternative to the voxel-basedmorphometry (VBM) approach called source-based morphometry (SBM), to study gray matter differences between patients and healthy controls. The SBM approach begins with the same preprocessing procedures as VBM. Next, independent component analysis is used to identify naturally grouping, maximally independent sources. Finally, statistical analyses are used to determine the significant sources and their relationship to other variables. The identified "source networks," groups of spatially distinct regions with common covariation among subjects, provide information about localization of gray matter changes and their variation among individuals. In this study, we first compared VBM and SBM via a simulation and then applied both methods to real data obtained from 120 chronic schizophrenia patients and 120 healthy controls. SBM identified five gray matter sources as significantly associated with schizophrenia. These included sources in the bilateral temporal lobes, thalamus, basal ganglia, parietal lobe, and frontotemporal regions. None of these showed an effect of sex. Two sources in the bilateral temporal and parietal lobes showed age-related reductions. The most significant source of schizophrenia-related gray matter changes identified by SBM occurred in the bilateral temporal lobe, while the most significant change found by VBM occurred in the thalamus. The SBM approach found changes not identified by VBM in basal ganglia, parietal, and occipital lobe. These findings show that SBM is a multivariate alternative to VBM, with wide applicability to studying changes in brain structure. Hum Brain Mapp 30:711-724, 2009. (c) 2008 Wiley-Liss, Inc.
Alzheimer's disease (AD) is a progressive and often fatal brain disease that destroys brain cells, resulting in memory loss as well as other cognitive and behavioral problems. Here we propose a novel method combin...
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ISBN:
(纸本)9781479951512
Alzheimer's disease (AD) is a progressive and often fatal brain disease that destroys brain cells, resulting in memory loss as well as other cognitive and behavioral problems. Here we propose a novel method combining independent components from MRI measures and clinical assessments to distinguish Alzheimer's patients or mild cognitive impairment (MCI) subjects from healthy elderly controls. Our method includes the following steps: pre-processing, estimating the number of independent components, extracting effective voxels for classification, and classification using a support vector machine (SVM)-based classifier. We found that (1) both AD and MCI subjects showed brain tissue loss, but the volumes of gray matter lost in MCI subjects was far less, in line with the notion that MCI is a prodromal stage of AD;and (2) combining gray matter features from MRI and three commonly used measures of mental status/cognitive function improves classification accuracy, sensitivity and specificity compared with classification using only independent components or clinical measurements. As a result, for classifying AD from healthy controls, we achieved a classification accuracy of 97.7%, sensitivity of 99.2%, and specificity of 96.7%;for differentiating MCI from healthy controls, we achieved a classification accuracy of 87.8%, a sensitivity of 86.0%, and a specificity of 89.6%;these results are better than those obtained with clinical measurements alone (accuracy of 79.5%;sensitivity of 74.0%, and specificity of 85.1%).
Deja vu (DV) is a widespread, fascinating and mysterious human experience. It occurs both in health and in disease, notably as an aura of temporal lobe epilepsy. This feeling of inappropriate familiarity has attracted...
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Deja vu (DV) is a widespread, fascinating and mysterious human experience. It occurs both in health and in disease, notably as an aura of temporal lobe epilepsy. This feeling of inappropriate familiarity has attracted interest from psychologists and neuroscientists for over a century, but still there is no widely agreed explanation for the phenomenon of non-pathological DV. Here we investigated differences in brain morphology between healthy subjects with and without DV using a novel multivariate neuroimaging technique, source-based morphometry. The analysis revealed a set of cortical (predominantly mesiotemporal) and subcortical regions in which there was significantly less gray matter in subjects reporting DV. In these regions gray matter volume was inversely correlated with the frequency of DV. Our results demonstrate a structural correlate of DV in healthy individuals for the first time and support a neurological explanation for the phenomenon. We hypothesis that the observed local gray matter decrease in subjects experiencing DV reflects an alteration of hippocampal function and postnatal neurogenesis with resulting changes of volume in remote brain regions. (C) 2012 Elsevier Srl. All rights reserved.
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