scholarly journals Quantitative Longitudinal Predictions of Alzheimer’s Disease by Multi-Modal Predictive Learning

2021 ◽  
Vol 79 (4) ◽  
pp. 1533-1546
Author(s):  
Mithilesh Prakash ◽  
Mahmoud Abdelaziz ◽  
Linda Zhang ◽  
Bryan A. Strange ◽  
Jussi Tohka ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prediction Models ◽  
Predictive Performance ◽  
Absolute Error ◽  
Disease Stage ◽  
Disease Assessment ◽  
Disease Category ◽  
Modal Data ◽  
Predictive Learning

Background: Quantitatively predicting the progression of Alzheimer’s disease (AD) in an individual on a continuous scale, such as the Alzheimer’s Disease Assessment Scale-cognitive (ADAS-cog) scores, is informative for a personalized approach as opposed to qualitatively classifying the individual into a broad disease category. Objective: To evaluate the hypothesis that the multi-modal data and predictive learning models can be employed for future predicting ADAS-cog scores. Methods: Unimodal and multi-modal regression models were trained on baseline data comprised of demographics, neuroimaging, and cerebrospinal fluid based markers, and genetic factors to predict future ADAS-cog scores for 12, 24, and 36 months. We subjected the prediction models to repeated cross-validation and assessed the resulting mean absolute error (MAE) and cross-validated correlation (ρ) of the model. Results: Prediction models trained on multi-modal data outperformed the models trained on single modal data in predicting future ADAS-cog scores (MAE12, 24 & 36 months= 4.1, 4.5, and 5.0, ρ12, 24 & 36 months= 0.88, 0.82, and 0.75). Including baseline ADAS-cog scores to prediction models improved predictive performance (MAE12, 24 & 36 months= 3.5, 3.7, and 4.6, ρ12, 24 & 36 months= 0.89, 0.87, and 0.80). Conclusion: Future ADAS-cog scores were predicted which could aid clinicians in identifying those at greater risk of decline and apply interventions at an earlier disease stage and inform likely future disease progression in individuals enrolled in AD clinical trials.

scholarly journals Quantitative longitudinal predictions of Alzheimer's disease by multi-modal predictive learning

2020 ◽  
Author(s):  
Mithilesh Prakash ◽  
Mahmoud Abdelaziz ◽  
Linda Zhang ◽  
Bryan A Strange ◽  
Jussi Tohka
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prediction Models ◽  
Predictive Performance ◽  
Absolute Error ◽  
Disease Stage ◽  
Disease Category ◽  
Modal Data ◽  
Predictive Learning ◽  
The Individual

Abstract Background: Quantitatively predicting the progression of Alzheimer’s disease (AD) in an individual on a continuous scale, such as AD assessment scale-cognitive (ADAS-cog) scores, is informative for a personalized approach as opposed to qualitatively classifying the individual into a broad disease category. We hypothesize that multi-modal data and predictive learning models can be employed for longitudinally predicting ADAS-cog scores.Methods: Multivariate regression techniques were employed to model baseline multi-modal data (demographics, neuroimaging, and cerebrospinal fluid based markers, and genetic factors) and future ADAS-cog scores. Prediction models were subjected to repeated cross-validation and the resulting mean absolute error and cross-validated correlation of the model assessed.Results: Prediction models on multi-modal data outperformed single modal data up to 36 months. Incorporating baseline ADAS-cog scores to prediction models marginally improved predictive performance.Conclusions: Future ADAS-cog scores were successfully estimated via predictive learning aiding clinicians in identifying those at greater risk of decline and apply interventions at an earlier disease stage and inform likely future disease progression in individuals enrolled in AD clinical trials.

scholarly journals Quantitative longitudinal predictions of Alzheimer’s disease by multi-modal predictive learning

2020 ◽  
Author(s):  
M. Prakash ◽  
M. Abdelaziz ◽  
L. Zhang ◽  
B.A. Strange ◽  
J. Tohka ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prediction Models ◽  
Predictive Performance ◽  
Absolute Error ◽  
Disease Stage ◽  
Disease Category ◽  
Modal Data ◽  
Predictive Learning ◽  
The Individual

AbstractBackgroundQuantitatively predicting the progression of Alzheimer’s disease (AD) in an individual on a continuous scale, such as AD assessment scale-cognitive (ADAS-cog) scores, is informative for a personalized approach as opposed to qualitatively classifying the individual into a broad disease category. We hypothesize that multi-modal data and predictive learning models can be employed for longitudinally predicting ADAS-cog scores.MethodsMultivariate regression techniques were employed to model baseline multi-modal data (demographics, neuroimaging, and cerebrospinal fluid based markers, and genetic factors) and future ADAS-cog scores. Prediction models were subjected to repeated cross-validation and the resulting mean absolute error and cross-validated correlation of the model assessed.ResultsPrediction models on multi-modal data outperformed single modal data up to 36 months. Incorporating baseline ADAS-cog scores to prediction models marginally improved predictive performance.ConclusionsFuture ADAS-cog scores were successfully estimated via predictive learning aiding clinicians in identifying those at greater risk of decline and apply interventions at an earlier disease stage and inform likely future disease progression in individuals enrolled in AD clinical trials.

scholarly journals Cognitive reserve and clinical progression in Alzheimer disease

Neurology ◽  
2019 ◽  
Vol 93 (4) ◽  
pp. e334-e346 ◽  
Author(s):  
Anna Catharina van Loenhoud ◽  
Wiesje Maria van der Flier ◽  
Alle Meije Wink ◽  
Ellen Dicks ◽  
Colin Groot ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Alzheimer Disease ◽  
Gray Matter ◽  
Cognitive Reserve ◽  
Disease Stage ◽  
Disease Assessment ◽  
White Matter Hyperintensity ◽  
Cerebral Damage ◽  
Clinical Progression

ObjectiveTo investigate the relationship between cognitive reserve (CR) and clinical progression across the Alzheimer disease (AD) spectrum.MethodsWe selected 839 β-amyloid (Aβ)–positive participants with normal cognition (NC, n = 175), mild cognitive impairment (MCI, n = 437), or AD dementia (n = 227) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). CR was quantified using standardized residuals (W scores) from a (covariate-adjusted) linear regression with global cognition (13-item Alzheimer's Disease Assessment Scale–cognitive subscale) as an independent variable of interest, and either gray matter volumes or white matter hyperintensity volume as dependent variables. These W scores, reflecting whether an individual's degree of cerebral damage is lower or higher than clinically expected, were tested as predictors of diagnostic conversion (i.e., NC to MCI/AD dementia, or MCI to AD dementia) and longitudinal changes in memory (ADNI-MEM) and executive functions (ADNI-EF).ResultsThe median follow-up period was 24 months (interquartile range 6–42). Corrected for age, sex, APOE4 status, and baseline cerebral damage, higher gray matter volume-based W scores (i.e., greater CR) were associated with a lower diagnostic conversion risk (hazard ratio [HR] 0.22, p < 0.001) and slower decline in memory (β = 0.48, p < 0.001) and executive function (β = 0.67, p < 0.001). Stratified by disease stage, we found similar results for NC (diagnostic conversion: HR 0.30, p = 0.038; ADNI-MEM: β = 0.52, p = 0.028; ADNI-EF: β = 0.42, p = 0.077) and MCI (diagnostic conversion: HR 0.21, p < 0.001; ADNI-MEM: β = 0.43, p = 0.003; ADNI-EF: β = 0.59, p < 0.001), but opposite findings (i.e., more rapid decline) for AD dementia (ADNI-MEM: β = −0.91, p = 0.002; ADNI-EF: β = −0.77, p = 0.081).ConclusionsAmong Aβ-positive individuals, greater CR related to attenuated clinical progression in predementia stages of AD, but accelerated cognitive decline after the onset of dementia.

Alzheimer's Disease Assessment Scale

1984 ◽  
Author(s):  
Wilma G. Rosen ◽  
Richard C. Mohs ◽  
Kenneth L. Davis
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Assessment Scale ◽  
Disease Assessment

Validation of the Hungarian version of Alzheimer’s Disease Assessment Scale – Cognitive Subscale

2012 ◽  
Vol 153 (12) ◽  
pp. 461-466 ◽  
Author(s):  
Magdolna Pákáski ◽  
Gergely Drótos ◽  
Zoltán Janka ◽  
János Kálmán
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Mental State ◽  
Mini Mental State Examination ◽  
Assessment Scale ◽  
Disease Assessment ◽  
Control Subjects ◽  
Cognitive Subscale ◽  
State Examination ◽  
Hungarian Version

The cognitive subscale of the Alzheimer’s Disease Assessment Scale is the most widely used test in the diagnostic and research work of Alzheimer’s disease. Aims: The aim of this study was to validate and investigate reliability of the Hungarian version of the Alzheimer’s Disease Assessment Scale in patients with Alzheimer’s disease and healthy control subjects. Methods: syxty-six patients with mild and moderate Alzheimer’s disease and 47 non-demented control subjects were recruited for the study. The cognitive status was established by the Hungarian version of the Alzheimer’s Disease Assessment Scale and Mini Mental State Examination. Discriminative validity, the relation between age and education and Alzheimer’s Disease Assessment Scale, and the sensitivity and specificity of the test were determined. Results: Both the Mini Mental State Examination and the Alzheimer’s Disease Assessment Scale had significant potential in differentiating between patients with mild and moderate stages of Alzheimer’s disease and control subjects. A very strong negative correlation was established between the scores of the Mini Mental State Examination and the Alzheimer’s Disease Assessment Scale in the Alzheimer’s disease group. The Alzheimer’s Disease Assessment Scale showed slightly negative relationship between education and cognitive performance, whereas a positive correlation between age and Alzheimer’s Disease Assessment Scale scores was detected only in the control group. According to the analysis of the ROC curve, the values of sensitivity and specificity of the Alzheimer’s Disease Assessment Scale were high. Conclusions: The Hungarian version of the Alzheimer’s Disease Assessment Scale was found to be highly reliable and valid and, therefore, the application of this scale can be recommended for the establishment of the clinical stage and follow-up of patients with Alzheimer’s disease. However, the current Hungarian version of the Alzheimer’s Disease Assessment Scale is not sufficient; the list of words and linguistic elements should be selected according to the Hungarian standard in the future. Orv. Hetil., 2012, 153, 461–466.

Deformation-based Statistical Shape Analysis of the Corpus Callosum in Mild Cognitive Impairment and Alzheimer’s Disease

2018 ◽  
Vol 15 (12) ◽  
pp. 1151-1160 ◽  
Author(s):  
Zihan Jiang ◽  
Huilin Yang ◽  
Xiaoying Tang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Corpus Callosum ◽  
Shape Analysis ◽  
Disease Assessment ◽  
Statistical Shape Analysis ◽  
Superior Part ◽  
Statistical Shape

Objective: In this study, we investigated the influence that the pathology of Alzheimer’s disease (AD) exerts upon the corpus callosum (CC) using a total of 325 mild cognitive impairment (MCI) subjects, 155 AD subjects, and 185 healthy control (HC) subjects. Method: Regionally-specific morphological CC abnormalities, as induced by AD, were quantified using a large deformation diffeomorphic metric curve mapping based statistical shape analysis pipeline. We also quantified the association between the CC shape phenotype and two cognitive measures; the Mini Mental State Examination (MMSE) and the Alzheimer’s Disease Assessment Scale-Cognitive Behavior Section (ADAS-cog). To identify AD-relevant areas, CC was sub-divided into three subregions; the genu, body, and splenium (gCC, bCC, and sCC). Results: We observed significant shape compressions in AD relative to that in HC, mainly concentrated on the superior part of CC, across all three sub-regions. The HC-vs-MCI shape abnormalities were also concentrated on the superior part, but mainly occurred on bCC and sCC. The significant MCI-vs-AD shape differences, however, were only detected in part of sCC. In the shape-cognition association, significant negative correlations to ADAS-cog were detected for shape deformations at regions belonging to gCC and sCC and significant positive correlations to MMSE at regions mainly belonging to sCC. Conclusion: Our results suggest that the callosal shape deformation patterns, especially those of sCC, linked tightly to the cognitive decline in AD, and are potentially a powerful biomarker for monitoring the progression of AD.

Microglia in Alzheimer’s Disease

2020 ◽  
Vol 17 (1) ◽  
pp. 29-43 ◽  
Author(s):  
Patrick Süß ◽  
Johannes C.M. Schlachetzki
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Current Knowledge ◽  
Imaging Techniques ◽  
Amyloid Β ◽  
Disease Stage ◽  
Disease Modifying Therapy ◽  
Transcriptomic Signature

: Alzheimer’s Disease (AD) is the most frequent neurodegenerative disorder. Although proteinaceous aggregates of extracellular Amyloid-β (Aβ) and intracellular hyperphosphorylated microtubule- associated tau have long been identified as characteristic neuropathological hallmarks of AD, a disease- modifying therapy against these targets has not been successful. An emerging concept is that microglia, the innate immune cells of the brain, are major players in AD pathogenesis. Microglia are longlived tissue-resident professional phagocytes that survey and rapidly respond to changes in their microenvironment. Subpopulations of microglia cluster around Aβ plaques and adopt a transcriptomic signature specifically linked to neurodegeneration. A plethora of molecules and pathways associated with microglia function and dysfunction has been identified as important players in mediating neurodegeneration. However, whether microglia exert either beneficial or detrimental effects in AD pathology may depend on the disease stage. : In this review, we summarize the current knowledge about the stage-dependent role of microglia in AD, including recent insights from genetic and gene expression profiling studies as well as novel imaging techniques focusing on microglia in human AD pathology and AD mouse models.

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 Disease progression modelling from preclinical Alzheimer’s disease (AD) to AD dementia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Soo Hyun Cho ◽  
Sookyoung Woo ◽  
Changsoo Kim ◽  
Hee Jin Kim ◽  
Hyemin Jang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Disease Assessment ◽  
Time Interval ◽  
Disease Course ◽  
Preclinical Alzheimer’S Disease ◽  
Apoe Ε4 ◽  
Preclinical Ad ◽  
Cognitive Subscale

AbstractTo characterize the course of Alzheimer’s disease (AD) over a longer time interval, we aimed to construct a disease course model for the entire span of the disease using two separate cohorts ranging from preclinical AD to AD dementia. We modelled the progression course of 436 patients with AD continuum and investigated the effects of apolipoprotein E ε4 (APOE ε4) and sex on disease progression. To develop a model of progression from preclinical AD to AD dementia, we estimated Alzheimer’s Disease Assessment Scale-Cognitive Subscale 13 (ADAS-cog 13) scores. When calculated as the median of ADAS-cog 13 scores for each cohort, the estimated time from preclinical AD to MCI due to AD was 7.8 years and preclinical AD to AD dementia was 15.2 years. ADAS-cog 13 scores deteriorated most rapidly in women APOE ε4 carriers and most slowly in men APOE ε4 non-carriers (p < 0.001). Our results suggest that disease progression modelling from preclinical AD to AD dementia may help clinicians to estimate where patients are in the disease course and provide information on variation in the disease course by sex and APOE ε4 status.

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