Alzheimer’s disease (AD) is a progressive neurodegenerative disease with the clinical symptom of the continuous deterioration of cognitive and memory functions. Multiple diffusion tensor imaging (DTI) indices such as fractional anisotropy (FA) and mean diffusivity (MD) can successfully explain the white matter damages in AD patients. However, most studies focused on the univariate measures (voxel-based analysis) to examine the differences between AD patients and normal controls (NCs). In this investigation, we applied a multivariate independent component analysis (ICA) to investigate the white matter covariances based on FA measurement from DTI data in 35 AD patients and 45 NCs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We found that six independent components (ICs) showed significant FA reductions in white matter covariances in AD compared with NC, including the genu and splenium of corpus callosum (IC-1 and IC-2), middle temporal gyral of temporal lobe (IC-3), sub-gyral of frontal lobe (IC-4 and IC-5) and sub-gyral of parietal lobe (IC-6). Our findings revealed covariant white matter loss in AD patients and suggest that the unsupervised data-driven ICA method is effective to explore the changes of FA in AD. This study assists us in understanding the mechanism of white matter covariant reductions in the development of AD.
Multi-voxel pattern analysis (MVPA) has been widely used in the object category classification of functional magnetic resonance imaging (fMRI) data. Feature selection is an essential operation in pattern classification. Searchlight, based on information mapping, is one method of feature selection. In contrast with traditional methods based on activation, searchlight has more sensitivity and then provides higher statistical power. In this study, we applied two
different feature selection methods, searchlight and activation, combined with linear support vector machine (SVM)
classifier, to investigate the classification effect in classifying 4-category objects on fMRI data. We found that the
average classification accuracies of searchlight were 0.8095 (house vs. face), 0.7240 (house vs. car), 0.7247 (house vs.
cat), 0.6980 (face vs. car), 0.5982 (face vs. cat) and 0.6860 (car vs. cat). For house vs. car, the average classification
accuracy based on searchlight was better than that based on activation (0.7240 vs. 0.7143). Specially, searchlight method performed better than activation for some subjects. The results showed that object category classification of fMRI data based on information mapping were significantly reliable. Our findings suggest that information mapping can be applied in pattern classification in future work.
Support Vector Machine (SVM) is an accurate pattern recognition method which has been widely used in functional
MRI (fMRI) data classification. Voxel selection is a very important part in classification. In general, voxel selection is
based on brain regions associated with activation caused by different experiment conditions or stimulations. However,
negative blood oxygenation level-dependent responses (deactivation) which have also been found in humans or animals
contribute to the classification of different cognitive tasks. Different from traditional studies which focused merely on
the activation voxel selection methods, our aim is to investigate the deactivation voxel selection methods in the
classification of fMRI data using SVM. In this study, three different voxel selection methods (deactivation, activation,
the combination of deactivation and activation) are applied to decide which voxel is included in SVM classifier with
linear kernel in classifying 4-category objects on fMRI data. The average accuracies of deactivation classification were
73.36%(house vs. face), 60.34%(house vs. car), 60.94%(house vs. cat), 71.43%(face vs. car), 63.17%(face vs. cat)
and 61.61%(car vs. cat). The classification results of deactivation were significantly above the chance level which
implies the deactivation is informative. The accuracies of combination of activation and deactivation method were close
to that of activation method, and it was even better for some representative subjects. These results suggest deactivation
provides useful information in the object category classification on fMRI data and the method of voxel selection based
on both activation and deactivation will be a significant method in classification in the future.
Gray matter volume and cortical thickness are two indices of concern in brain structure magnetic resonance imaging
research. Gray matter volume reflects mixed-measurement information of cerebral cortex, while cortical thickness
reflects only the information of distance between inner surface and outer surface of cerebral cortex. Using Scaled
Subprofile Modeling based on Principal Component Analysis (SSM_PCA) and Pearson's Correlation Analysis, this
study further provided quantitative comparisons and depicted both global relevance and local relevance to
comprehensively investigate morphometrical abnormalities in cerebral cortex in Alzheimer's disease (AD). Thirteen
patients with AD and thirteen age- and gender-matched healthy controls were included in this study. Results showed that
factor scores from the first 8 principal components accounted for ~53.38% of the total variance for gray matter volume,
and ~50.18% for cortical thickness. Factor scores from the fifth principal component showed significant correlation. In
addition, gray matter voxel-based volume was closely related to cortical thickness alterations in most cortical cortex,
especially, in some typical abnormal brain regions such as insula and the parahippocampal gyrus in AD. These findings
suggest that these two measurements are effective indices for understanding the neuropathology in AD. Studies using
both gray matter volume and cortical thickness can separate the causes of the discrepancy, provide complementary
information and carry out a comprehensive description of the morphological changes of brain structure.
Gray matter volume and cortical thickness are two important indices widely used to detect neuropathological changes in
brain structural magnetic resonance imaging. Using optimized voxel-based morphometry (VBM) protocol and
surface-based cortical thickness measure, this study comprehensively investigated the regional changes in cortical gray
matter volume and cortical thickness in Alzheimer's disease (AD). Thirteen patients with AD and fourteen age- and
gender-matched healthy controls were included in this study. Results showed that voxel-based gray matter volume and
cortical thickness reductions were highly correlated in the temporal lobe and its medial structure in AD. Moreover
significant reduced cortical regions of gray matter volume were obviously more than that of cortical thickness. These
findings suggest that gray matter volume and cortical thickness, as two important imaging markers, are effective indices
for detecting the neuroanatomical alterations and help us understand the neuropathology from different views in AD.
Using optimized voxel-based morphometry (VBM), this study systematically investigated the differences and similarities
of brain structural changes during the early three developmental periods of human lives: childhood, adolescence and
young adulthood. These brain changes were discussed in relationship to the corresponding cognitive function
development during these three periods. Magnetic Resonance Imaging (MRI) data from 158 Chinese healthy children,
adolescents and young adults, aged 7.26 to 22.80 years old, were included in this study. Using the customized brain
template together with the gray matter/white matter/cerebrospinal fluid prior probability maps, we found that there were
more age-related positive changes in the frontal lobe, less in hippocampus and amygdala during childhood, but more in
bilateral hippocampus and amygdala and left fusiform gyrus during adolescence and young adulthood. There were more
age-related negative changes near to central sulcus during childhood, but these changes extended to the frontal and
parietal lobes, mainly in the parietal lobe, during adolescence and young adulthood, and more in the prefrontal lobe
during young adulthood. So gray matter volume in the parietal lobe significantly decreased from childhood and
continued to decrease till young adulthood. These findings may aid in understanding the age-related differences in
cognitive function.
Using optimized voxel-based morphometry (VBM), this study systematically investigated gender differences in brain
development through magnetic resonance imaging (MRI) data in 158 Chinese normal children and adolescents aged 7.26
to 22.80 years (mean age 15.03±4.70 years, 78 boys and 80 girls). Gender groups were matched for measures of age,
handedness, education level. The customized brain templates, including T1-weighted image and gray matter (GM)/white
matter (WM)/cerebro-spinal fluid (CSF) prior probability maps, were created from all participants. Results showed that
the total intracranial volume (TIV), global absolute GM and global WM volume in girls were significantly smaller than
those in boys. The hippocampus grew faster in girls than that in boys, but the amygdala grew faster in boys than that in
girls. The rate of regional GM decreases with age was steeper in the left superior parietal lobule, bilateral inferior parietal
lobule, left precuneus, and bilateral supramarginal gyrus in boys compared to girls, which was possibly related to better
spatial processing ability in boys. Regional GM volumes were greater in bilateral superior temporal gyrus, bilateral
inferior frontal gyrus and bilateral middle frontal gyrus in girls. Regional WM volumes were greater in the left temporal
lobe, right inferior parietal and bilateral middle frontal gyrus in girls. The gender differences in the temporal and frontal
lobe maybe be related to better language ability in girls. These findings may aid in understanding the differences in
cognitive function between boys and girls.
Voxel-based morphometry (VBM) is an automated and objective image analysis technique for detecting differences in
regional concentration or volume of brain tissue composition based on structural magnetic resonance (MR) images.
VBM has been used widely to evaluate brain morphometric differences between different populations, but there isn't an
evaluation system for its validation until now. In this study, a quantitative and objective evaluation system was
established in order to assess VBM performance. We recruited twenty normal volunteers (10 males and 10 females, age
range 20-26 years, mean age 22.6 years). Firstly, several focal lesions (hippocampus, frontal lobe, anterior cingulate,
back of hippocampus, back of anterior cingulate) were simulated in selected brain regions using real MRI data. Secondly,
optimized VBM was performed to detect structural differences between groups. Thirdly, one-way ANOVA and post-hoc
test were used to assess the accuracy and sensitivity of VBM analysis. The results revealed that VBM was a good
detective tool in majority of brain regions, even in controversial brain region such as hippocampus in VBM study.
Generally speaking, much more severity of focal lesion was, better VBM performance was. However size of focal lesion
had little effects on VBM analysis.
This study examined regional gray matter abnormalities across the whole brain in 19 patients with schizophrenia (12 males and 7 females), comparing with 11 normal volunteers (7 males and 4 females). The customized brain templates
were created in order to improve spatial normalization and segmentation. Then automated preprocessing of magnetic
resonance imaging (MRI) data was conducted using optimized voxel-based morphometry (VBM). The statistical voxel based analysis was implemented in terms of two-sample t-test model. Compared with normal controls, regional gray matter concentration in patients with schizophrenia was significantly reduced in the bilateral superior temporal gyrus, bilateral middle frontal and inferior frontal gyrus, right insula, precentral and parahippocampal areas, left thalamus and hypothalamus as well as, however, significant increases in gray matter concentration were not observed across the whole brain in the patients. This study confirms and extends some earlier findings on gray matter abnormalities in schizophrenic patients. Previous behavior and fMRI researches on schizophrenia have suggested that cognitive capacity
decreased and self-conscious weakened in schizophrenic patients. These regional gray matter abnormalities determined through structural MRI with optimized VBM may be potential anatomic underpinnings of schizophrenia.
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