The Clinical Value of Large Neuroimaging Data Sets in Alzheimer's Disease

The Open Access Series of Imaging Studies is a series of neuroimaging data sets that are publicly available for study and analysis. The present MRI data set consists of a longitudinal collection of 150 subjects aged 60 to 96 years all acquired on the same scanner using identical sequences. Each subject was scanned on two or more visits, separated by at least 1 year for a total of 373 imaging sessions. Subjects were characterized using the Clinical Dementia Rating (CDR) as either nondemented or with very mild to mild Alzheimer's disease. Seventy-two of the subjects were characterized as nondemented throughout the study. Sixty-four of the included subjects were characterized as demented at the time of their initial visits and remained so for subsequent scans, including 51 individuals with CDR 0.5 similar level of impairment to individuals elsewhere considered to have “mild cognitive impairment.” Another 14 subjects were characterized as nondemented at the time of their initial visit (CDR 0) and were subsequently characterized as demented at a later visit (CDR > 0). The subjects were all right-handed and include both men (n = 62) and women (n = 88). For each scanning session, three or four individual T1-weighted MRI scans were obtained. Multiple within-session acquisitions provide extremely high contrast to noise, making the data amenable to a wide range of analytic approaches including automated computational analysis. Automated calculation of whole-brain volume is presented to demonstrate use of the data for measuring differences associated with normal aging and Alzheimer's disease.

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Computer-aided methods for 3-D visualization of serial sections and thick biological specimens

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100129930 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1060-1061

Author(s):

Mark Ellisman ◽

Maryann Martone ◽

Gabriel Soto ◽

Eleizer Masliah ◽

David Hessler ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Three Dimensional ◽

Neuritic Plaque ◽

Dimensional Structure ◽

Data Sets ◽

Molecular Physiology ◽

Research Activities ◽

Computer Aided ◽

Dimensional Reconstruction

Structurally-oriented biologists examine cells, tissues, organelles and macromolecules in order to gain insight into cellular and molecular physiology by relating structure to function. The understanding of these structures can be greatly enhanced by the use of techniques for the visualization and quantitative analysis of three-dimensional structure. Three projects from current research activities will be presented in order to illustrate both the present capabilities of computer aided techniques as well as their limitations and future possibilities.The first project concerns the three-dimensional reconstruction of the neuritic plaques found in the brains of patients with Alzheimer's disease. We have developed a software package “Synu” for investigation of 3D data sets which has been used in conjunction with laser confocal light microscopy to study the structure of the neuritic plaque. Tissue sections of autopsy samples from patients with Alzheimer's disease were double-labeled for tau, a cytoskeletal marker for abnormal neurites, and synaptophysin, a marker of presynaptic terminals.

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IC-P-144: BLAZER: A VERSATILE AND EFFICIENT WORKFLOW FOR ANALYZING PET/MR NEUROIMAGING DATA IN ALZHEIMER'S DISEASE

Alzheimer s & Dementia ◽

10.1016/j.jalz.2019.06.4258 ◽

2019 ◽

Vol 15 ◽

pp. P117-P118

Author(s):

Fabio Raman ◽

Sameera Grandhi ◽

Charles F. Murchison ◽

Richard E. Kennedy ◽

Susan M. Landau ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Neuroimaging Data

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Race-Related Association between APOE Genotype and Alzheimer’s Disease: A Systematic Review and Meta-Analysis

Journal of Alzheimer s Disease ◽

10.3233/jad-210549 ◽

2021 ◽

pp. 1-10

Author(s):

Wei Qin ◽

Wenwen Li ◽

Qi Wang ◽

Min Gong ◽

Tingting Li ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Black People ◽

Late Onset ◽

Meta Analysis ◽

Group Analysis ◽

Apoe Genotype ◽

Cochrane Library ◽

Data Sets ◽

Apoe Genotypes

Background: The global race-dependent association of Alzheimer’s disease (AD) and apolipoprotein E (APOE) genotype is not well understood. Transethnic analysis of APOE could clarify the role of genetics in AD risk across populations. Objective: This study aims to determine how race and APOE genotype affect the risks for AD. Methods: We performed a systematic search of PubMed, Embase, Web of Science, and the Cochrane Library since 1993 to Aug 25, 2020. A total of 10,395 reports were identified, and 133 were eligible for analysis with data on 77,402 participants. Studies contained AD clinical diagnostic and APOE genotype data. Homogeneous data sets were pooled in case-control analyses. Odds ratios and 95% confidence intervals for developing AD were calculated for populations of different races and APOE genotypes. Results: The proportion of APOE genotypes and alleles differed between populations of different races. Results showed that APOE ɛ4 was a risk factor for AD, whereas APOE ɛ2 protected against it. The effects of APOE ɛ4 and ɛ2 on AD risk were distinct in various races, they were substantially attenuated among Black people. Sub-group analysis found a higher frequency of APOE ɛ4/ɛ4 and lower frequency of APOE ɛ3/ɛ3 among early-onset AD than late-onset AD in a combined group and different races. Conclusion: Our meta-analysis suggests that the association of APOE genotypes and AD differ between races. These results enhance our understanding of APOE-related risk for AD across race backgrounds and provide new insights into precision medicine for AD.

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Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

International Journal of Molecular Sciences ◽

10.3390/ijms22157911 ◽

2021 ◽

Vol 22 (15) ◽

pp. 7911

Author(s):

Eugene Lin ◽

Chieh-Hsin Lin ◽

Hsien-Yuan Lane

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Future Research ◽

Learning Approaches ◽

Learning Models ◽

Learning Techniques ◽

Neuroimaging Data ◽

Similarities And Differences ◽

Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

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Antioxidant strategies for Alzheimer's disease

Proceedings of The Nutrition Society ◽

10.1079/pns2002146 ◽

2002 ◽

Vol 61 (2) ◽

pp. 191-202 ◽

Cited By ~ 110

Author(s):

Michael Grundman ◽

Patrick Delaney

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Vitamin E ◽

Oxidative Damage ◽

Functional Decline ◽

Antioxidant Properties ◽

Epidemiological Studies ◽

Clinical Value ◽

Antioxidant Defences ◽

Ad Prevention

Oxidative damage is present within the brains of patients with Alzheimer's disease (AD), and is observed within every class of biomolecule, including nucleic acids, proteins, lipids and carbohydrates. Oxidative injury may develop secondary to excessive oxidative stress resulting from β-amyloid-induced free radicals, mitochondrial abnormalities, inadequate energy supply, inflammation or altered antioxidant defences. Treatment with antioxidants is a promising approach for slowing disease progression to the extent that oxidative damage may be responsible for the cognitive and functional decline observed in AD. Although not a uniformly consistent observation, a number of epidemiological studies have found a link between antioxidant intake and a reduced incidence of dementia, AD and cognitive decline in elderly populations. In AD clinical trials molecules with antioxidant properties such as vitamin E andGinkgo bilobaextract have shown modest benefit. A clinical trial with vitamin E is currently ongoing to determine if it can delay progression to AD in individuals with mild cognitive impairment. Combinations of antioxidants might be of even greater potential benefit for AD, especially if the agents worked in different cellular compartments or had complementary activity (e.g. vitamins E, C and ubiquinone). Naturally-occurring compounds with antioxidant capacity are available and widely marketed (e.g. vitamin C, ubiquinone, lipoic acid, β-carotene, creatine, melatonin, curcumin) and synthetic compounds are under development by industry. Nevertheless, the clinical value of these agents for AD prevention and treatment is ambiguous, and will remain so until properly designed human trials have been performed.

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Ambient Intelligence Based on IoT for Assisting People with Alzheimer’s Disease Through Context Histories

Electronics ◽

10.3390/electronics10111260 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1260

Author(s):

Savanna Denega Machado ◽

João Elison da Rosa Tavares ◽

Márcio Garcia Martins ◽

Jorge Luis Victória Barbosa ◽

Gabriel Villarrubia González ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

State Of The Art ◽

Physiological Data ◽

Data Sets ◽

Scientific Contribution ◽

Daily Lives ◽

Iot Applications ◽

Methodological Aspects ◽

Dangerous Behaviors

New Internet of Things (IoT) applications are enabling the development of projects that help with monitoring people with different diseases in their daily lives. Alzheimer’s is a disease that affects neurological functions and needs support to maintain maximum independence and security of patients during this stage of life, as the cure and reversal of symptoms have not yet been discovered. The IoT-based monitoring system provides the caregivers’ support in monitoring people with Alzheimer’s disease (AD). This paper presents an ontology-based computational model that receives physiological data from external IoT applications, allowing identification of potentially dangerous behaviors for patients with AD. The main scientific contribution of this work is the specification of a model focusing on Alzheimer’s disease using the analysis of context histories and context prediction, which, considering the state of the art, is the only one that uses analysis of context histories to perform predictions. In this research, we also propose a simulator to generate activities of the daily life of patients, allowing the creation of data sets. These data sets were used to evaluate the contributions of the model and were generated according to the standardization of the ontology. The simulator generated 1026 scenarios applied to guide the predictions, which achieved average accurary of 97.44%. The experiments also allowed the learning of 20 relevant lessons on technological, medical, and methodological aspects that are recorded in this article.

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PET imaging of 18F-florbetapir in cognitively impaired individuals: Lack of activity within the cerebellar cortex

Neurology International ◽

10.4081/ni.2018.7666 ◽

2018 ◽

Vol 10 (3) ◽

Cited By ~ 2

Author(s):

Michael A. Meyer ◽

Allison Caccia ◽

Danielle Martinez ◽

Mark A. Mingos

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

White Matter ◽

Cerebellar Cortex ◽

Pet Imaging ◽

Parent Compound ◽

Hepatic Metabolism ◽

Subcortical White Matter ◽

Clinical Value ◽

Type Pattern

Ten individuals suspected of having possible Alzheimer disease underwent PET imaging using 18F-Flubetapir. Only one of ten individuals had a pattern typical for normal elderly control subjects with 9 of the 10 showing a Alzheimer type pattern for the cerebral cortex yet all 10 subjects had uniformly low to absent tracer localization to the cerebellar cortex; significantly high tracer activity was noted within the subcortical white matter of the cerebellum in a symmetric manner in all cases. In consideration of studies that have shown amyloid deposits within the cerebellar cortex in 90% of pathologically proven cases of Alzheimer’s disease, these findings raise questions about the actual clinical value of florbetapir PET imaging in evaluating cerebellar involvement and raises questions whether PET imaging of this tracer accurately portrays patterns of amyloid deposition, as there is rapid hepatic metabolism of the parent compound after intravenous injection. Possible links to Alzheimer’s disease related alterations in blood-brain barrier permeability to the parent compound and subsequent radiolabelled metabolites are discussed as potential mechanisms that could explain the associated localization of the tracer to the brainstem and subcortical white matter within the cerebrum and cerebellum of Alzheimer’s disease patients.

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Alzheimer’s Disease and the Volume of Hippocampal Subfields: Cluster Analysis Using Neuroimaging Data

VipIMAGE 2019 - Lecture Notes in Computational Vision and Biomechanics ◽

10.1007/978-3-030-32040-9_31 ◽

2019 ◽

pp. 296-306 ◽

Cited By ~ 1

Author(s):

C. G. Fonseca Pachi ◽

L. Torralbo ◽

J. F. Yamamoto

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cluster Analysis ◽

Hippocampal Subfields ◽

Neuroimaging Data

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Data Analysis With Shapley Values For Automatic Subject Selection in Alzheimer's Disease Data Sets Using Interpretable Machine Learning

10.21203/rs.3.rs-245707/v1 ◽

2021 ◽

Author(s):

Louise Bloch ◽

Christoph M. Friedrich

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Test Data ◽

Noisy Data ◽

Training Data ◽

Data Sets ◽

Data Set ◽

Model Interpretation ◽

Percentage Points ◽

Shapley Values

Abstract Background: The prediction of whether Mild Cognitive Impaired (MCI) subjects will prospectively develop Alzheimer's Disease (AD) is important for the recruitment and monitoring of subjects for therapy studies. Machine Learning (ML) is suitable to improve early AD prediction. The etiology of AD is heterogeneous, which leads to noisy data sets. Additional noise is introduced by multicentric study designs and varying acquisition protocols. This article examines whether an automatic and fair data valuation method based on Shapley values can identify subjects with noisy data. Methods: An ML-workow was developed and trained for a subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The validation was executed for an independent ADNI test data set and for the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) cohort. The workow included volumetric Magnetic Resonance Imaging (MRI) feature extraction, subject sample selection using data Shapley, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) for model training and Kernel SHapley Additive exPlanations (SHAP) values for model interpretation. This model interpretation enables clinically relevant explanation of individual predictions. Results: The XGBoost models which excluded 116 of the 467 subjects from the training data set based on their Logistic Regression (LR) data Shapley values outperformed the models which were trained on the entire training data set and which reached a mean classification accuracy of 58.54 % by 14.13 % (8.27 percentage points) on the independent ADNI test data set. The XGBoost models, which were trained on the entire training data set reached a mean accuracy of 60.35 % for the AIBL data set. An improvement of 24.86 % (15.00 percentage points) could be reached for the XGBoost models if those 72 subjects with the smallest RF data Shapley values were excluded from the training data set. Conclusion: The data Shapley method was able to improve the classification accuracies for the test data sets. Noisy data was associated with the number of ApoEϵ4 alleles and volumetric MRI measurements. Kernel SHAP showed that the black-box models learned biologically plausible associations.

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