Predicting conversion from mild cognitive impairment to Alzheimer's disease using neuropsychological tests and multivariate methods

Neurocognitive correlates of cerebral mitochondrial function and energy metabolism using phosphorus magnetic resonance spectroscopy in older adults

The goal of the current study was to learn about the role of cerebral mitochondrial function on cognition. Based on established cognitive neuroscience, clinical neuropsychology, and cognitive aging literature, we hypothesized mitochondrial function within a focal brain region would map onto cognitive behaviors linked to that brain region. To test this hypothesis, we used phosphorous (31P) magnetic resonance spectroscopy (MRS) to derive indirect markers of mitochondrial function and energy metabolism across two regions of the brain (bifrontal, left temporal). We administered cognitive tasks sensitive to frontal-executive or temporal-hippocampal systems to a sample of 70 cognitively unimpaired older adults with subjective memory complaints and a first-degree family history of Alzheimer's disease and predicted better executive function and recent memory performance would be related to greater frontal and temporal 31P MRS indirect markers, respectively. Results of separate hierarchical linear regressions indicated better recent memory scores were related to 31P MRS indirect markers of lower static energy and higher energy reserve within the left temporal voxel; these findings were associated with moderate effect sizes. Contrary to predictions, executive function performance was unrelated to 31P MRS indirect markers within the bilateral frontal voxel, which may reflect a combination of theoretical and/or methodological issues. Findings represent a snapshot of the relationship between cognition and 31P MRS indirect markers of mitochondrial function, providing potential avenues for future work investigating mitochondrial underpinnings of cognition. 31P MRS may provide a sensitive neuroimaging marker for differences in aspects of memory among persons at-risk for mild cognitive impairment or dementia.

Combined cognitive assessment and automated MRI volumetry improves the diagnostic accuracy of detecting MCI due to Alzheimer's disease

BACKGROUND

Mild cognitive impairment (MCI) confers a high annual risk of 10-15 % of conversion to Alzheimer's disease (AD) dementia. MRI atrophy patterns derived from automated ROI analysis, particularly hippocampal subfield volumes, were reported to be useful in diagnosing early clinical stages of Alzheimer's disease.

OBJECTIVE

The aim of the present study was to combine automated ROI MRI morphometry of hippocampal subfield volumes and cortical thickness estimates using FreeSurfer 6.0 with cognitive measures to predict disease progression and time to conversion from MCI to AD dementia.

METHODS

Baseline (Neuropsychology, MRI) and clinical follow-up data from 62 MCI patients were analysed retrospectively. Individual cortical thickness and volumetric measures were obtained from T1-weighted MRI. Linear discriminant analysis (LDA) of both, cognitive measures and MRI measures (hippocampal subfields, temporal and parietal lobe volumes), were performed to differentiate MCI converters from stable MCI patients.

RESULTS

Out of 62 MCI patients 21 (34 %) converted to AD dementia within a mean follow-up time of 74.7 ± 36.8 months (mean ± SD, range 12 to 130 months). LDA identified temporal lobe atrophy and hippocampal subfield volumes in combination with cognitive measures of verbal memory, verbal fluency and executive functions to correctly classify 71.4.% of MCI subjects converting to AD dementia and 92.7 % with stable MCI. Lower baseline GM volume of the subiculum and the superior temporal gyrus was associated with faster disease progression of MCI converters.

CONCLUSION

Combining cognitive assessment with automated ROI MRI morphometry is superior to using a single test in order to distinguish MCI due to AD from non converting MCI patients.

Assessment of visuospatial functions in post-Covid 19 patients: Beyond the traditional paradigm

Several studies indicate that some cognitive changes occur after COVID-19. Visuospatial alterations have been reported in 24-40 %. These alterations may be useful as early biomarkers of neurodegenerative disease. Thus, we can emphasize the importance of visuospatial processes in cognition through quantitative and qualitative analysis of performance on the Clock Test (CDT) and the Rey-Osterrieth Complex Figure (FCRO). Our objective was to describe the performance of post COVID 19 patients in visuospatial tests, with different degrees of respiratory impairment and to perform a qualitative analysis of the performance to check its relationship with alterations in attention and executive functions. This will allow highlighting the executive component of the performance of the CDT and ROCF and differentiate patients with possible cognitive impairment. 77 patients with SARS-CoV-2 infection were evaluated (3 months post-infection) with a complete neuropsychological battery and MRI. Overall, there is a significant difference between FCRO and CDT, with FCRO having only 9 % change and CDT having 51.9 % change. Regarding the correlations observed between groups (VM Inv, VM non I and non hospitalized) the highest correlations were observed between Boston with FCRO copy (r=0.497; p=0.001) and with FCRO memory (r=0.429; p=0.001). Comparing the performance between groups by severity, significant differences were observed only in the TMT A (13.706 p=0.001) and B (9.583 p=0.008) tests and in the phonological fluency letter A (13.445 p=0.001), we observed that the group of non-hospitalized patients had a better performance. Neuropsychological deficits often have a direct impact on daily life by affecting the ability to learn and adapt. Thus, a useful strategy for the neuropsychological characterization of post-COVID-19 patients is the qualitative analysis of visuospatial abilities in conjunction with executive functions that cannot be analyzed in isolation.

Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models

INTRODUCTION

Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials.

METHODS

We applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews of n = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases.

RESULTS

Our best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI-to-AD progression within 6 years.

DISCUSSION

The proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy-to-administer screening tool for MCI-to-AD progression prediction, facilitating development of remote assessment.

HIGHLIGHTS

Voice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment. The study leveraged AI methods for speech recognition and processed the resulting text using language models. The developed AI-powered pipeline can lead to fully automated assessment that could enable remote and cost-effective screening and prognosis for Alzehimer's disease.

Adult norms for the decision-making MindPulse Digital Test

We present adult normalized data for MindPulse (MP), a new tool evaluating attentional and executive functioning (AEF) in decision-making. We recruited 722 neurotypical participants (18-80 years), with 149 retested. The MP test includes three tasks: Simple Reaction Time (SRT), Go/No-go, and complex Go/No-go, involving perceptual components, motor responses, and measurements of reaction time (RT) and correctness. We compare responses, evaluating 14 cognitive indices (including new composite indices to describe AEF: Executive Speed and Reaction to Difficulty). We adjust for age/sex effects, introduce a difficulty scale, and consider standard deviations, aberrant times, and Spearman Correlation for speed-accuracy balance. Wilcoxon unpaired rank test is used to assess sex effects, and linear regression is employed to assess the age linear dependency model on the normalized database. The study demonstrated age and sex effects on RTs, in all three subtests, and the ability to correct it for individual results. The test showed excellent validity (Cronbach Alpha for the three subtasks is 92, 87, 95%) and high internal consistency (p < 0.001 for each subtask significantly faster than the more complex subtask) of the MP across the wide age range. Results showed correlation within the three RT parts of the test (p < .001 for each) and the independence of SRT, RD, and ES indices. The Retest effect was lower than intersubject variance, showing consistency over time. This study highlights the MP test's strong validity on a homogeneous, large adult sample. It emphasizes assessing AEF and Reaction to Difficulty dynamically with high sensitivity.

Normative scores for attentional tests used by the Spanish consortium for ageing normative data (SCAND) study: Trail Making Test, Digit Symbol and Letter Cancellation

Objective: This paper reports normative data for different attentional tests obtained from a sample of middle-aged and older native Spanish adults and considering effects of age, educational level and sex. Method: 2,597 cognitively intact participants, aged from 50 to 98 years old, participated voluntarily in the SCAND consortium studies. The statistical procedure included conversion of percentile ranges into scaled scores. The effects of age, education and sex were taken into account. Linear regressions were used to calculate adjusted scaled scores. Results: Scaled scores and percentiles corresponding to the TMT, Digit Symbol and Letter Cancellation Task are shown. Additional tables show the values to be added to or subtracted from the scaled scores, for age and education in the case of the TMT and Letter Cancellation Task measures, and for education in the case of the Digit Symbol subtest. Conclusions: The current norms provide clinically useful data for evaluating Spanish people aged 50 to 98 years old and contribute to improving detection of initial symptoms of cognitive impairment.

Executive function deficits and medial temporal lobe atrophy in late-life depression and Alzheimer's disease: a comparative study

Objectives

Alzheimer's disease (AD) and late-life depression (LLD) frequently exhibit executive function deficits (EFD) and medial temporal lobe atrophy (MTA) as shared characteristics. The objective of this research was to examine the utility of the Trail Making Test (TMT) and the MTA scale in distinguishing between LLD and AD.

Methods

A study of 100 patients, 50 with AD and 50 with LLD, was conducted using a cross-sectional design. The individuals were subjected to clinical evaluations to assess their level of depression and overall cognitive abilities, which included the Geriatric Depression Scale (GDS), Mini-Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA). We evaluated executive function deficits (EFD) through the use of the TMT, which includes both TMT-A and TMT-B. MTA was measured using magnetic resonance imaging. To evaluate the ability of TMT and MTA scale to distinguish between the two groups, a receiver operating characteristic (ROC) curve was utilized. To investigate the connections between MTA and neuropsychological measures, a correlation analysis was performed.

Results

AD patients exhibited notably reduced MMSE, MoCA, and GDS scores, as well as an increased MTA total scores, time spent on TMT-A, and TMT-B compared to LLD patients (p < 0.05). TMT-A and TMT-B both exhibited excellent discriminatory power between AD and LLD, achieving area under curve (AUC) values of 92.2 and 94.2%, respectively. In AD patients, there was a negative correlation between MMSE and MoCA scores and MTA scores, while in LLD patients, there was a positive correlation between time spent on TMT-A and GDS scores and MTA scores.

Conclusion

AD patients experience more severe EFD and MTA than LLD patients. The differential diagnosis of AD and LLD can be aided by the useful tool known as TMT. It is important to acknowledge that TMT is capable of capturing only a fraction of the executive function, thus necessitating a cautious interpretation of research findings.

Gender-specific factors associated with the Japanese version of the trail making test among Japanese workers

[Purpose] The Trail Making Test is a valuable tool for predicting the transition from mild cognitive impairment to dementia. This cross-sectional study aimed to investigate gender-specific factors associated with the Trail Making Test using body composition and motor function among Japanese workers. [Participants and Methods] Demographic data, body composition, motor function, and cognitive and attentional functions (Trail Making Test, Part B) were analyzed among 627 workers who underwent health assessments during the 2019 fiscal year. After conducting univariate analysis, multiple regression analysis was performed. [Results] The presence of metabolic syndrome risk factors was found to significantly prolonged the performance time of the Trail Making Test-B in male workers. In addition, low fat-free mass and the 30-second chair stand test also significantly prolonged the performance time of the Trail Making Test-B in male workers. Among female workers, the presence of metabolic syndrome risk factors affected the performance time of the Trail Making Test-B. Therefore, MetS risk factors affect the performance times of the Trail Making Test-B in both male and female workers. [Conclusion] As male and female workers exhibit different body composition and motor function items in the Trail Making Test-B, gender differences should be considered when formulating measures to prevent cognitive and attentional decline.

Serotonin Degeneration and Amyloid-β Deposition in Mild Cognitive Impairment: Relationship to Cognitive Deficits

BACKGROUND

Neuropathological and neuroimaging studies have demonstrated degeneration of the serotonin system in Alzheimer's disease (AD). Neuroimaging studies have extended these observations to the preclinical stages of AD, mild cognitive impairment (MCI). Serotonin degeneration has been observed also in transgenic amyloid mouse models, prior to widespread cortical distribution of amyloid-β (Aβ).

OBJECTIVE

The present study evaluated the regional distribution of the serotonin transporter (5-HTT) and of Aβ in individuals with MCI and healthy older controls, as well as the contribution of 5-HTT and Aβ to cognitive deficits.

METHODS

Forty-nine MCI participants and 45 healthy older controls underwent positron emission tomography (PET) imaging of 5-HTT and Aβ, structural magnetic resonance imaging and neuropsychological assessments.

RESULTS

Lower cortical, striatal, and limbic 5-HTT and higher cortical Aβ was observed in MCIs relative to healthy controls. Lower 5-HTT, mainly in limbic regions, was correlated with greater deficits in auditory-verbal and visual-spatial memory and semantic, not phonemic fluency. Higher cortical A β was associated with greater deficits in auditory-verbal and visual-spatial memory and in semantic, not phonemic fluency. When modeling the association between cognition, gray matter volumes and Aβ, inclusion of 5-HTT in limbic and in select cortical regions significantly improved model fit for auditory-verbal and visual-spatial memory and semantic, but not phonemic fluency.

CONCLUSIONS

These results support the role of serotonin degeneration in the memory and semantic fluency deficits observed in MCI.

Research progress on vestibular dysfunction and visual-spatial cognition in patients with Alzheimer's disease

Alzheimer's disease (AD) or vestibular dysfunction may impair visual-spatial cognitive function. Recent studies have shown that vestibular dysfunction is increasingly common in patients with AD, and patients with AD with vestibular impairment show more visual-spatial cognitive impairment. By exploring the relationship and interaction mechanism among the vestibular system, visual-spatial cognitive ability, and AD, this study aims to provide new insights for the screening, diagnosis, and rehabilitation intervention of patients with AD. In contrast, routine vestibular function tests are particularly important for understanding the vestibular function of patients with AD. The efficacy of vestibular function test as a tool for the early screening of patients with AD must also be further studied. Through the visual-spatial cognitive ability test, the "spatial impairment" subtype of patients with AD, which may be significant in caring for patients with AD to prevent loss and falls, can also be determined. Additionally, the visual-spatial cognitive ability test has great benefits in preventing and alleviating cognitive decline of patients with AD.

Longitudinal changes on the Independent Living Scale in amnestic mild cognitive impairment

The Independent Living Scales (ILS) is an objective measure of day-to-day functioning, which can be used to aid in diagnosing dementia in older adults with cognitive impairments. However, no studies have examined this measure longitudinally in individuals with mild cognitive impairment (MCI), a prodromal phase of dementia. Three subscales of the ILS (Managing Money, Managing Home and Transportation, Health and Safety) were administered to a sample of 94 individuals with amnestic MCI twice across 15 months. A measure of global cognition (Total Scale score on the Repeatable Battery for the Assessment of Neuropsychological Status [RBANS]) was also administered twice. In this MCI sample, two of the three subscales of the ILS showed a significant decline over time, where the third ILS subscale and the Total Scale score of the RBANS did not change. Regression-based change models showed that baseline ILS scores were most strongly predictive of follow-up ILS scores compared to RBANS scores at baseline and follow-up and demographic variables (age, education, and sex). These results provide additional information on the longitudinal change on the ILS in a sizeable cohort of older individuals with amnestic MCI, which may make this scale more useful in identifying progression to dementia.

A Deep Longitudinal Model for Mild Cognitive Impairment to Alzheimer’s Disease Conversion Prediction in Low-Income Countries

Alzheimer’s disease (AD) is a progressive and fatal disease, due to the nonavailability of any permanent cure. Some treatments are under experimentation that can slow down and possibly pause the progression of the disease only if the disease is diagnosed earlier. The onset of AD can only be detected at the mild cognitive impairment (MCI) stage in which slight memory loss is observed but daily routine functions are intact. A small fraction of the patient progresses from MCI to AD. In this research, we have designed a cascaded deep neural network model to identify those MCI subjects who will progress to AD in the following year. The analysis and experimentation have been performed using twenty longitudinal neuropsychological measures (NMs) provided by Alzheimer’s Disease Neuroimaging Initiative (ADNI). After normalization and ranking of longitudinal data, the deep neural network regression model is trained and tuned to forecast the next in-sequence biomarker value using two previous follow-up readings for each marker. Then, the three time-domain window samples are fed into another deep neural network classifier model for the classification of MCI progressor (MCIp) and MCI stables (MCIs). Our model presented regression forecasting MAE of 0.13 and classification accuracy of 86.9% with AUC of 92.1% (Sensitivity: 67.7%, specificity: 92.3%) over 5-fold cross-validation. We conclude that time-domain measures of NM alone can deliver comparable MCI to AD conversion prediction performance without leveraging more expensive and invasive counterparts such as MR imaging, PET scans, and CSF measures. Middle and low-income countries will benefit from such cheap and effective solutions greatly.

Predicting conversion from mild cognitive impairment to Alzheimer's disease with multimodal latent factors

INTRODUCTION

We studied the ability of latent factor scores to predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) and investigated whether multimodal factor scores improve predictive power, relative to single-modal factor scores.

METHOD

We conducted exploratory factor analyses (EFAs) and confirmatory factor analyses (CFAs) of the baseline data of MCI subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) to generate factor scores for three data modalities: neuropsychological (NP), magnetic resonance imaging (MRI), and cerebrospinal fluid (CSF). Factor scores from single or multiple modalities were entered in logistic regression models to predict MCI to AD conversion for 160 ADNI subjects over a 2-year interval.

RESULTS

NP factors attained an area under the curve (AUC) of .80, with a sensitivity of .66 and a specificity of .77. MRI factors reached a comparable level of performance (AUC = .80, sensitivity = .66, specificity = .78), whereas CSF factors produced weaker prediction (AUC = .70, sensitivity = .56, specificity = .79). Combining NP factors with MRI or CSF factors produced better prediction than either MRI or CSF factors alone. Similarly, adding MRI factors to NP or CSF factors produced improvements in prediction relative to NP or CSF factors alone. However, adding CSF factors to either NP or MRI factors produced no improvement in prediction.

CONCLUSIONS

Latent factor scores provided good accuracy for predicting MCI to AD conversion. Adding NP or MRI factors to factors from other modalities enhanced predictive power but adding CSF factors did not.

Dual task walking costs in older adults with mild cognitive impairment: a systematic review and meta-analysis

OBJECTIVE

The objective of this systematic review and meta-analysis (PROSPERO registration No CRD42020192121) is to review existing literature focusing on effects of different dual task paradigms on walking speed in older adults with and without Mild Cognitive Impairment.

METHODS

(1) Data Sources: PubMEd, Cumulative Index of Nursing and Allied Health, Cochrane library, and Web of Science. (2) Study Selection: The key terms searched included those associated with dual task, walking speed, executive function, older adults, and MCI. (3) Data Extraction: The search yielded 140 results with 20 studies meeting the inclusion criteria, which were rated by two independent reviewers using the Quality Assessment Tool. Descriptions of each study including the single and dual task protocol, outcome measure, and final outcomes were extracted. Meta-analysis was performed to evaluate the dual task effects on walking costs in older adults with and without MCI.

RESULTS

Meta-analysis revealed that there were significant differences in the dual task walking costs among older adults with or without MCI (p < .05). Pooled effect sizes of the serial subtraction (9.54; 95%CI, 3.93-15.15) and verbal fluency tasks (10.06; 95%CI, 6.26-15.65) showed that there are higher motor dual-task costs in older adults with MCI than age-matched controls. For quality assessment, all studies ranged from 12 to 16 in score, out of 18 (high quality).

CONCLUSIONS

In the studies included in this review, mental tracking tasks, consisting of serial subtraction and verbal fluency, were found to be the most sensitive in detecting MCI-related changes in older adults, and could serve an important role as a target measure for evaluating the efficacy of interventions aimed at improving cognitive and motor function in older adults.

A Role for Prior Knowledge in Statistical Classification of the Transition from Mild Cognitive Impairment to Alzheimer's Disease

BACKGROUND

The transition from mild cognitive impairment (MCI) to dementia is of great interest to clinical research on Alzheimer's disease and related dementias. This phenomenon also serves as a valuable data source for quantitative methodological researchers developing new approaches for classification. However, the growth of machine learning (ML) approaches for classification may falsely lead many clinical researchers to underestimate the value of logistic regression (LR), which often demonstrates classification accuracy equivalent or superior to other ML methods. Further, when faced with many potential features that could be used for classifying the transition, clinical researchers are often unaware of the relative value of different approaches for variable selection.

OBJECTIVE

The present study sought to compare different methods for statistical classification and for automated and theoretically guided feature selection techniques in the context of predicting conversion from MCI to dementia.

METHODS

We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to evaluate different influences of automated feature preselection on LR and support vector machine (SVM) classification methods, in classifying conversion from MCI to dementia.

RESULTS

The present findings demonstrate how similar performance can be achieved using user-guided, clinically informed pre-selection versus algorithmic feature selection techniques.

CONCLUSION

These results show that although SVM and other ML techniques are capable of relatively accurate classification, similar or higher accuracy can often be achieved by LR, mitigating SVM's necessity or value for many clinical researchers.

Risk Assessment During Longitudinal Progression of Cognition in Older Adults: A Community-based Bayesian Networks Model

BACKGROUND

Cognitive dysfunction, particularly in Alzheimer's disease (AD), seriously affects the health and quality of life of older adults. Early detection can prevent and slow cognitive decline.

OBJECTIVE

This study aimed at evaluating the role of socio-demographic variables, lifestyle, and physical characteristics in cognitive decline during AD progression and analyzing the probable causes and predicting stages of the disease.

METHODS

By analyzing data of 301 subjects comprising normal elderly and patients with mild cognitive impairment (MCI) or AD from six communities in Taiyuan, China, we identified the influencing factors during AD progression by a Logistic Regression model (LR) and then assessed the associations between variables and cognition using a Bayesian Networks (BNs) model.

RESULTS

The LR revealed that age, sex, family status, education, income, character, depression, hypertension, disease history, physical exercise, reading, drinking, and job status were significantly associated with cognitive decline. The BNs model revealed that hypertension, education, job status, and depression affected cognitive status directly, while character, exercise, sex, reading, income, and family status had intermediate effects. Furthermore, we predicted probable cognitive stages of AD and analyzed probable causes of these stages using a model of causal and diagnostic reasoning.

CONCLUSION

The BNs model lays the foundation for causal analysis and causal inference of cognitive dysfunction, and the prediction model of cognition in older adults may help the development of strategies to control modifiable risk factors for early intervention in AD.

Harnessing forgetfulness: can episodic-memory tests predict early Alzheimer's disease?

A rapid increase in the number of patients with Alzheimer's disease (AD) is expected over the next decades. Accordingly, there is a critical need for early-stage AD detection methods that can enable effective treatment strategies. In this study, we consider the ability of episodic-memory measures to predict mild cognitive impairment (MCI) to AD conversion and thus, detect early-stage AD. For our analysis, we studied 307 participants with MCI across four years using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Using a binary logistic regression, we compared episodic-memory tests to each other and to prominent neuroimaging methods in MCI converter (MCI participants who developed AD) and MCI non-converter groups (MCI participants who did not develop AD). We also combined variables to test the accuracy of mixed-predictor models. Our results indicated that the best predictors of MCI to AD conversion were the following: a combined episodic-memory and neuroimaging model in year one (59.8%), the Rey Auditory Verbal Learning Test in year two (71.7%), a mixed episodic-memory predictor model in year three (77.7%) and the Logical Memory Test in year four (77.2%) of ADNI. Overall, we found that individual episodic-memory measure and mixed models performed similarly when predicting MCI to AD conversion. Comparatively, individual neuroimaging measures predicted MCI conversion worse than chance. Accordingly, our results indicate that episodic-memory tests could be instrumental in detecting early-stage AD and enabling effective treatment.

Trail Making Test Normative Data for the German Older Population

OBJECTIVE

We provide normative data for the Trail Making Test (TMT)-A and B and the derived scores B - A and B/A, for the German general population aged 57-84 years.

METHODS

Data were derived from the third examination of the population-based Heinz Nixdorf Recall study. We excluded participants with a history of dementia or stroke, a depression score above cutoff (CES-D Center for Epidemiologic Studies Depression Scale score ≥ 18), or mild cognitive impairment according to a neurocognitive test battery. The normative sample (n = 2,182) was stratified by age, using the interval superposition approach, and by three levels of educational attainment (up to 10 years of schooling; >10 years of schooling; and university degree).

RESULTS

We tabulated test performance scores at percentage rank thresholds 5, 10, 15, 20, 25, 50, 75, and 90. In multiple linear regression, TMT-A performance declined by 1 s each year of life, and TMT-B performance by 3 s; educational level had an impact of up to 30 s in TMT-B. TMT-B/A was only weakly associated with age and education. TMT-B and B - A correlated r = 0.96. Completion of the TMT-B within the time limit of 300 s was not achieved by 10.9% of participants >74 years, and especially by those >74 years who were on the lowest educational level (13.9%).

CONCLUSIONS

For TMT-A, TMT-B, and B - A, the narrow age categorization and distinction between three educational levels proved meaningful. The 300 s limit for the TMT-B impedes the identification of thresholds for very low performance in this age group and needs reconsideration.

Semantic Memory as an Early Cognitive Marker of Alzheimer's Disease: Role of Category and Phonological Verbal Fluency Tasks

BACKGROUND

The assessment of semantic memory may be a useful marker to identify individuals with mild cognitive impairment (MCI) who will progress to Alzheimer's disease (AD) in the early stages of the disease.

OBJECTIVE

The aim of this five-year follow-up longitudinal study is to assess whether semantic assessment could predict progression in MCI.

METHODS

A population of MCI (N = 251); mild (N = 178) and moderate AD (N = 114); and a sample of healthy participants (HP; N = 262) was investigated. The five-year follow-up of the MCI group was completed by 178 patients. Semantic and episodic memory measures were used, including a measure of the discrepancy between categorical and phonological verbal fluency, the semantic-phonological delta (SPD). The main outcome was the progression of MCI due to AD to dementia.

RESULTS

A general linear model showed a significant effect of diagnosis on SPD (Wilks' Lambda = 0.591; p < 0.001). The estimated marginal means were -0.91 (SE = 0.185) in HP, -1.83 (SE = 0.187) in MCI, -1.16 (SE = 0.218) in mild AD, and -1.02 (SE = 0.275) in moderate AD. Post-hoc comparisons showed a significant difference between MCI and HP (p < 0.001). The follow-up was completed by 178 MCI individuals. SPD in MCI patients who progress to dementia was significantly lower than in MCI that will not progress (p = 0.003). Together with the Mini-Mental State Examination, the SPD was the only measure with a significant predicting effect at the five-years follow-up (p = 0.016).

CONCLUSION

The SPD indicates the impairment of semantic memory in individuals with underlying AD at the MCI early stage, reflecting the early involvement of perirhinal and entorhinal cortices in the earliest stages of AD neuropathological process.

Reaction time and response inhibition in autosomal dominant Alzheimer's disease

OBJECTIVE

Subtle deficits in several cognitive domains characterize the neuropsychological profile of preclinical Alzheimer's disease (AD). Assessment of preclinical individuals with genes causing autosomal dominant AD (ADAD) provides a model for prodromal disease. We sought to sensitively evaluate attention and working memory using a computerized battery in non-demented persons carrying ADAD mutations.

METHOD

A total of 71 non-demented Latinos at-risk for ADAD mutations were recruited [40 mutation carriers (MCs), 31 non-mutation carriers (NCs)] and completed a Spanish language chronometric battery of speeded decision and working memory tasks.

RESULTS

On two complex reaction time (RT) tasks involving decision-making and response inhibition, MCs exhibited slower RTs than NCs as they approached their anticipated age of dementia diagnosis. Education moderated these effects, but only in younger MCs. APOE ε4 status was not associated with age-related slowing among NCs or MCs on any of the tests.

CONCLUSIONS

Our findings indicate MCs respond more slowly as they approach the age of dementia onset on tasks with greater demands on executive function. Our results also suggest these effects were not explained by APOE ε4 status independently of ADAD mutation status. Computerized reaction time tests can provide sensitive measures of the earliest cognitive changes in AD.

Combining Cognitive Markers to Identify Individuals at Increased Dementia Risk: Influence of Modifying Factors and Time to Diagnosis

OBJECTIVE

We investigated the extent to which combining cognitive markers increases the predictive value for future dementia, when compared to individual markers. Furthermore, we examined whether predictivity of markers differed depending on a range of modifying factors and time to diagnosis.

METHOD

Neuropsychological assessment was performed for 2357 participants (60+ years) without dementia from the population-based Swedish National Study on Aging and Care in Kungsholmen. In the main sample analyses, the outcome was dementia at 6 years. In the time-to-diagnosis analyses, a subsample of 407 participants underwent cognitive testing 12, 6, and 3 years before diagnosis, with dementia diagnosis at the 12-year follow-up.

RESULTS

Category fluency was the strongest individual predictor of dementia 6 years before diagnosis [area under the curve (AUC) = .903]. The final model included tests of verbal fluency, episodic memory, and perceptual speed (AUC = .913); these three domains were found to be the most predictive across a range of different subgroups. Twelve years before diagnosis, pattern comparison (perceptual speed) was the strongest individual predictor (AUC = .686). However, models 12 years before diagnosis did not show significantly increased predictivity above that of the covariates.

CONCLUSIONS

This study shows that combining markers from different cognitive domains leads to increased accuracy in predicting future dementia 6 years later. Markers from the verbal fluency, episodic memory, and perceptual speed domains consistently showed high predictivity across subgroups stratified by age, sex, education, apolipoprotein E ϵ4 status, and dementia type. Predictivity increased closer to diagnosis and showed highest accuracy up to 6 years before a dementia diagnosis. (JINS, 2020, 00, 1-13).

Predicting Alzheimer's disease based on survival data and longitudinally measured performance on cognitive and functional scales

This study assessed how well longitudinally taken cognitive and functional scales from people with mild cognitive impairment (MCI) predict conversion to Alzheimer's disease (AD). Participants were individuals with baseline MCI from the Alzheimer's Disease Neuroimaging Initiative. Scales included the Alzheimer Disease Assessment Scale-Cognitive (ADAS-Cog) 11 and 13, the Mini Mental State Examination (MMSE), and the Functional Assessment Questionnaire (FAQ). A joint modelling approach compared performance on the four scales for dynamic prediction of risk for AD. The goodness of fit measures included log likelihood, the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The area under the curve (AUC) of the receiver operating characteristic assessed predictive accuracy. The parameter α in the ADAS-Cog11, ADAS-Cog13, MMSE, and FAQ joint models was statistically significant. Joint MMSE and FAQ models had better goodness of fit. FAQ had the best predictive accuracy. Cognitive and functional impairment assessment scales are strong screening predictors when repeated measures are available. They could be useful for predicting risk for AD in primary healthcare.

Altered Low-Frequency EEG Connectivity in Mild Cognitive Impairment as a Sign of Clinical Progression

BACKGROUND

Mild cognitive impairment (MCI) is associated with clinical progression to Alzheimer's disease (AD) but not all patients with MCI convert to AD. However, it is important to have methods that can differentiate between patients with MCI who progress (pMCI) and those who remain stable (sMCI), i.e., for timely administration of disease-modifying drugs.

OBJECTIVE

In the current study, we wanted to investigate whether quantitative EEG coherence and imaginary part of coherency (iCoh) could be used to differentiate between pMCI and sMCI.

METHODS

17 patients with AD, 27 patients with MCI, and 38 older healthy controls were recruited and followed for three years and 2nd year was used to determine progression. EEGs were recorded at baseline and coherence and iCoh were calculated after thorough preprocessing.

RESULTS

Between pMCI and sMCI, the largest difference in total coherence was found in the theta and delta bands. Here, the significant differences for coherence and iCoh were found in the lower frequency bands involving the temporal-frontal connections for coherence and parietal-frontal connections for iCoh. Furthermore, we found a significant negative correlation between theta coherence and the Addenbrooke's Cognitive Examination (ACE) (p = 0.0378; rho = -0.2388).

CONCLUSION

These findings suggest that low frequency coherence and iCoh can be used to determine, which patients with MCI will progress to AD and is associated with the ACE score. Low-frequency coherence has previously been associated with increased hippocampal atrophy and degeneration of the cholinergic system and may be an early marker of AD pathology.

Longitudinal survival analysis and two-group comparison for predicting the progression of mild cognitive impairment to Alzheimer's disease

BACKGROUND

Longitudinal studies using structural magnetic resonance imaging (MRI) and neuropsychological measurements (NMs) allow a noninvasive means of following the subtle anatomical changes occurring during the evolution of AD.

NEW METHOD

This paper compared two approaches for the construction of longitudinal predictive models: a) two-group comparison between converter and nonconverter MCI subjects and b) longitudinal survival analysis. Predictive models combined MRI-based markers with NMs and included demographic and clinical information as covariates. Both approaches employed linear mixed effects modeling to capture the longitudinal trajectories of the markers. The two-group comparison approaches used linear discriminant analysis and the survival analysis used risk ratios obtained from the extended Cox model and logistic regression.

RESULTS

The proposed approaches were developed and evaluated using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with a total of 1330 visits from 321 subjects. With both approaches, a very small number of features were selected. These markers are easily interpretable, generating robust, verifiable and reliable predictive models. Our best models predicted conversion with 78% accuracy at baseline (AUC = 0.860, 79% sensitivity, 76% specificity). As more visits were made, longitudinal predictive models improved their predictions with 85% accuracy (AUC = 0.944, 86% sensitivity, 85% specificity).

COMPARISON WITH EXISTING METHOD

Unlike the recently published models, there was also an improvement in the prediction accuracy of the conversion to AD when considering the longitudinal trajectory of the patients.

CONCLUSIONS

The survival-based predictive models showed a better balance between sensitivity and specificity with respect to the models based on the two-group comparison approach.

Lower Serum Calcium as a Potentially Associated Factor for Conversion of Mild Cognitive Impairment to Early Alzheimer's Disease in the Japanese Alzheimer's Disease Neuroimaging Initiative

BACKGROUND

Effect of serum calcium level to the incidence of mild cognitive impairment (MCI) conversion to early Alzheimer's disease (AD) remains uncertain.

OBJECTIVE

To investigate association between baseline serum calcium and the MCI conversion in the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) study cohort.

METHODS

In this sub-analysis of J-ADNI study, we reviewed data from MCI participants at baseline regarding their conversion to early AD during the 3 years of observation period and assessed the associated factors including serum calcium level. In addition, we compared our results from the J-ADNI study with the corresponding results from the North American (NA)-ADNI.

RESULTS

Of 234 eligible MCI participants from the J-ADNI cohort, 121 (51.7%) converted to AD during the first 36 months of observation. Using univariate analysis, being female, having shorter years of education, and lower serum calcium level were correlated with increased risk of MCI-to-AD conversion exclusively in J-ADNI cohort. The lower corrected serum calcium level remained as one of conversion-associated factors in the J-ADNI cohort even after adjustment for multiple confounding variables, although this was not observed in the NA-ADNI cohort.

CONCLUSION

Our findings suggest that lower serum calcium may be associated with an increased risk of MCI conversion to AD in Japanese cohorts. The reason for this correlation remains unclear and further external validation using other Asian cohorts is needed. It would be interesting for future AD studies to obtain serum calcium levels and other related factors, such as vitamin D levels, culture-specific dietary or medication information.

Artificial intelligence and neuropsychological measures: The case of Alzheimer's disease

One of the current challenges in the field of Alzheimer's disease (AD) is to identify patients with mild cognitive impairment (MCI) that will convert to AD. Artificial intelligence, in particular machine learning (ML), has established as one of more powerful approach to extract reliable predictors and to automatically classify different AD phenotypes. It is time to accelerate the translation of this knowledge in clinical practice, mainly by using low-cost features originating from the neuropsychological assessment. We performed a meta-analysis to assess the contribution of ML and neuropsychological measures for the automated classification of MCI patients and the prediction of their conversion to AD. The pooled sensitivity and specificity of patients' classifications was obtained by means of a quantitative bivariate random-effect meta-analytic approach. Although a high heterogeneity was observed, the results of meta-analysis show that ML applied to neuropsychological measures can lead to a successful automatic classification, being more specific as screening rather than prognosis tool. Relevant categories of neuropsychological tests can be extracted by ML that maximize the classification accuracy.

Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease

Alzheimer's disease (AD) is characterised by a dynamic process of neurocognitive changes from normal cognition to mild cognitive impairment (MCI) and progression to dementia. However, not all individuals with MCI develop dementia. Predicting whether individuals with MCI will decline (i.e. progressive MCI) or remain stable (i.e. stable MCI) is impeded by patient heterogeneity due to comorbidities that may lead to MCI diagnosis without progression to AD. Despite the importance of early diagnosis of AD for prognosis and personalised interventions, we still lack robust tools for predicting individual progression to dementia. Here, we propose a novel trajectory modelling approach based on metric learning (Generalised Metric Learning Vector Quantization) that mines multimodal data from MCI patients in the Alzheimer's disease Neuroimaging Initiative (ADNI) cohort to derive individualised prognostic scores of cognitive decline due to AD. We develop an integrated biomarker generation- using partial least squares regression- and classification methodology that extends beyond binary patient classification into discrete subgroups (i.e. stable vs. progressive MCI), determines individual profiles from baseline (i.e. cognitive or biological) data and predicts individual cognitive trajectories (i.e. change in memory scores from baseline). We demonstrate that a metric learning model trained on baseline cognitive data (memory, executive function, affective measurements) discriminates stable vs. progressive MCI individuals with high accuracy (81.4%), revealing an interaction between cognitive (memory, executive functions) and affective scores that may relate to MCI comorbidity (e.g. affective disturbance). Training the model to perform the same binary classification on biological data (mean cortical β-amyloid burden, grey matter density, APOE 4) results in similar prediction accuracy (81.9%). Extending beyond binary classifications, we develop and implement a trajectory modelling approach that shows significantly better performance in predicting individualised rate of future cognitive decline (i.e. change in memory scores from baseline), when the metric learning model is trained with biological (r = -0.68) compared to cognitive (r = -0.4) data. Our trajectory modelling approach reveals interpretable and interoperable markers of progression to AD and has strong potential to guide effective stratification of individuals based on prognostic disease trajectories, reducing MCI patient misclassification, that is critical for clinical practice and discovery of personalised interventions.

Prevalence, Incidence, Influence Factors, and Cognitive Characteristics of Amnestic Mild Cognitive Impairment Among Older Adult: A 1-Year Follow-Up Study in China

BACKGROUND

The risk and protective factors of amnestic mild cognitive impairment (aMCI) and its prevalence as well as incidence among old adult in Chinese community are still unclear.

METHODS

We carried out this 1-year longitudinal study to survey a random sample of 3,246 community elders aged 60 and over in China. All subjects were required to complete a comprehensive clinical assessment, physical examination and several neuropsychological tests at baseline and follow-up. What's more, we also collected their lifestyle information by a standardized questionnaire.

RESULTS

We found that the prevalence of aMCI was 17.1%, while the incidence of aMCI among Chinese old adult was 70.57 per 1,000 person-years. By using Cox regression analysis, we found that male sex (p = 0.001, OR = 0.489, 95%CI 0.319~0.751) and reading (p = 0.023, OR = 0.533, 95%CI 0.310~0.917) were protective factors for against aMCI. Old adult who developed aMCI in the future showed multiple cognitive impairments (such as immediate memory, associative learning memory and executive function) in their early stage, and Wechsler's Block Design (p = 0.027, OR = 0.969, 95%CL 0.943~0.996) could predict whether subjects would turn into aMCI in the future.

CONCLUSIONS

The present study suggests that aMCI is a considerable health problem in China. Executive dysfunction may be an indicator of future development of aMCI in the old normal adult.

Characteristics of Mild Cognitive Impairment in Northern Japanese Community-Dwellers from the ORANGE Registry

A gradually increasing prevalence of mild cognitive impairment (MCI) is recognized in the super-aging society that Japan faces, and early detection and intervention in community-dwellers with MCI are critical issues to prevent dementia. Although many previous studies have revealed MCI/non-MCI differences in older individuals, information on the prevalence and characteristics of MCI in rural older adults is limited. The aim of this study was to investigate differential characteristics between older adults with and without MCI. The investigation was conducted over one year from 2018 to 2019. Participants were recruited from Akita in northern Japan. Neuropsychological assessments were applied to classify MCI, including the National Center for Geriatrics and Gerontology Functional Assessment Tool (NCGG-FAT) and the Touch panel-type Dementia Assessment Scale (TDAS) based on the Alzheimer's disease assessment scale. Our samples consisted of 103 older adults divided into 54 non-MCI and 49 MCI. The MCI group had lower scores of all cognitive items. Our results showed that individuals with MCI had significantly slower walking speed (WS) and worse geriatric depression scale (GDS) compared to non-MCI. In addition, WS was significantly associated with some cognitive items in non-MCI, but not in MCI. Finally, we showed that predictive variables of MCI were WS and GDS. Our study provides important information about MCI in rural community-dwellers. We suggest that older adults living in a super-aging society should receive lower limb training, and avoiding depression in older adults through interaction of community-dwellers may contribute to preventing the onset of MCI.

Hypersynchronization in mild cognitive impairment: the ‘X’ model

Hypersynchronization has been considered as a biomarker of synaptic dysfunction along the Alzheimeŕs disease continuum. In a longitudinal MEG study, Pusil et al. reveal changes in functional connectivity upon progression from MCI to Alzheimer’s disease. They propose the ‘X’ model to explain their findings, and suggest that hypersynchronization predicts conversion.

A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual

Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression, and k-Nearest Neighbor for regression and Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbor for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to consistently have higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R ² = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.

Accelerated Ovarian Failure as a Unique Model to Study Peri-Menopause Influence on Alzheimer's Disease

Despite decades of extensive research efforts, efficacious therapies for Alzheimer's disease (AD) are lacking. The multi-factorial nature of AD neuropathology and symptomatology has taught us that a single therapeutic approach will most likely not fit all. Women constitute ~70% of the affected AD population, and pathology and rate of symptoms progression are 2-3 times higher in women than men. Epidemiological data suggest that menopausal estrogen loss may be causative of the more severe symptoms observed in AD women, however, results from clinical trials employing estrogen replacement therapy are inconsistent. AD pathological hallmarks-amyloid β (Aβ), neurofibrillary tangles (NFTs), and chronic gliosis-are laid down during a 20-year prodromal period before clinical symptoms appear, which coincides with the menopause transition (peri-menopause) in women (~45-54-years-old). Peri-menopause is marked by widely fluctuating estrogen levels resulting in periods of irregular hormone-receptor interactions. Recent studies showed that peri-menopausal women have increased indicators of AD phenotype (brain Aβ deposition and hypometabolism), and peri-menopausal women who used hormone replacement therapy (HRT) had a reduced AD risk. This suggests that neuroendocrine changes during peri-menopause may be a trigger that increases risk of AD in women. Studies on sex differences have been performed in several AD rodent models over the years. However, it has been challenging to study the menopause influence on AD due to lack of optimal models that mimic the human process. Recently, the rodent model of accelerated ovarian failure (AOF) was developed, which uniquely recapitulates human menopause, including a transitional peri-AOF period with irregular estrogen fluctuations and a post-AOF stage with low estrogen levels. This model has proven useful in hypertension and cognition studies with wild type animals. This review article will highlight the molecular mechanisms by which peri-menopause may influence the female brain vulnerability to AD and AD risk factors, such as hypertension and apolipoprotein E (APOE) genotype. Studies on these biological mechanisms together with the use of the AOF model have the potential to shed light on key molecular pathways underlying AD pathogenesis for the development of precision medicine approaches that take sex and hormonal status into account.

Decreased Parietal Beta Power as a Sign of Disease Progression in Patients with Mild Cognitive Impairment

BACKGROUND

Electroencephalography (EEG) power has previously been used to compare mild cognitive impairment (MCI) patients who progress to Alzheimer's disease (pMCI) with patients with MCI who remain stable (sMCI) by using beta power. However, the beta band is very broad and smaller frequency bands may improve accuracy.

OBJECTIVE

In the present study, we wanted to investigate whether it was possible to find any differences between pMCI and sMCI using relative power and whether these differences were correlated to cognitive function or neuropathology markers.

METHODS

17 patients with AD, 27 patients with MCI, and 38 older healthy controls were recruited from two memory clinics and followed for three years. EEGs were recorded at baseline for all participants and relative power was calculated. All participants underwent adjusted batteries of standardized cognitive tests and lumbar puncture.

RESULTS

We found that pMCI showed decreased baseline relative power in the parietal electrodes in the beta1 band (13-17.99 Hz). At 2-year follow-up, we found changes in all baseline beta bands but most pronounced in the beta1 band. In addition, we found that qEEG parietal power was correlated with amyloid-β42 and anterograde memory.

CONCLUSION

These findings suggests that relative power in the parietal electrodes in the beta1 band may be a better way to discriminate between pMCI and sMCI at the time of diagnosis than the broad beta band. Similar findings have also been found with resting state fMRI. In addition, we found that anterograde memory was correlated to qEEG parietal beta1 power.

Risk classification for conversion from mild cognitive impairment to Alzheimer's disease in primary care

There is a pressing need to identify individuals at high risk of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) based on available repeated cognitive measures in primary care. Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we applied a joint latent class mixed model (JLCM) to derive a 3-class solution: low risk (72.65%), medium risk (20.41%) and high risk (6.94%). In the low-risk group, individuals with lower daily activity and ApoEε4 carriers were at greater risk of conversion from MCI to AD. In the medium-risk group, being female, single, and an ApoEε4 carrier increased risk of conversion to AD. In the high-risk group, individuals with lower education level and single individuals were at greater risk of conversion to AD. Individual dynamic prediction for conversion from MCI to AD after 10 years was derived. Accurate identification of conversion from MCI to AD contributes to earlier close monitoring, appropriate management, and targeted interventions. Thereby, it can reduce avoidable hospitalizations for the high-risk MCI population. Moreover, it can avoid expensive follow-up tests that may provoke unnecessary anxiety for low-risk individuals and their families.

Evaluating Peripersonal Space through the Functional Transcranial Doppler: Are We Paving the Way for Early Detecting Mild Cognitive Impairment to Dementia Conversion?

BACKGROUND

Identifying the patients with mild cognitive impairment (MCI) who may develop dementia (MDC) is challenging. The study of peripersonal space (PPS) by using functional transcranial Doppler (fTCD) could be used for this purpose.

OBJECTIVE

To identify changes in cerebral blood flow (CBF) during motor tasks targeting PPS, which can predict MDC.

METHODS

We evaluated the changes in CBF in 22 patients with MCI and 23 with dementia [Alzheimer's disease (AD) and vascular dementia (VaD)] during a motor task (passive mobilization, motor imagery, and movement observation) in which the hand of the subject moved forward and backward the face.

RESULTS

CBF increased when the hand approached the face and decreased when the hand moved from the face in the healthy controls (HCs). CBF changed were detectable only in patients with MCI but not in those with the AD and those who were MDC after 8-month follow-up. On the other hand, the patients with VaD presented a paradoxical response to the motor task (i.e., a decrease of CBF rather than an increase, as observed in HCs and MCI). Therefore, we found a modulation of PPS-related CBF only in HCs and patients with stable MCI (at the 8-month follow-up).

CONCLUSIONS

fTCD may allow preliminarily differentiating and following-up the patients with MCI and MDC, thus allowing the physician to plan beforehand more individualized cognitive rehabilitative training.

Refining Mild-to-Moderate Alzheimer Disease Screening: A Tool for Clinicians

OBJECTIVES

Recent evidence suggests that a substantial minority of people clinically diagnosed with probable Alzheimer disease (AD) in fact do not fulfill the neuropathological criteria for the disease. A clinical hallmark of these phenocopies of AD is that these individuals tend to remain cognitively stable for extended periods of time, in contrast to their peers with confirmed AD who show a progressive decline. We aimed to examine the prevalence of patients clinically diagnosed with mild-to-moderate AD who do not experience the expected clinically significant cognitive decline and identify markers easily available in routine medical practice predictive of a stable cognitive prognosis in this population.

DESIGN

Data were obtained from two independent, longitudinal, observational multicenter studies in patients with mild-to-moderate AD.

SETTING

The two studies were the European "Impact of Cholinergic Treatment Use" (ICTUS) and the French "REseau sur la maladie d'Alzheimer FRançais" (REAL.FR).

PARTICIPANTS

We used prospective data of 756 patients enrolled in ICTUS and 340 enrolled in REAL.FR.

MEASUREMENTS

A prediction rule of cognitive decline was derived on ICTUS using classification and regression tree analysis and then cross-validated on REAL.FR. A range of demographic, clinical and cognitive variables were tested as predictor variables.

RESULTS

Overall, 27.9% of patients in ICTUS and 20.9% in REAL.FR did not decline over 2 years. We identified optimized cut-points on the verbal memory items of the Alzheimer Disease Assessment Scale-Cognitive Subscale capable of classifying patients at baseline into those who went on to decline and those who remained stable or improved over the duration of the trial.

CONCLUSION

The application of this simple rule would allow the identification of dementia cases where a more detailed differential diagnostic examination (eg, with biomarkers) is warranted. These findings are promising toward the refinement of AD screening in the clinic. For a further optimization of our classification rule, we encourage others to use our methodological approach on other episodic memory assessment tools designed to detect even small cognitive changes in patients with AD.

Can visuospatial measures improve the diagnosis of Alzheimer's disease?

Introduction

Overlapping and evolving symptoms lead to ambiguity in the diagnosis of dementia. Visuospatial function relies on parietal lobe function, which may be affected in the early stages of Alzheimer's disease (AD). This review evaluates visuospatial dysfunction in patients with AD, frontotemporal dementia, dementia with Lewy bodies, and vascular dementia to determine the diagnostic and prognostic potential of visuospatial tasks in AD.

Methods

A systematic search of studies (1960–2016) investigating visuospatial dysfunction in dementia was conducted.

Results

Tests measuring construction, specifically Block Design and Clock Drawing Test, and visual memory, specifically Rey-Osterrieth Complex Figure recall and topographical tasks, show the greatest diagnostic potential in dementia. The Benton visual retention, Doors and People, and topographical memory tests show potential as prognostic markers.

Discussion

Tests of visuospatial function demonstrate significant diagnostic and prognostic potential in dementia. Further studies with larger samples of pathologically confirmed cases are required to verify clinical utility.

•

Memory deficits have been demonstrated in Alzheimer's and non-Alzheimer's dementias.

•

Parietal lobes are uniquely affected in the early stages of Alzheimer's disease.

•

Visuospatial tasks demonstrate significant diagnostic and prognostic potential.

•

Computerized test protocols have been developed to test aspects of visuospatial function and memory.

•

Novel topographical memory tasks demonstrated the greatest prognostic potential.

Subjective rating of executive functions in mild Alzheimer's disease

OBJECTIVES

Subjective cognitive decline is considered to be a core feature of pre-Alzheimer's disease (AD) conditions, the vast majority of literature having focused on memory concerns. Neuropsychological studies have implicated executive dysfunction on objective performance measures in AD, but no research has evaluated whether individuals with AD have concerns about their executive functions and whether it differs from their caregiver's concerns. In the present study, we sought to evaluate self- and informant ratings of executive functioning in patients with mild AD.

METHOD

Participants were 23 patients with mild AD and 32 healthy elderly controls (HC) and their informants who completed the Behavior Rating Inventory of Executive Function - Adult version.

RESULTS

Patients with AD and their informants reported greater executive dysfunction than the HC group and their informants, respectively, and patients reported greater difficulty than their informants. The largest effect size for both self- and informant ratings was obtained for the Working Memory scale.

CONCLUSIONS

These findings indicate that subjective cognitive concerns in mild AD extend beyond the memory domain to executive functions. That greater difficulty was endorsed by patients than their informants suggests that at least in the mild stage of AD some awareness of executive dysfunction may be maintained in some patients. Implications for clinical care are discussed.

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

BACKGROUND

Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion.

METHODS

In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question "Will a MCI patient convert to dementia somewhere in the future" to the question "Will a MCI patient convert to dementia in a specific time window".

RESULTS

The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set.

CONCLUSIONS

Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.

Multistage Grading of Amnestic Mild Cognitive Impairment: The Associated Brain Gray Matter Volume and Cognitive Behavior Characterization

Background and Purpose: It is well known that there is a wide range of different pathological stages related to Alzheimer's disease (AD) among patients with amnestic mild cognitive impairment (aMCI). Further refinement of the stages based on neuropsychological and neuroimaging methods is important for earlier disease detection, as well as for the development and evaluation of disease-modifying interventions.

Materials and Methods: In this cross-sectional study, 125 aMCI patients were classified into declined progressively three stages of mild, moderate and severe, utilizing the extreme groups approach (EGA) based on their memory function. Fifty-two patients, in addition to 24 cognitively normal subjects, were included in further structural MRI analyses. Characteristics of cognitive functions and brain structures across these newly defined stages were explored through general linear models.

Results: Almost all the non-memory cognitive functions showed progressive decline as memory function deteriorated. In addition, medial structures including the right hippocampus, right lingual and left fusiform gyrus, presented with greater gray matter (GM) atrophy during the later stages of aMCI (corrected p < 0.05). Correlations were found between GM volume of the lingual gyrus and processing speed (r = 0.419, p = 0.003) and between the fusiform gyrus and general cognitive function (r = 0.281, p = 0.046). Moreover, both cognitive function and GM volume presented non-linear trajectories over stages of aMCI.

Conclusion: Our study characterized the cognitive profiles along with the degree of episodic memory impairment, and these three stages of aMCI showed non-linear progressive decline in cognitive functions and GM volumes.

Prediction of Conversion to Alzheimer's Disease with Longitudinal Measures and Time-To-Event Data

BACKGROUND

Identifying predictors of conversion to Alzheimer's disease (AD) is critically important for AD prevention and targeted treatment.

OBJECTIVE

To compare various clinical and biomarker trajectories for tracking progression and predicting conversion from amnestic mild cognitive impairment to probable AD.

METHODS

Participants were from the ADNI-1 study. We assessed the ability of 33 longitudinal biomarkers to predict time to AD conversion, accounting for demographic and genetic factors. We used joint modelling of longitudinal and survival data to examine the association between changes of measures and disease progression. We also employed time-dependent receiver operating characteristic method to assess the discriminating capability of the measures.

RESULTS

23 of 33 longitudinal clinical and imaging measures are significant predictors of AD conversion beyond demographic and genetic factors. The strong phenotypic and biological predictors are in the cognitive domain (ADAS-Cog; RAVLT), functional domain (FAQ), and neuroimaging domain (middle temporal gyrus and hippocampal volume). The strongest predictor is ADAS-Cog 13 with an increase of one SD in ADAS-Cog 13 increased the risk of AD conversion by 2.92 times.

CONCLUSION

Prediction of AD conversion can be improved by incorporating longitudinal change information, in addition to baseline characteristics. Cognitive measures are consistently significant and generally stronger predictors than imaging measures.

Dissociating Normal Aging from Alzheimer's Disease: A View from Cognitive Neuroscience

Both normal aging and Alzheimer's disease (AD) are associated with changes in cognition, grey and white matter volume, white matter integrity, neural activation, functional connectivity, and neurotransmission. Obviously, all of these changes are more pronounced in AD and proceed faster providing the basis for an AD diagnosis. Since these differences are quantitative, however, it was hypothesized that AD might simply reflect an accelerated aging process. The present article highlights the different neurocognitive changes associated with normal aging and AD and shows that, next to quantitative differences, there are multiple qualitative differences as well. These differences comprise different neurocognitive dissociations as different cognitive deficit profiles, different weights of grey and white matter atrophy, and different gradients of structural decline. These qualitative differences clearly indicate that AD cannot be simply described as accelerated aging process but on the contrary represents a solid entity.

Allocentric Spatial Memory Testing Predicts Conversion from Mild Cognitive Impairment to Dementia: An Initial Proof-of-Concept Study

The hippocampus is one of the first regions to exhibit neurodegeneration in Alzheimer's disease (AD), and knowledge of its role in allocentric spatial memory may therefore aid early diagnosis of AD. The 4 Mountains Test (4MT) is a short and easily administered test of spatial memory based on the cognitive map theory of hippocampal function as derived from rodent single cell and behavioral studies. The 4MT has been shown in previous cross-sectional studies to be sensitive and specific for mild cognitive impairment (MCI) due to AD. This report describes the initial results of a longitudinal study testing the hypothesis that allocentric spatial memory is predictive of conversion from MCI to dementia. Fifteen patients with MCI underwent baseline testing on the 4MT in addition to CSF amyloid/tau biomarker studies, volumetric MRI and neuropsychological assessment including the Rey Auditory Verbal Learning Test (RAVLT) and Trail Making Test "B" (TMT-B). At 24 months, 9/15 patients had converted to AD dementia. The 4MT predicted conversion to AD with 93% accuracy (Cohen's d = 2.52). The predictive accuracies of the comparator measures were as follows: CSF tau/β-amyloid_1-42 ratio 92% (d = 1.81), RAVLT 64% (d = 0.41), TMT-B 78% (d = 1.56), and hippocampal volume 77% (d = 0.65). CSF tau levels were strongly negatively correlated with 4MT scores (r = -0.71). This proof-of-concept study provides initial support for the hypothesis that allocentric spatial memory testing is a predictive cognitive marker of hippocampal neurodegeneration in pre-dementia AD. The 4MT is a brief, non-invasive, straightforward spatial memory test and is therefore ideally suited for use in routine clinical diagnostic practice. This is of particular importance given the current unmet need for simple accurate diagnostic tests for early AD and the ongoing development of potential disease-modifying therapeutic agents, which may be more efficacious when given earlier in the disease course. By applying a test based on studies of hippocampal function in rodents to patient populations, this work represents the first step in the development of translatable biomarkers of hippocampal involvement in early AD for use in both animal models and human subjects.

A comparison of theoretical and statistically derived indices for predicting cognitive decline

INTRODUCTION

Both theoretical and statistically derived approaches have been used in research settings for predicting cognitive decline.

METHODS

Fifty-eight cognitively normal (NC) and 71 mild cognitive impairment (MCI) subjects completed a comprehensive cognitive battery for up to 5 years of follow-up. Composite indices of cognitive function were derived using a classic theoretical approach and exploratory factor analysis (EFA). Cognitive variables comprising each factor were averaged to form the EFA composite indices. Logistic regression was used to investigate whether these cognitive composites can reliably predict cognitive outcomes.

RESULTS

Neither method predicted decline in NC. The theoretical memory, executive, attention, and language composites and the EFA-derived "attention/executive" and "verbal memory" composites were significant predictors of decline in MCI. The best models achieved an area under the curve of 0.94 in MCI.

CONCLUSIONS

The theoretical and the statistically derived cognitive composite approaches are useful in predicting decline in MCI but not in NC.

Bilingualism, dementia, cognitive and neural reserve

PURPOSE OF REVIEW

We discuss the role of bilingualism as a source of cognitive reserve and we propose the putative neural mechanisms through which lifelong bilingualism leads to a neural reserve that delays the onset of dementia.

RECENT FINDINGS

Recent findings highlight that the use of more than one language affects the human brain in terms of anatomo-structural changes. It is noteworthy that recent evidence from different places and cultures throughout the world points to a significant delay of dementia onset in bilingual/multilingual individuals. This delay has been reported not only for Alzheimer's dementia and its prodromal mild cognitive impairment phase, but also for other dementias such as vascular and fronto-temporal dementia, and was found to be independent of literacy, education and immigrant status.

SUMMARY

Lifelong bilingualism represents a powerful cognitive reserve delaying the onset of dementia by approximately 4 years. As to the causal mechanism, because speaking more than one language heavily relies upon executive control and attention, brain systems handling these functions are more developed in bilinguals resulting in increases of gray and white matter densities that may help protect from dementia onset. These neurocognitive benefits are even more prominent when second language proficiency and exposure are kept high throughout life.

The Trail Making Test

The Trail Making Test (TMT) is used as an indicator of visual scanning, graphomotor speed, and executive function. The aim of this study was to examine the TMT relationships with several neuropsychological measures and to provide normative data in community-dwelling participants of 55 years and older. A population-based Spanish-speaking sample of 2,564 participants was used. The TMT, Symbol Digit Test, Stroop Color–Word Test, Digit Span Test, Verbal Fluency tests, and the MacQuarrie Test for Mechanical Ability tapping subtest were administered. Exploratory factor analyses and regression lineal models were used. Normative data for the TMT scores were obtained. A total of 1,923 participants (76.3%) participated, 52.4% were women, and the mean age was 66.5 years (Digit Span = 8.0). The Symbol Digit Test, MacQuarrie Test for Mechanical Ability tapping subtest, Stroop Color–Word Test, and Digit Span Test scores were associated in the performance of most TMT scores, but the contribution of each measure was different depending on the TMT score. Normative tables according to significant factors such as age, education level, and sex were created. Measures of visual scanning, graphomotor speed, and visuomotor processing speed were more related to the performance of the TMT-A score, while working memory and inhibition control were mainly associated with the TMT-B and derived TMT scores.

Neuropsychological tests for predicting cognitive decline in older adults

AIM

To determine neuropsychological tests likely to predict cognitive decline.

METHODS

A sample of nonconverters (n = 106) was compared with those who declined in cognitive status (n = 24). Significant univariate logistic regression prediction models were used to create multivariate logistic regression models to predict decline based on initial neuropsychological testing.

RESULTS

Rey-Osterrieth Complex Figure Test (RCFT) Retention predicted conversion to mild cognitive impairment (MCI) while baseline Buschke Delay predicted conversion to Alzheimer's disease (AD). Due to group sample size differences, additional analyses were conducted using a subsample of demographically matched nonconverters. Analyses indicated RCFT Retention predicted conversion to MCI and AD, and Buschke Delay predicted conversion to AD.

CONCLUSION

Results suggest RCFT Retention and Buschke Delay may be useful in predicting cognitive decline.

Linking CSF and cognition in Alzheimer's disease: Reanalysis of clinical data

OBJECTIVES

Memory and executive deficits are important cognitive markers of Alzheimer's disease (AD). Moreover, in the past decade, cerebrospinal fluid (CSF) biomarkers have been increasingly utilized in clinical practice. Both cognitive and CSF markers can be used to differentiate between AD patients and healthy seniors with high diagnostic accuracy. However, the extent to which performance on specific mnemonic or executive tasks enables reliable estimations of the concentrations of different CSF markers and their ratios remains unclear.

METHODS

To address the above issues, we examined the association between neuropsychological data and CSF biomarkers in 51 AD patients using hierarchical multiple regression analyses. In the first step of these analyses, age, education and sex were entered as predictors to control for possible confounding effects. In the second step, data from a neuropsychological test battery assessing episodic memory, semantic memory and executive functioning were included to determine whether these variables significantly increased (compared to step 1) the explained variance in Aβ42 concentration, p-tau concentration, t-tau concentration, Aβ42/t-tau ratio, and Aβ42/Aβ40 ratio.

RESULTS

The different models explained 52% of the variance in Aβ42/t-tau ratio, 27% of the variance in Aβ42 concentration, and 28% of the variance in t-tau concentration. In particular, Aβ42/t-tau ratio was associated with verbal recognition and code shifting, with Aβ42 being related to verbal recognition and t-tau being related to code shifting. By contrast, the inclusion of neuropsychological data did not allow reliable estimations of Aβ42/Aβ40 ratio or p-tau concentration.

CONCLUSION

Our results showed that strong associations exist between the cognitive key symptoms of AD and the concentrations and ratios of specific CSF markers. In addition, we revealed a specific combination of neuropsychological tests that may facilitate reliable estimations of CSF concentrations, thereby providing important diagnostic information for non-invasive early AD detection.