scholarly journals Predicting Clinical Dementia Rating Using Blood RNA Levels

2019 ◽  
Author(s):  
Justin B. Miller ◽  
John S.K. Kauwe ◽  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Microarray Data ◽  
Predictive Accuracy ◽  
Cognitive Status ◽  
Clinical Dementia Rating ◽  
P Values ◽  
Intracellular Channel ◽  
Rna Levels

Structured AbstractINTRODUCTIONThe Clinical Dementia Rating (CDR) is commonly used to assess cognitive decline in Alzheimer’s disease patients.METHODSWe divided 741 participants with blood microarray data in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) into three groups based on their most recent CDR assessment: cognitive normal (CDR=0), mild dementia (CDR=0.5), and probable AD (CDR≥1.0). We then used machine learning to predict cognitive status using only blood RNA levels.RESULTSOne chloride intracellular channel 1 (CLIC1) probe was significant. By combining nonsignificant probes with p-values less than 0.1, we averaged 87.87 (s = 1.02)% predictive accuracy in classifying the three groups, compared to a 55.46% baseline for this study.DISCUSSIONWe identified one significant probe in CLIC1. However, CLIC1 levels alone were not sufficient to determine dementia status. We propose that combining individually suggestive, but nonsignificant, blood RNA levels can significantly improve diagnostic accuracy.

scholarly journals Predicting Clinical Dementia Rating Using Blood RNA Levels

Genes ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 706
Author(s):  
Justin B. Miller ◽  
John S. K. Kauwe
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Operating Characteristic ◽  
Predictive Accuracy ◽  
Cognitive Status ◽  
Clinical Dementia Rating ◽  
Intracellular Channel ◽  
Rna Levels

The Clinical Dementia Rating (CDR) is commonly used to assess cognitive decline in Alzheimer’s disease patients and is included in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. We divided 741 ADNI participants with blood microarray data into three groups based on their most recent CDR assessment: cognitive normal (CDR = 0), mild cognitive impairment (CDR = 0.5), and probable Alzheimer’s disease (CDR ≥ 1.0). We then used machine learning to predict cognitive status using only blood RNA levels. Only one probe for chloride intracellular channel 1 (CLIC1) was significant after correction. However, by combining individually nonsignificant probes with p-values less than 0.1, we averaged 87.87% (s = 1.02) predictive accuracy for classifying the three groups, compared to a 55.46% baseline for this study due to unequal group sizes. The best model had an overall precision of 0.902, recall of 0.895, and a receiver operating characteristic (ROC) curve area of 0.904. Although we identified one significant probe in CLIC1, CLIC1 levels alone were not sufficient to predict dementia status and cannot be used alone in a clinical setting. Additional analyses combining individually suggestive, but nonsignificant, blood RNA levels were significantly predictive and may improve diagnostic accuracy for Alzheimer’s disease. Therefore, we propose that patient features that do not individually predict cognitive status might still contribute to overall cognitive decline through interactions that can be elucidated through machine learning.

scholarly journals Predictors of rapid cognitive decline in Alzheimer's disease: results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of ageing

2011 ◽  
Vol 24 (2) ◽  
pp. 197-204 ◽  
Author(s):  
Alessandro Sona ◽  
Ping Zhang ◽  
David Ames ◽  
Ashley I. Bush ◽  
Nicola T. Lautenschlager ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Rating Scale ◽  
Cognitive Status ◽  
Imaging Biomarkers ◽  
Clinical Dementia Rating ◽  
Factors Associated ◽  
Biological Variables ◽  
Rapid Cognitive Decline

ABSTRACTBackground: The AIBL study, which commenced in November 2006, is a two-center prospective study of a cohort of 1112 volunteers aged 60+. The cohort includes 211 patients meeting NINCDS-ADRDA criteria for Alzheimer's disease (AD) (180 probable and 31 possible). We aimed to identify factors associated with rapid cognitive decline over 18 months in this cohort of AD patients.Methods: We defined rapid cognitive decline as a drop of 6 points or more on the Mini-Mental State Examination (MMSE) between baseline and 18-month follow-up. Analyses were also conducted with a threshold of 4, 5, 7 and 8 points, as well as with and without subjects who had died or were too severely affected to be interviewed at 18 months and after, both including and excluding subjects whose AD diagnosis was “possible” AD. We sought correlations between rapid cognitive decline and demographic, clinical and biological variables.Results: Of the 211 AD patients recruited at baseline, we had available data for 156 (73.9%) patients at 18 months. Fifty-one patients were considered rapid cognitive decliners (32.7%). A higher Clinical Dementia Rating scale (CDR) and higher CDR “sum of boxes” score at baseline were the major predictors of rapid cognitive decline in this population. Furthermore, using logistic regression model analysis, patients treated with a cholinesterase inhibitor (CheI) had a higher risk of being rapid cognitive decliners, as did males and those of younger age.Conclusions: Almost one third of patients satisfying established research criteria for AD experienced rapid cognitive decline. Worse baseline functional and cognitive status and treatment with a CheI were the major factors associated with rapid cognitive decline over 18 months in this population.

scholarly journals Apathy in Alzheimer's disease: Contribution to a clinical view on progression of dementia

2010 ◽  
Vol 4 (3) ◽  
pp. 188-193 ◽  
Author(s):  
Florindo Stella ◽  
Larissa Pires de Andrade ◽  
Thays Martins Vital ◽  
Flávia Gomes de Melo Coelho ◽  
Carla Manuela Crispim Nascimento ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Mini Mental State Examination ◽  
Cognitive Status ◽  
Clinical Dementia Rating ◽  
Global Deterioration Scale ◽  
Severe Dementia ◽  
Coefficient Corrélation ◽  
The Relationship ◽  
State Examination

Abstract In addition to cognitive impairment, apathy is increasingly recognized as an important neuropsychiatric syndrome in Alzheimer's disease (AD). Aims: To identify the relationship between dementia severity and apathy levels, and to discuss the association of this condition with other psychopathological manifestations in AD patients. Methods: This study involved 15 AD patients (mean age: 77 years; schooling: 4.9 years), with mild, moderate and severe dementia, living in Rio Claro SP, Brazil. Procedures included evaluation of cognitive status by the Mini-Mental State Examination, Clinical Dementia Rating, and Global Deterioration Scale. Apathy syndrome was assessed by the Apathy Evaluation Scale and Neuropsychiatric Inventory (NPI-apathy domain). Other psychopathological manifestations such as depression were also considered. Results: Patients with more severe dementia presented higher levels of apathy, reinforcing the hypothesis that apathy severity aggravates as the disease progresses. Using the Spearman coefficient correlation an association was identified between the MMSE and Apathy Evaluation Scale (r=0.63; p=0.01), and also between the MMSE and NPI-apathy domain (r=0.81; p=0.01). Associations were also found between the Global Deterioration Scale and Apathy Evaluation Scale (r=0.58; p=0.02), and between the Global Deterioration Scale and NPI-apathy domain (r=0.81; p=0.01). Conclusions: Apathy is a distinct syndrome among patients with AD and increases with global deterioration.

scholarly journals Predicting Alzheimer's disease from Clinical Dementia Rating, Estimated total intracranial volume & Atlas Scaling Factor : Incorporating Ensemble approach into Automated Machine Learning

2021 ◽  
Author(s):  
Ali Haider Bangash ◽  
Tauseef Ullah ◽  
Inayat Ullah Khan ◽  
Haris Khan ◽  
Arshiya Fatima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Weighted Average ◽  
Scaling Factor ◽  
Intracranial Volume ◽  
Clinical Dementia Rating ◽  
Ensemble Approach ◽  
Automated Machine Learning ◽  
Detection Score

The current state-of-the-art for automated machine learning is adopted to predict Alzheimer's disease (AD) by adopting variables such as Mini Mental State Examination score, estimated total intracranial volume and Atlas Scaling Factor. A macro-weighted average Area under the Response-operating Curve of 0.96 is achieved with a close-to-perfect AD detection score after incorporating the ensemble approach. Such predictive models shall serve to optimize risk stratification and management protocols for this enfeebling ailment.

Case Studies in Dementia-Related Anxiety

GeroPsych ◽  
2020 ◽  
pp. 1-6
Author(s):  
Molly Maxfield ◽  
Jennifer R. Roberts ◽  
JoAnna Dieker
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Social Withdrawal ◽  
Cognitive Status ◽  
Generalized Anxiety ◽  
Neurocognitive Disorder ◽  
High Genetic Risk ◽  
Disease Case ◽  
Negative Perceptions

Abstract. Two clients seeking neuropsychological assessment reported anxiety about their cognitive status. We review the cases to increase our understanding of factors contributing to dementia-related anxiety. Case 1 met the criteria for mild neurocognitive disorder; the client’s memory was impaired, and she had a high genetic risk for Alzheimer’s disease. The client reported anxiety about negative perceptions of quality of life among individuals diagnosed with Alzheimer’s disease. Case 2 did not meet the criteria for a neurocognitive disorder. Anxiety about this client’s cognitive status appeared attributable to generalized anxiety disorder, given his anxiety about diverse topics. Both clients reported embarrassment about forgetfulness and social withdrawal. Dementia-related anxiety is believed to be relatively common, to exist on a continuum, to have unique social implications, and to stem from various sources, necessitating differing interventions.

Alzheimer's Disease Brain Areas: The Machine Learning Support for Blind Localization

2016 ◽  
Vol 13 (5) ◽  
pp. 498-508 ◽  
Author(s):  
V. Vigneron ◽  
A. Kodewitz ◽  
A. M. Tome ◽  
S. Lelandais ◽  
E. Lang
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Learning Support ◽  
Brain Areas

scholarly journals Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1071
Author(s):  
Lucia Billeci ◽  
Asia Badolato ◽  
Lorenzo Bachi ◽  
Alessandro Tonacci
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Classification Problem ◽  
Computer Algorithms ◽  
Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease

2020 ◽  
Vol 16 (1s) ◽  
pp. 1-35 ◽  
Author(s):  
M. Tanveer ◽  
B. Richhariya ◽  
R. U. Khan ◽  
A. H. Rashid ◽  
P. Khanna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Machine Learning Techniques ◽  
Learning Techniques
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