A Community-Based Study Identifying Metabolic Biomarkers of Mild Cognitive Impairment and Alzheimer’s Disease Using Artificial Intelligence and Machine Learning

2020 ◽  
Vol 78 (4) ◽  
pp. 1381-1392
Author(s):  
Ali Yilmaz ◽  
Ilyas Ustun ◽  
Zafer Ugur ◽  
Sumeyya Akyol ◽  
William T. Hu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Lipid Metabolism ◽  
Mild Cognitive Impairment ◽  
Proton Nuclear Magnetic Resonance ◽  
Community Based ◽  
Depth Analysis

Background: Currently, there is no objective, clinically available tool for the accurate diagnosis of Alzheimer’s disease (AD). There is a pressing need for a novel, minimally invasive, cost friendly, and easily accessible tool to diagnose AD, assess disease severity, and prognosticate course. Metabolomics is a promising tool for discovery of new, biologically, and clinically relevant biomarkers for AD detection and classification. Objective: Utilizing artificial intelligence and machine learning, we aim to assess whether a panel of metabolites as detected in plasma can be used as an objective and clinically feasible tool for the diagnosis of mild cognitive impairment (MCI) and AD. Methods: Using a community-based sample cohort acquired from different sites across the US, we adopted an approach combining Proton Nuclear Magnetic Resonance Spectroscopy (1H NMR), Liquid Chromatography coupled with Mass Spectrometry (LC-MS) and various machine learning statistical approaches to identify a biomarker panel capable of identifying those patients with AD and MCI from healthy controls. Results: Of the 212 measured metabolites, 5 were identified as optimal to discriminate between controls, and individuals with MCI or AD. Our models performed with AUC values in the range of 0.72–0.76, with the sensitivity and specificity values ranging from 0.75–0.85 and 0.69–0.81, respectively. Univariate and pathway analysis identified lipid metabolism as the most perturbed biochemical pathway in MCI and AD. Conclusion: A comprehensive method of acquiring metabolomics data, coupled with machine learning techniques, has identified a strong panel of diagnostic biomarkers capable of identifying individuals with MCI and AD. Further, our data confirm what other groups have reported, that lipid metabolism is significantly perturbed in those individuals suffering with dementia. This work may provide additional insight into AD pathogenesis and encourage more in-depth analysis of the AD lipidome.

Using Fractional Anisotropy Imaging to Detect Mild Cognitive Impairment and Alzheimer’s Disease among Mexican Americans and Non-Hispanic Whites: A HABLE Study

2021 ◽  
pp. 1-8
Author(s):  
James R. Hall ◽  
Leigh A. Johnson ◽  
Fan Zhang ◽  
Melissa Petersen ◽  
Arthur W. Toga ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Fractional Anisotropy ◽  
Mexican Americans ◽  
Diffusion Tensor ◽  
Characteristic Curve ◽  
Aging Brain ◽  
Community Based

<b><i>Introduction:</i></b> Alzheimer’s disease (AD) is the most frequently occurring neurodegenerative disease; however, little work has been conducted examining biomarkers of AD among Mexican Americans. Here, we examined diffusion tensor MRI marker profiles for detecting mild cognitive impairment (MCI) and dementia in a multi-ethnic cohort. <b><i>Methods:</i></b> 3T MRI measures of fractional anisotropy (FA) were examined among 1,636 participants of the ongoing community-based Health &amp; Aging Brain among Latino Elders (HABLE) community-based study (Mexican American <i>n</i> = 851; non-Hispanic white <i>n</i> = 785). <b><i>Results:</i></b> The FA profile was highly accurate in detecting both MCI (area under the receiver operating characteristic curve [AUC] = 0.99) and dementia (AUC = 0.98). However, the FA profile varied significantly not only between diagnostic groups but also between Mexican Americans and non-Hispanic whites. <b><i>Conclusion:</i></b> Findings suggest that diffusion tensor imaging markers may have a role in the neurodiagnostic process for detecting MCI and dementia among diverse populations.

scholarly journals A machine learning-based linguistic battery for diagnosing mild cognitive impairment due to Alzheimer’s disease

PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0229460
Author(s):  
Sylvester Olubolu Orimaye ◽  
Karl Goodkin ◽  
Ossama Abid Riaz ◽  
Jean-Maurice Miranda Salcedo ◽  
Thabit Al-Khateeb ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment

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.

scholarly journals Machine Learning Methods for Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease. A Systematic Review.

2021 ◽  
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 ◽  
Multidimensional Data ◽  
Support Vector

Abstract Background: Increase in life-span 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 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.Methods: We conducted a systematic review following PRISMA guidelines of studies where Machine Learning was applied to neuroimaging data in order to predict the progression from Mild Cognitive Impairment to Alzheimer’s disease. After removing duplicates, we screened 159 studies and selected 47 for a qualitative analysis. Results: Most studies used Magnetic Resonance Image and Positron Emission Tomography data but also Magnetoencephalography. The datasets were mainly extracted from the Alzheimer’s disease Neuroimage Initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common were support vector machines, but more complex models such as Deep Learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, biological, and behavioral) achieved the best performance. Conclusions: Although performance of the different models 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.

Characteristics of Mild Cognitive Impairment Using the Thai Version of the Consortium to Establish a Registry for Alzheimer’s Disease Tests: A Multivariate and Machine Learning Study

2018 ◽  
Vol 45 (1-2) ◽  
pp. 38-48 ◽  
Author(s):  
Chavit Tunvirachaisakul ◽  
Thitiporn Supasitthumrong ◽  
Sookjareon Tangwongchai ◽  
Solaphat Hemrunroj ◽  
Phenphichcha Chuchuen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Support Vector ◽  
Test Machine ◽  
Characteristic Analysis ◽  
Adaptive Boosting ◽  
Normal Controls

Background: The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) developed a neuropsychological battery (CERAD-NP) to screen patients with Alzheimer’s dementia. Mild cognitive impairment (MCI) has received attention as a pre-dementia stage. Objectives: To delineate the CERAD-NP features of MCI and their clinical utility to externally validate MCI diagnosis. Methods: The study included 60 patients with MCI, diagnosed using the Clinical Dementia Rating, and 63 normal controls. Data were analysed employing receiver operating characteristic analysis, Linear Support Vector Machine, Random Forest, Adaptive Boosting, Neural Network models, and t-distributed stochastic neighbour embedding (t-SNE). Results: MCI patients were best discriminated from normal controls using a combination of Wordlist Recall, Wordlist Memory, and Verbal Fluency Test. Machine learning showed that the CERAD features learned from MCI patients and controls were not strongly predictive of the diagnosis (maximal cross-validation 77.2%), whilst t-SNE showed that there is a considerable overlap between MCI and controls. Conclusions: The most important features of the CERAD-NP differentiating MCI from normal controls indicate impairments in episodic and semantic memory and recall. While these features significantly discriminate MCI patients from normal controls, the tests are not predictive of MCI.

scholarly journals Machine Learning methods for predicting conversion from mild cognitive impairment to Alzheimer’s disease. A systematic review.

2021 ◽  
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 ◽  
Multidimensional Data ◽  
Support Vector

Increase in life-span in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease (AD) being the most prevalent. Advances in medical imaging and computational power, enable new methods for 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 (MCI), sometimes a prodromal stage of AD. We conducted a systematic review following PRISMA guidelines of studies where Machine Learning was applied to neuroimaging data in order to predict the progression from MCI to AD. After removing duplicates, we screened 159 studies and selected 47 for a qualitative analysis. Most studies used MRI and PET data but also MEG. The datasets were mainly extracted from the ADNI database with some exceptions. Regarding the algorithms used, the most common were support vector machines, but more complex models such as Deep Learning, combined with multimodal and multidimensional data (neuroimage, clinical, cognitive, biological, and behavioral) achieved the best performance. Although performance of the different models still have 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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