A prospective study of cognitive function and onset of dementia in cognitively healthy elders

Cognitive-Cognitive Dual-task in aging: A cross-sectional online study

The prevalence of neurodegenerative disorders, particularly dementia, is on the rise across many countries worldwide. This negative trend calls for improving our understanding of cognitive aging. While motor-cognitive dual-task approaches have already been proven valuable for clinical diagnosis, comparatively less research is available on the application of Cognitive-Cognitive Dual-Tasking (CCDT), across several cognitive domains. Moreover, there is limited understanding about how healthy aging affects performance in such dual-tasks in the general population. CCDT entails engaging individuals in multiple cognitive tasks simultaneously and holds promise for remote e-Health interventions. In this cross-sectional study, our objective was to evaluate the suitability of a newly developed, self-administered, online tool for examining age-related differences in memory performance under dual-tasking. 337 healthy adults aged 50-90 underwent a visual memory test (Memo) under both single and dual-task conditions (attend to auditory letters). Additional measures included questionnaires on subjective memory complaints (MAC-Q), on cognitive reserve (CR), and a cognitive screening (auto-GEMS). As expected, the accuracy of visual memory performance exhibited a negative correlation with age and MAC-Q, and a positive correlation with CR and auto-GEMS scores. Dual-tasking significantly impaired performance, and its detrimental effect decreased with increasing age. Furthermore, the protective effect of cognitive reserve diminished with advancing age. These findings suggest that the commonly observed age-related increase in dual-task costs is not universally applicable across all tasks and cognitive domains. With further refinement, a longitudinal implementation of this approach may assist in identifying individuals with a distinct cognitive trajectory and potentially at a higher risk of developing cognitive decline.

Subjective hearing loss is not associated with an increased risk of Alzheimer's disease dementia

Hearing loss is a risk-factor for dementia but the reasons for this are unclear. Subjective hearing loss is related to increased future dementia risk, however, this metric has not been previously examined with cognitive, neuroimaging and biochemical measures that are relevant to Alzheimer's disease. We assessed Cognitively Normal and Mild Cognitively Impaired participants from the Alzheimer's Disease Neuroimaging Initiative with subjective hearing loss to examine if they had faster decline in episodic memory scores, hippocampal volume and greater pTau positivity. The likelihood of a dementia diagnosis in hearing impaired participants over a 5-year period was also assessed. There were no statistically significant differences between the hearing subgroups over a 5-year period nor were there in conversions to a dementia diagnosis. Objective hearing loss metrics may provide a more reliable link between hearing loss and dementia risk.

GLACIER: GLASS-BOX TRANSFORMER FOR INTERPRETABLE DYNAMIC NEUROIMAGING

Deep learning models can perform as well or better than humans in many tasks, especially vision related. Almost exclusively, these models are used to perform classification or prediction. However, deep learning models are usually of black-box nature, and it is often difficult to interpret the model or the features. The lack of interpretability causes a restrain from applying deep learning to fields such as neuroimaging, where the results must be transparent, and interpretable. Therefore, we present a 'glass-box' deep learning model and apply it to the field of neuroimaging. Our model mixes spatial and temporal dimensions in succession to estimate dynamic connectivity between the brain's intrinsic networks. The interpretable connectivity matrices produced by our model result in beating state-of-the-art models on many tasks using multiple functional MRI datasets. More importantly, our model estimates task-based flexible connectivity matrices, unlike static methods such as Pearson's correlation coefficients.

Left and Right Cortical Activity Arising from Preferred Walking Speed in Older Adults

Cortical activity and walking speed are known to decline with age and can lead to an increased risk of falls in the elderly. Despite age being a known contributor to this decline, individuals age at different rates. This study aimed to analyse left and right cortical activity changes in elderly adults regarding their walking speed. Cortical activation and gait data were obtained from 50 healthy older individuals. Participants were then grouped into a cluster based on their preferred walking speed (slow or fast). Analyses on the differences of cortical activation and gait parameters between groups were carried out. Within-subject analyses on left and right-hemispheric activation were also performed. Results showed that individuals with a slower preferred walking speed required a higher increase in cortical activity. Individuals in the fast cluster presented greater changes in cortical activation in the right hemisphere. This work demonstrates that categorizing older adults by age is not necessarily the most relevant method, and that cortical activity can be a good indicator of performance with respect to walking speed (linked to fall risk and frailty in the elderly). Future work may wish to explore how physical activity training influences cortical activation over time in the elderly.

Multimodal Machine Learning for 10-Year Dementia Risk Prediction: The Framingham Heart Study

BACKGROUND

Early prediction of dementia risk is crucial for effective interventions. Given the known etiologic heterogeneity, machine learning methods leveraging multimodal data, such as clinical manifestations, neuroimaging biomarkers, and well-documented risk factors, could predict dementia more accurately than single modal data.

OBJECTIVE

This study aims to develop machine learning models that capitalize on neuropsychological (NP) tests, magnetic resonance imaging (MRI) measures, and clinical risk factors for 10-year dementia prediction.

METHODS

This study included participants from the Framingham Heart Study, and various data modalities such as NP tests, MRI measures, and demographic variables were collected. CatBoost was used with Optuna hyperparameter optimization to create prediction models for 10-year dementia risk using different combinations of data modalities. The contribution of each modality and feature for the prediction task was also quantified using Shapley values.

RESULTS

This study included 1,031 participants with normal cognitive status at baseline (age 75±5 years, 55.3% women), of whom 205 were diagnosed with dementia during the 10-year follow-up. The model built on three modalities demonstrated the best dementia prediction performance (AUC 0.90±0.01) compared to single modality models (AUC range: 0.82-0.84). MRI measures contributed most to dementia prediction (mean absolute Shapley value: 3.19), suggesting the necessity of multimodal inputs.

CONCLUSION

This study shows that a multimodal machine learning framework had a superior performance for 10-year dementia risk prediction. The model can be used to increase vigilance for cognitive deterioration and select high-risk individuals for early intervention and risk management.

Factors associated with verbal fluency in older adults living with HIV in West Africa: A longitudinal study

OBJECTIVE

Verbal fluency decline, observed both in aging and HIV infection, has been related to lower quality of life. This study aimed to evaluate the factors associated with categorical fluency in people living with HIV (PLHIV) aged ≥60 years living in West Africa.

METHODS

In this longitudinal study, PLHIV aged ≥60 years, on antiretroviral therapy (ART) for ≥6 months were included in three clinics (two in Côte d'Ivoire, one in Senegal) participating in the West Africa International epidemiological Databases to Evaluate AIDS (IeDEA) collaboration. Categorical fluency was evaluated with the Isaacs Set Test at 60 s at baseline and 2 years later. Factors associated with verbal fluency baseline performance and annual rates of changes were evaluated using multivariate linear regression models.

RESULTS

Ninety-seven PLHIV were included with 41 of them (42%) having a 2-year follow-up visit. The median age was 64 (62-67), 45.4% were female, and 89.7% had an undetectable viral load. The median annual change in categorical fluency scores was -0.9 (IQR: -2.7 to 1.8). Low baseline categorical fluency performance and its decline were associated with older age and being a female. Low educational level was associated with low baseline categorical fluency performance but not with its decline. Categorical fluency decline was also associated with marital status and hypertension.

CONCLUSIONS

Among older West African PLHIV, usual socio-demographic variables and hypertension were the main factors associated with low categorical fluency performance and/or its decline. Interventions that focus on supporting cardiometabolic health are highly recommended to prevent cognitive disorders in PLHIV.

Through the looking glass: Deep interpretable dynamic directed connectivity in resting fMRI

Brain network interactions are commonly assessed via functional (network) connectivity, captured as an undirected matrix of Pearson correlation coefficients. Functional connectivity can represent static and dynamic relations, but often these are modeled using a fixed choice for the data window Alternatively, deep learning models may flexibly learn various representations from the same data based on the model architecture and the training task. However, the representations produced by deep learning models are often difficult to interpret and require additional posthoc methods, e.g., saliency maps. In this work, we integrate the strengths of deep learning and functional connectivity methods while also mitigating their weaknesses. With interpretability in mind, we present a deep learning architecture that exposes a directed graph layer that represents what the model has learned about relevant brain connectivity. A surprising benefit of this architectural interpretability is significantly improved accuracy in discriminating controls and patients with schizophrenia, autism, and dementia, as well as age and gender prediction from functional MRI data. We also resolve the window size selection problem for dynamic directed connectivity estimation as we estimate windowing functions from the data, capturing what is needed to estimate the graph at each time-point. We demonstrate efficacy of our method in comparison with multiple existing models that focus on classification accuracy, unlike our interpretability-focused architecture. Using the same data but training different models on their own discriminative tasks we are able to estimate task-specific directed connectivity matrices for each subject. Results show that the proposed approach is also more robust to confounding factors compared to standard dynamic functional connectivity models. The dynamic patterns captured by our model are naturally interpretable since they highlight the intervals in the signal that are most important for the prediction. The proposed approach reveals that differences in connectivity among sensorimotor networks relative to default-mode networks are an important indicator of dementia and gender. Dysconnectivity between networks, specially sensorimotor and visual, is linked with schizophrenic patients, however schizophrenic patients show increased intra-network default-mode connectivity compared to healthy controls. Sensorimotor connectivity was important for both dementia and schizophrenia prediction, but schizophrenia is more related to dysconnectivity between networks whereas, dementia bio-markers were mostly intra-network connectivity.

Interpreting models interpreting brain dynamics

Brain dynamics are highly complex and yet hold the key to understanding brain function and dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging data are noisy, high-dimensional, and not readily interpretable. The typical approach of reducing this data to low-dimensional features and focusing on the most predictive features comes with strong assumptions and can miss essential aspects of the underlying dynamics. In contrast, introspection of discriminatively trained deep learning models may uncover disorder-relevant elements of the signal at the level of individual time points and spatial locations. Yet, the difficulty of reliable training on high-dimensional low sample size datasets and the unclear relevance of the resulting predictive markers prevent the widespread use of deep learning in functional neuroimaging. In this work, we introduce a deep learning framework to learn from high-dimensional dynamical data while maintaining stable, ecologically valid interpretations. Results successfully demonstrate that the proposed framework enables learning the dynamics of resting-state fMRI directly from small data and capturing compact, stable interpretations of features predictive of function and dysfunction.

Effects of Age, Level of Education and HIV Status on Cognitive Performance in West African Older Adults: The West Africa IeDEA Cohort Collaboration

An in-depth understanding of the impact of aging, cognitive reserve, and HIV status on cognitive function is needed in older West African adults. Ninety-nine HIV-negative and 334 HIV-positive adults aged ≥ 50 years were enrolled in three clinics (Senegal and Côte d'Ivoire) participating in the IeDEA West Africa collaboration. All subjects underwent the Free and Cued Selective Reminding Test (FCSRT) and the Isaacs Set Test (IST). Age (both linear and quadratic), education level, and HIV status effects on Z-scores were assessed using multivariate linear regression models. Interactions between HIV status and age or educational level were tested. In the present cohort of older West African adults, the role of age and educational level on episodic memory and verbal fluency was observed without revealing an interaction between HIV status and age effect. As age had quadratic effects, older HIV-positive adults should not be considered as a unique group irrespective of their age. Low-educated HIV-positive patients had the lowest verbal fluency performance compared to others. Further studies are needed to duplicate these results. In clinical settings, screening and adapted programs focusing on improving cognition in those patients are needed.

The role of intraindividual cognitive variability in posttraumatic stress syndromes and cognitive aging: a literature search and proposed research agenda

OBJECTIVES

Cognitive impairments are directly related to severity of symptoms and are a primary cause for functional impairment. Intraindividual cognitive variability likely plays a role in both risk and resiliency from symptoms. In fact, such cognitive variability may be an earlier marker of cognitive decline and emergent psychiatric symptoms than traditional psychiatric or behavioral symptoms. Here, our objectives were to survey the literature linking intraindividual cognitive variability, trauma, and dementia and to suggest a potential research agenda.

DESIGN

A wide body of literature suggests that exposure to major stressors is associated with poorer cognitive performance, with intraindividual cognitive variability in particular linked to the development of posttraumatic stress disorder (PTSD) in the aftermath of severe trauma.

MEASUREMENTS

In this narrative review, we survey the empirical studies to date that evaluate the connection between intraindividual cognitive variability, PTSD, and pathological aging including dementia.

RESULTS

The literature suggests that reaction time (RT) variability within an individual may predict future cognitive impairment, including premature cognitive aging, and is significantly associated with PTSD symptoms.

CONCLUSIONS

Based on our findings, we argue that intraindividual RT variability may serve as a common pathological indicator for trauma-related dementia risk and should be investigated in future studies.

Brainstem atrophy in the early stage of Alzheimer's disease: a voxel-based morphometry study

Postmortem studies on patients with Alzheimer's disease (AD) have confirmed that the dorsal raphe nucleus (DRN) in the brainstem is the first brain structure affected in the earliest stage of AD. The present study examined the brainstem in the early stage of AD using magnetic resonance (MR) imaging. T1-weighted MR images of the brains of 81 subjects were obtained from the publicly available Open Access Series of Imaging Studies (OASIS) database, including 27 normal control (NC) subjects, 27 patients with very mild AD (AD-VM) and 27 patients with mild AD (AD-M). The brainstem was interactively segmented from the MR images using ITK-SNAP. The present voxel-based morphometry (VBM) study was designed to investigate the brainstem differences between the AD-VM/AD-M groups and the NC group. The results showed bilateral loss in the pons and the left part of the midbrain in the AD-M group compared to the NC group. The AD-M group showed greater loss in the left midbrain than the AD-VM group (P_FWEcorrected < 0.05). The results revealed that brainstem atrophy occurs in the early stages of AD (Clinical Dementia Rating = 0.5 and 1.0). Most of these findings were also investigated in a multicenter dataset. This is the first VBM study that provides evidence of brainstem alterations in the early stage of AD.

Bayesian model selection favors parametric over categorical fMRI subsequent memory models in young and older adults

Subsequent memory paradigms allow to identify neural correlates of successful encoding by separating brain responses as a function of memory performance during later retrieval. In functional magnetic resonance imaging (fMRI), the paradigm typically elicits activations of medial temporal lobe, prefrontal and parietal cortical structures in young, healthy participants. This categorical approach is, however, limited by insufficient memory performance in older and particularly memory-impaired individuals. A parametric modulation of encoding-related activations with memory confidence could overcome this limitation. Here, we applied cross-validated Bayesian model selection (cvBMS) for first-level fMRI models to a visual subsequent memory paradigm in young (18-35 years) and older (51-80 years) adults. Nested cvBMS revealed that parametric models, especially with non-linear transformations of memory confidence ratings, outperformed categorical models in explaining the fMRI signal variance during encoding. We thereby provide a framework for improving the modeling of encoding-related activations and for applying subsequent memory paradigms to memory-impaired individuals.

Is there a dose-response relationship between musical instrument playing and later-life cognition? A cohort study using EPIC-Norfolk data

INTRODUCTION

Musical instrument playing provides intellectual stimulation, which is hypothesised to generate cognitive reserve that protects against cognitive impairment. Studies to date have classified musicianship as a binary entity. This investigation draws on the dataset of the European Prospective Investigation of Cancer Norfolk study to examine the effect of frequency of playing on later-life cognition.

METHODS

We compared three categorisations of self-reported musical playing frequency in late mid-life (12-month period) against cognitive performance measured after a 4-11 year delay, adjusted for relevant health and social confounders. Logistic regression models estimated the adjusted association between frequency of musical playing and the likelihood of being in the top and bottom cognitive deciles.

RESULTS

A total of 5,693 participants (745 musicians) provided data on music playing, cognition and all co-variables. Classification of musicianship by frequency of playing demonstrated key differences in socio-demographic factors. Musicians outperformed non-musicians in cognition generally. Compared with non-musicians, frequent musicians had 80% higher odds of being in the top cognitive decile (OR 1.80 [95% CI 1.19-2.73]), whereas musicians playing at any frequency had 29% higher odds (95% CI 1.03-1.62). There was evidence of a threshold effect, rather than a linear dose-response relationship.

DISCUSSION

This study supports a positive association between late mid-life musical instrument playing and later-life cognition, although causation cannot be assumed. Musicians playing frequently demonstrated the best cognition. 'Musicians' are a heterogeneous group and frequency of music playing seems a more informative measure than binary classification. Ideally, this more nuanced measure would be collected for different life course phases.

Exploring a Cost-Efficient Model for Predicting Cerebral Aβ Burden Using MRI and Neuropsychological Markers in the ADNI-2 Cohort

Many studies have focused on the early detection of Alzheimer’s disease (AD). Cerebral amyloid beta (Aβ) is a hallmark of AD and can be observed in vivo via positron emission tomography imaging using an amyloid tracer or cerebrospinal fluid assessment. However, these methods are expensive. The current study aimed to identify and compare the ability of magnetic resonance imaging (MRI) markers and neuropsychological markers to predict cerebral Aβ status in an AD cohort using machine learning (ML) approaches. The prediction ability of candidate markers for cerebral Aβ status was examined by analyzing 724 participants from the ADNI-2 cohort. Demographic variables, structural MRI markers, and neuropsychological test scores were used as input in several ML algorithms to predict cerebral Aβ positivity. Out of five combinations of candidate markers, neuropsychological markers with demographics showed the most cost-efficient result. The selected model could distinguish abnormal levels of Aβ with a prediction ability of 0.85, which is the same as that for MRI-based models. In this study, we identified the prediction ability of MRI markers using ML approaches and showed that the neuropsychological model with demographics can predict Aβ positivity, suggesting a more cost-efficient method for detecting cerebral Aβ status compared to MRI markers.

MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer's Disease: A Survey

Many neurological diseases and delineating pathological regions have been analyzed, and the anatomical structure of the brain researched with the aid of magnetic resonance imaging (MRI). It is important to identify patients with Alzheimer's disease (AD) early so that preventative measures can be taken. A detailed analysis of the tissue structures from segmented MRI leads to a more accurate classification of specific brain disorders. Several segmentation methods to diagnose AD have been proposed with varying complexity. Segmentation of the brain structure and classification of AD using deep learning approaches has gained attention as it can provide effective results over a large set of data. Hence, deep learning methods are now preferred over state-of-the-art machine learning methods. We aim to provide an outline of current deep learning-based segmentation approaches for the quantitative analysis of brain MRI for the diagnosis of AD. Here, we report how convolutional neural network architectures are used to analyze the anatomical brain structure and diagnose AD, discuss how brain MRI segmentation improves AD classification, describe the state-of-the-art approaches, and summarize their results using publicly available datasets. Finally, we provide insight into current issues and discuss possible future research directions in building a computer-aided diagnostic system for AD.

Exploring the Hierarchical Influence of Cognitive Functions for Alzheimer Disease: The Framingham Heart Study

BACKGROUND

Although some neuropsychological (NP) tests are considered more central for the diagnosis of Alzheimer disease (AD), there is a lack of understanding about the interaction between different cognitive tests.

OBJECTIVE

This study aimed to demonstrate a global view of hierarchical probabilistic dependencies between NP tests and the likelihood of cognitive impairment to assist physicians in recognizing AD precursors.

METHODS

Our study included 2091 participants from the Framingham Heart Study. These participants had undergone a variety of NP tests, including Wechsler Memory Scale, Wechsler Adult Intelligence Scale, and Boston Naming Test. Heterogeneous cognitive Bayesian networks were developed to understand the relationship between NP tests and the cognitive status. The performance of probabilistic inference was evaluated by the 10-fold cross validation.

RESULTS

A total of 4512 NP tests were used to build the Bayesian network for the dementia diagnosis. The network demonstrated conditional dependency between different cognitive functions that precede the development of dementia. The prediction model reached an accuracy of 82.24%, with sensitivity of 63.98% and specificity of 92.74%. This probabilistic diagnostic system can also be applied to participants that exhibit more heterogeneous profiles or with missing responses for some NP tests.

CONCLUSIONS

We developed a probabilistic dependency network for AD diagnosis from 11 NP tests. Our study revealed important psychological functional segregations and precursor evidence of AD development and heterogeneity.

Links between autobiographical memory richness and temporal discounting in older adults

When making choices between smaller, sooner rewards and larger, later ones, people tend to discount future outcomes. Individual differences in temporal discounting in older adults have been associated with episodic memory abilities and entorhinal cortical thickness. The cause of this association between better memory and more future-oriented choice remains unclear, however. One possibility is that people with perceptually richer recollections are more patient because they also imagine the future more vividly. Alternatively, perhaps people whose memories focus more on the meaning of events (i.e., are more "gist-based") show reduced temporal discounting, since imagining the future depends on interactions between semantic and episodic memory. We examined which categories of episodic details - perception-based or gist-based - are associated with temporal discounting in older adults. Older adults whose autobiographical memories were richer in perception-based details showed reduced temporal discounting. Furthermore, in an exploratory neuroanatomical analysis, both discount rates and perception-based details correlated with entorhinal cortical thickness. Retrieving autobiographical memories before choice did not affect temporal discounting, however, suggesting that activating episodic memory circuitry at the time of choice is insufficient to alter discounting in older adults. These findings elucidate the role of episodic memory in decision making, which will inform interventions to nudge intertemporal choices.

Classification of Alzheimer's Disease with and without Imagery using Gradient Boosted Machines and ResNet-50

Background. Alzheimer’s is a disease for which there is no cure. Diagnosing Alzheimer’s disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and magnetic resonance imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and a mini-mental state exam (MMSE). A residual network with 50 layers (ResNet-50) predicted the clinical dementia rating (CDR) presence and severity from MRI’s (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.

Functional Neuroimaging in the New Era of Big Data

The field of functional neuroimaging has substantially advanced as a big data science in the past decade, thanks to international collaborative projects and community efforts. Here we conducted a literature review on functional neuroimaging, with focus on three general challenges in big data tasks: data collection and sharing, data infrastructure construction, and data analysis methods. The review covers a wide range of literature types including perspectives, database descriptions, methodology developments, and technical details. We show how each of the challenges was proposed and addressed, and how these solutions formed the three core foundations for the functional neuroimaging as a big data science and helped to build the current data-rich and data-driven community. Furthermore, based on our review of recent literature on the upcoming challenges and opportunities toward future scientific discoveries, we envisioned that the functional neuroimaging community needs to advance from the current foundations to better data integration infrastructure, methodology development toward improved learning capability, and multi-discipline translational research framework for this new era of big data.

Cognitive Profiling Related to Cerebral Amyloid Beta Burden Using Machine Learning Approaches

Background: Cerebral amyloid beta (Aβ) is a hallmark of Alzheimer’s disease (AD). Aβ can be detected in vivo with amyloid imaging or cerebrospinal fluid assessments. However, these technologies can be both expensive and invasive, and their accessibility is limited in many clinical settings. Hence the current study aims to identify multivariate cost-efficient markers for Aβ positivity among non-demented individuals using machine learning (ML) approaches.

Methods: The relationship between cost-efficient candidate markers and Aβ status was examined by analyzing 762 participants from the Alzheimer’s Disease Neuroimaging Initiative-2 cohort at baseline visit (286 cognitively normal, 332 with mild cognitive impairment, and 144 with AD; mean age 73.2 years, range 55–90). Demographic variables (age, gender, education, and APOE status) and neuropsychological test scores were used as predictors in an ML algorithm. Cerebral Aβ burden and Aβ positivity were measured using ¹⁸F-florbetapir positron emission tomography images. The adaptive least absolute shrinkage and selection operator (LASSO) ML algorithm was implemented to identify cognitive performance and demographic variables and distinguish individuals from the population at high risk for cerebral Aβ burden. For generalizability, results were further checked by randomly dividing the data into training sets and test sets and checking predictive performances by 10-fold cross-validation.

Results: Out of neuropsychological predictors, visuospatial ability and episodic memory test results were consistently significant predictors for Aβ positivity across subgroups with demographic variables and other cognitive measures considered. The adaptive LASSO model using out-of-sample classification could distinguish abnormal levels of Aβ. The area under the curve of the receiver operating characteristic curve was 0.754 in the mild change group, 0.803 in the moderate change group, and 0.864 in the severe change group, respectively.

Conclusion: Our results showed that the cost-efficient neuropsychological model with demographics could predict Aβ positivity, suggesting a potential surrogate method for detecting Aβ deposition non-invasively with clinical utility. More specifically, it could be a very brief screening tool in various settings to recruit participants with potential biomarker evidence of AD brain pathology. These identified individuals would be valuable participants in secondary prevention trials aimed at detecting an anti-amyloid drug effect in the non-demented population.

An Examination of Semantic Impairment in Amnestic MCI and AD: What Can We Learn From Verbal Fluency?

Introduction

The Verbal Fluency Test (VF) is commonly used in neuropsychology. Some studies have demonstrated a marked impairment of semantic VF compared to phonemic VF in Alzheimer’s disease (AD). Since amnestic Mild Cognitive Impairment (aMCI) is associated with increased risk of conversion to incident AD, it is relevant to examine whether a similar impairment is observed in this population. The objective of the present empirical study is to compare VF performance of aMCI patients to those of AD and elderly controls matched one-to-one for age and education.

Method

Ninety-six participants divided into three equal groups (N = 32: AD, aMCI and Controls) were included in this study. Participants in each group were, on average, 76 years of age and had 13 years of education. A repeated measures ANOVA with the Group (AD, aMCI, NC) as between-subject factor and the Fluency condition (“P” and “animals”) as within-subject factor was performed. T-tests and simple ANOVAs were also conducted to examine the interaction.

Results

There was a significant interaction between the groups and the verbal fluency condition. In AD, significantly fewer words were produced in both conditions. In contrast, participants with aMCI demonstrated a pattern similar to controls in the phonemic condition, but generated significantly fewer words in the semantic condition.

Conclusion

These results indicate a semantic memory impairment in aMCI revealed by a simple, commonly-used neuropsychological test. Future studies are needed to investigate if semantic fluency deficits can help predict future conversion to AD.

A cross-sectional study to assess prevalence and factors associated with mild cognitive impairment among older adults in an urban area of Kerala, South India

OBJECTIVES

To assess the prevalence and factors associated with mild cognitive impairment (MCI) among older adults in an urban area of South India.

SETTING

The study was conducted in the capital city of Thiruvananthapuram in the South Indian state of Kerala.

PARTICIPANTS

The study participants were community-dwelling individuals aged 60 years and above.

PRIMARY OUTCOME MEASURE

MCI was the primary outcome measure and was defined using the criteria by European Alzheimer's Disease Consortium. Cognitive assessment was done using the Malayalam version of Addenbrooke's Cognitive Examination tool. Data were also collected on sociodemographic variables, self-reported comorbidities like hypertension and diabetes, lifestyle factors, depression, anxiety and activities of daily living.

RESULTS

The prevalence of MCI was found to be 26.06% (95% CI of 22.12 to 30.43). History of imbalance on walking (adjusted OR 2.75; 95 % CI of 1.46 to 5.17), presence of depression (adjusted OR 2.17, 95 % CI of 1.21 to 3.89), anxiety (adjusted OR 2.22; 95 % CI of 1.21 to 4.05) and alcohol use (adjusted OR 1.99; 95 % CI of 1.02 to 3.86) were positively associated with MCI while leisure activities at home (adjusted OR 0.33; 95 % CI of 0.11 to 0.95) were negatively associated.

CONCLUSION

The prevalence of MCI is high in Kerala. It is important that the health system and the government take up urgent measures to tackle this emerging public health issue.

Affective problems and decline in cognitive state in older adults: a systematic review and meta-analysis

Evidence suggests that affective problems, such as depression and anxiety, increase risk for late-life dementia. However, the extent to which affective problems influence cognitive decline, even many years prior to clinical diagnosis of dementia, is not clear. The present study systematically reviews and synthesises the evidence for the association between affective problems and decline in cognitive state (i.e., decline in non-specific cognitive function) in older adults. An electronic search of PubMed, PsycInfo, Cochrane, and ScienceDirect was conducted to identify studies of the association between depression and anxiety separately and decline in cognitive state. Key inclusion criteria were prospective, longitudinal designs with a minimum follow-up period of 1 year. Data extraction and methodological quality assessment using the STROBE checklist were conducted independently by two raters. A total of 34 studies (n = 71 244) met eligibility criteria, with 32 studies measuring depression (n = 68 793), and five measuring anxiety (n = 4698). A multi-level meta-analysis revealed that depression assessed as a binary predictor (OR 1.36, 95% CI 1.05-1.76, p = 0.02) or a continuous predictor (B = -0.008, 95% CI -0.015 to -0.002, p = 0.012; OR 0.992, 95% CI 0.985-0.998) was significantly associated with decline in cognitive state. The number of anxiety studies was insufficient for meta-analysis, and they are described in a narrative review. Results of the present study improve current understanding of the temporal nature of the association between affective problems and decline in cognitive state. They also suggest that cognitive function may need to be monitored closely in individuals with affective disorders, as these individuals may be at particular risk of greater cognitive decline.

When does cognitive decline begin? A systematic review of change point studies on accelerated decline in cognitive and neurological outcomes preceding mild cognitive impairment, dementia, and death

Older adults who ultimately develop dementia experience accelerated cognitive decline long before diagnosis. A similar acceleration in cognitive decline occurs in the years before death as well. To evaluate preclinical and terminal cognitive decline, past researchers have incorporated change points in their analyses of longitudinal data, identifying point estimates of how many years prior to diagnosis or death that decline begins to accelerate. The current systematic review aimed to summarize the published literature on preclinical and terminal change points in relation to mild cognitive impairment (MCI), dementia, and death, identifying the order in which cognitive and neurological outcomes decline and factors that modify the onset and rate of decline. A systematic search protocol yielded 35 studies, describing 16 longitudinal cohorts, modeling change points for cognitive and neurological outcomes preceding MCI, dementia, or death. Change points for cognitive abilities ranged from 3-7 years prior to MCI diagnosis, 1-11 years prior to dementia diagnosis, and 3-15 years before death. No sequence of decline was observed preceding MCI or death, but the following sequence was tentatively accepted for Alzheimer's disease: verbal memory, visuospatial ability, executive functions and fluency, and last, verbal IQ. Some of the modifiers of the onset and rate of decline examined by previous researchers included gender, education, genetics, neuropathology, and personality. Change point analyses evidence accelerated decline preceding MCI, dementia, and death, but moderators of the onset and rate of decline remain ambiguous due to between-study modeling differences, and coordinated analyses may improve comparability across future studies. (PsycINFO Database Record

Correlates of Cognitive Impairment among Indian Urban Elders

BACKGROUND

Cognitive impairment among elderly is increasing owing to increases in life expectancy globally. The problem is multifactorial. The objective of the present paper was to study the correlates of cognitive impairment in an urban elderly population in India.

METHODS

A cross sectional study was conducted among 100 randomly selected urban elderly population. Data was collected upon household visits using a predesigned pretested questionnaire administered by a trained investigator. Measurements included cognitive function assessment using Mini Mental State Examination, depression assessment using Geriatric Depression Scale, blood pressure measurement and anthropometry. Cognitive impairment was defined at MMSE score <24. Logistic regression was done to identify independently associated factors with cognitive impairment.

RESULTS

Prevalence of cognitive impairment among elderly was 10%. Women had a higher prevalence than men. Higher age, no schooling, living single, lower weight, lower waist and hip ratios, difficulty in activities of daily living, poor self-reported health, bedridden and depression significantly associated with cognitive impairment. The independently associated factors upon logistic regression were increasing age, no schooling and bedridden status for past six months.

CONCLUSION

Although the current prevalence of cognitive impairment among Indian urban elderly is low, several associated factors exist in this population that may increase the burden in future. Geriatric health policy should address the modifiable risk factors to manage the problem of cognitive impairment and its consequent outcomes.

Callosal circularity as an early marker for Alzheimer's disease

Background

Although brain atrophy is considered to be a downstream marker of Alzheimer's disease (AD), subtle changes may allow to identify healthy subjects at risk of developing AD. As the ability to select at-risk persons is considered to be important to assess the efficacy of drugs and as MRI is a widely available imaging technique we have recently developed a reliable segmentation algorithm for the corpus callosum (CC). Callosal atrophy within AD has been hypothesized to reflect both myelin breakdown and Wallerian degeneration.

Methods

We applied our fully automated segmentation and feature extraction algorithm to two datasets: the OASIS database consisting of 316 healthy controls (HC) and 100 patients affected by either mild cognitive impairment (MCI) or Alzheimer's disease dementia (ADD) and a second database that was collected at the Memory Clinic of Hospital Network Antwerp and consists of 181 subjects, including healthy controls, subjects with subjective cognitive decline (SCD), MCI, and ADD. All subjects underwent (among others) neuropsychological testing including the Mini-Mental State Examination (MMSE). The extracted features were the callosal area (CCA), the circularity (CIR), the corpus callosum index (CCI) and the thickness profile.

Results

CIR and CCI differed significantly between most groups. Furthermore, CIR allowed us to discriminate between SCD and HC with an accuracy of 77%. The more detailed callosal thickness profile provided little added value towards the discrimination of the different AD stages. The largest effect of normal ageing on callosal thickness was found in the frontal callosal midbody.

Conclusions

To the best of our knowledge, this is the first study investigating changes in corpus callosum morphometry in normal ageing and AD by exploring both summarizing features (CCA, CIR and CCI) and the complete CC thickness profile in two independent cohorts using a completely automated algorithm. We showed that callosal circularity allows to discriminate between an important subgroup of the early AD spectrum (SCD) and age and sex matched healthy controls.

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Callosal circularity allows to discriminate between subjects with subjective cognitive decline and matched healthy controls

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Callosal circularity is smaller in subjects with AD dementia as compared to matched subjects with mild cognitive impairment

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The callosal thickness profile differs between AD and HC, but not between the different clinical AD stages

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The AD thickness profile strongly correlates with age in HCs

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Callosal circularity correlates with CSF biomarkers (T-tau and P-tau) in MCI.

Tau and Aβ imaging, CSF measures, and cognition in Alzheimer's disease

Alzheimer's disease (AD) is characterized by two molecular pathologies: cerebral β-amyloidosis in the form of β-amyloid (Aβ) plaques and tauopathy in the form of neurofibrillary tangles, neuritic plaques, and neuropil threads. Until recently, only Aβ could be studied in humans using positron emission tomography (PET) imaging owing to a lack of tau PET imaging agents. Clinical pathological studies have linked tau pathology closely to the onset and progression of cognitive symptoms in patients with AD. We report PET imaging of tau and Aβ in a cohort of cognitively normal older adults and those with mild AD. Multivariate analyses identified unique disease-related stereotypical spatial patterns (topographies) for deposition of tau and Aβ. These PET imaging tau and Aβ topographies were spatially distinct but correlated with disease progression. Cerebrospinal fluid measures of tau, often used to stage preclinical AD, correlated with tau deposition in the temporal lobe. Tau deposition in the temporal lobe more closely tracked dementia status and was a better predictor of cognitive performance than Aβ deposition in any region of the brain. These data support models of AD where tau pathology closely tracks changes in brain function that are responsible for the onset of early symptoms in AD.

Which computer-use behaviours are most indicative of cognitive decline? Insights from an expert reference group

Computer use is becoming ubiquitous among older adults. As computer use depends on complex cognitive functions, measuring individuals’ computer-use behaviours over time may provide a way to detect changes in their cognitive functioning. However, it is uncertain which computer-use behaviour changes are most likely to be associated with declines of particular cognitive functions. To address this, we convened six experts from clinical and cognitive neurosciences to take part in two workshops and a follow-up survey to gain consensus on which computer-use behaviours would likely be the strongest indicators of cognitive decline. This resulted in a list of 21 computer-use behaviours that the majority of experts agreed would offer a ‘strong indication’ of decline in a specific cognitive function, across Memory, Executive function, Language and Perception and Action domains. This list enables a hypothesis-driven approach to analysing computer-use behaviours predicted to be markers of cognitive decline.

AV-1451 PET imaging of tau pathology in preclinical Alzheimer disease: Defining a summary measure

Utilizing [18F]-AV-1451 tau positron emission tomography (PET) as an Alzheimer disease (AD) biomarker will require identification of brain regions that are most important in detecting elevated tau pathology in preclinical AD. Here, we utilized an unsupervised learning, data-driven approach to identify brain regions whose tau PET is most informative in discriminating low and high levels of [18F]-AV-1451 binding. 84 cognitively normal participants who had undergone AV-1451 PET imaging were used in a sparse k-means clustering with resampling analysis to identify the regions most informative in dividing a cognitively normal population into high tau and low tau groups. The highest-weighted FreeSurfer regions of interest (ROIs) separating these groups were the entorhinal cortex, amygdala, lateral occipital cortex, and inferior temporal cortex, and an average SUVR in these four ROIs was used as a summary metric for AV-1451 uptake. We propose an AV-1451 SUVR cut-off of 1.25 to define high tau as described by imaging. This spatial distribution of tau PET is a more widespread pattern than that predicted by pathological staging schemes. Our data-derived metric was validated first in this cognitively normal cohort by correlating with early measures of cognitive dysfunction, and with disease progression as measured by β-amyloid PET imaging. We additionally validated this summary metric in a cohort of 13 Alzheimer disease patients, and showed that this measure correlates with cognitive dysfunction and β-amyloid PET imaging in a diseased population.

Genetic variants associated with risk of Alzheimer's disease contribute to cognitive change in midlife: The Atherosclerosis Risk in Communities Study

Alzheimer's disease (AD) is the most common form of dementia and is characterized by impairment in memory, behavioral changes, and gradual loss of autonomy. Since there is a long latent period prior to diagnosis, the aim of this study was to determine whether twenty single nucleotide polymorphisms identified in genome-wide association analyses of AD are associated with cognitive change in 8,320 white and 2,039 African-American middle-aged adults enrolled in the prospective Atherosclerosis Risk in Communities (ARIC) study. Cognition was evaluated using the Delayed Word Recall Test (DWRT; verbal memory), Digit Symbol Substitution Test (DSST; processing speed), and Word Fluency Test (WFT; executive function). General linear models were used to assess mean differences in 6-year change in test scores among individuals categorized by genotype after adjusting for age, gender, and years of education. Addition of the minor allele for rs670139 (MS4A4E), rs9331896 (CLU), and rs12155159 (NME8) was nominally associated with change on the DWRT, DSST, and WFT, respectively, in whites. The ZCWPW1 (rs1476679) and CDS33 (rs3865444) variants were nominally associated with change on the DWRT and WFT in African-Americans. For rs670139 and rs9331896 the association was only significant in individuals bearing at least one APOE ϵ4 allele in stratified analyses. An unweighted genetic risk score aggregating the risk alleles for 15 polymorphisms was not associated with change in cognitive function. Although the AD-associated genetic variants appear to have small effects on early cognitive change, replication will be required to establish whether there is a discernible influence on cognitive status in midlife. © 2016 Wiley Periodicals, Inc.

Practice Effects on Story Memory and List Learning Tests in the Neuropsychological Assessment of Older Adults

Two of the most commonly used methods to assess memory functioning in studies of cognitive aging and dementia are story memory and list learning tests. We hypothesized that the most commonly used story memory test, Wechsler's Logical Memory, would generate more pronounced practice effects than a well validated but less common list learning test, the Neuropsychological Assessment Battery (NAB) List Learning test. Two hundred eighty-seven older adults, ages 51 to 100 at baseline, completed both tests as part of a larger neuropsychological test battery on an annual basis. Up to five years of recall scores from participants who were diagnosed as cognitively normal (n = 96) or with mild cognitive impairment (MCI; n = 72) or Alzheimer's disease (AD; n = 121) at their most recent visit were analyzed with linear mixed effects regression to examine the interaction between the type of test and the number of times exposed to the test. Other variables, including age at baseline, sex, education, race, time (years) since baseline, and clinical diagnosis were also entered as fixed effects predictor variables. The results indicated that both tests produced significant practice effects in controls and MCI participants; in contrast, participants with AD declined or remained stable. However, for the delayed-but not the immediate-recall condition, Logical Memory generated more pronounced practice effects than NAB List Learning (b = 0.16, p < .01 for controls). These differential practice effects were moderated by clinical diagnosis, such that controls and MCI participants-but not participants with AD-improved more on Logical Memory delayed recall than on delayed NAB List Learning delayed recall over five annual assessments. Because the Logical Memory test is ubiquitous in cognitive aging and neurodegenerative disease research, its tendency to produce marked practice effects-especially on the delayed recall condition-suggests a threat to its validity as a measure of new learning, an essential construct for dementia diagnosis.

Alzheimer's Disease: Prototype of Cognitive Deterioration, Valuable Lessons to Understand Human Cognition

It is important for neurologists to become more familiar with neuropsychological evaluation for Alzheimer disease. The growth of this method in research, as an available, inexpensive, and noninvasive diagnostic approach, which can be administered even by non-specialist-trained examiners, makes this knowledge more necessary than ever. Such knowledge has a basic role in planning national programs in primary health care systems for prevention and early detection of Alzheimer disease. This is more crucial in developing countries, which have higher rates of dementia prevalence along with cardiovascular risk factors, lack of public knowledge about dementia, and limited social support. In addition compared to the neurological hard signs which are tangible and measurable, the concept of cognition seems to be more difficult for the neurologists to evaluate and for the students to understand. Dementia in general and Alzheimer's disease as the prototype of cognitive disorders specifically, play an important role to explore all domains of human cognition through its symptomatology and neuropsychological deficits.

Dissociative global and local task-switching costs across younger adults, middle-aged adults, older adults, and very mild Alzheimer's disease individuals

A task-switching paradigm was used to examine differences in attentional control across younger adults, middle-aged adults, healthy older adults, and individuals classified in the earliest detectable stage of Alzheimer's disease (AD). A large sample of participants (570) completed a switching task in which participants were cued to classify the letter (consonant/vowel) or number (odd/even) task-set dimension of a bivalent stimulus (e.g., A 14), respectively. A pure block consisting of single-task trials and a switch block consisting of nonswitch and switch trials were completed. Local (switch vs. nonswitch trials) and global (nonswitch vs. pure trials) costs in mean error rates, mean response latencies, underlying reaction time (RT) distributions, along with stimulus-response congruency effects were computed. Local costs in errors were group invariant, but global costs in errors systematically increased as a function of age and AD. Response latencies yielded a strong dissociation: Local costs decreased across groups whereas global costs increased across groups. Vincentile distribution analyses revealed that the dissociation of local and global costs primarily occurred in the slowest response latencies. Stimulus-response congruency effects within the switch block were particularly robust in accuracy in participants in the very mild AD group. We argue that the results are consistent with the notion that the impaired groups show a reduced local cost because the task sets are not as well tuned, and hence produce minimal cost on switch trials. In contrast, global costs increase because of the additional burden on working memory of maintaining 2 task sets.

Dementia and Dentistry

Dementia is increasing in prevalence: by 2025 it is estimated that there will be over a million people in the UK with this diagnosis. The condition is likely to affect us all as healthcare providers, whether in our patients, our relatives or ourselves. This article gives an overview of dementia: causes, treatment, how it affects people and provides advice on how to manage patients with dementia who require dental care. CPD/CLINICAL RELEVANCE: By identifying the patient with dementia and being aware of the challenges in providing care the clinician can provide better treatment and reduce the chance of dental problems as the condition progresses.

Episodic memory in normal aging and Alzheimer disease: Insights from imaging and behavioral studies

Age-related cognitive changes often include difficulties in retrieving memories, particularly those that rely on personal experiences within their temporal and spatial contexts (i.e., episodic memories). This decline may vary depending on the studied phase (i.e., encoding, storage or retrieval), according to inter-individual differences, and whether we are talking about normal or pathological (e.g., Alzheimer disease; AD) aging. Such cognitive changes are associated with different structural and functional alterations in the human neural network that underpins episodic memory. The prefrontal cortex is the first structure to be affected by age, followed by the medial temporal lobe (MTL), the parietal cortex and the cerebellum. In AD, however, the modifications occur mainly in the MTL (hippocampus and adjacent structures) before spreading to the neocortex. In this review, we will present results that attempt to characterize normal and pathological cognitive aging at multiple levels by integrating structural, behavioral, inter-individual and neuroimaging measures of episodic memory.

Cognitive, genetic, and brain perfusion factors associated with four year incidence of Alzheimer's disease from mild cognitive impairment

BACKGROUND

There is a range of factors that predict the development of Alzheimer's disease (AD) dementia among patients with amnestic mild cognitive impairment (MCI).

OBJECTIVES

To identify the neuropsychological, genetic, and functional brain imaging data that best predict conversion to AD dementia in patients with amnestic MCI.

METHODS

From an initial group of 42 amnestic MCI patients assessed with neurological, neuropsychological, and brain SPECT, 39 (25 converters, 14 non-converters) were followed for 4 years, and 36 had APOE ε4 genotyping. Baseline neuropsychological data and brain SPECT data were used to predict which of the MCI patients would develop dementia by the end of the 4 years of observation.

RESULTS

The MCI patients who had converted to AD dementia had poorer performance on long-term visual memory and Semantic Fluency tests. The MCI subjects who developed dementia were more likely to carry at least one copy of the APOE ε4 allele (Hazard Risk = 4.22). There was lower brain perfusion in converters than non-converters, mainly in postcentral gyrus. An additional analysis of the SPECT data found differences between the MCI subjects and controls in the posterior cingulate gyrus and the basal forebrain. When the brain imaging and neuropsychological test data were combined in the same Cox regression model, only the neuropsychological test data were significantly associated with time to dementia.

CONCLUSION

Although the presence of reduced brain perfusion in postcentral gyrus and basal forebrain indicated an at-risk condition, it was the extent of memory impairment that was linked to the speed of decline from MCI to AD.

Cognitive changes preceding clinical symptom onset of mild cognitive impairment and relationship to ApoE genotype

BACKGROUND

This study had two goals (1) to evaluate changes in neuropsychological performance among cognitively normal individuals that might precede the onset of clinical symptoms, and (2) to examine the impact of Apolipoprotein E (ApoE) genotype on these changes.

METHODS

Longitudinal neuropsychological, clinical assessments and consensus diagnoses were completed prospectively in 268 cognitively normal individuals. The mean duration of follow-up was 9.2 years (+/- 3.3). 208 participants remained normal and 60 developed cognitive decline, consistent with a diagnosis of MCI or dementia. Cox regression analyses were completed, for both baseline scores and rate of change in scores, in relation to time to onset of clinical symptoms. Analyses were completed both with and without ApoE-4 status included. Interactions with ApoE-4 status were also examined.

RESULTS

Lower baseline test scores, as well as greater rate of change in test scores, were associated with time to onset of clinical symptoms (p<0.001). The mean time from baseline to onset of clinical symptoms was 6.15 (+/- 3.4) years. The presence of an ApoE-4 allele doubled the risk of progression. The rate of change in two of the test scores was significantly different in ApoE-4 carriers vs. non-carriers.

CONCLUSIONS

Cognitive performance declines prior to the onset of clinical symptoms that are a harbinger of a diagnosis of MCI. Cognitive changes in normal individuals who will subsequently decline may be observed at least 6.5 years prior to symptom onset. In addition, the risk of decline is doubled among individuals with an ApoE-4 allele.

Free Recall Episodic Memory Performance Predicts Dementia Ten Years prior to Clinical Diagnosis: Findings from the Betula Longitudinal Study

BACKGROUND/AIMS

Early dementia diagnosis is a considerable challenge. The present study examined the predictive value of cognitive performance for a future clinical diagnosis of late-onset Alzheimer's disease or vascular dementia in a random population sample.

METHODS

Cognitive performance was retrospectively compared between three groups of participants from the Betula longitudinal cohort. Group 1 developed dementia 11-22 years after baseline testing (n = 111) and group 2 after 1-10 years (n = 280); group 3 showed no deterioration towards dementia during the study period (n = 2,855). Multinomial logistic regression analysis was used to investigate the predictive value of tests reflecting episodic memory performance, semantic memory performance, visuospatial ability, and prospective memory performance.

RESULTS

Age- and education-corrected performance on two free recall episodic memory tests significantly predicted dementia 10 years prior to clinical diagnosis. Free recall performance also predicted dementia 11-22 years prior to diagnosis when controlling for education, but not when age was added to the model.

CONCLUSION

The present results support the suggestion that two free recall-based tests of episodic memory function may be useful for detecting individuals at risk of developing dementia 10 years prior to clinical diagnosis.

The trajectory of cognitive decline in the pre-dementia phase in memory clinic visitors: findings from the 4C-MCI study

BACKGROUND

We investigated the course of decline in multiple cognitive domains in non-demented subjects from a memory clinic setting, and compared pattern, onset and magnitude of decline between subjects who progressed to Alzheimer's disease (AD) dementia at follow-up and subjects who did not progress.

METHOD

In this retrospective cohort study 819 consecutive non-demented patients who visited the memory clinics in Maastricht or Amsterdam between 1987 and 2010 were followed until they became demented or for a maximum of 10 years (range 0.5-10 years). Differences in trajectories of episodic memory, executive functioning, verbal fluency, and information processing speed/attention between converters to AD dementia and subjects remaining non-demented were compared by means of random effects modelling.

RESULTS

The cognitive performance of converters and non-converters could already be differentiated seven (episodic memory) to three (verbal fluency and executive functioning) years prior to dementia diagnosis. Converters declined in these three domains, while non-converters remained stable on episodic memory and executive functioning and showed modest decline in verbal fluency. There was no evidence of decline in information processing speed/attention in either group.

CONCLUSIONS

Differences in cognitive performance between converters to AD dementia and subjects remaining non-demented could be established 7 years prior to diagnosis for episodic memory, with verbal fluency and executive functioning following several years later. Therefore, in addition to early episodic memory decline, decline in executive functions may also flag incident AD dementia. By contrast, change in information processing speed/attention seems less informative.

Predicting cognitive function from clinical measures of physical function and health status in older adults

INTRODUCTION

Current research suggests that the neuropathology of dementia-including brain changes leading to memory impairment and cognitive decline-is evident years before the onset of this disease. Older adults with cognitive decline have reduced functional independence and quality of life, and are at greater risk for developing dementia. Therefore, identifying biomarkers that can be easily assessed within the clinical setting and predict cognitive decline is important. Early recognition of cognitive decline could promote timely implementation of preventive strategies.

METHODS

We included 89 community-dwelling adults aged 70 years and older in our study, and collected 32 measures of physical function, health status and cognitive function at baseline. We utilized an L1-L2 regularized regression model (elastic net) to identify which of the 32 baseline measures were strongly predictive of cognitive function after one year. We built three linear regression models: 1) based on baseline cognitive function, 2) based on variables consistently selected in every cross-validation loop, and 3) a full model based on all the 32 variables. Each of these models was carefully tested with nested cross-validation.

RESULTS

Our model with the six variables consistently selected in every cross-validation loop had a mean squared prediction error of 7.47. This number was smaller than that of the full model (115.33) and the model with baseline cognitive function (7.98). Our model explained 47% of the variance in cognitive function after one year.

DISCUSSION

We built a parsimonious model based on a selected set of six physical function and health status measures strongly predictive of cognitive function after one year. In addition to reducing the complexity of the model without changing the model significantly, our model with the top variables improved the mean prediction error and R-squared. These six physical function and health status measures can be easily implemented in a clinical setting.

Visuospatial characteristics of an elderly Chinese population: results from the WAIS-R block design test

Visuospatial deficits have long been recognized as a potential predictor of dementia, with visuospatial ability decline having been found to accelerate in later stages of dementia. We, therefore, believe that the visuospatial performance of patients with mild cognitive impairment (MCI) and dementia (Dem) might change with varying visuospatial task difficulties. This study administered the Wechsler Adult Intelligence Scale-Revised (WAIS-R) Block Design Test (BDT) to determine whether visuospatial ability can help discriminate between MCI patients from Dem patients and normal controls (NC). Results showed that the BDT could contribute to the discrimination between MCI and Dem. Specifically, simple BDT task scores could best distinguish MCI from Dem patients, while difficult BDT task scores could contribute to discriminating between MCI and NC. Given the potential clinical value of the BDT in the diagnosis of Dem and MCI, normative data stratified by age and education for the Chinese elderly population are presented for use in research and clinical settings.

CANTAB object recognition and language tests to detect aging cognitive decline: an exploratory comparative study

Objective

The recognition of the limits between normal and pathological aging is essential to start preventive actions. The aim of this paper is to compare the Cambridge Neuropsychological Test Automated Battery (CANTAB) and language tests to distinguish subtle differences in cognitive performances in two different age groups, namely young adults and elderly cognitively normal subjects.

Method

We selected 29 young adults (29.9±1.06 years) and 31 older adults (74.1±1.15 years) matched by educational level (years of schooling). All subjects underwent a general assessment and a battery of neuropsychological tests, including the Mini Mental State Examination, visuospatial learning, and memory tasks from CANTAB and language tests. Cluster and discriminant analysis were applied to all neuropsychological test results to distinguish possible subgroups inside each age group.

Results

Significant differences in the performance of aged and young adults were detected in both language and visuospatial memory tests. Intragroup cluster and discriminant analysis revealed that CANTAB, as compared to language tests, was able to detect subtle but significant differences between the subjects.

Conclusion

Based on these findings, we concluded that, as compared to language tests, large-scale application of automated visuospatial tests to assess learning and memory might increase our ability to discern the limits between normal and pathological aging.

Plasma vitamin D levels and cognitive function in aging women: the nurses' health study

BACKGROUND

Vitamin D may play a role in preserving cognitive function. However, there is a paucity of prospective studies on the relationship between vitamin D and cognition with aging. The aim of this study was to examine the association between plasma levels of vitamin D and subsequent cognitive function.

METHODS

This is a prospective study including 1,185 women aged 60-70 years from the Nurses' Health Study, who had plasma 25-hydroxy-vitamin D levels measured in 1989-1990 and completed an initial Telephone Interview of Cognitive Status approximately 9 years later. Subsequently, three follow-up cognitive assessments were conducted at 1.5-2.0 years intervals. We used multivariable-adjusted linear regression to model initial cognitive function, and mixed linear regression to model change in cognitive function over time.

RESULTS

Lower vitamin D levels were associated with significantly worse cognitive function 9 years later. For example, the mean global composite score averaging all the cognitive tests was 0.20 lower (95% Confidence Interval (CI):-0.33,-0.08; p-trend=0.009) in women in the lowest quintile (median=14.1 ng/mL) compared with women in the highest quintile of vitamin D (median=38.4 ng/mL). The observed differences were equivalent to the effect estimates we found for women who were approximately 4-6 years apart in age. However, vitamin D levels were not significantly associated with subsequent cognitive decline during 6 years of follow-up.

CONCLUSIONS

Higher levels of plasma vitamin D in women aged 60-70 years were associated with better cognitive function about a decade later but were not associated with cognitive decline during 6 years of follow-up.

Diagnostic thresholds with three ordinal groups

In practice, there exist many disease processes with three ordinal disease classes; for example, in the detection of Alzheimer's disease (AD) a patient can be classified as healthy (disease-free stage), mild cognitive impairment (early disease stage), or AD (full disease stage). The treatment interventions and effectiveness of such disease processes will depend on the disease stage. Therefore, it is important to develop diagnostic tests with the ability to discriminate between the three disease stages. Measuring the overall ability of diagnostic tests to discriminate between the three classes has been discussed extensively in the literature. However, there has been little proposed on how to select clinically meaningful thresholds for such diagnostic tests, except for a method based on the generalized Youden index by Nakas et al. (2010). In this article, we propose two new criteria for selecting diagnostic thresholds in the three-class setting. The numerical study demonstrated that the proposed methods may provide thresholds with less variability and more balance among the correct classification rates for the three stages. The proposed methods are applied to two real examples: the clinical diagnosis of AD from the Washington University Alzheimer's Disease Research Center and the detection of liver cancer (LC) using protein segments.

Lexical retrieval in discourse: an early indicator of Alzheimer's dementia

We examined the progression of lexical-retrieval deficits in individuals with neuropathologically determined Alzheimer's disease (AD; n = 23) and a comparison group without criteria for AD (n = 24) to determine whether linguistic changes were a significant marker of the disease. Our participants underwent multiple administrations of a neuropsychological battery, with initial administration occurring on average 16 years prior to death. The battery included the Boston Naming Test (BNT), a letter fluency task (FAS) and written description of the Cookie Theft Picture (CTP). Repeated measures analysis revealed that the AD-group showed progressively greater decline in FAS and CTP lexical performance than the comparison group. Cross-sectional time-specific group comparisons indicated that the CTP differentiated performance between the two groups at 7-9 years prior to death and FAS and BNT only at 2-4 years. These results suggest that lexical-retrieval deficits in written discourse serve as an early indicator of AD.

Confidence interval estimation of the difference between two sensitivities to the early disease stage

Although most of the statistical methods for diagnostic studies focus on disease processes with binary disease status, many diseases can be naturally classified into three ordinal diagnostic categories, that is normal, early stage, and fully diseased. For such diseases, the volume under the ROC surface (VUS) is the most commonly used index of diagnostic accuracy. Because the early disease stage is most likely the optimal time window for therapeutic intervention, the sensitivity to the early diseased stage has been suggested as another diagnostic measure. For the purpose of comparing the diagnostic abilities on early disease detection between two markers, it is of interest to estimate the confidence interval of the difference between sensitivities to the early diseased stage. In this paper, we present both parametric and non-parametric methods for this purpose. An extensive simulation study is carried out for a variety of settings for the purpose of evaluating and comparing the performance of the proposed methods. A real example of Alzheimer's disease (AD) is analyzed using the proposed approaches.

Mild cognitive impairment

PURPOSE OF REVIEW

The term mild cognitive impairment (MCI) is used to describe older subjects with demonstrable cognitive impairment who have not crossed the threshold for dementia. Because patients with MCI have an increased risk of developing dementia, especially Alzheimer disease (AD), there is significant interest in the clinical characterization of these subjects and in understanding the pathophysiology of the transition from MCI to AD.

RECENT FINDINGS

The MCI syndrome, as an expression of an incipient disorder that may lead to dementia, is extremely heterogeneous and may coexist with systemic, neurologic, or psychiatric disorders that can cause cognitive deficits. Recent clinical criteria were designed to take into account the different forms of clinical presentation of the syndrome, and introduced the possible contribution of biomarkers to the clinical diagnosis. Bedside diagnosis of MCI can be difficult, since patients who report having cognitive problems may have normal scores in global cognitive scales or in brief neuropsychological instruments.

SUMMARY

This article presents the evolution of the clinical concept of MCI, the operationalization of its current definitions, the development of biomarkers that can help to identify an underlying neurodegenerative process as the etiology of the syndrome, and its proposed treatments.

Brain imaging and cognitive predictors of stroke and Alzheimer disease in the Framingham Heart Study

BACKGROUND AND PURPOSE

Exposure to vascular risk factors has a gradual deleterious effect on brain MRI and cognitive measures. We explored whether a pattern of these measures exists that predicts stroke and Alzheimer disease (AD) risk.

METHODS

A cognitive battery was administered to 1679 dementia and stroke-free Framingham offspring (age, >55 years; mean, 65.7±7.0) between 1999 and 2004; participants were also free of other neurological conditions that could affect cognition and >90% also had brain MRI examination. We related cognitive and MRI measures to risks of incident stroke and AD ≤10 years of follow-up. As a secondary analysis, we explored these associations in The Framingham Heart Study original cohort (mean age, 67.5±7.3 and 84.8±3.3 years at the cognitive assessment and MRI examination, respectively).

RESULTS

A total of 55 Offspring participants sustained strokes and 31 developed AD. Offspring who scored <1.5 SD below predicted mean scores, for age and education, on an executive function test, had a higher risk of future stroke (hazard ratio [HR], 2.27; 95% confidence interval [CI], 1.06-4.85) and AD (HR, 3.60; 95% CI, 1.52-8.52); additional cognitive tests also predicted AD. Participants with low (<20 percentile) total brain volume and high (>20 percentile) white matter hyperintensity volume had a higher risk of stroke (HR, 1.97; 95% CI, 1.03-3.77 and HR, 2.74; 95% CI, 1.51-5.00, respectively) but not AD. Hippocampal volume at the bottom quintile predicted AD in the offspring and original cohorts (HR, 4.41; 95% CI, 2.00-9.72 and HR, 2.37; 95% CI, 1.12-5.00, respectively). A stepwise increase in stroke risk was apparent with increasing numbers of these cognitive and imaging markers.

CONCLUSIONS

Specific patterns of cognitive and brain structural measures observed even in early aging predict stroke risk and may serve as biomarkers for risk prediction.

Using Voxel-Based Morphometry to Examine the Relationship between Regional Brain Volumes and Memory Performance in Amnestic Mild Cognitive Impairment

Alzheimer’s disease (AD) is a slowly progressive neurodegenerative disorder, in which morphological alterations of brain tissue develop many years before the first neuropsychological and clinical changes occur. Among the first and most prominent symptoms are deficiencies of declarative memory functions. This stage of precursory symptoms to AD has been described as amnestic mild cognitive impairment (aMCI) and is discussed as a potential AD prodrome. As therapy in the later stages of AD has been shown to be of limited impact, aMCI would be the key target for early intervention. For that purpose a comprehensive neuropsychological and anatomical characterization of this group is necessary. Previous neuropsychological investigations identified tests which are highly sensitive in diagnosing aMCI and very early AD. However, the sensitivity of those neuropsychological tests to the particular structural neuropathology in aMCI remains to be specified. To this end, we investigated 25 patients with single-domain aMCI. All participants underwent extensive neuropsychological testing and anatomical scanning with structural magnetic resonance imaging. Voxel-based morphometry (VBM) was performed to identify brain regions that show a significant correlation between regional brain volume and behavioral measures of memory and executive functioning. We found that performance in a variety of mnemonic tests was directly related to the integrity of the medial temporal lobe cortex (MTLC). Moreover, impairment of memory sub-functions in aMCI might be detected earlier than overt structural damage. By this, these findings contribute to the identification of cerebral structures associated with memory deficits in aMCI.