Walking stabilizes cognitive functioning in Alzheimer's disease (AD) across one year.

AD is a public health epidemic, which seriously impacts cognition, mood and daily activities; however, one type of activity, exercise, has been shown to alter these states. Accordingly, we sought to investigate the relationship between exercise and mood, in early-stage AD patients (N=104) from California, over a 1-year period. Patients completed the Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Blessed-Roth Dementia Rating Scale (BRDRS), while their caregivers completed the Yale Physical Activity Survey (YALE), Profile of Mood States (POMS), the Neuropsychiatric Inventory (NPI) and Functional Abilities Questionnaire (FAQ). Approximately half of the participants were female, from a variety of ethnic groups (Caucasian=69.8%; Latino/Hispanic Americans=20.1%). Our results demonstrated that the patients spent little time engaged in physical activity in general, their overall activity levels decreased over time, and this was paired with a change in global cognition (e.g., MMSE total score) and affect/mood (e.g., POMS score). Patients were parsed into Active and Sedentary groups based on their Yale profiles, with Active participants engaged in walking activities, weekly, over 1 year. Here, Sedentary patients had a significant decline in MMSE scores, while the Active patients had an attenuation in global cognitive decline. Importantly, among the Active AD patients, those individuals who engaged in walking for more than 2 h/week had a significant improvement in MMSE scores. Structured clinical trials which seek to increase the amount of time AD patients were engaged in walking activities and evaluate the nature and scope of beneficial effects in the brain are warranted.

The variability of practice hypothesis in motor learning: does it apply to Alzheimer's disease?

Based on Schmidt's (1975) variability of practice hypothesis, this study examined acquisition and transfer of a gross motor skill, namely tossing, in 58 patients with Alzheimer's disease (AD) and 58 healthy older adults under constant, blocked, and random practice conditions. While healthy older adults were able to learn the tossing task equally well under the three practice conditions, only AD patients receiving constant practice showed significant improvements. Tests of intermediate transfer yielded the expected random practice advantage in healthy controls but not AD patients. None of the practice conditions facilitated intermediate transfer in AD patients; however, constant practice did benefit these impaired individuals on tests of near transfer. These results indicate that the variability of practice hypothesis does not extend to AD patients. As motor learning and transfer were clearly a function of constant practice, future attempts to retrain basic activities of daily living in AD patients should emphasize consistency in training.

Two-Stage Machine Learning model for guideline development.

We present a Two-Stage Machine Learning (ML) model as a data mining method to develop practice guidelines and apply it to the problem of dementia staging. Dementia staging in clinical settings is at present complex and highly subjective because of the ambiguities and the complicated nature of existing guidelines. Our model abstracts the two-stage process used by physicians to arrive at the global Clinical Dementia Rating Scale (CDRS) score. The model incorporates learning intermediate concepts (CDRS category scores) in the first stage that then become the feature space for the second stage (global CDRS score). The sample consisted of 678 patients evaluated in the Alzheimer's Disease Research Center at the University of California, Irvine. The demographic variables, functional and cognitive test results used by physicians for the task of dementia severity staging were used as input to the machine learning algorithms. Decision tree learners and rule inducers (C4.5, Cart, C4.5 rules) were selected for our study as they give expressive models, and Naive Bayes was used as a baseline algorithm for comparison purposes. We first learned the six CDRS category scores (memory, orientation, judgement and problem solving, personal care, home and hobbies, and community affairs). These learned CDRS category scores were then used to learn the global CDRS scores. The Two-Stage ML model classified as well as or better than the published inter-rater agreements for both the category and global CDRS scoring by dementia experts. Furthermore, for the most critical distinction, normal versus very mildly impaired, the Two-Stage ML model was 28.1 and 6.6% more accurate than published performances by domain experts. Our study of the CDRS examined one of the largest, most diverse samples in the literature, suggesting that our findings are robust. The Two-Stage ML model also identified a CDRS category, Judgment and Problem Solving, which has low classification accuracy similar to published reports. Since this CDRS category appears to be mainly responsible for misclassification of the global CDRS score when it occurs, further attribute and algorithm research on the Judgment and Problem Solving CDRS score could improve its accuracy as well as that of the global CDRS score.

Simple models for estimating dementia severity using machine learning.

Estimating dementia severity using the Clinical Dementia Rating (CDR) Scale is a two-stage process that currently is costly and impractical in community settings, and at best has an interrater reliability of 80%. Because staging of dementia severity is economically and clinically important, we used Machine Learning (ML) algorithms with an Electronic Medical Record (EMR) to identify simpler models for estimating total CDR scores. Compared to a gold standard, which required 34 attributes to derive total CDR scores, ML algorithms identified models with as few as seven attributes. The classification accuracy varied with the algorithm used with naïve Bayes giving the highest. (76%) The mildly demented severity class was the only one with significantly reduced accuracy (59%). If one groups the severity classes into normal, very mild-to-mildly demented, and moderate-to-severely demented, then classification accuracies are clinically acceptable (85%). These simple models can be used in community settings where it is currently not possible to estimate dementia severity due to time and cost constraints.

A multinomial modeling analysis of memory deficits in Alzheimer's disease and vascular dementia.

Data from the immediate recall task of the Consortium to Establish a Registry for Alzheimer's Disease neuropsychological test battery were disaggregated into nine subject groups and analyzed with traditional statistics as well as with a general processing tree (GPT) model of free recall. The groups represented four levels of severity of Alzheimer's and vascular dementia, as well as a ninth group of healthy elderly controls. It was demonstrated that the patterns of success and failure of recall to individual items across successive trials contained much more information than the marginal trial-to-trial performance scores traditionally used in scoring the test. The GPT model analyzed recall performance in terms of three levels of item storage: unstored, intermediate, and long-term. Associated with the intermediate and long-term storage levels were respective retrieval parameters. Statistical methods enable one to estimate the parameters for each group, and the analyses revealed group differences in long-term storage that were not evident in a statistical analysis of the marginal trial-to-trial performance scores.

Ishihara test performance and dementia.

Ishihara trail-tracing (TT) and number-naming (NN) tests were administered to a clinical sample of 378 demented patients. Error counts on TT and NN tests were best fit by a negative binomial (overdispersed Poisson) distribution. TT, NN, and combined (TT + NN) error counts were regressed on patient characteristics (sex, age, and education), severity of cognitive impairment (Mini-Mental State Exam: MMSE), dementia stage (Clinical Dementia Rating: CDR), etiology, onset age, and symptom duration in a negative binomial generalized linear model. Patient characteristics, onset age, and symptom duration had no significant effects on any error count. The effects of MMSE, CDR, and etiology, on the other hand, were highly significant and appear to help discriminate vascular dementia from Alzheimer's disease. MMSE (which taps cognitive skills) correlated with both TT and NN errors. CDR (which taps both cognitive and functional skills) correlated only with TT errors and dementia etiology correlated only with NN errors. These distinct correlational patterns reflect differences between the TT and NN tasks (i.e., trail-tracing vs. number-naming) related to specific brain loci and associated functions. This aspect of the phenomenon suggests that Ishihara tests have useful clinical applications in dementia.

Acquisition and long-term retention of a gross motor skill in Alzheimer's disease patients under constant and varied practice conditions.

This study examined the acquisition and long-term retention of a gross motor skill, namely, tossing, in 23 moderately to severely demented Alzheimer's disease (AD) patients and 22 health older adults. To identify optimal learning strategies, subjects received 10 weeks of training under either constant or variable practice conditions. Accuracy at the tossing task was assessed immediately, one week, and one month following training. AD patients given constant practice were able to learn and retain the tossing task as well as healthy adults. Although controls performed equally well in both conditions, AD patients showed significantly less improvement when practiced at various distances from the target. By the one-month post-test, these patients had lost any minimal gains achieved through practice. In comparison, AD patients receiving constant practice showed essentially no forgetting across post-tests. The inability to benefit from varied practice suggests that AD patients may have difficulty accessing and/or forming motor schemas.

Full-information models for multiple psychometric tests: annualized rates of change in normal aging and dementia.

The rates of change for five widely used psychometric tests were analyzed to compare how much more variance reduction can be achieved using full-information methods relative to the single-equation methods previously used in dementia research. Nondemented controls and subjects with Alzheimer disease (AD), probable/ possible vascular dementia (VD), or mixed dementia (MD) were evaluated. A cohort design was followed, with follow-up of three demented groups and one normal control group; data were analyzed in a multiple-equation regression model estimated with full-information methods. The study was conducted at Alzheimer's Disease Research Center sites at the University of California, Irvine, and at the University of Southern California. In all, 226 patients and controls who had completed initial assessment and at least one annual reassessment were included in the study. Dependent variables were annualized rates of change in the Mini-Mental State Examination (MMSE), the Short-Blessed Dementia Rating Scale (DRS), the Consortium to Establish a Registry for Alzheimer's Disease drawings test (CD), the WAIS-R Block Design test (WRB), and the Boston Naming Test (BNT). Independent variables were dementia severity, diagnosis (AD, VD, MD, or control), sex, age, marital status, education, and age at onset. Full-information methods reduced the variance in the change scores by > or = 25% compared with previous studies. The model's prediction of a test's rate of change was almost entirely due to dementia stage and diagnosis. The effects of other explanatory variables (sex, marital status, age, and education) were weak and statistically insignificant. When the effects of other independent variables were controlled, AD and MD patients were found to decline at significantly faster rates than VD patients. Full-information methods, relative to single-equation methods, substantially reduce the variance of rates of change for multiple psychometric tests. They do so by simultaneously considering the correlated error terms in the regression for each dependent psychometric change score variable. The robustness of these results to minor variations in follow-up time suggests that annualization is a reasonably valid procedure for making change scores comparable. This study's results suggest that change scores in psychometric tests provide information that can be used to aid differential diagnosis. However, the large variances of change scores preclude many other uses. Finally, since standardization of psychometric change scores translates all tests to the same scale (0-100%), standardized change scores are easier to interpret. The analysis of standardized change scores deserves further investigation.

Acquisition and long-term retention of a fine motor skill in Alzheimer's disease.

This study investigated the acquisition and long-term retention of the rotary pursuit task under varying amounts of practice in 12 moderate-to-severely demented AD patients and 12 healthy older adults. Equal numbers of AD and control subjects were randomly assigned to either 40, 80, or 120 trials of training (40 trials/day) on the rotary pursuit task, followed by 15-trial retention tests 20 min, 2 days, 7 days, and 37 days following the end of practice. Performance improved significantly in both groups during the first 40 trials, while additional practice provided no ensuing positive or negative effects. Further, subjects in both groups showed minimal forgetting across the four retention tests. Therefore, the results demonstrated that AD patients can effectively learn and retain a motor skill for at least 1 month.

Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.

Memory for action events in Alzheimer-type dementia: further evidence of an encoding failure.

The purpose of the present research was to examine the nature of the encoding problem in patients with dementia of the Alzheimer type (DAT) using a nonstrategic memory task, namely the recall of action events or subject-performed tasks (SPTs). The first experiment investigated the retention of SPTs and the verbal descriptions of action events in patients with mild-to-moderate DAT, young, and old adults. While the healthy older adults showed significantly higher recall for SPTs than for verbal descriptions, the DAT patients failed to exhibit this effect. A follow-up study replicated this same pattern using SPTs and tasks performed by the experimenter. As the multimodal and contextually rich encoding environment present in SPTs had no effect on the patients' retention, this suggests that manipulations designed to enhance encoding in this population will be unsuccessful. The relevance of the results to (1) memory compensation in the aged, and (2) the development of mnemonic training programs for the elderly are discussed.

Memory for internally generated words in Alzheimer-type dementia: breakdown in encoding and semantic memory.

The "generation effect" is a phenomenon in which words that are generated by the subject are remembered better than words which are read. The present experiments examined this effect in patients with mild-to-moderate dementia of the Alzheimer type (DAT), healthy elderly adults, and young adults under a variety of different encoding and retrieval conditions. Experiment 1 employed an intentional learning task with multiple study/test trials using the same list of words. While both the young and elderly adults exhibited higher recall for internally generated words than read words, the DAT patients failed to demonstrate the effect even after repeated exposures to the same stimulus list. Experiment 2 replicated this same pattern of results using an incidental learning paradigm with both recall and recognition tests. Various explanations as to why the DAT patients failed to show the generation effect were discussed with particular emphasis placed on the role of semantic memory and encoding failure.

The preselection effect on the recall facilitation of motor movements in Alzheimer-type dementia.

Two experiments examined the coding and retention of psychomotor information in patients with mild to moderate dementia of the Alzheimer-type (DAT), healthy elderly controls, and young adults. Experiment 1 compared recall accuracy for preselected (subject-defined) and constrained (experimenter-defined) movements under three different retention conditions. Not surprisingly, the DAT patients showed significantly larger reproduction errors than did the controls. In all three groups, however, preselected movements were recalled more accurately than constrained movements. This preselection advantage was replicated in Experiment 2 using a new group of DAT patients with recall being performed under both same- and switch-limb conditions. While the results suggested that DAT patients suffer from both an encoding deficiency and a rapid loss of information from short-term memory, they also indicate that patients are capable of coding meaningful aspects of motor movements. Most importantly, the results suggest that in the motor domain, efforts to enhance encoding can facilitate the recall of DAT patients.