Cognitive variability-A marker for incident MCI and AD: An analysis for the Alzheimer's Disease Neuroimaging Initiative

Development and Validity of Norms for Cognitive Dispersion on the Uniform Data Set 3.0 Neuropsychological Battery

OBJECTIVE

Cognitive dispersion indexes intraindividual variability in performance across a battery of neuropsychological tests. Measures of dispersion show promise as markers of cognitive dyscontrol and everyday functioning difficulties; however, they have limited practical applicability due to a lack of normative data. This study aimed to develop and evaluate normed scores for cognitive dispersion among older adults.

METHOD

We analyzed data from 4,283 cognitively normal participants aged ≥50 years from the Uniform Data Set (UDS) 3.0. We describe methods for calculating intraindividual standard deviation (ISD) and coefficient of variation (CoV), as well as associated unadjusted scaled scores and demographically adjusted z-scores. We also examined the ability of ISD and CoV scores to differentiate between cognitively normal individuals (n = 4,283) and those with cognitive impairment due to Lewy body disease (n = 282).

RESULTS

We generated normative tables to map raw ISD and CoV scores onto a normal distribution of scaled scores. Cognitive dispersion indices were associated with age, education, and race/ethnicity but not sex. Regression equations were used to develop a freely accessible Excel calculator for deriving demographically adjusted normed scores for ISD and CoV. All measures of dispersion demonstrated excellent diagnostic utility when evaluated by the area under the curve produced from receiver operating characteristic curves.

CONCLUSIONS

Results of this study provide evidence for the clinical utility of sample-based and demographically adjusted normative standards for cognitive dispersion on the UDS 3.0. These standards can be used to guide interpretation of intraindividual variability among older adults in clinical and research settings.

Lexical Speech Features of Spontaneous Speech in Older Persons With and Without Cognitive Impairment: Reliability Analysis

Background

Speech analysis data are promising digital biomarkers for the early detection of Alzheimer disease. However, despite its importance, very few studies in this area have examined whether older adults produce spontaneous speech with characteristics that are sufficiently consistent to be used as proxy markers of cognitive status.

Objective

This preliminary study seeks to investigate consistency across lexical characteristics of speech in older adults with and without cognitive impairment.

Methods

A total of 39 older adults from a larger, ongoing study (age: mean 81.1, SD 5.9 years) were included. Participants completed neuropsychological testing and both picture description tasks and expository tasks to elicit speech. Participants with T-scores of ≤40 on ≥2 cognitive tests were categorized as having mild cognitive impairment (MCI). Speech features were computed automatically by using Python and the Natural Language Toolkit.

Results

Reliability indices based on mean correlations for picture description tasks and expository tasks were similar in persons with and without MCI (with r ranging from 0.49 to 0.65 within tasks). Intraindividual variability was generally preserved across lexical speech features. Speech rate and filler rate were the most consistent indices for the cognitively intact group, and speech rate was the most consistent for the MCI group.

Conclusions

Our findings suggest that automatically calculated lexical properties of speech are consistent in older adults with varying levels of cognitive impairment. These findings encourage further investigation of the utility of speech analysis and other digital biomarkers for monitoring cognitive status over time.

Plasma Aβ42/40 and cognitive variability are associated with cognitive function in Black Americans: Findings from the AA-FAIM cohort

Introduction

It is critical to develop more inclusive Alzheimer's disease (AD) research protocols to ensure that historically excluded groups are included in preclinical research and have access to timely diagnosis and treatment. If validated in racialized groups, plasma AD biomarkers and measures of subtle cognitive dysfunction could provide avenues to expand diversity in preclinical AD research. We sought to evaluate the utility of two easily obtained, low-burden disease markers, plasma amyloid beta (Aβ)42/40, and intra-individual cognitive variability (IICV), to predict concurrent and longitudinal cognitive performance in a sample of Black adults.

Methods

Two hundred fifty-seven Black participants enrolled in the African Americans Fighting Alzheimer's in Midlife (AA-FAIM) study underwent at least one cognitive assessment visit; a subset of n = 235 had plasma samples. Baseline IICV was calculated as the standard deviation across participants' z scores on five cognitive measures: Rey Auditory Verbal Learning Test Delayed Recall, Trail Making Test Parts A and B (Trails A and B), and Boston Naming Test. Using mixed effects regression models, we compared concurrent and longitudinal models to baseline plasma Aβ42/40 or IICV by age interactions. PrecivityAD assays quantified baseline plasma Aβ42/40.

Results

IICV was associated with concurrent/baseline performance on several outcomes but did not modify associations between age and cognitive decline. In contrast, plasma Aβ42/40 was unrelated to baseline cognitive performance, but a pattern emerged in interactions with age in longitudinal models of Trails A and B and Rey Auditory Verbal Learning Test total learning trials. Although not significant after correcting for multiple comparisons, low Aβ42/40 was associated with faster cognitive declines over time.

Discussion

Our results are promising as they extend existing findings to an Black American sample using low-cost, low-burden methods that can be implemented outside of a research center, thus supporting efforts for inclusive AD biomarker research.

Intra-individual variability in cognitive performance predicts functional decline in Parkinson's disease

BACKGROUND

Cognitive deficits contribute to disability in Parkinson's disease (PD). Cognitive intra-individual variability (IIV) is associated with cognitive decline in age-related disorders, but IIV has not been related to functional ability in PD. We examined IIV in predicting functional ability in participants with PD.

METHODS

De-identified National Alzheimer's Coordinating Center data (N = 1,228) from baseline and follow-up visits included participants with PD propensity score matched to control participants at baseline on age (M = 72), education (M = 15), and gender (28% female). PD symptom duration averaged 6 years. Outcome measures included the Functional Ability Questionnaire (FAQ), overall test battery mean (OTBM) of ten cognitive variables, IIV calculated as the standard deviation of cognitive data for each participant, Geriatric Depression Scale (GDS), and Unified PD Rating Scale gait and posture items. Baseline FAQ status in the PD group was predicted using logistic regression with age, education, cognition, GDS, and motor function as predictors. We compared baseline characteristics of PD participants with and without functional impairment at follow up.

RESULTS

PD participants showed lower OTBM and greater IIV, GDS, and motor dysfunction than controls (p < .0001). Education, OTBM, IIV, GDS, and gait predicted functional status (77% overall classification; AUC = .84). PD participants with functional impairment at follow up showed significantly lower OTBM and greater IIV, GDS, and motor dysfunction at baseline (p < .001).

CONCLUSION

IIV independently predicts functional status in participants with PD while controlling for other variables. PD participants with functional impairment at follow up showed greater IIV than those without functional impairment at follow up.

Intraindividual Cognitive Variability and Magnetic Resonance Imaging in Aging American Indians: Data from the Strong Heart Study

BACKGROUND

American Indians have high prevalence of risk factors for Alzheimer's disease and related dementias (ADRD) compared to the general population, yet dementia onset and frequency in this population are understudied. Intraindividual cognitive variability (IICV), a measure of variability in neuropsychological test performance within a person at a single timepoint, may be a novel, noninvasive biomarker of neurodegeneration and early dementia.

OBJECTIVE

To characterize the cross-sectional associations between IICV and hippocampal, total brain volume, and white matter disease measured by magnetic resonance imaging (MRI) among older American Indians.

METHODS

IICV measures for memory, executive function, and processing speed, and multidomain cognition were calculated for 746 American Indians (aged 64-95) who underwent MRI. Regression models were used to examine the associations of IICV score with hippocampal volume, total brain volume, and graded white matter disease, adjusting for age, sex, education, body mass index, intracranial volume, diabetes, stroke, hypertension, hypercholesterolemia, alcohol use, and smoking.

RESULTS

Higher memory IICV measure was associated with lower hippocampal volume (Beta = -0.076; 95% CI -0.499, -0.023; p = 0.031). After adjustment for Bonferroni or IICV mean scores in the same tests, the associations were no longer significant. No IICV measures were associated with white matter disease or total brain volume.

CONCLUSION

These findings suggest that the IICV measures used in this research cannot be robustly associated with cross-sectional neuroimaging features; nonetheless, the results encourage future studies investigating the associations between IICV and other brain regions, as well as its utility in the prediction of neurodegeneration and dementia in American Indians.

Effect of Metabolic Syndrome Risk Factors on Processing Speed and Executive Function in Three Racialized Groups

BACKGROUND

Metabolic syndrome (MetS) has been associated with increased risk for Alzheimer's disease and related dementias (ADRD). Understanding the association of MetS risk factors to processing speed and executive function in the pre-clinical stages of ADRD in under-represented groups would offer insight on potential mechanisms through which MetS associates with ADRD risk.

OBJECTIVE

Examine association of MetS features and processing speed and executive function across three racial groups.

METHODS

Cognitively unimpaired adults from the Wisconsin Alzheimer's Disease Research Center and the Wisconsin Registry for Alzheimer's Disease Prevention completed blood-draws and neuropsychological testing. Six cognitive outcomes were assessed in association to MetS risk factors: Trailmaking Tests A and B, Animal Fluency, Digit Symbol, and composite scores for Processing Speed and Executive Function. Linear mixed effect models were used to assess the relationship between MetS risk factor count and longitudinal cognitive performance across three racialized groups.

RESULTS

Participant sample sizes varied by outcome analyzed (N = 714-1,088). African American and Native American groups exhibited higher rates of MetS than non-Hispanic Whites. MetS was associated with processing speed and executive function across all racialized groups. Three-way interaction by racialized group was limited to one cognitive outcome: Trailmaking Test A.

CONCLUSION

Metabolic dysfunction incrementally affects cognitive trajectory, with generally similar associations across racial groups. Since racialized groups exhibit higher levels of both MetS and ADRD, MetS may represent a driving factor for increased ADRD risk experience by racialized group and an important and modifiable target through which to reduce risk of ADRD.

Psychological well-being and cognitive aging in Black, Native American, and White Alzheimer's Disease Research Center participants

Psychological well-being is associated with cognition in later life but has not been examined across diverse populations-including minoritized communities at disproportionately high risk of dementia. Further, most previous work has not been able to examine links between specific facets of psychological well-being and performance within distinct cognitive domains that can capture subclinical impairment. Using a well-characterized sample followed through enrollment in an NIH-funded Alzheimer's Disease Center, we sought to test these associations within three racial groups at baseline. Participants were N = 529 cognitively unimpaired Black, American Indian/Alaska Native (AI/AN), and white middle-aged and older adults (mean age = 63.6, SD = 8.1, range = 45-88 years) enrolled in the Wisconsin Alzheimer's Disease Research Center's Clinical Core. Predictors included validated NIH Toolbox Emotion Battery scales assessing positive affect, general life satisfaction, and meaning and purpose. Outcomes included performance on widely used tests of executive functioning and episodic memory. We conducted race-stratified regression models to assess within-group relationships. Black and AI/AN participants reported lower life satisfaction than white participants. Racial disparities were not observed for positive affect or meaning and purpose scores. Across groups, life satisfaction predicted better executive functioning. Similar associations were observed for positive affect in Black and AI/AN samples but not among whites. In general, well-being measures were not related to performance on tests of episodic memory. Our results highlight well-being as a potentially important determinant of late-life cognitive health, particularly executive functioning, that is modifiable if older adults are connected with appropriate resources and supports. Further, psychological well-being may represent a potent target for brain health interventions tailored for Black and Native communities.

Intra-individual cognitive variability in neuropsychological assessment: a sign of neural network dysfunction

Intra-Individual Cognitive Variability (IICV) predicts progression in neurocognitive disorders . Given important clinical applications, we investigated the association between IICV and multiple brain metrics across 17 networks to better understand the brain mechanisms underlying this performance measure. Sixty-three middle-aged and older adults without dementia underwent a neuropsychological battery, resting-state fMRI, and structural MRI scans. In a linear mixed effect model, higher IICV was associated with lower functional connectivity in control C network relative to medial occipital network (the reference). A multivariate partial least squares analysis revealed that lower mean and higher variability were both associated with lower connectivity in sensorimotor and default mode networks, while higher mean and higher variability were associated with lower volume in default mode and limbic networks. This study suggests that IICV signals widespread network dysfunction across multiple brain networks. These brain abnormalities offer new insights into mechanisms of early cognitive dysfunction. Clinical implications are discussed.

Interindividual and intraindividual variability in amnestic mild cognitive impairment (aMCI) measured with an online cognitive assessment

INTRODUCTION

Mean cognitive performance is worse in amnestic mild cognitive impairment (aMCI) compared to control groups. However, studies on variability of cognitive performance in aMCI have yielded inconclusive results, with many differences in variability measures and samples from one study to another.

METHODS

We examined variability in aMCI using an existing older adult sample (n = 91; 51 with aMCI, 40 with normal cognition for age), measured with an online self-administered computerized cognitive assessment (Cogniciti's Brain Health Assessment). Our methodology extended past findings by using pure measures of variability (controlling for confounding effects of group performance or practice), and a clinically representative aMCI sample (reflecting the continuum of cognitive performance between normal cognition and aMCI).

RESULTS

Between-group t-tests showed significantly greater between-person variability (interindividual variability or diversity) in overall cognitive performance in aMCI than controls, although the effect size was with a small to moderate effect size, d = 0.44. No significant group differences were found in within-person variability (intraindividual variability) across cognitive tasks (dispersion) or across trials of a response time task (inconsistency), which may be because we used a sample measuring the continuum of cognitive performance. Exploratory correlation analyses showed that a worse overall score was associated with greater inter- and intraindividual variability, and that variability measures were correlated with each other, indicating people with worse cognitive performance were more variable.

DISCUSSION

The current study demonstrates that self-administered online tests can be used to remotely assess different types of variability in people at risk of Alzheimer`s. Our findings show small but significantly more interindividual differences in people with aMCI. This diversity is considered as "noise" in standard assessments of mean performance, but offers an interesting and cognitively informative "signal" in itself.

Multilevel Survival Modeling with Structured Penalties for Disease Prediction from Imaging Genetics data

This paper introduces a framework for disease prediction from multimodal genetic and imaging data. We propose a multilevel survival model which allows predicting the time of occurrence of a future disease state in patients initially exhibiting mild symptoms. This new multilevel setting allows modeling the interactions between genetic and imaging variables. This is in contrast with classical additive models which treat all modalities in the same manner and can result in undesirable elimination of specific modalities when their contributions are unbalanced. Moreover, the use of a survival model allows overcoming the limitations of previous approaches based on classification which consider a fixed time frame. Furthermore, we introduce specific penalties taking into account the structure of the different types of data, such as a group lasso penalty over the genetic modality and a L2-penalty over the imaging modality. Finally, we propose a fast optimization algorithm, based on a proximal gradient method. The approach was applied to the prediction of Alzheimer's disease (AD) among patients with mild cognitive impairment (MCI) based on genetic (single nucleotide polymorphisms - SNP) and imaging (anatomical MRI measures) data from the ADNI database. The experiments demonstrate the effectiveness of the method for predicting the time of conversion to AD. It revealed how genetic variants and brain imaging alterations interact in the prediction of future disease status. The approach is generic and could potentially be useful for the prediction of other diseases.

Cognitive dispersion and ApoEe4 genotype predict dementia diagnosis in 8-year follow-up of the oldest-old

BACKGROUND

Cognitive dispersion, or inconsistencies in performance across cognitive domains, has been posited as a cost-effective tool to predict conversion to dementia in older adults. However, there is a dearth of studies exploring cognitive dispersion in the oldest-old (>80 years) and its relationship to dementia incidence.

OBJECTIVE

The main aim of this study was to examine whether higher cognitive dispersion at baseline was associated with dementia incidence within an 8-year follow-up of very old adults, while controlling for established risk factors and suggested protective factors for dementia.

METHODS

Participants (n = 468) were from the Origins of Variance in the Old-Old: Octogenarian Twins study, based on the Swedish Twin Registry. Cox regression analyses were performed to assess the association between baseline cognitive dispersion scores and dementia incidence, while controlling for sociodemographic variables, ApoEe4 carrier status, co-morbidities, zygosity and lifestyle engagement scores. An additional model included a composite of average cognitive performance.

RESULTS

Cognitive dispersion and ApoEe4 were significantly associated with dementia diagnosis. These variables remained statistically significant when global cognitive performance was entered into the model. Likelihood ratio tests revealed that cognitive dispersion and cognitive composite scores entered together in the same model was superior to either predictor alone in the full model.

CONCLUSIONS

The study underscores the usefulness of cognitive dispersion metrics for dementia prediction in the oldest-old and highlights the influence of ApoEe4 on cognition in very late age. Our findings concur with others suggesting that health and lifestyle factors pose little impact upon cognition in very advanced age.

Cognitive variability, brain aging, and cognitive decline in late-life major depression

OBJECTIVES

Older adults with late-life major depression (LLMD) are at increased risk of dementia. Dispersion, or within-person performance variability across cognitive tests, is a potential marker of cognitive decline. This study examined group differences in dispersion between LLMD and nondepressed healthy controls (HC) and investigated whether dispersion was a predictor of cognitive performance 1 year later in LLMD. We also explored demographic, clinical, and structural imaging correlates of dispersion in LLMD and HC. We hypothesized that dispersion would be greater in LLMD compared with HC and would be associated with worse cognitive performance 1 year later in LLMD.

DESIGN

Participants were enrolled in the Neurobiology of Late-Life Depression, a naturalistic longitudinal investigation of the predictors of poor illness course in LLMD.

PARTICIPANTS

The baseline sample consisted of 121 older adults with LLMD and 39 HC; of these subjects, 94 LLMD and 35 HC underwent magnetic resonance imaging (MRI). One-year cognitive data were available for 107 LLMD patients.

MEASUREMENTS

All participants underwent detailed clinical and structural MRI at baseline. LLMD participants also completed a comprehensive cognitive evaluation 1 year later.

RESULTS

Higher test dispersion was evident in LLMD when compared with nondepressed controls. Greater baseline dispersion predicted 1-year cognitive decline in LLMD patients even when controlling for baseline cognitive functioning and demographic and clinical confounders. Dispersion was correlated with white matter lesions in LLMD but not HC. Dispersion was also correlated with anxiety in both LLMD and HC.

CONCLUSIONS

Dispersion is a marker of neurocognitive integrity that requires further exploration in LLMD.

Cerebrospinal fluid Aβ42 moderates the relationship between brain functional network dynamics and cognitive intraindividual variability

As Alzheimer's disease (AD) pathology accumulates, resting-state functional connectivity (rs-fc) within and between brain networks decreases, and fluctuations in cognitive performance known as intraindividual variability (IIV) increase. Here, we assessed the relationship between IIV and anticorrelations in rs-fc between the default mode network (DMN)-dorsal attention network (DAN) in cognitively normal older adults and symptomatic AD participants. We also evaluated the relationship between cerebrospinal fluid (CSF) biomarkers of AD (amyloid-beta [Aβ₄₂] and tau) and IIV-anticorrelation in rs-fc. We observed that cognitive IIV and anticorrelations between DMN × DAN were higher in individuals with AD compared with cognitively normal participants. As DMN × DAN relationship became more positive, cognitive IIV increased, indicating that stronger anticorrelations between networks support more consistent cognitive performance. Moderation analyses indicated that continuous CSF Aβ₄₂, but not CSF total tau, moderated the relationship between cognitive IIV and DMN × DAN, collectively demonstrating that greater amyloid burden and alterations in functional network dynamics are associated with cognitive changes seen in AD. These findings are valuable, as they suggest that amyloid affects cognitive functioning during the early stages of AD.

Cognitive Dispersion Is Not Associated with Cerebrospinal Fluid Biomarkers of Alzheimer's Disease: Results from the European Prevention of Alzheimer's Dementia (EPAD) v500.0 Cohort

BACKGROUND

Cognitive dispersion, variation in performance across cognitive domains, is posited as a non-invasive and cost-effective marker of early neurodegeneration. Little work has explored associations between cognitive dispersion and Alzheimer's disease (AD) biomarkers in healthy older adults. Even less is known about the influence or interaction of biomarkers reflecting brain pathophysiology or other risk factors on cognitive dispersion scores.

OBJECTIVE

The main aim of this study was to examine whether higher cognitive dispersion was associated with cerebrospinal fluid (CSF) levels of amyloid-β (Aβ42), total tau (t-tau), phosphorylated tau (p-tau), and amyloid positivity in a cohort of older adults at various severities of AD. A secondary aim was to explore which AD risk factors were associated with cognitive dispersion scores.

METHODS

Linear and logistic regression analyses explored the associations between dispersion and CSF levels of Aβ42, t-tau, and p-tau and amyloid positivity (Aβ42 < 1000 pg/ml). Relationships between sociodemographics, APOEɛ4 status, family history of dementia, and levels of depression and dispersion were also assessed.

RESULTS

Dispersion did not emerge as associated with any of the analytes nor amyloid positivity. Older (β= -0.007, SE = 0.002, p = 0.001) and less educated (β= -0.009, SE = 0.003, p = 0.009) individuals showed greater dispersion.

CONCLUSION

Dispersion was not associated with AD pathology, but was associated with age and years of education, highlighting individual differences in cognitive aging. The use of this metric as a screening tool for existing AD pathology is not supported by our analyses. Follow-up work will determine if dispersion scores can predict changes in biomarker levels and/or positivity status longitudinally.

Identification of a Simple and Novel Cut-Point Based Cerebrospinal Fluid and MRI Signature for Predicting Alzheimer's Disease Progression that Reinforces the 2018 NIA-AA Research Framework

The 2018 NIA-AA research framework proposes a classification system with Amyloid-β deposition, pathologic Tau, and Neurodegeneration (ATN) for diagnosis and staging of Alzheimer's disease (AD). Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database can be utilized to identify diagnostic signatures for predicting AD progression, and to determine the utility of this NIA-AA research framework. Profiles of 320 peptides from baseline cerebrospinal fluid (CSF) samples of 287 normal, mild cognitive impairment (MCI), and AD subjects followed over a 3-10-year period were measured via multiple reaction monitoring mass spectrometry. CSF Aβ42, total-Tau (tTau), phosphorylated-Tau (pTau-181), and hippocampal volume were also measured. From these candidate markers, optimal signatures with decision thresholds to separate AD and normal subjects were first identified via unbiased regression and tree-based algorithms. The best performing signature determined via cross-validation was then tested in an independent group of MCI subjects to predict future progression. This multivariate analysis yielded a simple diagnostic signature comprising CSF pTau-181 to Aβ42 ratio, MRI hippocampal volume, and low CSF levels of a novel PTPRN peptide, with a decision threshold on each marker. When applied to a separate MCI group at baseline, subjects meeting these signature criteria experience 4.3-fold faster progression to AD compared to a 2.2-fold faster progression using only conventional markers. This novel 4-marker signature represents an advance over the current diagnostics based on widely used markers, and is easier to use in practice than recently published complex signatures. This signature also reinforces the ATN construct from the 2018 NIA-AA research framework.

Characterizing the Effects of Sex, APOE ɛ4, and Literacy on Mid-life Cognitive Trajectories: Application of Information-Theoretic Model Averaging and Multi-model Inference Techniques to the Wisconsin Registry for Alzheimer's Prevention Study

OBJECTIVES

Prior research has identified numerous genetic (including sex), education, health, and lifestyle factors that predict cognitive decline. Traditional model selection approaches (e.g., backward or stepwise selection) attempt to find one model that best fits the observed data, risking interpretations that only the selected predictors are important. In reality, several predictor combinations may fit similarly well but result in different conclusions (e.g., about size and significance of parameter estimates). In this study, we describe an alternative method, Information-Theoretic (IT) model averaging, and apply it to characterize a set of complex interactions in a longitudinal study on cognitive decline.

METHODS

Here, we used longitudinal cognitive data from 1256 late-middle aged adults from the Wisconsin Registry for Alzheimer's Prevention study to examine the effects of sex, apolipoprotein E (APOE) ɛ4 allele (non-modifiable factors), and literacy achievement (modifiable) on cognitive decline. For each outcome, we applied IT model averaging to a set of models with different combinations of interactions among sex, APOE, literacy, and age.

RESULTS

For a list-learning test, model-averaged results showed better performance for women versus men, with faster decline among men; increased literacy was associated with better performance, particularly among men. APOE had less of an association with cognitive performance in this age range (∼40-70 years).

CONCLUSIONS

These results illustrate the utility of the IT approach and point to literacy as a potential modifier of cognitive decline. Whether the protective effect of literacy is due to educational attainment or intrinsic verbal intellectual ability is the topic of ongoing work. (JINS, 2019, 25, 119-133).

Going beyond the mean: Intraindividual variability of cognitive performance in prodromal and early neurodegenerative disorders

OBJECTIVE

Intraindividual variability (IIV), generally defined as short-term variations in behavior, has been proposed as a sign of subtle early impairment in neurodegenerative disorders, presumably associated with the disintegration of neuronal network connectivity. We aim to provide a review of IIV as a sensitive cognitive marker in prodromal neurodegenerative disorders.

METHOD

A narrative review focusing not only on theoretical and methodological definitions, including an overview on the neural correlates of IIV, but mainly on results from population-based and clinical-based studies on the role of IIV as a reliable predictor of mild cognitive impairment (MCI) and conversion to dementia in neurodegenerative disorders, mostly Alzheimer's and Parkinson's disease.

RESULTS

Most studies focus on MCI and Alzheimer's disease and demonstrate that IIV is a reliable cognitive marker. IIV is partly more sensitive than mean performance in the prediction of cognitive impairment or progressive deterioration and is independent of socio-demographic variables and disease mediators (e.g., genetic susceptibility). Neuroimaging data, mostly from healthy subjects, suggest a relationship between IIV and dysfunction of the default mode network, presumably mediated by white matter disintegration in frontal and parietal areas.

CONCLUSIONS

IIV measures may provide valuable information about diagnosis and progression in prodromal stages of neurodegenerative disorders. Thus, further conceptual and methodological clarifications are needed to justify the inclusion of IIV as a sensible cognitive marker in routine clinical neuropsychological assessment.

The combination of apolipoprotein E4, age and Alzheimer's Disease Assessment Scale - Cognitive Subscale improves the prediction of amyloid positron emission tomography status in clinically diagnosed mild cognitive impairment

BACKGROUND AND PURPOSE

Randomized clinical trials involving anti-amyloid interventions focus on the early stages of Alzheimer's disease (AD) with proven amyloid pathology, using amyloid positron emission tomography (amyloid-PET) imaging or cerebrospinal fluid analysis. However, these investigations are either expensive or invasive and are not readily available in resource-limited centres. Hence, the identification of cost-effective clinical alternatives to amyloid-PET is highly desirable. This study aimed to investigate the accuracy of combined clinical markers in predicting amyloid-PET status in mild cognitive impairment (MCI) individuals.

METHODS

In all, 406 MCI participants from the Alzheimer's Disease Neuroimaging Initiative database were dichotomized into amyloid-PET(+) and amyloid-PET(-) using a cut-off of >1.11. The accuracies of single clinical markers [apolipoprotein E4 (ApoE4) genotype, demographics, cognitive measures and cerebrospinal fluid analysis] in predicting amyloid-PET status were evaluated using receiver operating characteristic curve analysis. A logistic regression model was then used to determine the optimal model with combined clinical markers to predict amyloid-PET status.

RESULTS

Cerebrospinal fluid amyloid-β (Aβ) showed the best predictive accuracy of amyloid-PET status [area under the curve (AUC) = 0.927]. Whilst ApoE4 genotype (AUC = 0.737) and Alzheimer's Disease Assessment Scale - Cognitive Subscale (ADAS-Cog) 13 (AUC = 0.724) independently discriminated amyloid-PET(+) and amyloid-PET(-) MCI individuals, the combination of clinical markers (ApoE4 carrier, age >60 years and ADAS-Cog 13 > 13.5) improved the predictive accuracy of amyloid-PET status (AUC = 0.827, P < 0.001).

CONCLUSIONS

Cerebrospinal fluid Aβ, which is an invasive procedure, is most accurate in predicting amyloid-PET status in MCI individuals. The combination of ApoE4, age and ADAS-Cog 13 also accurately predicts amyloid-PET status. As this combination of clinical markers is cheap, non-invasive and readily available, it offers an attractive surrogate assessment for amyloid status amongst MCI individuals in resource-limited settings.

Haplotype analysis of APOE intragenic SNPs

Background

APOE ε4 allele is most common genetic risk factor for Alzheimer’s disease (AD) and cognitive decline. However, it remains poorly understood why only some carriers of APOE ε4 develop AD and how ethnic variabilities in APOE locus contribute to AD risk. Here, to address the role of APOE haplotypes, we reassessed the diversity of APOE locus in major ethnic groups and in Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset on patients with AD, and subjects with mild cognitive impairment (MCI), and control non-demented individuals.

Results

We performed APOE gene haplotype analysis for a short block of five SNPs across the gene using the ADNI whole genome sequencing dataset. The compilation of ADNI data with 1000 Genomes identified the APOE ε4 linked haplotypes, which appeared to be distant for the Asian, African and European populations. The common European ε4-bearing haplotype is associated with AD but not with MCI, and the Africans lack this haplotype. Haplotypic inference revealed alleles that may confer protection against AD. By assessing the DNA methylation profile of the APOE haplotypes, we found that the AD-associated haplotype features elevated APOE CpG content, implying that this locus can also be regulated by genetic-epigenetic interactions.

Conclusions

We showed that SNP frequency profiles within APOE locus are highly skewed to population-specific haplotypes, suggesting that the ancestral background within different sites at APOE gene may shape the disease phenotype. We propose that our results can be utilized for more specific risk assessment based on population descent of the individuals and on higher specificity of five site haplotypes associated with AD.

Electronic supplementary material

The online version of this article (10.1186/s12868-018-0413-4) contains supplementary material, which is available to authorized users.

Two Different Populations within the Healthy Elderly: Lack of Conflict Detection in Those at Risk of Cognitive Decline

During healthy aging, inhibitory processing is affected at the sensorial, perceptual, and cognitive levels. The assessment of event-related potentials (ERPs) during the Stroop task has been used to study age-related decline in the efficiency of inhibitory processes. Studies using ERPs have found that the P300 amplitude increases and the N500 amplitude is attenuated in healthy elderly adults compared to those in young adults. On the other hand, it has been reported that theta excess in resting EEG with eyes closed is a good predictor of cognitive decline during aging 7 years later, while a normal EEG increases the probability of not developing cognitive decline. The behavioral and ERP responses during a Counting-Stroop task were compared between 22 healthy elderly subjects with normal EEG (Normal-EEG group) and 22 healthy elderly subjects with an excess of EEG theta activity (Theta-EEG group). Behaviorally, the Normal-EEG group showed a higher behavioral interference effect than the Theta-EEG group. ERP patterns were different between the groups, and two facts are highlighted: (a) the P300 amplitude was higher in the Theta-EEG group, with both groups showing a P300 effect in almost all electrodes, and (b) the Theta-EEG group did not show an N500 effect. These results suggest that the diminishment in inhibitory control observed in the Theta-EEG group may be compensated by different processes in earlier stages, which would allow them to perform the task with similar efficiency to that of participants with a normal EEG. This study is the first to show that healthy elderly subjects with an excess of theta EEG activity not only are at risk of developing cognitive decline but already have a cognitive impairment.

Cognitive Variability Predicts Incident Alzheimer's Disease and Mild Cognitive Impairment Comparable to a Cerebrospinal Fluid Biomarker

BACKGROUND

Alzheimer's disease (AD) biomarkers are emerging as critically important for disease detection and monitoring. Most biomarkers are obtained through invasive, resource-intense procedures. A cognitive marker, intra-individual cognitive variability (IICV) may provide an alternative or adjunct marker of disease risk for individuals unable or disinclined to undergo lumbar puncture.

OBJECTIVE

To contrast risk of incident AD and mild cognitive impairment (MCI) associated with IICV to risk associated with well-established biomarkers: cerebrospinal fluid (CSF) phosphorylated tau protein (p-tau181) and amyloid-β 42 (Aβ42) peptide.

METHODS

Dispersion in cognitive performance, IICV, was estimated with a published algorithm, and included Trail Making Test A and B, Rey Auditory Verbal Learning Test (RAVLT), and the American National Adult Reading Test (ANART). CSF biomarkers were expressed as a ratio: p-tau181/Aβ42, wherein high values signified pathognomonic profiles. Logistic regression models included longitudinal data from 349 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants who completed lumbar puncture. All subjects were cognitively healthy (n = 105) or diagnosed with MCI (n = 244) at baseline. We examined odds of conversion associated with baseline elevations in IICV and/or ratio of CSF p-tau181/Aβ42.

RESULTS

When included in models alone or in combination with CSF p-tau181/Aβ42, one standard IICV unit higher was associated with an estimated odds ratio for incident AD or MCI of 2.81 (95% CI: 1.83-4.33) in the most inclusive sample, and an odds ratio of 3.41 (95% CI: 2.03-5.73) when restricted to participants with MCI. Iterative analyses suggested that IICV independently improved model fit even when individual index components were included in comparative models.

CONCLUSIONS

These analyses provide preliminary support for IICV as a marker of incident AD and MCI. This easily-disseminated, non-invasive marker compared favorably to well-established CSF biomarkers.