scholarly journals Prediction of Mild Cognitive Impairment Conversion Using Auxiliary Information

2019 ◽  
Author(s):  
Xiaofeng Zhu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Auxiliary Information ◽  
Feature Selection Method ◽  
Positron Emission ◽  
Public Data ◽  
Target Data

In this paper, we propose a new feature selection method to exploit the issue of High Dimension Low Sample Size (HDLSS) for the prediction of Mild Cognitive Impairment (MCI) conversion. Specially, by regarding the Magnetic Resonance Imaging (MRI) information of MCI subjects as the target data, this paper proposes to integrate auxiliary information with the target data in a unified feature selection framework for distinguishing progressive MCI (pMCI) subjects from stable MCI (sMCI) subjects, i.e., the MCI conversion classification for short in this paper, based on their MRI information. The auxiliary information includes the Positron Emission Tomography (PET) information of the target data, the MRI information of Alzheimer’s Disease (AD) subjects and Normal Control (NC) subjects, and the ages of the target data and the AD and NC subjects. As a result, the proposed method jointly selects features from the auxiliary data and the target data by taking into account the influence of outliers and aging of these two kinds of data. Experimental results on the public data of Alzheimer’s Disease Neuroimaging Initiative (ADNI) verified the effectiveness of our proposed method, compared to three state-of-the-art feature selection methods, in terms of four classification evaluation metrics.

An Embedded Feature Selection and Multi-Class Classification Method for Detection of the Progression from Mild Cognitive Impairment to Alzheimer's Disease

2020 ◽  
Vol 10 (2) ◽  
pp. 370-379 ◽  
Author(s):  
Jie Cai ◽  
Lingjing Hu ◽  
Zhou Liu ◽  
Ke Zhou ◽  
Huailing Zhang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Treatment Plan ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Feature Subset

Background: Mild cognitive impairment (MCI) patients are a high-risk group for Alzheimer's disease (AD). Each year, the diagnosed of 10–15% of MCI patients are converted to AD (MCI converters, MCI_C), while some MCI patients remain relatively stable, and unconverted (MCI stable, MCI_S). MCI patients are considered the most suitable population for early intervention treatment for dementia, and magnetic resonance imaging (MRI) is clinically the most recommended means of imaging examination. Therefore, using MRI image features to reliably predict the conversion from MCI to AD can help physicians carry out an effective treatment plan for patients in advance so to prevent or slow down the development of dementia. Methods: We proposed an embedded feature selection method based on the least squares loss function and within-class scatter to select the optimal feature subset. The optimal subsets of features were used for binary classification (AD, MCI_C, MCI_S, normal control (NC) in pairs) based on a support vector machine (SVM), and the optimal 3-class features were used for 3-class classification (AD, MCI_C, MCI_S, NC in triples) based on one-versus-one SVMs (OVOSVMs). To ensure the insensitivity of the results to the random train/test division, a 10-fold cross-validation has been repeated for each classification. Results: Using our method for feature selection, only 7 features were selected from the original 90 features. With using the optimal subset in the SVM, we classified MCI_C from MCI_S with an accuracy, sensitivity, and specificity of 71.17%, 68.33% and 73.97%, respectively. In comparison, in the 3-class classification (AD vs. MCI_C vs. MCI_S) with OVOSVMs, our method selected 24 features, and the classification accuracy was 81.9%. The feature selection results were verified to be identical to the conclusions of the clinical diagnosis. Our feature selection method achieved the best performance, comparing with the existing methods using lasso and fused lasso for feature selection. Conclusion: The results of this study demonstrate the potential of the proposed approach for predicting the conversion from MCI to AD by identifying the affected brain regions undergoing this conversion.

scholarly journals Neuroimage Biomarker Identification of the Conversion of Mild Cognitive Impairment to Alzheimer’s Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Te-Han Kung ◽  
Tzu-Cheng Chao ◽  
Yi-Ru Xie ◽  
Ming-Chyi Pai ◽  
Yu-Min Kuo ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Structural Changes ◽  
Feature Selection Method ◽  
Cognitive Capacity ◽  
Principal Curvatures ◽  
Hippocampal Subfields

An efficient method to identify whether mild cognitive impairment (MCI) has progressed to Alzheimer’s disease (AD) will be beneficial to patient care. Previous studies have shown that magnetic resonance imaging (MRI) has enabled the assessment of AD progression based on imaging findings. The present work aimed to establish an algorithm based on three features, namely, volume, surface area, and surface curvature within the hippocampal subfields, to model variations, including atrophy and structural changes to the cortical surface. In this study, a new biomarker, the ratio of principal curvatures (RPC), was proposed to characterize the folding patterns of the cortical gyrus and sulcus. Along with volumes and surface areas, these morphological features associated with the hippocampal subfields were assessed in terms of their sensitivity to the changes in cognitive capacity by two different feature selection methods. Either the extracted features were statistically significantly different, or the features were selected through a random forest model. The identified subfields and their structural indices that are sensitive to the changes characteristic of the progression from MCI to AD were further assessed with a multilayer perceptron classifier to help facilitate the diagnosis. The accuracy of the classification based on the proposed method to distinguish whether a MCI patient enters the AD stage amounted to 79.95%, solely using the information from the features selected by a logical feature selection method.

Monte Carlo feature selection and rule-based models to predict Alzheimer’s disease in mild cognitive impairment

2012 ◽  
Vol 119 (7) ◽  
pp. 821-831 ◽  
Author(s):  
Marcin Kruczyk ◽  
Henrik Zetterberg ◽  
Oskar Hansson ◽  
Sindre Rolstad ◽  
Lennart Minthon ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Monte Carlo ◽  
Feature Selection ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Rule Based ◽  
Monte Carlo Feature Selection

scholarly journals β-Amyloid is Associated with Aberrant Metabolic Connectivity in Subjects with Mild Cognitive Impairment

2014 ◽  
Vol 34 (7) ◽  
pp. 1169-1179 ◽  
Author(s):  
Felix Carbonell ◽  
Arnaud Charil ◽  
Alex P Zijdenbos ◽  
Alan C Evans ◽  
Barry J Bedell ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Disease Process ◽  
Brain Regions ◽  
Apoe Ε4 ◽  
Positron Emission ◽  
Β Amyloid ◽  
Glucose Hypometabolism

Positron emission tomography (PET) studies using [18F]2-fluoro-2-deoxyglucose (FDG) have identified a well-defined pattern of glucose hypometabolism in Alzheimer's disease (AD). The assessment of the metabolic relationship among brain regions has the potential to provide unique information regarding the disease process. Previous studies of metabolic correlation patterns have demonstrated alterations in AD subjects relative to age-matched, healthy control subjects. The objective of this study was to examine the associations between β-amyloid, apolipoprotein ε4 (APOE ε4) genotype, and metabolic correlations patterns in subjects diagnosed with mild cognitive impairment (MCI). Mild cognitive impairment subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study were categorized into β-amyloid-low and β-amyloid-high groups, based on quantitative analysis of [18F]florbetapir PET scans, and APOE ε4 non-carriers and carriers based on genotyping. We generated voxel-wise metabolic correlation strength maps across the entire cerebral cortex for each group, and, subsequently, performed a seed-based analysis. We found that the APOE ε4 genotype was closely related to regional glucose hypometabolism, while elevated, fibrillar β-amyloid burden was associated with specific derangements of the metabolic correlation patterns.

scholarly journals Validity Evidence for the Research Category, “Cognitively Unimpaired – Declining,” as a Risk Marker for Mild Cognitive Impairment and Alzheimer’s Disease

2021 ◽  
Vol 13 ◽  
Author(s):  
Rebecca Langhough Koscik ◽  
Bruce P. Hermann ◽  
Samantha Allison ◽  
Lindsay R. Clark ◽  
Erin M. Jonaitis ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Decline ◽  
Operational Definition ◽  
Cognitive Status ◽  
Validity Evidence ◽  
Positron Emission ◽  
Clinically Significant

While clinically significant cognitive impairment is the key feature of the symptomatic stages of the Alzheimer’s disease (AD) continuum, subtle cognitive decline is now known to occur years before a clinical diagnosis of mild cognitive impairment (MCI) or dementia due to AD is made. The primary aim of this study was to examine criterion validity evidence for an operational definition of “cognitively unimpaired-declining” (CU-D) in the Wisconsin Registry for Alzheimer’s Prevention (WRAP), a longitudinal cohort study following cognition and risk factors from mid-life and on. Cognitive status was determined for each visit using a consensus review process that incorporated internal norms and published norms; a multi-disciplinary panel reviewed cases first to determine whether MCI or dementia was present, and subsequently whether CU-D was present, The CU-D group differed from CU-stable (CU-S) and MCI on concurrent measures of cognition, demonstrating concurrent validity. Participants who changed from CU-S to CU-D at the next study visit demonstrated greater declines than those who stayed CU-S. In addition, those who were CU-D were more likely to progress to MCI or dementia than those who were CU-S (predictive validity). In a subsample with positron emission tomography (PET) imaging, the CU-D group also differed from the CU-S and MCI/Dementia groups on measures of amyloid and tau burden, indicating that biomarker evidence of AD was elevated in those showing sub-clinical (CU-D) decline. Together, the results corroborate other studies showing that cognitive decline begins long before a dementia diagnosis and indicate that operational criteria can detect subclinical decline that may signal AD or other dementia risk.

A Role for Prior Knowledge in Statistical Classification of the Transition from Mild Cognitive Impairment to Alzheimer’s Disease

2021 ◽  
Vol 83 (4) ◽  
pp. 1859-1875
Author(s):  
Zihuan Liu ◽  
Tapabrata Maiti ◽  
Andrew R. Bender ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Support Vector ◽  
Statistical Classification ◽  
Relative Value ◽  
Data Source ◽  
Feature Selection Techniques

Background: The transition from mild cognitive impairment (MCI) to dementia is of great interest to clinical research on Alzheimer’s disease and related dementias. This phenomenon also serves as a valuable data source for quantitative methodological researchers developing new approaches for classification. However, the growth of machine learning (ML) approaches for classification may falsely lead many clinical researchers to underestimate the value of logistic regression (LR), which often demonstrates classification accuracy equivalent or superior to other ML methods. Further, when faced with many potential features that could be used for classifying the transition, clinical researchers are often unaware of the relative value of different approaches for variable selection. Objective: The present study sought to compare different methods for statistical classification and for automated and theoretically guided feature selection techniques in the context of predicting conversion from MCI to dementia. Methods: We used data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to evaluate different influences of automated feature preselection on LR and support vector machine (SVM) classification methods, in classifying conversion from MCI to dementia. Results: The present findings demonstrate how similar performance can be achieved using user-guided, clinically informed pre-selection versus algorithmic feature selection techniques. Conclusion: These results show that although SVM and other ML techniques are capable of relatively accurate classification, similar or higher accuracy can often be achieved by LR, mitigating SVM’s necessity or value for many clinical researchers.

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