Early Detection of Mild Cognitive Impairment and Mild Alzheimer’s Disease in Elderly using CBF Activation during Verbally-based Cognitive Tests

Abstract BACKGROUND Alzheimer’s disease (AD) involves the abnormal activity of transition metals and metal ion dyshomeostasis. The present study aimed to assess the potential of 36 trace elements in predicting cognitive decline in patients with amnestic mild cognitive impairment (aMCI) or AD. METHODS All participants (controls, aMCI, and AD) underwent baseline cognitive tests, which included the Mini-Mental State Examination (MMSE) and plasma biomarker examinations. We conducted a trend analysis for the cognitive tests and plasma trace elements and examined the correlations between the latter and annual MMSE changes during follow-up. RESULTS An increase in the disease severity was linked to lowered boron (B), bismuth (Bi), thorium (Th), and uranium (U) plasma concentrations (adjusted P < 0.05). “B”, mercury (Hg) and “Th” levels could detect different cognitive stages. “B” displayed high area under the receiver operating characteristic curves (AUCs) for aMCI and AD versus controls (97.6%, cut-off value: ≤73.1 ug/l and 100%, cut-off value: ≤47.1 ug/l, respectively). “Hg” displayed the highest AUC result to differentiate AD from aMCI (79.9%, cut-off value: ≤1.02 ug/l). Higher baseline levels of calcium (r = 0.50, p = 0.026) were associated with less annual cognitive decline. While higher baseline levels of “B” (r=-0.70, p = 0.001), zirconium (r=-0.58, p = 0.007), “Th” (r=-0.52, p = 0.020) were associated with rapid annual cognitive decline in the aMCI group, those of manganese (r=-0.91, p = 0.035) were associated with rapid annual cognitive decline in the AD group. CONCLUSION Plasma metal level biomarkers can be used as an in vivo tool to study and identify patients with aMCI and AD.

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

Alzheimer s Research & Therapy ◽

10.1186/s13195-021-00900-w ◽

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

10.21203/rs.3.rs-288564/v1 ◽

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.

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State-of-science review: SR-E11: Early detection of mild cognitive impairment and Alzheimer's disease: An example using the CANTAB PAL

PsycEXTRA Dataset ◽

10.1037/e592872011-001 ◽

2008 ◽

Author(s):

Mischa de Rover ◽

Sharon Morein-Zamir ◽

Barbara Sahakian

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Early Detection ◽

Science Review

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Predictive Model for Early Detection of Mild Cognitive Impairment and Alzheimer’s Disease

Optimization and Data Analysis in Biomedical Informatics - Fields Institute Communications ◽

10.1007/978-1-4614-4133-5_4 ◽

2012 ◽

pp. 83-97 ◽

Cited By ~ 1

Author(s):

Eva K. Lee ◽

Tsung-Lin Wu ◽

Felicia Goldstein ◽

Allan Levey

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Early Detection ◽

Predictive Model

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

10.31234/osf.io/vur7e ◽

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.

Download Full-text