Alzheimer’s Disease (AD) is a devastating neurodegenerative disease. Recent advances in tau-positron emission tomography (PET) imaging allow quantitating and mapping out the regional distribution of one important hallmark of AD across the brain. There is a need to develop machine learning (ML) algorithms to interrogate the utility of this new imaging modality. While there are some recent studies showing promise of using ML to differentiate AD patients from normal controls (NC) based on tau-PET images, there is limited work to investigate if tau-PET, with the help of ML, can facilitate predicting the risk of converting to AD while an individual is still at the early Mild Cognitive Impairment (MCI) stage. We developed an early AD risk predictor for subjects with MCI based on tau-PET using Machine Learning (ML). Our ML algorithms achieved good accuracy in predicting the risk of conversion to AD for a given MCI subject. Important features contributing to the prediction are consistent with literature reports of tau susceptible regions. This work demonstrated the feasibility of developing an early AD risk predictor for subjects with MCI based on tau-PET and ML.
Multi-modality images usually exist for diagnosis/prognosis of a disease, such as Alzheimer’s Disease (AD), but with different levels of accessibility and accuracy. MRI is used in the standard of care, thus having high accessibility to patients. On the other hand, imaging of pathologic hallmarks of AD such as amyloid-PET and tau-PET has low accessibility due to cost and other practical constraints, even though they are expected to provide higher diagnostic/prognostic accuracy than standard clinical MRI. We proposed Cross-Modality Transfer Learning (CMTL) for accurate diagnosis/prognosis based on standard imaging modality with high accessibility (mod_HA), with a novel training strategy of using not only data of mod_HA but also knowledge transferred from the model based on advanced imaging modality with low accessibility (mod_LA). We applied CMTL to predict conversion of individuals with Mild Cognitive Impairment (MCI) to AD using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) datasets, demonstrating improved performance of the MRI (mod_HA)-based model by leveraging the knowledge transferred from the model based on tau-PET (mod_LA).
Biomarker-assisted diagnosis and intervention in Alzheimer’s disease (AD) may be the key to prevention breakthroughs. One of the hallmarks of AD is the accumulation of tau plaques in the human brain. However, current methods to detect tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (Tau PET). In our previous work, structural MRI-based hippocampal multivariate morphometry statistics (MMS) showed superior performance as an effective neurodegenerative biomarker for preclinical AD and Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) has excellent ability to generate low-dimensional representations with strong statistical power for brain amyloid prediction. In this work, we apply this framework together with ridge regression models to predict Tau deposition in Braak12 and Braak34 brain regions separately. We evaluate our framework on 925 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Each subject has one pair consisting of a PET image and MRI scan which were collected at about the same times. Experimental results suggest that the representations from our MMS and PASCS-MP have stronger predictive power and their predicted Braak12 and Braak34 are closer to the real values compared to the measures derived from other approaches such as hippocampal surface area and volume, and shape morphometry features based on spherical harmonics (SPHARM).
Collectively, vast quantities of brain imaging data exist across hospitals and research institutions, providing valuable resources to study brain disorders such as Alzheimer’s disease (AD). However, in practice, putting all these distributed datasets into a centralized platform is infeasible due to patient privacy concerns, data restrictions and legal regulations. In this study, we propose a novel federated feature selection framework that can analyze the data at each individual institution without data-sharing or accessing private patient information. In this framework, we first propose a federated group lasso optimization method based on block coordinate descent. We employ stability selection to determine statistically significant features, by solving the group lasso problem with a sequence of regularization parameters. To accelerate the stability selection, we further propose a federated screening rule, which can identify and exclude the irrelevant features before solving the group lasso. Here, we use this framework for patch based feature selection on hippocampal morphometry. Shape is characterized through two different kinds of local measures, the radial distance and the surface area determined via tensor-based morphometry (TBM). The method is tested on 1,127 T1-weighted brain magnetic resonance images (MRI) of AD, mild cognitive impairment (MCI) and elderly control subjects, randomly assigned to five independent hypothetical institutions for testing purpose. We examine the association of MRI-based anatomical measures with general cognitive assessment and amyloid burden to identify the morphometry changes related to AD deterioration and plaque accumulation. Finally, we visualize the significance of the association on the hippocampal surfaces. Our experimental results successfully demonstrate the efficiency and effectiveness of our method.
An emerging trend in AD research is brain network development including graphic metrics and graph mining techniques. To construct a brain structural network, Diffusion Tensor Imaging (DTI) in conjunction with T1 weighted Magnetic Resonance Imaging (MRI) can be used to isolate brain regions as nodes, white matter tracts as the edge, and the density of the tracts as the weight to the edge. To study such network, its sub-network is often obtained by excluding unrelated nodes or edges. Existing research has heavily relied on domain knowledge or single-thresholding individual subject based network metrics to identify the sub network. In this research, we develop a bi-threshold frequent subgraph mining method (BT-FSG) to automatically filter out less important edges in responding to the clinical questions. Using this method, we are able to discover a subgraph of human brain network that can significantly reveal the difference between cognitively unimpaired APOE-4 carriers and noncarriers based on the correlations between the age vs. network local metric and age vs. network or global metric. This can potentially become a brain network marker for evaluating the AD risks for preclinical individuals.
Alzheimer’s Disease (AD) is the most common cause of dementia and currently has no cure. Treatments targeting early stages of AD such as Mild Cognitive Impairment (MCI) may be most effective to deaccelerate AD, thus attracting increasing attention. However, MCI has substantial heterogeneity in that it can be caused by various underlying conditions, not only AD. To detect MCI due to AD, NIA-AA published updated consensus criteria in 2011, in which the use of multi-modality images was highlighted as one of the most promising methods. It is of great interest to develop a CAD system based on automatic, quantitative analysis of multi-modality images and machine learning algorithms to help physicians more adequately diagnose MCI due to AD. The challenge, however, is that multi-modality images are not universally available for many patients due to cost, access, safety, and lack of consent. We developed a novel Missing Modality Transfer Learning (MMTL) algorithm capable of utilizing whatever imaging modalities are available for an MCI patient to diagnose the patient’s likelihood of MCI due to AD. Furthermore, we integrated MMTL with radiomics steps including image processing, feature extraction, and feature screening, and a post-processing for uncertainty quantification (UQ), and developed a CAD system called “ADMultiImg” to assist clinical diagnosis of MCI due to AD using multi-modality images together with patient demographic and genetic information. Tested on ADNI date, our system can generate a diagnosis with high accuracy even for patients with only partially available image modalities (AUC=0.94), and therefore may have broad clinical utility.
Fluorodeoxyglucose (FDG) positron emission tomography (PET) measures the decline in the regional cerebral metabolic rate for glucose, offering a reliable metabolic biomarker even on presymptomatic Alzheimer’s disease (AD) patients. PET scans provide functional information that is unique and unavailable using other types of imaging. However, the computational efficacy of FDG-PET data alone, for the classification of various Alzheimers Diagnostic categories, has not been well studied. This motivates us to correctly discriminate various AD Diagnostic categories using FDG-PET data. Deep learning has improved state-of-the-art classification accuracies in the areas of speech, signal, image, video, text mining and recognition. We propose novel methods that involve probabilistic principal component analysis on max-pooled data and mean-pooled data for dimensionality reduction, and multilayer feed forward neural network which performs binary classification. Our experimental dataset consists of baseline data of subjects including 186 cognitively unimpaired (CU) subjects, 336 mild cognitive impairment (MCI) subjects with 158 Late MCI and 178 Early MCI, and 146 AD patients from Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. We measured F1-measure, precision, recall, negative and positive predictive values with a 10-fold cross validation scheme. Our results indicate that our designed classifiers achieve competitive results while max pooling achieves better classification performance compared to mean-pooled features. Our deep model based research may advance FDG-PET analysis by demonstrating their potential as an effective imaging biomarker of AD.
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.
Real-time fMRI (rtfMRI) is a new technology which allows human subjects to observe and control their own BOLD
signal change from one or more localized brain regions during scanning. Current rtfMRI-neurofeedback studies mainly
focused on the target region itself without considering other related regions influenced by the real-time feedback.
However, there always exits important directional influence between many of cooperative regions. On the other hand,
rtfMRI based on motor imagery mainly aimed at somatomotor cortex or primary motor area, whereas supplement motor
area (SMA) was a relatively more integrated and pivotal region. In this study, we investigated whether the activities of
SMA can be controlled utilizing different motor imagery strategies, and whether there exists any possible impact on an
unregulated but related region, primary motor cortex (M1). SMA was first localized using overt finger tapping task, the
activities of SMA were feedback to subjects visually on line during each of two subsequent imagery motor movement
sessions. All thirteen healthy participants were found to be able to successfully control their SMA activities by self-fit
imagery strategies which involved no actual motor movements. The activation of right M1 was also found to be
significantly reduced in both intensity and extent with the neurofeedback process targeted at SMA, suggestive that not
only the part of motor cortex activities were influenced under the regulation of a key region SMA, but also the increased
difference between SMA and M1 might reflect the potential learning effect.
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.
Spatial Independent component analysis (sICA) has been successfully used to analyze functional magnetic resonance
(fMRI) data. However, the application of ICA was limited in multi-task fMRI data due to the potential spatial
dependence between task-related components. Long et al. (2009) proposed ICA with linear projection (ICAp) method
and demonstrated its capacity to solve the interaction among task-related components in multi-task fMRI data of single
subject. However, it's unclear that how to perform ICAp over a group of subjects. In this study, we proposed a group
analysis framework on multi-task fMRI data by combining ICAp with the temporal concatenation method reported by
Calhoun (2001). The results of real fMRI experiment containing multiple visual processing tasks demonstrated the
feasibility and effectiveness of the group ICAp method. Moreover, compared to the GLM method, the group ICAp
method is more sensitive to detect the regions specific to each task.
KEYWORDS: Functional magnetic resonance imaging, Independent component analysis, Data modeling, Brain, Data analysis, Neural networks, Magnetic resonance imaging, Data processing, Convolution, Feature extraction
The existence of the potential non-independency between task-related components in multi-task functional magnetic
resonance imaging (fMRI) studies limits the general application of Independent Component Analysis (ICA) method. The
ICA with projection (ICAp) method proposed by Long (2009, HBM) demonstrated its capacity to solve the interaction
among task-related components of multi-task fMRI data. The basic idea of projection is to remove the influence of the
uninteresting tasks through projection in order to extract one interesting task-related component. However, both the
stimulus paradigm of each task and the homodynamic response function (HRF) are essential for the projection. Due to
the noises in the data and the variability of the HRF across the voxels and subjects, the ideal time course of each task for
projection would be deviant from the true value, which might worsen the ICAp results. In order to make the time courses
for projection closer to the true value, the iterative ICAp is proposed in this study. The iterative ICAp is based on the
assumption that the task-related time courses extracted from the fMRI data by ICAp is more approximate to the true
value than the ideal reference function. Simulated experiment proved that both the spatial detection power and the
temporal accuracy of time course were increased for each task-related component. Moreover, the results of the real
two-task fMRI data were also improved by the iterative ICAp method.
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.
Functional network connectivity (FNC) measures the temporal dependency among the time courses of functional
networks. However, the marginal correlation between two networks used in the classic FNC analysis approach doesn't
separate the FNC from the direct/indirect effects of other networks. In this study, we proposed an alternative approach
based on partial correlation to evaluate the FNC, since partial correlation based FNC can reveal the direct interaction
between a pair of networks, removing dependencies or influences from others. Previous studies have demonstrated less
task-specific activation and less rest-state activity in Alzheimer's disease (AD). We applied present approach to contrast
FNC differences of resting state network (RSN) between AD and normal controls (NC). The fMRI data under resting
condition were collected from 15 AD and 16 NC. FNC was calculated for each pair of six RSNs identified using Group
ICA, thus resulting in 15 (2 out of 6) pairs for each subject. Partial correlation based FNC analysis indicated 6 pairs
significant differences between groups, while marginal correlation only revealed 2 pairs (involved in the partial
correlation results). Additionally, patients showed lower correlation than controls among most of the FNC differences.
Our results provide new evidences for the disconnection hypothesis 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.
KEYWORDS: Brain, Independent component analysis, Functional magnetic resonance imaging, Statistical analysis, Neuroimaging, Testing and analysis, Data modeling, Principal component analysis, Data acquisition, Analytical research
This work proposed to use the linear Gaussian Bayesian network (BN) to construct the effective connectivity model of
the brain's default mode network (DMN), a set of regions characterized by more increased neural activity during
rest-state than most goal-oriented tasks. In a complete unsupervised data-driven manner, Bayesian information criterion
(BIC) based learning approach was utilized to identify a highest scored network whose nodes (brain regions) were
selected based on the result from the group independent component analysis (Group ICA) examining the DMN. We put
forward to adopt the statistical significance testing method for regression coefficients used in stepwise regression
analysis to further refine the network identified by BIC. The final established BN, learned from the functional magnetic
resonance imaging (fMRI) data acquired from 12 healthy young subjects during rest-state, revealed that the hippocampus
(HC) was the most influential brain region that affected activities in all other regions included in the BN. In contrast, the
posterior cingulate cortex (PCC) was influenced by other regions, but had no reciprocal effects on any other region.
Overall, the configuration of our BN illustrated that a prominent connection from HC to PCC existed in the DMN.
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.
Magnetic resonance image (MRI) has provided an imageological support into the clinical diagnosis and prediction of
Alzheimer disease (AD) progress. Currently, the clinical use of MRI data on AD diagnosis is qualitative via visual
inspection and less accurate. To provide assistance to physicians in improving the accuracy and sensitivity of the AD
diagnose and the clinical outcome of the disease, we developed a computer-assisted analysis package that analyzed the
MRI data of an individual patient in comparison with a group of normal controls. The package is based on the principle
of the well established and widely used voxel-based morphometry (VBM) and SPM software. All analysis procedure is
automated and streamlined. With only one mouse-click, the whole procedure was finished within 15 minutes. With the
interactive display and anatomical automatic labeling toolbox, the final result and report supply the brain regional
structure difference, the quantitative assessment and visual inspections by physicians and scientific researcher. The brain
regions which affected by AD are consonant in the main with the clinical diagnosis, which are reviewed by physicians.
In result, the computer package provides physician with an automatic and assistant tool for prediction using MRI. This
package could be valuable tool assisting physicians in making their clinical diagnosis decisions.
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.
KEYWORDS: Independent component analysis, Functional magnetic resonance imaging, Principal component analysis, Brain, Signal detection, Data acquisition, Solids, Shape memory alloys, Data centers, Neuroimaging
Independent component analysis (ICA) method can be used to separate fMRI data into some task-related independent components, including one consistently task-related (CTR) and several transiently task-related (TTR) components. However, the weights, with which the CTR and TTRs contribute to the final task component, are often unknown, but are important for finding its relevant spatial activation area. Here we propose a new ICA post-processing method alternative to combine not only these CTR and TTRs which sometimes are judged in a subjective manner, but also others in an effort to identify a comprehended and summed spatial pattern that is responsible for the behavior under investigation. This proposed procedure has been successfully used in principal component analysis (PCA) based scaled subprofile modeling (SSM). Adopting this newly proposed approach, we essentially refer the ICA exploratory findings to a hypothesized temporal brain response pattern (reference function). Basically, we will use linear regression method to seek the relationship between the reference function and time courses of multi components generated from the ICA procedure. The linear regression coefficients are then used as relative weights in generating the final summed spatial pattern. Moreover, this approach allows a researcher to use T-test to statistically infer the importance of each independent component in its contribution to the final pattern and consequently the contribution to the cognitive process. Experiment result also shows that the spatial activation of the final task component becomes more accurate.
Kewei Chen, Xiaolin Ge, Li Yao, Dan Bandy, Gene Alexander, Anita Prouty, Christine Burns, Xiaojie Zhao, Xiaotong Wen, Ronald Korn, Michael Lawson, Eric Reiman
Having approved fluorodeoxyglucose positron emission tomography (FDG PET) for the diagnosis of Alzheimer's disease (AD) in some patients, the Centers for Medicare and Medicaid Services suggested the need to develop and test analysis techniques to optimize diagnostic accuracy. We developed an automated computer package comparing an individual's FDG PET image to those of a group of normal volunteers. The normal control group includes FDG-PET images from 82 cognitively normal subjects, 61.89±5.67 years of age, who were characterized demographically, clinically, neuropsychologically, and by their apolipoprotein E genotype (known to be associated with a differential risk for AD). In addition, AD-affected brain regions functionally defined as based on a previous study (Alexander, et al, Am J Psychiatr, 2002) were also incorporated. Our computer package permits the user to optionally select control subjects, matching the individual patient for gender, age, and educational level. It is fully streamlined to require minimal user intervention. With one mouse click, the program runs automatically, normalizing the individual patient image, setting up a design matrix for comparing the single subject to a group of normal controls, performing the statistics, calculating the glucose reduction overlap index of the patient with the AD-affected brain regions, and displaying the findings in reference to the AD regions. In conclusion, the package automatically contrasts a single patient to a normal subject database using sound statistical procedures. With further validation, this computer package could be a valuable tool to assist physicians in decision making and communicating findings with patients and patient families.
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.
Spatial normalization is a very important step in the processing of magnetic resonance imaging (MRI) data. So the quality of brain templates is crucial for the accuracy of MRI analysis. In this paper, using the classical protocol and the optimized protocol plus nonlinear deformation, we constructed the T1 whole brain templates and apriori brain tissue data from 69 Chinese pediatric MRI data (age 7-16 years). Then we proposed a new assessment method to evaluate our templates. 10 pediatric subjects were chosen to do the assessment as the following steps. First, the cerebellum region, the region of interest (ROI), was located on both the pediatric volume and the template volume by an experienced neuroanatomist. Second, the pediatric whole brain was mapped to the template with affine and nonlinear deformation. Third, the parameter, derived from the second step, was used to only normalize the ROI of the child to the ROI of the template. Last, the overlapping ratio, which described the overlapping rate between the ROI of the template and the normalized ROI of the child, was calculated. The mean of overlapping ratio normalized to the classical template was 0.9687, and the mean normalized to the optimized template was 0.9713. The results show that the two Chinese pediatric brain templates are comparable and their accuracy is adequate to our studies.
We investigated the use of the Generalized Linear Least Squares (GLLS) method for fast estimation of myocardial blood flow (MBF) with N-13 ammonia Positron Emission Tomography (PET). GLLS was based on a high order integral equation converted from the state variable differential equations describing the kinetics of a PET tracer. Two error sources, spillover and the measurement noise, were studied. The estimation of the spillover coefficients between plasma time activity curve (TAC) and tissue TAC was incorporated into the GLLS. The GLLS procedure was modified accordingly. It was found, in computer simulation, that spillover correction incorporated GLLS provided as reliable MBF estimation as the model fitting method accounting for spillover. Since the linear kinetic model relating the plasma TAC to the tissue TAC was equivalent to the one relating the integral of plasma TAC (accumulated counts) to the integral of the tissue TAC, the GLLS method could be directly applied to the accumulated PET counts. It was found that the direct use of the accumulated counts reduced the random fluctuation observed in TAC data from PET images; and that this noise reduction significantly improved the accuracy of estimated MBF.
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