scholarly journals Early Detection of Alzheimer’s Disease Using Neuropsychological Tests: a Predict-Diagnose Approach Using Neural Networks

2021 ◽  
Author(s):  
Devarshi Mukherji ◽  
Manibrata Mukherji ◽  
Nivedita Mukherji
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Cognitive Impairment ◽  
Early Detection ◽  
Neuropsychological Tests ◽  
Neuropsychological Test ◽  
Trial Participation ◽  
Cognitive States ◽  
The Future

Abstract Alzheimer’s Disease (AD) is the most expensive and currently incurable disease that affects a large number of the elderly globally. One in five Medicare dollars is spent on AD-related tests and treatments. Accurate AD diagnosis is critical but often involves invasive and expensive tests that include brain scans and spinal taps. Recommending these tests for only patients who are likely to develop the disease will save families of cognitively normal individuals and hospitals from unnecessary expenditures. Moreover, many of the subjects chosen for clinical trials for AD therapies never develop any cognitive impairment and prove not to be ideal candidates for those trials. It is thereby critical to find inexpensive ways to first identify individuals who are likely to develop cognitive impairment and focus attention on them for in-depth testing, diagnosing, and clinical trial participation. Research shows that AD is a slowly progressing disease. This slow progression allows for early detection and treatment, but more importantly, gives the opportunity to predict the likelihood of disease development from early indications of memory lapses. Neuropsychological tests have been shown to be effective in identifying cognitive impairment. Relying exclusively on a set of longitudinal neuropsychological test data available from the ADNI database, this paper has developed Recurrent Neural Network (RNN) models to predict future neuropsychological test results and Multi-Level Perceptron (MLP) models to diagnose the future cognitive states of individuals based on those predicted results. The RNNs use sequence prediction techniques to predict test scores for two to four years in the future. The predicted scores and predictions of cognitive states based on them showed a high level of accuracy for a group of test subjects, when compared with their known future cognitive assessments conducted by ADNI. This shows that a battery of neuropsychological tests can be used to track the cognitive states of people above a certain age and identify those who are likely to develop cognitive impairment in the future. This ability to triage individuals into those who are likely to remain normal and those who will develop cognitive impairment in the future, advances the quest to find appropriate candidates for invasive tests like spinal taps for disease identification, and the ability to identify suitable candidates for clinical trials.

scholarly journals Early Detection of Alzheimer’s Disease with Low-Cost Neuropsychological Tests: A Novel Predict-Diagnose Approach using Recurrent Neural Networks

2021 ◽  
Author(s):  
Devarshi Mukherji ◽  
Manibrata Mukherji ◽  
Nivedita Mukherji ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Cognitive Impairment ◽  
Early Detection ◽  
Recurrent Neural Networks ◽  
Neuropsychological Tests ◽  
Cognitive States ◽  
The Future

AbstractAlzheimer’s Disease (AD) is the most expensive and currently incurable disease that affects a large number of the elderly globally. One in five Medicare dollars is spent on AD-related tests and treatments. Accurate AD diagnosis is critical but often involves invasive and expensive tests that include brain scans and spinal taps. Recommending these tests for only patients who are likely to develop the disease will save families of cognitively normal individuals and hospitals from unnecessary expenditures. Moreover, many of the subjects chosen for clinical trials for AD therapies never develop any cognitive impairment and prove not to be ideal candidates for those trials. It is thereby critical to find inexpensive ways to first identify individuals who are likely to develop cognitive impairment and focus attention on them for in-depth testing, diagnosing, and clinical trial participation. Research shows that AD is a slowly progressing disease. This slow progression allows for early detection and treatment, but more importantly, gives the opportunity to predict the likelihood of disease development from early indications of memory lapses. Neuropsychological tests have been shown to be effective in identifying cognitive impairment. Relying exclusively on a set of longitudinal neuropsychological test data available from the ADNI database, this paper has developed Recurrent Neural Networks (RNN) to diagnose the current and predict the future cognitive states of individuals. The RNNs use sequence prediction techniques to predict test scores for two to four years in the future. The predicted scores and predictions of cognitive states based on them showed a high level of accuracy for a group of test subjects, when compared with their known future cognitive assessments conducted by ADNI. This shows that a battery of neuropsychological tests can be used to track the cognitive states of people above a certain age and identify those who are likely to develop cognitive impairment in the future. This ability to triage individuals into those who are likely to remain normal and those who will develop cognitive impairment in the future, advances the quest to find appropriate candidates for invasive tests like spinal taps for disease identification, and the ability to identify suitable candidates for clinical trials.

One-year Outcome of Shanghai Mild Cognitive Impairment Cohort Study

2019 ◽  
Vol 16 (2) ◽  
pp. 156-165 ◽  
Author(s):  
Bin Zhou ◽  
Qianhua Zhao ◽  
Shinsuke Kojima ◽  
Ding Ding ◽  
Satoshi Higashide ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cohort Study ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Detection ◽  
Neuropsychological Test ◽  
Clinical Trial Design ◽  
Disease Assessment ◽  
Factors Associated

Background & Objective: The purpose of this study is to identify the risk factors associated with the conversion from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) dementia for the early detection of AD.Methods:The study comprised a prospective cohort study that included 400 MCI subjects with annual follow-ups for 3 years.Results:During the first 12 months’ follow-up, 42 subjects converted to Alzheimer’s dementia (21 probable AD and 21 possible AD), two subjects converted to other types of dementia and 56 subjects lost follow. The factors associated with a greater risk of conversion from MCI to AD included gender, whole brain volume, and right hippocampal volume (rt. HV), as well as scores on the Revised Chinese version of the Alzheimer’s Disease Assessment Scale-Cognitive subscale 13 (ADAS-Cog-C), Clock Drawing Test (CDT), Symbol Digit Modalities Test (SDMT), and Rey-Osterrieth Complex Figure Test (ROCFT). The risk classification of the combined ADAS-Cog-C and Alzheimer Cognitive Composite (ACC) score with the rt. HV and left Entorhinal Cortex Volume (lt. ECV) showed a conversion difference among the groups.Conclusion:Early detection of AD and potential selection for clinical trial design should utilize the rt. HV, as well as neuropsychological test scores, including those of the ADAS-Cog-C and ACC.

Identification and Temporal Characterization of Features Associated with the Conversion from Mild Cognitive Impairment to Alzheimer’s Disease

2018 ◽  
Vol 15 (8) ◽  
pp. 751-763 ◽  
Author(s):  
Antonio Martinez-Torteya ◽  
Hugo Gomez-Rueda ◽  
Victor Trevino ◽  
Joshua Farber ◽  
Jose Tamez-Pena ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Neuropsychological Tests ◽  
Quantitative Mri ◽  
Rank Test ◽  
Longitudinal Models ◽  
Log Rank Test ◽  
Medical Histories

Background: Diagnosing Alzheimer’s disease (AD) in its earliest stages is important for therapeutic and support planning. Similarly, being able to predict who will convert from mild cognitive impairment (MCI) to AD would have clinical implications. Objectives: The goals of this study were to identify features from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database associated with the conversion from MCI to AD, and to characterize the temporal evolution of that conversion. Methods: We screened the publically available ADNI longitudinal database for subjects with MCI who have developed AD (cases: n=305), and subjects with MCI who have remained stable (controls: n=250). Analyses included 1,827 features from laboratory assays (n=12), quantitative MRI scans (n=1,423), PET studies (n=136), medical histories (n=72), and neuropsychological tests (n=184). Statistical longitudinal models identified features with significant differences in longitudinal behavior between cases and matched controls. A multiple-comparison adjusted log-rank test identified the capacity of the significant predictive features to predict early conversion. Results: 411 features (22.5%) were found to be statistically different between cases and controls at the time of AD diagnosis; 385 features were statistically different at least 6 months prior to diagnosis, and 28 features distinguished early from late conversion, 20 of which were obtained from neuropsychological tests. In addition, 69 features (3.7%) had statistically significant changes prior to AD diagnosis. Conclusion: Our results characterized features associated with disease progression from MCI to AD, and, in addition, the log-rank test identified features which are associated with the risk of early conversion.

A Biomarker Combining Imaging and Neuropsychological Assessment for Tracking Early Alzheimer's Disease in Clinical Trials

2018 ◽  
Vol 15 (5) ◽  
pp. 429-442 ◽  
Author(s):  
Nishant Verma ◽  
S. Natasha Beretvas ◽  
Belen Pascual ◽  
Joseph C. Masdeu ◽  
Mia K. Markey ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Cognitive Impairment ◽  
Latent Variable ◽  
Brain Regions ◽  
Disease Assessment ◽  
Latent Variable Analysis ◽  
The Relationship ◽  
Selection Of

Background: Combining optimized cognitive (Alzheimer's Disease Assessment Scale- Cognitive subscale, ADAS-Cog) and atrophy markers of Alzheimer's disease for tracking progression in clinical trials may provide greater sensitivity than currently used methods, which have yielded negative results in multiple recent trials. Furthermore, it is critical to clarify the relationship among the subcomponents yielded by cognitive and imaging testing, to address the symptomatic and anatomical variability of Alzheimer's disease. Method: Using latent variable analysis, we thoroughly investigated the relationship between cognitive impairment, as assessed on the ADAS-Cog, and cerebral atrophy. A biomarker was developed for Alzheimer's clinical trials that combines cognitive and atrophy markers. Results: Atrophy within specific brain regions was found to be closely related with impairment in cognitive domains of memory, language, and praxis. The proposed biomarker showed significantly better sensitivity in tracking progression of cognitive impairment than the ADAS-Cog in simulated trials and a real world problem. The biomarker also improved the selection of MCI patients (78.8±4.9% specificity at 80% sensitivity) that will evolve to Alzheimer's disease for clinical trials. Conclusion: The proposed biomarker provides a boost to the efficacy of clinical trials focused in the mild cognitive impairment (MCI) stage by significantly improving the sensitivity to detect treatment effects and improving the selection of MCI patients that will evolve to Alzheimer’s disease.

scholarly journals Saffron for mild cognitive impairment and dementia: a systematic review and meta-analysis of randomised clinical trials

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Zahra Ayati ◽  
Guoyan Yang ◽  
Mohammad Hossein Ayati ◽  
Seyed Ahmad Emami ◽  
Dennis Chang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Function ◽  
Daily Living ◽  
Randomised Clinical Trials ◽  
Disease Assessment ◽  
Significant Difference

Abstract Background Saffron (stigma of Crocus sativus L.) from Iridaceae family is a well-known traditional herbal medicine that has been used for hundreds of years to treat several diseases such as depressive mood, cancer and cardiovascular disorders. Recently, anti-dementia property of saffron has been indicated. However, the effects of saffron for the management of dementia remain controversial. The aim of the present study is to explore the effectiveness and safety of saffron in treating mild cognitive impairment and dementia. Methods An electronic database search of some major English and Chinese databases was conducted until 31st May 2019 to identify relevant randomised clinical trials (RCT). The primary outcome was cognitive function and the secondary outcomes included daily living function, global clinical assessment, quality of life (QoL), psychiatric assessment and safety. Rev-Man 5.3 software was applied to perform the meta-analyses. Results A total of four RCTs were included in this review. The analysis revealed that saffron significantly improves cognitive function measured by the Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-cog) and Clinical Dementia Rating Scale-Sums of Boxes (CDR-SB), compared to placebo groups. In addition, there was no significant difference between saffron and conventional medicine, as measured by cognitive scales such as ADAS-cog and CDR-SB. Saffron improved daily living function, but the changes were not statistically significant. No serious adverse events were reported in the included studies. Conclusions Saffron may have the potential to improve cognitive function and activities of daily living in patients with Alzheimer’s disease and mild cognitive impairment (MCI). However, due to limited high-quality studies there is insufficient evidence to make any recommendations for clinical use. Further clinical trials on larger sample sizes are warranted to shed more light on its efficacy and safety.

Non-Ceruloplasmin Copper as a Stratification Biomarker of Alzheimer’s Disease Patients: How to Measure and Use It

2021 ◽  
Vol 18 ◽  
Author(s):  
Rosanna Squitti ◽  
Mariacarla Ventriglia ◽  
Alberto Granzotto ◽  
Stefano L. Sensi ◽  
Mauro Ciro Antonio Rongioletti
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Cognitive Impairment ◽  
Cost Effective ◽  
The Elderly ◽  
The Body ◽  
Eligibility Criterion ◽  
Growing Body ◽  
Proper Balance

: Alzheimer’s disease (AD) is a type of dementia very common in the elderly. A growing body of recent evidence has linked AD pathogenesis to copper (Cu) dysmetabolism in the body. In fact, a subset of patients affected either by AD or by its prodromal form known as Mild Cognitive Impairment (MCI) have been observed to be unable to maintain a proper balance of Cu metabolism and distribution and are characterized by the presence in their serum of increased levels of Cu not bound to ceruloplasmin (non-ceruloplasmin Cu). Since serum non-ceruloplasmin Cu is a biomark- er of Wilson's disease (WD), a well-known condition of Cu-driven toxicosis, in this review, we pro- pose that in close analogy with WD, the assessment of non-ceruloplasmin Cu levels can be exploit- ed as a cost-effective stratification and susceptibility/risk biomarker for the identification of some AD/MCI individuals. The approach can also be used as an eligibility criterion for clinical trials aim- ing at investigating Cu-related interventions against AD/MCI.

scholarly journals Attention and inhibition in Mild Cognitive Impairment and Alzheimer's Disease

2013 ◽  
Vol 6 (3) ◽  
pp. 43-50
Author(s):  
Clara Zancada-Menéndez ◽  
Patricia Sampedro-Piquero ◽  
Azucena Begega ◽  
Laudino López ◽  
Jorge Luis Arias
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Reaction Time ◽  
Alzheimer Disease ◽  
Neuropsychological Test ◽  
Reaction Times ◽  
Stop Signal Task ◽  
Healthy Controls

Mild cognitive impairment is understood as a cognitive deficit of insufficient severity to fulfil the criteria for Alzheimer’s disease. Many studies have attempted to identify which cognitive functions are most affected by this type of impairment and which is the most sensitive neuropsychological test for early detection. This study investigated sustained and selective attention, processing speed, and the inhibition process using a sample of people divided into three groups mild cognitive impairment, Alzheimer disease and cognitively healthy controls selected and grouped based on their scores in the Mini Mental State Examination and Cambridge Cognitive Examination-revised. Three tests from the Cambridge Neuropsychological Test Automated Battery (Motor Screening Task, Stop Signal Task and Reaction time) were used as well as the d2 attention test. The results show that that participants with mild cognitive impairment and Alzheimer disease showed lower levels of concentration compared with the cognitively healthy controls group in the d2 test and longer reaction times in the Cambridge Neuropsychological Test Automated Battery, although the differences were not marked in the latter test. The impairments in basic cognitive processes, such as reaction time and sustained attention, indicate the need to take these functions into account in the test protocols when discriminating between normal aging and early and preclinical dementia processes.

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