scholarly journals Open Access Series of Imaging Studies: Longitudinal MRI Data in Nondemented and Demented Older Adults

2010 ◽  
Vol 22 (12) ◽  
pp. 2677-2684 ◽  
Author(s):  
Daniel S. Marcus ◽  
Anthony F. Fotenos ◽  
John G. Csernansky ◽  
John C. Morris ◽  
Randy L. Buckner
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Open Access ◽  
Data Sets ◽  
Imaging Studies ◽  
Clinical Dementia Rating ◽  
Data Set ◽  
Wide Range ◽  
Neuroimaging Data ◽  
Mri Scans

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.

scholarly journals Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults

2007 ◽  
Vol 19 (9) ◽  
pp. 1498-1507 ◽  
Author(s):  
Daniel S. Marcus ◽  
Tracy H. Wang ◽  
Jamie Parker ◽  
John G. Csernansky ◽  
John C. Morris ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Open Access ◽  
Intracranial Volume ◽  
Resonance Imaging ◽  
Imaging Studies ◽  
Cross Sectional ◽  
Data Set

The Open Access Series of Imaging Studies is a series of magnetic resonance imaging data sets that is publicly available for study and analysis. The initial data set consists of a cross-sectional collection of 416 subjects aged 18 to 96 years. One hundred of the included subjects older than 60 years have been clinically diagnosed with very mild to moderate Alzheimer's disease. The subjects are all right-handed and include both men and women. For each subject, three or four individual T1-weighted magnetic resonance imaging scans obtained in single imaging sessions are included. Multiple within-session acquisitions provide extremely high contrast-to-noise ratio, making the data amenable to a wide range of analytic approaches including automated computational analysis. Additionally, a reliability data set is included containing 20 subjects without dementia imaged on a subsequent visit within 90 days of their initial session. Automated calculation of whole-brain volume and estimated total intracranial volume are presented to demonstrate use of the data for measuring differences associated with normal aging and Alzheimer's disease.

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

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.

scholarly journals Alzheimer’s Disease: Classification and Detection using MRI Dataset

2021 ◽  
Vol 10 (5) ◽  
pp. 70-72
Author(s):  
V P Suhaira ◽  
Sita S ◽  
Joby George
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Heart Function ◽  
Brain Mri ◽  
Memory Loss ◽  
Disease Classification ◽  
Data Set ◽  
Mri Scans ◽  
Disease Experience ◽  
The Brain

Alzheimer's disease (AD) is a hereditary brain condition that is incurable and progresses over time. Patients with Alzheimer's disease experience memory loss, uncertainty, and difficulty speaking, reading, and writing as a result of this condition. Alzheimer's disease eventually affects the portion of the brain that controls breathing and heart function, leading to death. This framework proposes the OASIS (Open Access Series of Imaging Studies) dataset, which contains the existing MRI data set, which is comprised of a longitudinal sample of 150 subjects aged 60 to 96 who were all acquired on the same scanner using similar sequences. This paper uses a combination of brain MRI scans and psychological parameters to predict disease with high accuracy using various classifier algorithms, and the results can be compared to improve performance.

scholarly journals PL - 038 Habitual swimming exercise induced partial resistance to rat Alzheimer's disease

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Timon Cheng-Yi Liu ◽  
Quan-Guang Zhang ◽  
Chong-Yun Wu ◽  
Luo-Dan Yang ◽  
Ling Zhu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Golden Section ◽  
Quantitative Difference ◽  
P Value ◽  
Data Sets ◽  
Self Similarity ◽  
Data Set ◽  
First Order ◽  
Self Similar

Objective  In MSSE, we have divided male 2.5-month-old Sprague-Dawley rats into the following 4 groups: control (C), habitual swimming (SW), Alzheimer’s disease (AD) induction without swimming (AD), and habitual swimming and then AD induction (SA), and found the perfect resistance of habitual swimming to AD induction by using the P value statistics of the 5 behavior parameters of rats and the 23 physiological and biochemical parameters of their hippocampus. The topological difference  of four groups were further calculated in this paper by using quantitative difference (QD) and self-similar approach. Methods 1. The logarithm to base golden section τ (lt) is called golden logarithm. It was found that σ=ltσ ≈ 0.710439287156503. 2. For a process from x1 to x2, lx(1,2)=lt(x2/x1) and its absolute vale are called the process logarithm and its QD, QDx(1,2). There are QD threshold values (αx,βx,γx) of function x which can be calculated in terms of σ. The function x is kept to be constant if QDx(1,2) < αx. A function in/far from its function-specific homeostasis is called a normal/dysfunctional function. A normal function can resist a disturbance under its threshold so that QDx(1,2) < βx. A dysfunctional function is defined as the QD is significant if βx ≦QDx(1,2) < γx and extraordinarily significant if QDx(1,2) ≧ γx. 3. Self-similarity was studied in the fractal literature: a pattern is self-similar if it does not vary with spatial or temporal scale. First-order self-similarity condition leads to the power law between two data sets A = {xi} and B = {yi}; yi = ai xi if the QDi of ai and the average of {ai} is smaller than βmin=min{βi} and the average QD of {QDi} is smaller than αmin=min{αi}. 4. The σ algorithm for integrative biology was established based on high-order self-similarity. Those parameters that contribute to the topological difference were the biomarkers. Results The 28 dimension data set consisted of all the 28 parameters. The first-order self-similarity held true for the 28 dimension data sets between groups C and SW. The topological algorithm of other groups suggested three AD biomarkers, protein carbonyl, granules density of presynaptic synaptophysin in the hippocampal CA1 and malondialdehyde intensity. The first two biomarkers were completely reversed by exercise pretreatment, but the third biomarker was partially reversed. Conclusions  Exercise pretraining exerts partial benefits on AD that support its use as a promising new therapeutic option for prevention of neurodegeneration in the elderly and/or AD population. 

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

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.

Therapeutic Properties of Several Chemical Compounds from Salvia officinalis L. in Alzheimer’s Disease

2020 ◽  
Vol 21 ◽  
Author(s):  
Georgiana Uță ◽  
Denisa Ștefania Manolescu ◽  
Speranța Avram
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Side Effects ◽  
Chemical Compounds ◽  
Natural Compounds ◽  
Salvia Officinalis ◽  
Chemical Structures ◽  
In Silico Studies ◽  
Wide Range ◽  
Salvia Officinalis L

Background.: Currently, the pharmacological management in Alzheimer's disease is based on several chemical structures, represented by acetylcholinesterase and N-methyl-D-aspartate (NMDA) receptor ligands, with still unclear molecular mechanisms, but severe side effects. For this reason, a challenge for Alzheimer's disease treatment remains to identify new drugs with reduced side effects. Recently, the natural compounds, in particular certain chemical compounds identified in the essential oil of peppermint, sage, grapes, sea buckthorn, have increased interest as possible therapeutics. Objectives.: In this paper, we have summarized data from the recent literature, on several chemical compounds extracted from Salvia officinalis L., with therapeutic potential in Alzheimer's disease. Methods.: In addition to the wide range of experimental methods performed in vivo and in vitro, also we presented some in silico studies of medicinal compounds. Results. Through this mini-review, we present the latest information regarding the therapeutic characteristics of natural compounds isolated from Salvia officinalis L. in Alzheimer's disease. Conclusion.: Thus, based on the information presented, we can say that phytotherapy is a reliable therapeutic method in a neurodegenerative disease.

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

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

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

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.

scholarly journals NLRP3 Inflammasome: A Starring Role in Amyloid-β- and Tau-Driven Pathological Events in Alzheimer’s Disease

2021 ◽  
pp. 1-22
Author(s):  
Mariana Van Zeller ◽  
Diogo M. Dias ◽  
Ana M. Sebastião ◽  
Cláudia A. Valente
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Glial Cells ◽  
Neurofibrillary Tangles ◽  
Nlrp3 Inflammasome ◽  
Neuronal Death ◽  
Inflammatory Responses ◽  
Senile Plaques ◽  
Amyloid Β ◽  
Wide Range

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease commonly diagnosed among the elderly population. AD is characterized by the loss of synaptic connections, neuronal death, and progressive cognitive impairment, attributed to the extracellular accumulation of senile plaques, composed by insoluble aggregates of amyloid-β (Aβ) peptides, and to the intraneuronal formation of neurofibrillary tangles shaped by hyperphosphorylated filaments of the microtubule-associated protein tau. However, evidence showed that chronic inflammatory responses, with long-lasting exacerbated release of proinflammatory cytokines by reactive glial cells, contribute to the pathophysiology of the disease. NLRP3 inflammasome (NLRP3), a cytosolic multiprotein complex sensor of a wide range of stimuli, was implicated in multiple neurological diseases, including AD. Herein, we review the most recent findings regarding the involvement of NLRP3 in the pathogenesis of AD. We address the mechanisms of NLRP3 priming and activation in glial cells by Aβ species and the potential role of neurofibrillary tangles and extracellular vesicles in disease progression. Neuronal death by NLRP3-mediated pyroptosis, driven by the interneuronal tau propagation, is also discussed. We present considerable evidence to claim that NLRP3 inhibition, is undoubtfully a potential therapeutic strategy for AD.

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