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Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using MRI and Structural Network Features.

Wei R, Li C, Fogelson N, Li L - Front Aging Neurosci (2016)

Bottom Line: To obtain unbiased results, support vector machine (SVM) classifiers with nested cross validation were used for classification.The combination of 10 features including those from MRI and network measures attained accuracies of 66.04, 76.39, 74.66, and 73.91% for mixed conversion time, 6, 12, and 18 months before diagnosis of probable AD, respectively.Analysis of the diagnostic power of different time periods before diagnosis of probable AD showed that short-term prediction (6 and 12 months) achieved more stable and higher AUC scores compared with long-term prediction (18 months), with K-values from 1 to 30.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory for NeuroInformation of Ministry of Education, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China.

ABSTRACT
Optimized magnetic resonance imaging (MRI) features and abnormalities of brain network architectures may allow earlier detection and accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). In this study, we proposed a classification framework to distinguish MCI converters (MCIc) from MCI non-converters (MCInc) by using a combination of FreeSurfer-derived MRI features and nodal features derived from the thickness network. At the feature selection step, we first employed sparse linear regression with stability selection, for the selection of discriminative features in the iterative combinations of MRI and network measures. Subsequently the top K features of available combinations were selected as optimal features for classification. To obtain unbiased results, support vector machine (SVM) classifiers with nested cross validation were used for classification. The combination of 10 features including those from MRI and network measures attained accuracies of 66.04, 76.39, 74.66, and 73.91% for mixed conversion time, 6, 12, and 18 months before diagnosis of probable AD, respectively. Analysis of the diagnostic power of different time periods before diagnosis of probable AD showed that short-term prediction (6 and 12 months) achieved more stable and higher AUC scores compared with long-term prediction (18 months), with K-values from 1 to 30. The present results suggest that meaningful predictors composed of MRI and network measures may offer the possibility for early detection of progression from MCI to AD.

No MeSH data available.


Related in: MedlinePlus

Proposed prediction framework. (A) Feature extraction: T1-weigthed images are processed and individual thickness network is constructed based on the difference in cortical thickness of a pair of ROIs. (B) Classification: SVM classifier with nested cross validation is implemented for classification.
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Figure 1: Proposed prediction framework. (A) Feature extraction: T1-weigthed images are processed and individual thickness network is constructed based on the difference in cortical thickness of a pair of ROIs. (B) Classification: SVM classifier with nested cross validation is implemented for classification.

Mentions: The FreeSurfer 5.30 software package was utilized for cortical reconstruction and volumetric segmentation (FreeSurfer v5.30, http://surfer.nmr.mgh.harvard.edu/fswiki). In brief, the processing contains automated Talairach spaces transformation, intensity inhomogeneity correction, removal of non-brain tissue, intensity normalization, tissue segmentation (Fischl et al., 2002), automated topology correction, surface deformation to generate the gray/white matter boundary and gray matter/ Cerebrospinal Fluid (CSF) boundary, and parcellation of the cerebral cortex (Desikan et al., 2006). The quality of the raw MRI images, Talairach registration, intensity normalization, brain segmentation, and surface demarcation were assessed using a manual inspection protocol. The images that failed the stages of quality assurance were removed from subsequent analysis. The atlas used in FreeSurfer included 34 cortical ROIs per hemisphere (Table 2). For each cortical ROI, cortical thickness (CT), cortical volume (CV), and cortical surface area (CS) were calculated as three subtypes of MRI features. CT at each vertex of the cortex was calculated as the average shortest distance between white and pail surfaces. CS was calculated by computing the area of every triangle in a standardized spherical surface tessellation. CV at each vertex was computed by the product of the CS and CT at each surface vertex. This yielded a total of 204 cortical features for each subject (Figure 1A).


Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using MRI and Structural Network Features.

Wei R, Li C, Fogelson N, Li L - Front Aging Neurosci (2016)

Proposed prediction framework. (A) Feature extraction: T1-weigthed images are processed and individual thickness network is constructed based on the difference in cortical thickness of a pair of ROIs. (B) Classification: SVM classifier with nested cross validation is implemented for classification.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4836149&req=5

Figure 1: Proposed prediction framework. (A) Feature extraction: T1-weigthed images are processed and individual thickness network is constructed based on the difference in cortical thickness of a pair of ROIs. (B) Classification: SVM classifier with nested cross validation is implemented for classification.
Mentions: The FreeSurfer 5.30 software package was utilized for cortical reconstruction and volumetric segmentation (FreeSurfer v5.30, http://surfer.nmr.mgh.harvard.edu/fswiki). In brief, the processing contains automated Talairach spaces transformation, intensity inhomogeneity correction, removal of non-brain tissue, intensity normalization, tissue segmentation (Fischl et al., 2002), automated topology correction, surface deformation to generate the gray/white matter boundary and gray matter/ Cerebrospinal Fluid (CSF) boundary, and parcellation of the cerebral cortex (Desikan et al., 2006). The quality of the raw MRI images, Talairach registration, intensity normalization, brain segmentation, and surface demarcation were assessed using a manual inspection protocol. The images that failed the stages of quality assurance were removed from subsequent analysis. The atlas used in FreeSurfer included 34 cortical ROIs per hemisphere (Table 2). For each cortical ROI, cortical thickness (CT), cortical volume (CV), and cortical surface area (CS) were calculated as three subtypes of MRI features. CT at each vertex of the cortex was calculated as the average shortest distance between white and pail surfaces. CS was calculated by computing the area of every triangle in a standardized spherical surface tessellation. CV at each vertex was computed by the product of the CS and CT at each surface vertex. This yielded a total of 204 cortical features for each subject (Figure 1A).

Bottom Line: To obtain unbiased results, support vector machine (SVM) classifiers with nested cross validation were used for classification.The combination of 10 features including those from MRI and network measures attained accuracies of 66.04, 76.39, 74.66, and 73.91% for mixed conversion time, 6, 12, and 18 months before diagnosis of probable AD, respectively.Analysis of the diagnostic power of different time periods before diagnosis of probable AD showed that short-term prediction (6 and 12 months) achieved more stable and higher AUC scores compared with long-term prediction (18 months), with K-values from 1 to 30.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory for NeuroInformation of Ministry of Education, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China.

ABSTRACT
Optimized magnetic resonance imaging (MRI) features and abnormalities of brain network architectures may allow earlier detection and accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). In this study, we proposed a classification framework to distinguish MCI converters (MCIc) from MCI non-converters (MCInc) by using a combination of FreeSurfer-derived MRI features and nodal features derived from the thickness network. At the feature selection step, we first employed sparse linear regression with stability selection, for the selection of discriminative features in the iterative combinations of MRI and network measures. Subsequently the top K features of available combinations were selected as optimal features for classification. To obtain unbiased results, support vector machine (SVM) classifiers with nested cross validation were used for classification. The combination of 10 features including those from MRI and network measures attained accuracies of 66.04, 76.39, 74.66, and 73.91% for mixed conversion time, 6, 12, and 18 months before diagnosis of probable AD, respectively. Analysis of the diagnostic power of different time periods before diagnosis of probable AD showed that short-term prediction (6 and 12 months) achieved more stable and higher AUC scores compared with long-term prediction (18 months), with K-values from 1 to 30. The present results suggest that meaningful predictors composed of MRI and network measures may offer the possibility for early detection of progression from MCI to AD.

No MeSH data available.


Related in: MedlinePlus