Modeling the time-course of Alzheimer dementia

Episodic memory assessment: effects of sex and age on performance and response time during a continuous recognition task

Introduction

Continuous recognition tasks (CRTs) assess episodic memory (EM), the central functional disturbance in Alzheimer's disease and several related disorders. The online MemTrax computerized CRT provides a platform for screening and assessment that is engaging and can be repeated frequently. MemTrax presents complex visual stimuli, which require complex involvement of the lateral and medial temporal lobes and can be completed in less than 2 min. Results include number of correct recognitions (HITs), recognition failures (MISSes = 1-HITs), correct rejections (CRs), false alarms (FAs = 1-CRs), total correct (TC = HITs + CRs), and response times (RTs) for each HIT and FA. Prior analyses of MemTrax CRT data show no effects of sex but an effect of age on performance. The number of HITs corresponds to faster RT-HITs more closely than TC, and CRs do not relate to RT-HITs. RT-HITs show a typical skewed distribution, and cumulative RT-HITs fit a negative survival curve (RevEx). Thus, this study aimed to define precisely the effects of sex and age on HITS, CRs, RT-HITs, and the dynamics of RTs in an engaged population.

Methods

MemTrax CRT online data on 18,255 individuals was analyzed for sex, age, and distributions of HITs, CRs, MISSes, FAs, TC, and relationships to both RT-HITs and RT-FAs.

Results

HITs corresponded more closely to RT-HITs than did TC because CRs did not relate to RT-HITs. RT-FAs had a broader distribution than RT-HITs and were faster than RT-HITs in about half of the sample, slower in the other half. Performance metrics for men and women were the same. HITs declined with age as RT-HITs increased. CRs also decreased with age and RT-FAs increased, but with no correlation. The group over aged 50 years had RT-HITs distributions slower than under 50 years. For both age ranges, the RevEx model explained more than 99% of the variance in RT-HITs.

Discussion

The dichotomy of HITs and CRs suggests opposing cognitive strategies: (1) less certainty about recognitions, in association with slower RT-HITs and lower HIT percentages suggests recognition difficulty, leading to more MISSes, and (2) decreased CRs (more FAs) but faster RTs to HITs and FAs, suggesting overly quick decisions leading to errors. MemTrax CRT performance provides an indication of EM (HITs and RT-HITs may relate to function of the temporal lobe), executive function (FAs may relate to function of the frontal lobe), processing speed (RTs), cognitive ability, and age-related changes. This CRT provides potential clinical screening utility for early Alzheimer's disease and other conditions affecting EM, other cognitive functions, and more accurate impairment assessment to track changes over time.

Data-driven Stochastic Model for Quantifying the Interplay Between Amyloid-beta and Calcium Levels in Alzheimer's Disease

The abnormal aggregation of extracellular amyloid-β ( A β ) in senile plaques resulting in calcium C a + 2 dyshomeostasis is one of the primary symptoms of Alzheimer's disease (AD). Significant research efforts have been devoted in the past to better understand the underlying molecular mechanisms driving A β deposition and C a + 2 dysregulation. Importantly, synaptic impairments, neuronal loss, and cognitive failure in AD patients are all related to the buildup of intraneuronal A β accumulation. Moreover, increasing evidence show a feed-forward loop between A β and C a + 2 levels, i.e. A β disrupts neuronal C a + 2 levels, which in turn affects the formation of A β . To better understand this interaction, we report a novel stochastic model where we analyze the positive feedback loop between A β and C a + 2 using ADNI data. A good therapeutic treatment plan for AD requires precise predictions. Stochastic models offer an appropriate framework for modelling AD since AD studies are observational in nature and involve regular patient visits. The etiology of AD may be described as a multi-state disease process using the approximate Bayesian computation method. So, utilizing ADNI data from 2-year visits for AD patients, we employ this method to investigate the interplay between A β and C a + 2 levels at various disease development phases. Incorporating the ADNI data in our physics-based Bayesian model, we discovered that a sufficiently large disruption in either A β metabolism or intracellular C a + 2 homeostasis causes the relative growth rate in both C a + 2 and A β , which corresponds to the development of AD. The imbalance of C a + 2 ions causes A β disorders by directly or indirectly affecting a variety of cellular and subcellular processes, and the altered homeostasis may worsen the abnormalities of C a + 2 ion transportation and deposition. This suggests that altering the C a + 2 balance or the balance between A β and C a + 2 by chelating them may be able to reduce disorders associated with AD and open up new research possibilities for AD therapy.

Data-driven modelling of neurodegenerative disease progression: thinking outside the black box

Data-driven disease progression models are an emerging set of computational tools that reconstruct disease timelines for long-term chronic diseases, providing unique insights into disease processes and their underlying mechanisms. Such methods combine a priori human knowledge and assumptions with large-scale data processing and parameter estimation to infer long-term disease trajectories from short-term data. In contrast to 'black box' machine learning tools, data-driven disease progression models typically require fewer data and are inherently interpretable, thereby aiding disease understanding in addition to enabling classification, prediction and stratification. In this Review, we place the current landscape of data-driven disease progression models in a general framework and discuss their enhanced utility for constructing a disease timeline compared with wider machine learning tools that construct static disease profiles. We review the insights they have enabled across multiple neurodegenerative diseases, notably Alzheimer disease, for applications such as determining temporal trajectories of disease biomarkers, testing hypotheses about disease mechanisms and uncovering disease subtypes. We outline key areas for technological development and translation to a broader range of neuroscience and non-neuroscience applications. Finally, we discuss potential pathways and barriers to integrating disease progression models into clinical practice and trial settings.

Toward digitally screening and profiling AD: A GAMLSS approach of MemTrax in China

PURPOSES

To establish a normative range of MemTrax (MTx) metrics in the Chinese population.

METHODS

The correct response percentage (MTx-%C) and mean response time (MTx-RT) were obtained and the composite scores (MTx-Cp) calculated. Generalized additive models for location, shape and scale (GAMLSS) were applied to create percentile curves and evaluate goodness of fit, and the speed-accuracy trade-off was investigated.

RESULTS

26,633 subjects, including 13,771 (51.71%) men participated in this study. Age- and education-specific percentiles of the metrics were generated. Q tests and worm plots indicated adequate fit for models of MTx-RT and MTx-Cp. Models of MTx-%C for the low and intermediate education fit acceptably, but not well enough for a high level of education. A significant speed-accuracy trade-off was observed for MTx-%C from 72 to 94.

CONCLUSIONS

GAMLSS is a reliable method to generate smoothed age- and education-specific percentile curves of MTx metrics, which may be adopted for mass screening and follow-ups addressing Alzheimer's disease or other cognitive diseases.

HIGHLIGHTS

GAMLSS was applied to establish nonlinear percentile curves of cognitive decline. Subjects with a high level of education demonstrate a later onset and slower decline of cognition. Speed-accuracy trade-off effects were observed in a subgroup with moderate accuracy. MemTrax can be used as a mass-screen instrument for active cognition health management advice.

Cognitive Trajectories and Associated Biomarkers in Patients with Mild Cognitive Impairment

BACKGROUND

To diagnose mild cognitive impairment (MCI) patients at risk of progression to dementia is clinically important but challenging.

OBJECTIVE

We classified MCI patients based on cognitive trajectories and compared biomarkers among groups.

METHODS

This study analyzed amnestic MCI patients with at least three Clinical Dementia Rating (CDR) scores available over a minimum of 36 months from the Alzheimer's Disease Neuroimaging Initiative database. Patients were classified based on their progression using trajectory modeling with the CDR-sum of box scores. We compared clinical and neuroimaging biomarkers across groups.

RESULTS

Of 569 eligible MCI patients (age 72.7±7.4 years, women n = 223), three trajectory groups were identified: stable (58.2%), slow decliners (24.6%), and fast decliners (17.2%). In the fifth year after diagnosis, the CDR-sum of box scores increased by 1.2, 5.4, and 11.8 points for the stable, slow, and fast decliners, respectively. Biomarkers associated with cognitive decline were amyloid-β 42, total tau, and phosphorylated tau protein in cerebrospinal fluid, hippocampal volume, cortical metabolism, and amount of cortical and subcortical amyloid deposits. Cortical metabolism and the amount of amyloid deposits were associated with the rate of cognitive decline.

CONCLUSION

Data-driven trajectory analysis provides new insights into the various cognitive trajectories of MCI. Baseline brain metabolism, and the amount of cortical and subcortical amyloid burden can provide additional information on the rate of cognitive decline.

Differentiation of Subjective Cognitive Decline, Mild Cognitive Impairment, and Dementia Using qEEG/ERP-Based Cognitive Testing and Volumetric MRI in an Outpatient Specialty Memory Clinic

BACKGROUND

Distinguishing between subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia in a scalable, accessible way is important to promote earlier detection and intervention.

OBJECTIVE

We investigated diagnostic categorization using an FDA-cleared quantitative electroencephalographic/event-related potential (qEEG/ERP)-based cognitive testing system (eVox® by Evoke Neuroscience) combined with an automated volumetric magnetic resonance imaging (vMRI) tool (Neuroreader® by Brainreader).

METHODS

Patients who self-presented with memory complaints were assigned to a diagnostic category by dementia specialists based on clinical history, neurologic exam, neuropsychological testing, and laboratory results. In addition, qEEG/ERP (n = 161) and quantitative vMRI (n = 111) data were obtained. A multinomial logistic regression model was used to determine significant predictors of cognitive diagnostic category (SCD, MCI, or dementia) using all available qEEG/ERP features and MRI volumes as the independent variables and controlling for demographic variables. Area under the Receiver Operating Characteristic curve (AUC) was used to evaluate the diagnostic accuracy of the prediction models.

RESULTS

The qEEG/ERP measures of Reaction Time, Commission Errors, and P300b Amplitude were significant predictors (AUC = 0.79) of cognitive category. Diagnostic accuracy increased when volumetric MRI measures, specifically left temporal lobe volume, were added to the model (AUC = 0.87).

CONCLUSION

This study demonstrates the potential of a primarily physiological diagnostic model for differentiating SCD, MCI, and dementia using qEEG/ERP-based cognitive testing, especially when combined with volumetric brain MRI. The accessibility of qEEG/ERP and vMRI means that these tools can be used as adjuncts to clinical assessments to help increase the diagnostic certainty of SCD, MCI, and dementia.

Correctness and response time distributions in the MemTrax continuous recognition task: Analysis of strategies and a reverse-exponential model

A critical issue in addressing medical conditions is measurement. Memory measurement is difficult, especially episodic memory, which is disrupted by many conditions. On-line computer testing can precisely measure and assess several memory functions. This study analyzed memory performances from a large group of anonymous, on-line participants using a continuous recognition task (CRT) implemented at https://memtrax.com. These analyses estimated ranges of acceptable performance and average response time (RT). For 344,165 presumed unique individuals completing the CRT a total of 602,272 times, data were stored on a server, including each correct response (HIT), Correct Rejection, and RT to the thousandth of a second. Responses were analyzed, distributions and relationships of these parameters were ascertained, and mean RTs were determined for each participant across the population. From 322,996 valid first tests, analysis of correctness showed that 63% of these tests achieved at least 45 correct (90%), 92% scored at or above 40 correct (80%), and 3% scored 35 correct (70%) or less. The distribution of RTs was skewed with 1% faster than 0.62 s, a median at 0.890 s, and 1% slower than 1.57 s. The RT distribution was best explained by a novel model, the reverse-exponential (RevEx) function. Increased RT speed was most closely associated with increased HIT accuracy. The MemTrax on-line memory test readily provides valid and reliable metrics for assessing individual episodic memory function that could have practical clinical utility for precise assessment of memory dysfunction in many conditions, including improvement or deterioration over time.

Now is the Time to Improve Cognitive Screening and Assessment for Clinical and Research Advancement

Alzheimer’s disease (AD) is the only cause of death ranked in the top ten globally without precise early diagnosis or effective means of prevention or treatment. Further, AD was identified as a pandemic [1] well before COVID-19 was dubbed a 21st century pandemic [2]. And now, with the realization of the prominent secondary impacts of pandemics, there is a growing, widespread recognition of the tremendous magnitude of the impending burden from AD in an aging world population in the coming decades [3]. This appreciation has amplified the growing and pressing need for a new, efficacious, and practical platform to detect and track cognitive decline, beginning in the preliminary (prodromal) phases of the disease, sensitively, accurately, effectively, reliably, efficiently, and remotely [4–7]. Moreover, the parallel necessity of clarifying and understanding risk factors, developing successful prevention strategies [8–17], and discovering and monitoring viable and effective treatments could all benefit from accurate and efficient screening and assessment platforms. Modern recognition of AD [18] as a common affliction of the elderly began in 1968 with a paper by Blessed, Tomlinson, & Roth [19] in which two tests, one a brief assessment of cognitive function and the other a measure of daily function, demonstrated impairment which was associated with the postmortem counts of neurofibrillary tangles, composed mainly of microtubule-associated protein-tau (tau), in the brain, though not to senile plaques, composed mainly of amyloid-β (Aβ). Even in more recent analyses, the tangles correspond with the severity of dementia more than the plaques [20, 21]. Since 1960, a plethora of cognitive tests, paper and pencil [22, 23], simple screening models [24], and computerized [25–27], have been developed to assess the dysfunction associated with AD. However, there has been limited application of Modern Test Theory, which includes Item Characteristic Curve Analysis, used in the technological development of such tools [28–31], along with widespread failure to understand the underlying AD pathological process to guide test development [32, 33]. The lack of such development has likely been a major contributor to the failure of the field to develop timely screening approaches for AD [34, 35], inaccurate assessment of the progression of AD [36], and even now, failure to find an effective approach to stopping AD.

Evaluation of the Accuracy of Cognitive Screening Tests in Detecting Dementia Associated with Alzheimer's Disease: A Hierarchical Bayesian Latent Class Meta-Analysis

BACKGROUND

Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) are neuropsychological tests commonly used by physicians for screening cognitive dysfunction of Alzheimer's disease (AD). Due to different imperfect reference standards, the performance of MoCA and MMSE do not reach consensus. It is necessary to evaluate the consistence and differentiation of MoCA and MMSE in the absence of a gold standard for AD.

OBJECTIVE

We aimed to assess the accuracy of MoCA and MMSE in screening AD without a gold standard reference test.

METHODS

Studies were identified from PubMed, Web of Science, CNKI, Chinese Wanfang Database, China Science and Technology Journal Database, and Cochrane Library. Our search was limited to studies published in English and Chinese before August 2021. A hierarchical Bayesian latent class model was performed in meta-analysis when the gold standard was absent.

RESULTS

A total of 67 studies comprising 5,554 individuals evaluated for MoCA and 76,862 for MMSE were included in this meta-analysis. The pooled sensitivity was 0.934 (95% CI 0.906 to 0.954) for MoCA and 0.883 (95% CI 0.859 to 0.903) for MMSE, while the pooled specificity was 0.899 (95% CI 0.859 to 0.928) for MoCA and 0.903 (95% CI 0.879 to 0.923) for MMSE. MoCA was useful to rule out dementia associated with AD with lower negative likelihood ratio (LR-) (0.074, 95% CI 0.051 to 0.108). MoCA showed better performance with higher diagnostic odds ratio (DOR) (124.903, 95% CI 67.459 to 231.260).

CONCLUSION

MoCA had better performance than MMSE in screening dementia associated with AD from patients with mild cognitive impairment or healthy controls.

A Systematic Review of Neuroimaging Studies Comparing Individuals with Subjective Cognitive Decline to Healthy Controls

Background: Individuals with subjective cognitive decline (SCD) are hypothesized to be the earliest along the cognitive continuum between healthy aging and Alzheimer’s disease (AD), although more research is needed on this topic. Given that treatment approaches may be most effective pre-clinically, a primary objective of emerging research is to identify biological markers of SCD using neuroimaging methods. Objective: The current review aimed to comprehensively present the neuroimaging studies on SCD to date. Methods: PubMed and PsycINFO databases were searched for neuroimaging studies of individuals with SCD. Quality assessments were completed using the Appraisal tool for Cross-Sectional Studies. Results: In total, 62 neuroimaging studies investigating differences between participants with SCD and healthy controls were identified. Specifically, the number of studies were as follows: 36 MRI, 6 PET, 8 MRI/PET, 4 EEG, 7 MEG, and 1 SPECT. Across neuroimaging modalities, 48 of the 62 included studies revealed significant differences in brain structure and/or function between groups. Conclusion: Neuroimaging methods can identify differences between healthy controls and individuals with SCD. However, inconsistent results were found within and between neuroimaging modalities. Discrepancies across studies may be best accounted for by methodological differences, notably variable criteria for SCD, and differences in participant characteristics and risk factors for AD. Clinic based recruitment and cross-sectional study design were common and may bias the literature. Future neuroimaging investigations of SCD should consistently incorporate the standardized research criteria for SCD (as recommended by the SCD-Initiative), include more details of their SCD sample and their symptoms, and examine groups longitudinally.

Segmented Linear Mixed Model Analysis Reveals Association of the APOEɛ4 Allele with Faster Rate of Alzheimer's Disease Dementia Progression

Background:

APOEɛ4 allele carriers present with an increased risk for late-onset Alzheimer’s disease (AD), show cognitive symptoms at an earlier age, and are more likely to transition from mild cognitive impairment (MCI) to dementia but despite this, it remains unclear whether or not the ɛ4 allele controls the rate of disease progression.

Objective:

To determine the effects of the ɛ4 allele on rates of cognitive decline and brain atrophy during MCI and dementia stages of AD.

Methods:

A segmented linear mixed model was chosen for longitudinal modeling of cognitive and brain volumetric data of 73 ɛ3/ɛ3, 99 ɛ3/ɛ4, and 39 ɛ4/ɛ4 Alzheimer’s Disease Neuroimaging Initiative participants who transitioned during the study from MCI to AD dementia.

Results:

ɛ4 carriers showed faster decline on MMSE, ADAS-11, CDR-SB, and MoCA scales, with the last two measures showing significant ɛ4 allele-dose effects after dementia transition but not during MCI. The ɛ4 effect was more prevalent in younger participants and in females. ɛ4 carriers also demonstrated faster rates of atrophy of the whole brain, the hippocampus, the entorhinal cortex, the middle temporal gyrus, and expansion of the ventricles after transitioning to dementia but not during MCI.

Conclusion:

Possession of the ɛ4 allele is associated with a faster progression of dementia due to AD. Our observations support the notion that APOE genotype not only controls AD risk but also differentially regulates mechanisms of neurodegeneration underlying disease advancement. Furthermore, our findings carry significance for AD clinical trial design.

Episodic-Memory Performance in Machine Learning Modeling for Predicting Cognitive Health Status Classification

BACKGROUND

Memory dysfunction is characteristic of aging and often attributed to Alzheimer's disease (AD). An easily administered tool for preliminary assessment of memory function and early AD detection would be integral in improving patient management.

OBJECTIVE

Our primary aim was to utilize machine learning in determining initial viable models to serve as complementary instruments in demonstrating efficacy of the MemTrax online Continuous Recognition Tasks (M-CRT) test for episodic-memory screening and assessing cognitive impairment.

METHODS

We used an existing dataset subset (n = 18,395) of demographic information, general health screening questions (addressing memory, sleep quality, medications, and medical conditions affecting thinking), and test results from a convenience sample of adults who took the M-CRT test. M-CRT performance and participant features were used as independent attributes: true positive/negative, percent responses/correct, response time, age, sex, and recent alcohol consumption. For predictive modeling, we used demographic information and test scores to predict binary classification of the health-related questions (yes/no) and general health status (healthy/unhealthy), based on the screening questions.

RESULTS

ANOVA revealed significant differences among HealthQScore groups for response time true positive (p = 0.000) and true positive (p = 0.020), but none for true negative (p = 0.0551). Both % responses and % correct had significant differences (p = 0.026 and p = 0.037, respectively). Logistic regression was generally the top-performing learner with moderately robust prediction performance (AUC) for HealthQScore (0.648-0.680) and selected general health questions (0.713-0.769).

CONCLUSION

Our novel application of supervised machine learning and predictive modeling helps to demonstrate and validate cross-sectional utility of MemTrax in assessing early-stage cognitive impairment and general screening for AD.

Utility of MemTrax and Machine Learning Modeling in Classification of Mild Cognitive Impairment

Background:

The widespread incidence and prevalence of Alzheimer’s disease and mild cognitive impairment (MCI) has prompted an urgent call for research to validate early detection cognitive screening and assessment.

Objective:

Our primary research aim was to determine if selected MemTrax performance metrics and relevant demographics and health profile characteristics can be effectively utilized in predictive models developed with machine learning to classify cognitive health (normal versus MCI), as would be indicated by the Montreal Cognitive Assessment (MoCA).

Methods:

We conducted a cross-sectional study on 259 neurology, memory clinic, and internal medicine adult patients recruited from two hospitals in China. Each patient was given the Chinese-language MoCA and self-administered the continuous recognition MemTrax online episodic memory test on the same day. Predictive classification models were built using machine learning with 10-fold cross validation, and model performance was measured using Area Under the Receiver Operating Characteristic Curve (AUC). Models were built using two MemTrax performance metrics (percent correct, response time), along with the eight common demographic and personal history features.

Results:

Comparing the learners across selected combinations of MoCA scores and thresholds, Naïve Bayes was generally the top-performing learner with an overall classification performance of 0.9093. Further, among the top three learners, MemTrax-based classification performance overall was superior using just the top-ranked four features (0.9119) compared to using all 10 common features (0.8999).

Conclusion:

MemTrax performance can be effectively utilized in a machine learning classification predictive model screening application for detecting early stage cognitive impairment.

A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer's disease

BACKGROUND AND OBJECTIVES

Recently, longitudinal studies of Alzheimer's disease have gathered a substantial amount of neuroimaging data. New methods are needed to successfully leverage and distill meaningful information on the progression of the disease from the deluge of available data. Machine learning has been used successfully for many different tasks, including neuroimaging related problems. In this paper, we review recent statistical and machine learning applications in Alzheimer's disease using longitudinal neuroimaging.

METHODS

We search for papers using longitudinal imaging data, focused on Alzheimer's Disease and published between 2007 and 2019 on four different search engines.

RESULTS

After the search, we obtain 104 relevant papers. We analyze their approach to typical challenges in longitudinal data analysis, such as missing data and variability in the number and extent of acquisitions.

CONCLUSIONS

Reviewed works show that machine learning methods using longitudinal data have potential for disease progression modelling and computer-aided diagnosis. We compare results and models, and propose future research directions in the field.

The MemTrax Test Compared to the Montreal Cognitive Assessment Estimation of Mild Cognitive Impairment

Cognitive impairment is a leading cause of dysfunction in the elderly. When mild cognitive impairment (MCI) occurs in elderly, it is frequently a prodromal condition to dementia. The Montreal Cognitive Assessment (MoCA) is a commonly used tool to screen for MCI. However, this test requires a face-to-face administration and is composed of an assortment of questions whose responses are added together by the rater to provide a score whose precise meaning has been controversial. This study was designed to evaluate the performance of a computerized memory test (MemTrax), which is an adaptation of a continuous recognition task, with respect to the MoCA. Two outcome measures are generated from the MemTrax test: MemTrax_speed and MemTrax_correct. Subjects were administered the MoCA and the MemTrax test. Based on the results of the MoCA, subjects were divided in two groups of cognitive status: normal cognition (n = 45) and MCI (n = 37). Mean MemTrax scores were significantly lower in the MCI than in the normal cognition group. All MemTrax outcome variables were positively associated with the MoCA. Two methods, computing the average MTX score and linear regression were used to estimate the cutoff values of the MemTrax test to detect MCI. These methods showed that for the outcome MemTrax_speed a score below the range of 0.87 – 91 s^-1 is an indication of MCI, and for the outcome MemTrax_correct a score below the range of 85 – 90% is an indication for MCI.

Testing whether the progression of Alzheimer's disease changes with the year of publication, additional design, and geographical area: a modeling analysis of literature aggregate data

BACKGROUND

Our objectives were to develop a disease progression model for cognitive decline in Alzheimer's disease (AD) and to determine whether disease progression of AD is related to the year of publication, add-on trial design, and geographical regions.

METHODS

Placebo-controlled randomized AD clinical trials were systemically searched in public databases. Longitudinal placebo response (mean change from baseline in the cognitive subscale of the Alzheimer's Disease Assessment Scale [ADAS-cog]) and the corresponding demographic information were extracted to establish a disease progression model. Covariate screening and subgroup analyses were performed to identify potential factors affecting the disease progression rate.

RESULTS

A total of 134 publications (140 trials) were included in this model-based meta-analysis. The typical disease progression rate was 5.82 points per year. The baseline ADAS-cog score was included in the final model using an inverse U-type function. Age was found to be negatively correlated with disease progression rate. After correcting the baseline ADAS-cog score and the age effect, no significant difference in the disease progression rate was found between trials published before and after 2008 and between trials using an add-on design and those that did not use an add-on design. However, a significant difference was found among different trial regions. Trials in East Asian countries showed the slowest decline rate and the largest placebo effect.

CONCLUSIONS

Our model successfully quantified AD disease progression by integrating baseline ADAS-cog score and age as important predictors. These factors and geographic location should be considered when optimizing future trial designs and conducting indirect comparisons of clinical outcomes.

Brief Cognitive Tests Used in Primary Care Cannot Accurately Differentiate Mild Cognitive Impairment from Subjective Cognitive Decline

BACKGROUND

Differentiating mild cognitive impairment (MCI) from subjective cognitive decline (SCD) is important because of the higher progression rate to dementia for MCI and when considering future disease-modifying drugs that will have treatment indications at the MCI stage.

OBJECTIVE

We examined if the two most widely-used cognitive tests, the Mini-Mental State Examination (MMSE) and clock-drawing test (CDT), and a test of attention/executive function (AQT) accurately can differentiate MCI from SCD.

METHODS

We included 466 consecutively recruited non-demented patients with cognitive complaints from the BioFINDER study who had been referred to memory clinics, predominantly from primary care. They were classified as MCI (n = 258) or SCD (n = 208) after thorough neuropsychological assessments. The accuracy of MMSE, CDT, and AQT for identifying MCI was examined both in training and validation samples and in the whole population.

RESULTS

As a single test, MMSE had the highest accuracy (sensitivity 73%, specificity 60%). The best combination of two tests was MMSE < 27 points or AQT > 91 seconds (sensitivity 56%, specificity 78%), but in logistic regression models, their AUC (0.76) was not significantly better than MMSE alone (AUC 0.75). CDT and AQT performed significantly worse (AUC 0.71; p < 0.001-0.05); otherwise no differences were seen between any combination of two or three tests.

CONCLUSION

Neither single nor combinations of tests could differentiate MCI from SCD with adequately high accuracy. There is a great need to further develop, validate, and implement accurate screening-tests for primary care to improve accurate identification of MCI among individuals that seek medical care due to cognitive symptoms.

Analyses of natural courses of Japanese patients with Alzheimer's disease using placebo data from placebo-controlled, randomized clinical trials: Japanese Study on the Estimation of Clinical course of Alzheimer's disease

Introduction

Symptomatic anti-Alzheimer's disease (AD) drugs have been commonly used for the treatment of AD. Knowing the natural courses of patients with AD on placebo is highly relevant for clinicians to understand their efficacy and for investigators to design clinical studies.

Methods

The data on rating scales for dementia such as Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog) and Severe Impairment Battery were extracted from eight previous Japanese Phase II and III studies. Natural courses of Japanese AD patients in placebo groups were evaluated and statistically analyzed in a pooled and retrospective fashion.

Results

Decreases in ADAS-cog and Severe Impairment Battery was larger at week 22 or 24 than at week 12. Scores of ADAS-cog appeared to deteriorate faster in moderate AD than in mild AD.

Discussion

The present data will provide clinicians following up patients with AD with helpful information on how to manage AD patients and investigators with instruction for clinical study design.

A Computerized Continuous-Recognition Task for Measurement of Episodic Memory

Based on clinical observations of severe episodic memory (EM) impairment in dementia of Alzheimer’s disease (AD), a brief, computerized EM test was developed for AD patient evaluation. A continuous recognition task (CRT) was chosen because of its extensive use in EM research. Initial experience with this computerized CRT (CCRT) showed patients were very engaged in the test, but AD patients had marked failure in recognizing repeated images. Subsequently, the test was administered to audiences, and then a two-minute online version was implemented (http://www.memtrax.com). The online CCRT shows 50 images, 25 unique and 25 repeats, which subjects respectively either try to remember or indicate recognition as quickly as possible. The pictures contain 5 sets of 5 images of scenes or objects (e.g., mountains, clothing, vehicles, etc.). A French company (HAPPYneuron, SAS) provided the test for 2 years, with these results. Of 18,477 individuals, who indicated sex and age 21–99 years and took the test for the first time, 18,007 individuals performed better than chance. In this group, age explained 1.5% of the variance in incorrect responses and 3.5% of recognition time variance, indicating considerable population variability. However, when averaging for specific year of age, age explained 58% of percent incorrect variance and 78% of recognition time variance, showing substantial population variability but a major age effect. There were no apparent sex effects. Further studies are indicated to determine the value of this CCRT as an AD screening test and validity as a measure of EM impairment in other clinical conditions.

Longitudinal Genotype-Phenotype Association Study through Temporal Structure Auto-Learning Predictive Model

With the rapid development of high-throughput genotyping and neuroimaging techniques, imaging genetics has drawn significant attention in the study of complex brain diseases such as Alzheimer's disease (AD). Research on the associations between genotype and phenotype improves the understanding of the genetic basis and biological mechanisms of brain structure and function. AD is a progressive neurodegenerative disease; therefore, the study on the relationship between single nucleotide polymorphism (SNP) and longitudinal variations of neuroimaging phenotype is crucial. Although some machine learning models have recently been proposed to capture longitudinal patterns in genotype-phenotype association studies, most machine-learning models base the learning on fixed structure among longitudinal prediction tasks rather than automatically learning the interrelationships. In response to this challenge, we propose a new automated time structure learning model to automatically reveal the longitudinal genotype-phenotype interactions and exploits such learned structure to enhance the phenotypic predictions. We proposed an efficient optimization algorithm for our model and provided rigorous theoretical convergence proof. We performed experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for longitudinal phenotype prediction, including 3123 SNPs and 2 biomarkers (Voxel-Based Morphometry and FreeSurfer). The empirical results validate that our proposed model is superior to the counterparts. In addition, the best SNPs identified by our model have been replicated in the literature, which justifies our prediction.

The development of a stochastic mathematical model of Alzheimer's disease to help improve the design of clinical trials of potential treatments

Alzheimer's disease (AD) is a neurodegenerative disorder characterised by a slow progressive deterioration of cognitive capacity. Drugs are urgently needed for the treatment of AD and unfortunately almost all clinical trials of AD drug candidates have failed or been discontinued to date. Mathematical, computational and statistical tools can be employed in the construction of clinical trial simulators to assist in the improvement of trial design and enhance the chances of success of potential new therapies. Based on the analysis of a set of clinical data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) we developed a simple stochastic mathematical model to simulate the development and progression of Alzheimer's in a longitudinal cohort study. We show how this modelling framework could be used to assess the effect and the chances of success of hypothetical treatments that are administered at different stages and delay disease development. We demonstrate that the detection of the true efficacy of an AD treatment can be very challenging, even if the treatment is highly effective. An important reason behind the inability to detect signals of efficacy in a clinical trial in this therapy area could be the high between- and within-individual variability in the measurement of diagnostic markers and endpoints, which consequently results in the misdiagnosis of an individual's disease state.

A Longitudinal Item Response Theory Model to Characterize Cognition Over Time in Elderly Subjects

For drug development in neurodegenerative diseases such as Alzheimer's disease, it is important to understand which cognitive domains carry the most information on the earliest signs of cognitive decline, and which subject characteristics are associated with a faster decline. A longitudinal Item Response Theory (IRT) model was developed for the Basel Study on the Elderly, in which the Consortium to Establish a Registry for Alzheimer's Disease - Neuropsychological Assessment Battery (with additions) and the California Verbal Learning Test were measured on 1,750 elderly subjects for up to 13.9 years. The model jointly captured the multifaceted nature of cognition and its longitudinal trajectory. The word list learning and delayed recall tasks carried the most information. Greater age at baseline, fewer years of education, and positive APOEɛ4 carrier status were associated with a faster cognitive decline. Longitudinal IRT modeling is a powerful approach for progressive diseases with multifaceted endpoints.

Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-Learning Predictive Model

With rapid progress in high-throughput genotyping and neuroimaging, imaging genetics has gained significant attention in the research of complex brain disorders, such as Alzheimer's Disease (AD). The genotype-phenotype association study using imaging genetic data has the potential to reveal genetic basis and biological mechanism of brain structure and function. AD is a progressive neurodegenerative disease, thus, it is crucial to look into the relations between SNPs and longitudinal variations of neuroimaging phenotypes. Although some machine learning models were newly presented to capture the longitudinal patterns in genotype-phenotype association study, most of them required fixed longitudinal structures of prediction tasks and could not automatically learn the interrelations among longitudinal prediction tasks. To address this challenge, we proposed a novel temporal structure auto-learning model to automatically uncover longitudinal genotype-phenotype interrelations and utilized such interrelated structures to enhance phenotype prediction in the meantime. We conducted longitudinal phenotype prediction experiments on the ADNI cohort including 3,123 SNPs and 2 types of biomarkers, VBM and FreeSurfer. Empirical results demonstrated advantages of our proposed model over the counterparts. Moreover, available literature was identified for our top selected SNPs, which demonstrated the rationality of our prediction results. An executable program is available online at https://github.com/littleq1991/sparse_lowRank_regression.

Resource utilisation, costs and clinical outcomes in non-institutionalised patients with Alzheimer's disease: 18-month UK results from the GERAS observational study

BACKGROUND

Alzheimer's disease (AD), the commonest cause of dementia, represents a significant cost to UK society. This analysis describes resource utilisation, costs and clinical outcomes in non-institutionalised patients with AD in the UK.

METHODS

The GERAS prospective observational study assessed societal costs associated with AD for patients and caregivers over 18 months, stratified according to baseline disease severity (mild, moderate, or moderately severe/severe [MS/S]). All patients enrolled had an informal caregiver willing to participate in the study. Healthcare resource utilisation was measured using the Resource Utilization in Dementia instrument, and 18-month costs estimated by applying unit costs of services and products (2010 values). Total societal costs were calculated using an opportunity cost approach.

RESULTS

Overall, 526 patients (200 mild, 180 moderate and 146 MS/S at baseline) were recruited from 24 UK centres. Mini-Mental State Examination (MMSE) scores deteriorated most markedly in the MS/S patient group, with declines of 3.6 points in the mild group, 3.5 points in the moderate group and 4.7 points in the MS/S group; between-group differences did not reach statistical significance. Patients with MS/S AD dementia at baseline were more likely to be institutionalised (Kaplan-Meier probability 28% versus 9% in patients with mild AD dementia; p < 0.001 for difference across all severities) and had a greater probability of death (Kaplan-Meier probability 15% versus 5%; p = 0.013) at 18 months. Greater disease severity at baseline was also associated with concomitant increases in caregiver time and mean total societal costs. Total societal costs of £43,560 over 18 months were estimated for the MS/S group, versus £25,865 for the mild group and £30,905 for the moderate group (p < 0.001). Of these costs, over 50% were related to informal caregiver costs at each AD dementia severity level.

CONCLUSIONS

This study demonstrated a mean deterioration in MMSE score over 18 months in patients with AD. It also showed that AD is a costly disease, with costs increasing with disease severity, even when managed in the community: informal caregiver costs represented the main contributor to societal costs.

Instantiated mixed effects modeling of Alzheimer's disease markers

The assessment and prediction of a subject's current and future risk of developing neurodegenerative diseases like Alzheimer's disease are of great interest in both the design of clinical trials as well as in clinical decision making. Exploring the longitudinal trajectory of markers related to neurodegeneration is an important task when selecting subjects for treatment in trials and the clinic, in the evaluation of early disease indicators and the monitoring of disease progression. Given that there is substantial intersubject variability, models that attempt to describe marker trajectories for a whole population will likely lack specificity for the representation of individual patients. Therefore, we argue here that individualized models provide a more accurate alternative that can be used for tasks such as population stratification and a subject-specific prognosis. In the work presented here, mixed effects modeling is used to derive global and individual marker trajectories for a training population. Test subject (new patient) specific models are then instantiated using a stratified "marker signature" that defines a subpopulation of similar cases within the training database. From this subpopulation, personalized models of the expected trajectory of several markers are subsequently estimated for unseen patients. These patient specific models of markers are shown to provide better predictions of time-to-conversion to Alzheimer's disease than population based models.

Treatment of Alzheimer's Disease: The Legacy of the Cholinergic Hypothesis, Neuroplasticity, and Future Directions

In this issue, an article by Waring et al. provides a meta-analysis of the effects of apo-lipo-protein E (APOE) genotype on the beneficial effect of acetyl-cholinesterase inhibitors (AChEIs) in patients with Alzheimer's disease (AD). There was no significant effect found. As of 2015, AChEI medications are the mainstay of AD treatment, and APOE genotype is the most significant factor associated with AD causation. This lack of a significant effect of APOE is analyzed with respect to the "Cholinergic Hypothesis" of AD, dating from 1976, through the recognition that cholinergic neurons are not the sole target of AD, but rather that AD attacks all levels of neuroplasticity in the brain, an idea originated by Ashford and Jarvik in 1985 and which still provides the clearest explanation for AD dementia. The "Amyloid Hypothesis" is dissected back to the alpha/beta pathway switching mechanism affecting the nexin-amyloid pre-protein (NAPP switch). The NAPP switch may be the critical neuroplasticity component of all learning involving synapse remodeling and subserve all learning mechanisms. The gamma-secretase cleavage is discussed, and its normal complementary products, beta-amyloid and the NAPP intracellular domain (NAICD), appear to be involved in natural synapse removal, but the link to AD dementia may involve the NAICD rather than beta-amyloid. Understanding neuroplasticity and the critical pathways to AD dementia are needed to determine therapies and preventive strategies for AD. In particular, the effect of APOE on AD predisposition needs to be established and a means found to adjust its effect to prevent AD.

Danger and dementia: caregiver experiences and shifting social roles during a highly active hurricane season

This study examined disaster preparedness and decision-making by caregivers of community-dwelling persons diagnosed with Alzheimer's or a related dementia (ADRD). Interviews were conducted with 20 caregivers in South Florida. Twelve of these interviews include caregiving experiences during the highly active 2004-2005 hurricane seasons. Results indicate that persons in earlier stages of ADRD can, and often do, remain engaged in the disaster preparation and planning process. However, during the early stages, persons may also resist evacuation, even if the caregiver felt it was necessary. During later stages of the disease, caregivers reported less resistance to disaster-related decisions, however, with the tradeoff of less ability to assist with preparation.

Disease progression model for Clinical Dementia Rating-Sum of Boxes in mild cognitive impairment and Alzheimer's subjects from the Alzheimer's Disease Neuroimaging Initiative

BACKGROUND

The objective of this analysis was to develop a nonlinear disease progression model, using an expanded set of covariates that captures the longitudinal Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) scores. These were derived from the Alzheimer's Disease Neuroimaging Initiative ADNI-1 study, of 301 Alzheimer's disease and mild cognitive impairment patients who were followed for 2-3 years.

METHODS

The model describes progression rate and baseline disease score as a function of covariates. The covariates that were tested fell into five groups: a) hippocampal volume; b) serum and cerebrospinal fluid (CSF) biomarkers; c) demographics and apolipoprotein Epsilon 4 (ApoE4) allele status; d) baseline cognitive tests; and e) disease state and comedications.

RESULTS

Covariates associated with baseline disease severity were disease state, hippocampal volume, and comedication use. Disease progression rate was influenced by baseline CSF biomarkers, Trail-Making Test part A score, delayed logical memory test score, and current level of impairment as measured by CDR-SB. The rate of disease progression was dependent on disease severity, with intermediate scores around the inflection point score of 10 exhibiting high disease progression rate. The CDR-SB disease progression rate in a typical patient, with late mild cognitive impairment and mild Alzheimer's disease, was estimated to be approximately 0.5 and 1.4 points/year, respectively.

CONCLUSIONS

In conclusion, this model describes disease progression in terms of CDR-SB changes in patients and its dependency on novel covariates. The CSF biomarkers included in the model discriminate mild cognitive impairment subjects as progressors and nonprogressors. Therefore, the model may be utilized for optimizing study designs, through patient population enrichment and clinical trial simulations.

Disease progression model in subjects with mild cognitive impairment from the Alzheimer's disease neuroimaging initiative: CSF biomarkers predict population subtypes

AIM

The objective is to develop a semi-mechanistic disease progression model for mild cognitive impairment (MCI) subjects. The model aims to describe the longitudinal progression of ADAS-cog scores from the Alzheimer's disease neuroimaging initiative trial that had data from 198 MCI subjects with cerebrospinal fluid (CSF) information who were followed for 3 years.

METHOD

Various covariates were tested on disease progression parameters and these variables fell into six categories: imaging volumetrics, biochemical, genetic, demographic, cognitive tests and CSF biomarkers.

RESULTS

CSF biomarkers were associated with both baseline disease score and disease progression rate in subjects with MCI. Baseline disease score was also correlated with atrophy measured using hippocampal volume. Progression rate was also predicted by executive functioning as measured by the Trail B-test.

CONCLUSION

CSF biomarkers have the ability to discriminate MCI subjects into sub-populations that exhibit markedly different rates of disease progression on the ADAS-cog scale. These biomarkers can therefore be utilized for designing clinical trials enriched with subjects that carry the underlying disease pathology.

Prediction models for assessing long-term outcome in Alzheimer's disease: a review

In Alzheimer's disease (AD), placebo-controlled long-term studies of cholinesterase inhibitors (ChEIs) are not permitted for ethical reasons. Therefore, in these studies, patients' outcomes on cognitive and functional assessment scales must be compared with mathematical models or historical data from untreated cohorts. PubMed and previously published long-term extensions of clinical trials and naturalistic studies of ChEIs were examined to identify empirical statistical models and other approaches, such as use of data from historical cohorts or extrapolated changes from extension studies, that were used to draw comparisons between ChEI-treated and untreated patients. The models and methods were described. It is essential to be aware of the limitations of comparisons made with these approaches. Prediction models based on ChEI-treated patients can be used in the studies of new treatments when those treatments are added to ChEIs. More sophisticated models that also accommodate patient-specific characteristics should be developed for comparisons in future long-term AD studies.

Combining patient-level and summary-level data for Alzheimer's disease modeling and simulation: a β regression meta-analysis

Our objective was to develop a beta regression (BR) model to describe the longitudinal progression of the 11 item Alzheimer's disease (AD) assessment scale cognitive subscale (ADAS-cog) in AD patients in both natural history and randomized clinical trial settings, utilizing both individual patient and summary level literature data. Patient data from the coalition against major diseases database (3,223 patients), the Alzheimer's disease neruroimaging initiative study database (186 patients), and summary data from 73 literature references (representing 17,235 patients) were fit to a BR drug-disease-trial model. Treatment effects for currently available acetyl cholinesterase inhibitors, longitudinal changes in disease severity, dropout rate, placebo effect, and factors influencing these parameters were estimated in the model. Based on predictive checks and external validation, an adequate BR meta-analysis model for ADAS-cog using both summary-level and patient-level data was developed. Baseline ADAS-cog was estimated from baseline MMSE score. Disease progression was dependent on time, ApoE4 status, age, and gender. Study drop out was a function of time, baseline age, and baseline MMSE. The use of the BR constrained simulations to the 0-70 range of the ADAS-cog, even when residuals were incorporated. The model allows for simultaneous fitting of summary and patient level data, allowing for integration of all information available. A further advantage of the BR model is that it constrains values to the range of the original instrument for simulation purposes, in contrast to methodologies that provide appropriate constraints only for conditional expectations.

Prediction of preclinical Alzheimer's disease: longitudinal rates of change in cognition

Preclinical Alzheimer's disease (pAD) reflects neuropathological findings of AD in cognitively normal subjects. The present study represents an effort to determine if differences could be identified in the longitudinal patterns of cognitive performance in persons classified as pAD compared to those who did not meet criteria for AD at autopsy. We included 121 subjects who were cognitively normal from baseline through their last assessment before death and who underwent autopsy. Participants were classified into two groups: pathologically normal (PN; NIA-Reagan low or no-likelihood of AD, n = 89) and preclinical AD (pAD; NIA-Reagan criteria of intermediate or high-likelihood of AD in the absence of clinical dementia symptoms, n = 32) followed for a mean 7.5 years prior to death. Longitudinal rates and patterns of change in scores on a standard cognitive battery were compared between these two groups. While cognitive results at baseline and last evaluations revealed no clear cross sectional group differences after adjustment for age, ApoE status, education, and gender, statistically significant differences between the pAD and PN groups in slope of decline were seen on a composite score of cognitive function. Further analyses showed three components of this score reached significance: constructional praxis, delayed recall of a word list, and category verbal fluency. Despite being clinically viewed as normal at enrollment and at the final exam, there are significant differences in rates of cognitive decline in participants classified as pAD compared to those without this pathology. Longitudinal changes in slope of decline in specific cognitive test measures can serve as non-invasive methods for the detection of pAD.

A systematic review of assessment and treatment of moderate to severe Alzheimer's disease

OBJECTIVE

The systematic, large-scale study of therapies for moderate to severe Alzheimer's disease (AD) is a relatively recent advancement in the field. This review describes for the general practitioner the characterization of moderate to severe AD, discusses the development of metrics sensitive to the constellation of symptoms in these patients, and critically evaluates the use of those measures in moderate to severe AD clinical trials.

DATA SOURCES

Published clinical trials obtained by MEDLINE searches used the following key words: moderate AD, severe AD, donepezil, rivastigmine, galantamine, memantine, and anti-dementia agents. Clinical trials were limited by language (English), study type (clinical trial), and publication dates (1990-2005).

STUDY SELECTION

Nine clinical trials comprise the studies conducted to date in moderate to severe AD and include 5 prospective randomized clinical trials (3 for memantine, 2 for donepezil) and 4 retrospective subanalyses (2 for galantamine, 2 for rivastigmine) of primary datasets.

DATA EXTRACTION

Clinical trials are summarized and major findings are reviewed.

DATA SYNTHESIS

The data reviewed support the decision to initiate and maintain treatment in moderate to severe AD patients.

CONCLUSIONS

The development and implementation of improved metrics for moderate to severe AD patients has revealed that meaningful benefits are attainable in this patient population by treatment with the N-methyl-D-aspartate receptor antagonist memantine. Evidence also indicates a benefit from cholinesterase inhibitor treatment, although further study of these agents in this patient population is warranted.

Dependence as a unifying construct in defining Alzheimer's disease severity

This article reviews measures of Alzheimer's disease (AD) progression in relation to patient dependence and offers a unifying conceptual framework for dependence in AD. Clinicians typically characterize AD by symptomatic impairments in three domains: cognition, function, and behavior. From a patient's perspective, changes in these domains, individually and in concert, ultimately lead to increased dependence and loss of autonomy. Examples of dependence in AD range from a need for reminders (early AD) to requiring safety supervision and assistance with basic functions (late AD). Published literature has focused on the clinical domains as somewhat separate constructs and has given limited attention to the concept of patient dependence as a descriptor of AD progression. This article presents the concept of dependence on others for care needs as a potential method for translating the effect of changes in cognition, function, and behavior into a more holistic, transparent description of AD progression.

An improved model for disease progression in patients from the Alzheimer's disease neuroimaging initiative

The objective of this analysis was to develop a semi-mechanistic nonlinear disease progression model using an expanded set of covariates that captures the longitudinal change of Alzheimer's Disease Assessment Scale (ADAS-cog) scores from the Alzheimer's Disease Neuroimaging Initiative study that consisted of 191 Alzheimer disease patients who were followed for 2 years. The model describes the rate of progression and baseline disease severity as a function of influential covariates. The covariates that were tested fell into 4 categories: (1) imaging volumetric measures, (2) serum biomarkers, (3) demographic and genetic factors, and (4) baseline cognitive tests. Covariates found to affect baseline disease status were years since disease onset, hippocampal volume, and ventricular volume. Disease progression rate in the model was influenced by age, total cholesterol, APOE ε4 genotype, Trail Making Test (part B) score, and current levels of impairment as measured by ADAS-cog. Rate of progression was slower for mild and severe Alzheimer patients compared with moderate Alzheimer patients who exhibited faster rates of deterioration. In conclusion, this model describes disease progression in Alzheimer patients using novel covariates that are important for understanding the worsening of ADAS-cog scores over time and may be useful in the future for optimizing study designs through clinical trial simulations.

Imaging the Alzheimer brain

This supplement to the Journal of Alzheimer's Disease contains more than half of the chapters from The Handbook of Imaging the Alzheimer Brain, which was first presented at the International Conference on Alzheimer's Disease in Paris, in July, 2011.

While the Handbook contains 27 chapters that are modified articles from 2009, 2010, and 2011 issues of the Journal of Alzheimer's Disease, this supplement contains the 31 new chapters of that book and an introductory article drawn from the introductions to each section of the book.

The Handbook was designed to provide a multilevel overview of the full field of brain imaging related to Alzheimer's disease (AD). The Handbook, as well as this supplement, contains both reviews of the basic concepts of imaging, the latest developments in imaging, and various discussions and perspectives of the problems of the field and promising directions.

The Handbook was designed to be useful for students and clinicians interested in AD as well as scientists studying the brain and pathology related to AD.

Disease progression model for cognitive deterioration from Alzheimer's Disease Neuroimaging Initiative database

BACKGROUND

A mathematical model was developed to describe the longitudinal response in Alzheimer's Disease Assessment Scale-cognitive (ADAS-cog) obtained from the Alzheimer's Disease Neuroimaging Initiative.

METHODS

The model was fit to the longitudinal ADAS-cog scores from 817 patients. Risk factors (age, apolipoprotein ɛ4 [APOE ɛ4] genotype, gender, family history of AD, years of education) and baseline severity were tested as covariates.

RESULTS

Rate of disease progression increased with baseline severity. Age, APOE ɛ4 genotype, and gender were identified as potential covariates influencing disease progression. The rate of disease progression in patients with mild to moderate AD was estimated as approximately 5.5 points/yr.

CONCLUSIONS

A disease progression model adequately described the natural decline of ADAS-cog observed in Alzheimer's Disease Neuroimaging Initiative. Baseline severity is an important covariate to predict a curvilinear rate of disease progression in normal elderly, mild cognitive impairment, and AD patients. Age, APOE ɛ4 genotype, and gender also influence the rate of disease progression.

The Functional Transitions Model: Maximizing Ability in the Context of Progressive Disability Associated with Alzheimer's Disease

The Functional Transitions Model (FTM) integrates the theoretical notions of progressive functional decline associated with Alzheimer's disease (AD), excess disability, and transitions occurring intermittently along the trajectory of functional decline. Application of the Functional Transitions Model to clinical practice encompasses the paradox of attempting to minimize excess disability while anticipating the progressive functional decline associated with AD. It is suggested that times of functional transition are times of decision making and opportunities for interdisciplinary collaboration to identify and minimize excess disability, for revision of goals and expectations, and for provision of support to patients and caregivers. The model also is applicable as a conceptual framework for education and research.

Should older adults be screened for dementia?

The question of whether to screen for dementia and Alzheimer's disease (AD) has been discussed in many forums throughout the world. Generally, medical advisory groups and policy-making groups have recognized the importance of early diagnosis but have uniformly avoided making recommendations to screen at-risk populations. This presentation reflects the support for reconsidering the importance of screening individuals at risk or above a certain age. In this statement, the majority of the authors support the consideration of dementia risk factors in individuals at age 50, with routine yearly screening after 75. Other authors remain concerned that the benefits of treatments of early disease do not yet support a general screening recommendation. These statements are made to encourage progress toward the development of a consensus regarding the widespread institution of screening policy. Accordingly, members of the worldwide scientific community are invited to add their perspective by contributing short commentaries (1500 words) on this subject.

Screening for memory disorders, dementia and Alzheimer’s disease

Dementia and its most common cause, Alzheimer’s disease, affect memory and occur predominantly in the elderly. Dementia has become increasingly prevalent in the world as health has improved and life expectancy has increased. However, the fields of clinical care have not responded adequately to develop diagnostic tools and treatments for this rapidly increasing group of conditions. While scientists search for cures for the numerous causes of dementia, improvement of diagnostic measures are needed now and should begin with screening elderly populations for memory difficulties and other cognitive problems. This review examines the history of cognitive screening tests, the numerous excellent tests that are currently available and ready for use, and directions and methods that will lead to progressively better evaluations.

Strategies to reduce site differences in multisite studies: a case study of Alzheimer disease progression

OBJECTIVES

The objectives of this study were to evaluate the magnitude and sources of site differences in a multisite study of rates of cognitive decline among patients with Alzheimer disease and to seek strategies to reduce the magnitude of site differences in this and future such studies.

METHODS

A total of 3,280 participants from 15 different sites was analyzed. For each participant, the average rate of change in the Mini-Mental State Examination (MMSE) was calculated. Participants who declined at least three MMSE points per year were classified "rapid decliners." Site differences in sociodemographic distributions and the percentage of rapid decliners were examined, and a signal detection approach was used to identify the main correlates of rapid decline.

RESULTS

The percentage of rapid decliners for the 15 sites initially varied from 8%-40%. Two of the correlates of rapid decline were largely the result of different sampling protocols, namely baseline MMSE and elapsed time between the first and last MMSE. By selecting only those participants at each site with a baseline MMSE between 15 and 23, and limiting the follow-up time to a period of 11-24 months, the authors created greater homogeneity in the protocols across sites and reduced site variability of rapid decliners from 27%-50%.

CONCLUSION

Results of single-site studies are often nonreproducible, and multisite studies that follow different protocols and do not take site differences into account may be misleading. This study indicates the importance of site differences and how relatively simple efforts to impose common sampling, measurement, and design criteria can reduce, if not totally remove, site differences.

A general bilinear model to describe growth or decline time profiles

Linear models are widely used because of their unrivaled simplicity, but they cannot be applied for data that have a turning-or rate-change-point, even if the data show good linearity sufficiently far from this point. To describe such bilinear-type data, a completely generalized version of a linearized biexponential model (LinBiExp) is proposed here to make possible smooth and fully parametrizable transitions between two linear segments while still maintaining a clear connection with the linear models. Applications and brief conclusions are presented for various time profiles of biological and medical interest including growth profiles, such as those of human stature, agricultural crops and fruits, multicellular tumor spheroids, single fission yeast cells, or even labor productivity, and decline profiles, such as age-effects on cognition in patients who develop dementia and lactation yields in dairy cattle. In all these cases, quantitative model selection criteria such as the Akaike and the Schwartz Bayesian information criteria indicated the superiority of the bilinear model compared to adequate less parametrized alternatives such as linear, parabolic, exponential, or classical growth (e.g., logistic, Gompertz, Weibull, and Richards) models. LinBiExp provides a versatile and useful five-parameter bilinear functional form that is convenient to implement, is suitable for full optimization, and uses intuitive and easily interpretable parameters.

Neural networks for longitudinal studies in Alzheimer's disease

OBJECTIVE

Alzheimer's disease affects a growing population of elderly people today. The predictions about the course of the disease is a key component of health care decision making for patients with Alzheimer's. The physician's prognosis and predicted trajectory of cognitive decline often form the basis of treatment and health care decisions taken by patients and their families. These predictions are difficult to make because of the high variability and non-linearity exhibited by individual patterns of cognitive decline. This paper presents a new method of predicting the course of a disease using longitudinal data collected through multiple clinic visits. Longitudinal databases are similar to temporal databases, with some important differences--data is collected at irregular time intervals that are patient specific and also a varying number of observations are made for each patient, depending upon the number of times the patient visited the clinic. We propose a new type of neural network called the mixed effects neural network (MENN) model that can incorporate this type of longitudinal information.

MATERIAL AND METHODS

We have used longitudinal data on 704 subjects enrolled at the Layton aging and research center (LAARC) at Oregon Health and Science University. A back-propagation algorithm, modified for longitudinal data is used to obtain the weight parameters of the MENN. The modified back-propagation algorithm is further embedded in an iterative procedure that estimates the noise variance and the parameters that capture the longitudinal (temporal) correlation structure.

RESULTS

We have compared the performance of the MENN with linear mixed effects models and standard neural networks (NN). MENN show better performance (misclassification rate = 0.13 and relative MSE = 0.35) as compared to standard NN (misclassification rate = 0.34 and relative MSE = 2.74) and linear mixed effects models (misclassification rate = 0.14 and relative MSE = 0.4).

CONCLUSION

The results show that this method can be a useful tool for predicting non-linear disease trajectories and uncovering significant prognostic factors in longitudinal databases.