scholarly journals To Explore the Predictive Power of Visuomotor Network Dysfunctions in Mild Cognitive Impairment and Alzheimer’s Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Justine Staal ◽  
Francesco Mattace-Raso ◽  
Hennie A. M. Daniels ◽  
Johannes van der Steen ◽  
Johan J. M. Pel
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Predictive Power ◽  
Area Under The Curve ◽  
Classification Performance ◽  
Support Vector ◽  
Potential Biomarker

BackgroundResearch into Alzheimer’s disease has shifted toward the identification of minimally invasive and less time-consuming modalities to define preclinical stages of Alzheimer’s disease.MethodHere, we propose visuomotor network dysfunctions as a potential biomarker in AD and its prodromal stage, mild cognitive impairment with underlying the Alzheimer’s disease pathology. The functionality of this network was tested in terms of timing, accuracy, and speed with goal-directed eye-hand tasks. The predictive power was determined by comparing the classification performance of a zero-rule algorithm (baseline), a decision tree, a support vector machine, and a neural network using functional parameters to classify controls without cognitive disorders, mild cognitive impaired patients, and Alzheimer’s disease patients.ResultsFair to good classification was achieved between controls and patients, controls and mild cognitive impaired patients, and between controls and Alzheimer’s disease patients with the support vector machine (77–82% accuracy, 57–93% sensitivity, 63–90% specificity, 0.74–0.78 area under the curve). Classification between mild cognitive impaired patients and Alzheimer’s disease patients was poor, as no algorithm outperformed the baseline (63% accuracy, 0% sensitivity, 100% specificity, 0.50 area under the curve).Comparison with Existing Method(s)The classification performance found in the present study is comparable to that of the existing CSF and MRI biomarkers.ConclusionThe data suggest that visuomotor network dysfunctions have potential in biomarker research and the proposed eye-hand tasks could add to existing tests to form a clear definition of the preclinical phenotype of AD.

Performance of Validated MicroRNA Biomarkers for Alzheimer’s Disease in Mild Cognitive Impairment

2020 ◽  
Vol 78 (1) ◽  
pp. 245-263
Author(s):  
Ursula S. Sandau ◽  
Jack T. Wiedrick ◽  
Sierra J. Smith ◽  
Trevor J. McFarland ◽  
Theresa A. Lusardi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Stage ◽  
Linear Trend ◽  
Area Under The Curve ◽  
Classification Performance ◽  
T Tau ◽  
Median Expression

Background: Cerebrospinal fluid (CSF) microRNA (miRNA) biomarkers of Alzheimer’s disease (AD) have been identified, but have not been evaluated in prodromal AD, including mild cognitive impairment (MCI). Objective: To assess whether a set of validated AD miRNA biomarkers in CSF are also sensitive to early-stage pathology as exemplified by MCI diagnosis. Methods: We measured the expression of 17 miRNA biomarkers for AD in CSF samples from AD, MCI, and cognitively normal controls (NC). We then examined classification performance of the miRNAs individually and in combination. For each miRNA, we assessed median expression in each diagnostic group and classified markers as trending linearly, nonlinearly, or lacking any trend across the three groups. For trending miRNAs, we assessed multimarker classification performance alone and in combination with apolipoprotein E ɛ4 allele (APOE ɛ4) genotype and amyloid-β42 to total tau ratio (Aβ42:T-Tau). We identified predicted targets of trending miRNAs using pathway analysis. Results: Five miRNAs showed a linear trend of decreasing median expression across the ordered diagnoses (control to MCI to AD). The trending miRNAs jointly predicted AD with area under the curve (AUC) of 0.770, and MCI with AUC of 0.705. Aβ42:T-Tau alone predicted MCI with AUC of 0.758 and the AUC improved to 0.813 (p = 0.051) after adding the trending miRNAs. Multivariate correlation of the five trending miRNAs with Aβ42:T-Tau was weak. Conclusion: Selected miRNAs combined with Aβ42:T-Tau improved classification performance (relative to protein biomarkers alone) for MCI, despite a weak correlation with Aβ42:T-Tau. Together these data suggest that that these miRNAs carry novel information relevant to AD, even at the MCI stage. Preliminary target prediction analysis suggests novel roles for these biomarkers.

Gaussian Discriminant Analysis for Optimal Delineation of Mild Cognitive Impairment in Alzheimer’s Disease

2018 ◽  
Vol 28 (08) ◽  
pp. 1850017 ◽  
Author(s):  
Chen Fang ◽  
Chunfei Li ◽  
Mercedes Cabrerizo ◽  
Armando Barreto ◽  
Jean Andrian ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Discriminant Analysis ◽  
Classification Problem ◽  
Classification Performance ◽  
High Dimensional ◽  
Support Vector ◽  
Brain Hemispheres

Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer’s disease (AD) and its prodromal stage of mild cognitive impairment (MCI). Using multimodal biomarkers for this high-dimensional classification problem, the widely used algorithms include Support Vector Machines (SVM), Sparse Representation-based classification (SRC), Deep Belief Networks (DBN) and Random Forest (RF). These widely used algorithms continue to yield unsatisfactory performance for delineating the MCI participants from the cognitively normal control (CN) group. A novel Gaussian discriminant analysis-based algorithm is thus introduced to achieve a more effective and accurate classification performance than the aforementioned state-of-the-art algorithms. This study makes use of magnetic resonance imaging (MRI) data uniquely as input to two separate high-dimensional decision spaces that reflect the structural measures of the two brain hemispheres. The data used include 190 CN, 305 MCI and 133 AD subjects as part of the AD Big Data DREAM Challenge #1. Using 80% data for a 10-fold cross-validation, the proposed algorithm achieved an average F1 score of 95.89% and an accuracy of 96.54% for discriminating AD from CN; and more importantly, an average F1 score of 92.08% and an accuracy of 90.26% for discriminating MCI from CN. Then, a true test was implemented on the remaining 20% held-out test data. For discriminating MCI from CN, an accuracy of 80.61%, a sensitivity of 81.97% and a specificity of 78.38% were obtained. These results show significant improvement over existing algorithms for discriminating the subtle differences between MCI participants and the CN group.

Association of Androgens and Gonadotropins with Amnestic Mild Cognitive Impairment and Probable Alzheimer’s Disease in Chinese Elderly Men

2020 ◽  
Vol 78 (1) ◽  
pp. 277-290
Author(s):  
Yan Li ◽  
Sha Li ◽  
Shunjiang Xu ◽  
Hong Yu ◽  
Longmei Tang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Area Under The Curve ◽  
Amnestic Mild Cognitive Impairment ◽  
Elderly Men ◽  
Age Related ◽  
Potential Biomarker ◽  
Amci Group

Background: Age-related hormone changes play important roles in cognitive decline in older men, and apolipoprotein E ɛ4 (APOE ɛ4) is a risk factor for Alzheimer’s disease (AD). Objective: This study aimed to investigate the interactive role of androgen decline and APOE ɛ4 genotype in the pathogenesis of amnestic mild cognitive impairment (aMCI) and AD. Methods: In total, 576 elderly men over 65 years old from communities in Shijiazhuang were enrolled in this study, including 243 with normal cognition (NC), 271 with aMCI, and 62 with probable AD. Cognitive function was evaluated with a battery of neuropsychological tests. The serum levels of androgen and gonadotropin were detected by ELISA and chemiluminescence immunoassay. Results: The levels of free testosterone (FT) and dihydrotestosterone (DHT) were lower in the aMCI group (p < 0.05), and even lower in the AD group (p < 0.001), but the levels of follicle stimulating hormone (FSH) and luteinizing hormone (LH) were higher in AD group (p < 0.01), comparing with that in NC or aMCI group. The interaction of lower FT or DHT levels with APOE ɛ4 had a risk role in global cognitive impairment (p < 0.05). The area under the curve (AUC) of the ROC curve for predicting aMCI by serum FT levels was 0.745. Conclusion: These results indicated that the interaction of androgen decline and APOE ɛ4 genotype play a role in aMCI and AD. Serum FT levels have a predictive value for aMCI and might be a potential biomarker for prodromal AD.

scholarly journals 12-year prediction of mild cognitive impairment aided by Alzheimer’s brain signatures at mean age 56

2021 ◽  
Author(s):  
McKenna E Williams ◽  
Jeremy A Elman ◽  
Linda K McEvoy ◽  
Ole A Andreassen ◽  
Anders M Dale ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Mean Diffusivity ◽  
Area Under The Curve ◽  
Polygenic Risk Score ◽  
Age Difference ◽  
Cognitively Normal ◽  
Brain Age

Abstract Neuroimaging signatures based on composite scores of cortical thickness and hippocampal volume predict progression from mild cognitive impairment to Alzheimer’s disease. However, little is known about the ability of these signatures among cognitively normal adults to predict progression to mild cognitive impairment. Toward that end, a signature sensitive to microstructural changes that may predate macrostructural atrophy should be useful. We hypothesized that: 1) a validated MRI-derived Alzheimer’s disease signature based on cortical thickness and hippocampal volume in cognitively normal middle-aged adults would predict progression to mild cognitive impairment; and 2) a novel gray matter mean diffusivity signature would be a better predictor than the thickness/volume signature. This cohort study was part of the Vietnam Era Twin Study of Aging. Concurrent analyses compared cognitively normal and mild cognitive impairment groups at each of three study waves (ns = 246–367). Predictive analyses included 169 cognitively normal men at baseline (age = 56.1, range = 51–60). Our previously published thickness/volume signature derived from independent data, a novel mean diffusivity signature using the same regions and weights as the thickness/volume signature, age, and an Alzheimer’s disease polygenic risk score were used to predict incident mild cognitive impairment an average of 12 years after baseline (follow-up age = 67.2, range = 61–71). Additional analyses adjusted for predicted brain age difference scores (chronological age minus predicted brain age) to determine if signatures were Alzheimer-related and not simply aging-related. In concurrent analyses, individuals with mild cognitive impairment had higher (worse) mean diffusivity signature scores than cognitively normal participants, but thickness/volume signature scores did not differ between groups. In predictive analyses, age and polygenic risk score yielded an area under the curve of 0.74 (sensitivity = 80.00%; specificity = 65.10%). Prediction was significantly improved with addition of the mean diffusivity signature (area under the curve = 0.83; sensitivity = 85.00%; specificity = 77.85%; P=0.007), but not with addition of the thickness/volume signature. A model including both signatures did not improve prediction over a model with only the mean diffusivity signature. Results held up after adjusting for predicted brain age difference scores. The novel mean diffusivity signature was limited by being yoked to the thickness/volume signature weightings. An independently-derived mean diffusivity signature may thus provide even stronger prediction. The young age of the sample at baseline is particularly notable. Given that the brain signatures were examined when participants were only in their 50 s, our results suggest a promising step toward improving very early identification of Alzheimer’s disease risk and the potential value of mean diffusivity and/or multimodal brain signatures.

scholarly journals Detection of mild cognitive impairment and early stage dementia with an audio-recorded cognitive scale

2013 ◽  
Vol 25 (8) ◽  
pp. 1325-1333 ◽  
Author(s):  
Margaret C. Sewell ◽  
Xiaodong Luo ◽  
Judith Neugroschl ◽  
Mary Sano
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Verbal Memory ◽  
Early Stage ◽  
Neuropsychological Testing ◽  
Area Under The Curve ◽  
Cognitive Domain ◽  
Screening Measures

ABSTRACTBackground: Physicians often miss diagnosis of mild cognitive impairment (MCI) or early dementia and screening measures can be insensitive to very mild impairments. Other cognitive assessments may take too much time or be frustrating to seniors. This study examined the ability of an audio-recorded scale, developed in Australia, to detect MCI or mild Alzheimer's disease (AD) and compared cognitive domain-specific performance on the audio-recorded scale to in-person battery and common cognitive screens.Method: Seventy-six patients from the Mount Sinai Alzheimer's Disease Research Center were recruited. Patients were aged 75 years or older, with clinical diagnosis of AD or MCI (n = 51) or normal control (n = 25). Participants underwent in-person neuropsychological testing followed by testing with the audio-recorded cognitive screen (ARCS).Results: ARCS provided better discrimination between normal and impaired elderly individuals than either the Mini-Mental State Examination or the clock drawing test. The in-person battery and ARCS analogous variables were significantly correlated, most in the 0.4 to 0.7 range, including verbal memory, executive function/attention, naming, and verbal fluency. The area under the curve generated from the receiver operating characteristic curves indicated high and equivalent discrimination for ARCS and the in-person battery (0.972 vs. 0.988; p = 0.23).Conclusion: The ARCS demonstrated better discrimination between normal controls and those with mild deficits than typical screening measures. Performance on cognitive domains within the ARCS was well correlated with the in-person battery. Completion of the ARCS was accomplished despite mild difficulty hearing the instructions even in very elderly participants, indicating that it may be a useful measure in primary care settings.

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 Analysis of 27 vascular-related proteins reveals that NT-proBNP is a potential biomarker for Alzheimer's disease and mild cognitive impairment: A pilot-study

2014 ◽  
Vol 50 ◽  
pp. 114-121 ◽  
Author(s):  
Josef Marksteiner ◽  
Douglas Imarhiagbe ◽  
Michaela Defrancesco ◽  
Eberhard A. Deisenhammer ◽  
Georg Kemmler ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Pilot Study ◽  
Potential Biomarker ◽  
Related Proteins

Multi-Objective Genetic Algorithms to Find Most Relevant Volumes of the Brain Related to Alzheimer's Disease and Mild Cognitive Impairment

2018 ◽  
Vol 28 (09) ◽  
pp. 1850022 ◽  
Author(s):  
Olga Valenzuela ◽  
Xiaoyi Jiang ◽  
Antonio Carrillo ◽  
Ignacio Rojas
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genetic Algorithms ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Support Vector ◽  
Significant Information ◽  
Multi Objective ◽  
3D Dwt ◽  
The Brain

Computer-Aided Diagnosis (CAD) represents a relevant instrument to automatically classify between patients with and without Alzheimer's Disease (AD) using several actual imaging techniques. This study analyzes the optimization of volumes of interest (VOIs) to extract three-dimensional (3D) textures from Magnetic Resonance Image (MRI) in order to diagnose AD, Mild Cognitive Impairment converter (MCIc), Mild Cognitive Impairment nonconverter (MCInc) and Normal subjects. A relevant feature of the proposed approach is the use of 3D features instead of traditional two-dimensional (2D) features, by using 3D discrete wavelet transform (3D-DWT) approach for performing feature extraction from T-1 weighted MRI. Due to the high number of coefficients when applying 3D-DWT to each of the VOIs, a feature selection algorithm based on mutual information is used, as is the minimum Redundancy Maximum Relevance (mRMR) algorithm. Region optimization has been performed in order to discover the most relevant regions (VOIs) in the brain with the use of Multi-Objective Genetic Algorithms, being one of the objectives to be optimize the accuracy of the system. The error index of the system is computed by the confusion matrix obtained by the multi-class support vector machine (SVM) classifier. Principal Component Analysis (PCA) is used with the purpose of reducing the number of features to the classifier. The cohort of subjects used in the study consisted of 296 different patients. A first group of 206 patients was used to optimize VOI selection and another group of 90 independent subjects (that did not belong to the first group) was used to test the solutions yielded by the genetic algorithm. The proposed methodology obtains excellent results in multi-class classification achieving accuracies of 94.4% and also extracting significant information on the location of the most relevant points of the brain. This suggests that the proposed method could aid in the research of other neurodegenerative diseases, improving the accuracy of the diagnosis and finding the most relevant regions of the brain associated with them.

scholarly journals Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer’s disease dementia: a systematic review

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sergio Grueso ◽  
Raquel Viejo-Sobera
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Detection ◽  
Neurocognitive Disorders ◽  
Support Vector ◽  
Alzheimer’S Disease Dementia

Abstract Background An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer’s disease dementia. Methods We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer’s disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis. Results Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer’s disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance. Conclusions Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.

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