Reliability and validity of an algorithm for the diagnosis of normal cognition, mild cognitive impairment, and dementia: implications for multicenter research studies

Stability in cognitive classification as a function of severity of impairment and ethnicity: A longitudinal analysis

OBJECTIVE

The interaction of ethnicity, progression of cognitive impairment, and neuroimaging biomarkers of Alzheimer's Disease remains unclear. We investigated the stability in cognitive status classification (cognitively normal [CN] and mild cognitive impairment [MCI]) of 209 participants (124 Hispanics/Latinos and 85 European Americans).

METHODS

Biomarkers (structural MRI and amyloid PET scans) were compared between Hispanic/Latino and European American individuals who presented a change in cognitive diagnosis during the second or third follow-up and those who remained stable over time.

RESULTS

There were no significant differences in biomarkers between ethnic groups in any of the diagnostic categories. The frequency of CN and MCI participants who were progressors (progressed to a more severe cognitive diagnosis at follow-up) and non-progressors (either stable through follow-ups or unstable [progressed but later reverted to a diagnosis of CN]) did not significantly differ across ethnic groups. Progressors had greater atrophy in the hippocampus (HP) and entorhinal cortex (ERC) at baseline compared to unstable non-progressors (reverters) for both ethnic groups, and more significant ERC atrophy was observed among progressors of the Hispanic/Latino group. For European Americans diagnosed with MCI, there were 60% more progressors than reverters (reverted from MCI to CN), while among Hispanics/Latinos with MCI, there were 7% more reverters than progressors. Binomial logistic regressions predicting progression, including brain biomarkers, MMSE, and ethnicity, demonstrated that only MMSE was a predictor for CN participants at baseline. However, for MCI participants at baseline, HP atrophy, ERC atrophy, and MMSE predicted progression.

Development and clinical validation of blood-based multibiomarker models for the evaluation of brain amyloid pathology

Background and Objectives

Plasma biomarkers provide new tools to evaluate patients with mild cognitive impairment (MCI) for Alzheimer's disease (AD) pathology. Such tools are needed for anti-amyloid therapies that require efficient and accurate diagnostic evaluation to identify potential treatment candidates. This study sought to develop and evaluate the clinical performance of a multi-marker combination of plasma beta-amyloid 42/40 (Aβ42/40), ptau-217, and APOE genotype to predict amyloid PET positivity in a diverse cohort of patients at a memory clinic and evaluate >4,000 results from "real-world" specimens submitted for high-throughput clinical testing.

Methods

Study participants were from the 1Florida AD Research Center (ADRC). Demographics, clinical evaluations, and amyloid PET scan data were provided with plasma specimens for model development for the intended-use cohort (MCI/AD: n=215). Aβ42/40 and ApoE4 proteotype (reflecting high-risk APOE 4 alleles) were measured by mass spectrometry and ptau-217 by immunoassay. A likelihood score model was determined for each biomarker separately and in combination. Model performance was optimized using 2 cutpoints, 1 for high and 1 for low likelihood of PET positivity, to attain ≥90% specificity and sensitivity. These cutpoints were applied to categorize 4,326 real-world specimens and an expanded cohort stratified by cognitive status (normal cognition [NC], MCI, AD).

Results

For the intended-use cohort (46.0% prevalence of PET-positivity), a combination of Aβ42/40, ptau-217, and APOE4 allele count provided the best model with a receiver operating characteristic area under the curve (ROC-AUC) of 0.942 and with 2 cutpoints fixed at 91% sensitivity and 91% specificity yielding a high cutpoint with 88% positive predictive value (PPV) and 87% accuracy and a low cutpoint with 91% negative predictive value (NPV) and 85% accuracy. Incorporating APOE4 allele count also reduced the percentage of patients with indeterminate risk from 15% to 10%. The cutpoints categorized the real-world clinical specimens as having 42% high, 51% low, and 7% indeterminate likelihood for PET positivity and differentiated between NC, MCI, and AD dementia cognitive status in the expanded cohort.

Discussion

Combining plasma biomarkers Aβ42/40, ptau-217, and APOE4 allele count is a scalable approach for evaluating patients with MCI for suspected AD pathology.

Diffusion MRI relates to plasma Aβ42/40 in PET negative participants without dementia

INTRODUCTION

Magnetic resonance imaging (MRI) biomarkers are needed for indexing early biological stages of Alzheimer's disease (AD), such as plasma amyloid-β (Aβ42/40) positivity in Aβ positron emission tomography (PET) negative individuals.

METHODS

Diffusion free-water (FW) MRI was acquired in individuals with normal cognition (NC) and mild cognitive impairment (MCI) with Aβ plasma-/PET- (NC = 22, MCI = 60), plasma+/PET- (NC = 5, MCI = 20), and plasma+/PET+ (AD dementia = 21) biomarker status. Gray and white matter FW and fractional anisotropy (FAt) were compared cross-sectionally and the relationships between imaging, plasma and PET biomarkers were assessed.

RESULTS

Plasma+/PET- demonstrated increased FW (24 regions) and decreased FAt (66 regions) compared to plasma-/PET-. FW (16 regions) and FAt (51 regions) were increased in plasma+/PET+ compared to plasma+/PET-. Composite brain FW correlated with plasma Aβ42/40 and p-tau181.

DISCUSSION

FW imaging changes distinguish plasma Aβ42/40 positive and negative groups, independent of group differences in cognitive status, Aβ PET status, and other plasma biomarkers (i.e., t-tau, p-tau181, glial fibrillary acidic protein, neurofilament light).

HIGHLIGHTS

Plasma Aβ42/40 positivity is associated with brain microstructure decline. Plasma+/PET- demonstrated increased FW in 24 total GM and WM regions. Plasma+/PET- demonstrated decreased FAt in 66 total GM and WM regions. Whole-brain FW correlated with plasma Aβ42/40 and p-tau181 measures. Plasma+/PET- demonstrated decreased cortical volume and thickness.

Clinical utility of plasma Aβ42/40 ratio by LC-MS/MS in Alzheimer's disease assessment

Introduction

Plasma Aβ42/40 ratio can help predict amyloid PET status, but its clinical utility in Alzheimer's disease (AD) assessment is unclear.

Methods

Aβ42/40 ratio was measured by LC-MS/MS for 250 specimens with associated amyloid PET imaging, diagnosis, and demographic data, and for 6,192 consecutive clinical specimens submitted for Aβ42/40 testing.

Results

High diagnostic sensitivity and negative predictive value (NPV) for Aβ-PET positivity were observed, consistent with the clinical performance of other plasma LC-MS/MS assays, but with greater separation between Aβ42/40 values for individuals with positive vs. negative Aβ-PET results. Assuming a moderate prevalence of Aβ-PET positivity, a cutpoint was identified with 99% NPV, which could help predict that AD is likely not the cause of patients' cognitive impairment and help reduce PET evaluation by about 40%.

Conclusion

High-throughput plasma Aβ42/40 LC-MS/MS assays can help identify patients with low likelihood of AD pathology, which can reduce PET evaluations, allowing for cost savings.

Reliability of Spectral Features of Resting-State Brain Activity: A Magnetoencephalography Study

Background Cognition is a vital sign and its deterioration is a major concern in clinical medicine. It is usually evaluated using neuropsychological assessments, which have innate limitations such as the practice effect. To compensate for these assessments, the oscillatory power of resting-state brain activity has recently become available. The power is obtained noninvasively using magnetoencephalography and is summarized by spectral parameters such as the median frequency (MF), individual alpha frequency (IAF), spectral edge frequency 95 (SEF95), and Shannon's spectral entropy (SSE). As these parameters are less sensitive to practice effects, they are suitable for longitudinal studies. However, their reliability remains unestablished, hindering their proactive use in clinical practice. Therefore, we aimed to quantify the within-participant reliability of these parameters using repeated measurements of healthy participants to facilitate their clinical use and to evaluate the observed changes/differences in these parameters reported in previous studies. Methodology Resting-state brain activity with eyes closed was recorded using magnetoencephalography for five minutes from 15 healthy individuals (29.3 ± 4.6 years old: ranging from 23 to 28 years old). The following four spectral parameters were calculated: MF, IAF, SEF95, and SSE. To quantify reliability, the minimal detectable change (MDC) and intraclass correlation coefficient (ICC) were computed for each parameter. In addition, we used MDCs to evaluate the changes and differences in the spectral parameters reported in previous longitudinal and cross-sectional studies. Results The MDC at 95% confidence interval (MDC95) of MF, IAF, SEF95, and SSE were 0.61 Hz, 0.44 Hz, 2.91 Hz, and 0.028, respectively. The ICCs of these parameters were 0.96, 0.92, 0.94, and 0.83, respectively. The MDC95 of these parameters was smaller than the mean difference in the parameters between cognitively healthy individuals and patients with dementia, as reported in previous studies. Conclusions The spectral parameter changes/differences observed in prior studies were not attributed to measurement errors but rather reflected genuine effects. Furthermore, all spectral parameters exhibited high ICCs (>0.8), underscoring their robust within-participant reliability. Our results support the clinical use of these parameters, especially in the longitudinal monitoring and evaluation of the outcomes of interventions.

Study protocol for "In-vehicle sensors to detect changes in cognition of older drivers"

BACKGROUND

Driving is a complex behavior that may be affected by early changes in the cognition of older individuals. Early changes in driving behavior may include driving more slowly, making fewer and shorter trips, and errors related to inadequate anticipation of situations. Sensor systems installed in older drivers' vehicles may detect these changes and may generate early warnings of possible changes in cognition.

METHOD

A naturalistic longitudinal design is employed to obtain continuous information on driving behavior that will be compared with the results of extensive cognitive testing conducted every 3 months for 3 years. A driver facing camera, forward facing camera, and telematics unit are installed in the vehicle and data downloaded every 3 months when the cognitive tests are administered.

RESULTS

Data processing and analysis will proceed through a series of steps including data normalization, adding information on external factors (weather, traffic conditions), and identifying critical features (variables). Traditional prediction modeling results will be compared with Recurring Neural Network (RNN) approach to produce Driver Behavior Indices (DBIs), and algorithms to classify drivers within age, gender, ethnic group membership, and other potential group characteristics.

CONCLUSION

It is well established that individuals with progressive dementias are eventually unable to drive safely, yet many remain unaware of their cognitive decrements. Current screening and evaluation services can test only a small number of individuals with cognitive concerns, missing many who need to know if they require treatment. Given the increasing number of sensors being installed in passenger vehicles and pick-up trucks and their increasing acceptability, reconfigured in-vehicle sensing systems could provide widespread, low-cost early warnings of cognitive decline to the large number of older drivers on the road in the U.S. The proposed testing and evaluation of a readily and rapidly available, unobtrusive in-vehicle sensing system could provide the first step toward future widespread, low-cost early warnings of cognitive change for this large number of older drivers in the U.S. and elsewhere.

Clinical utility of plasma Aβ42/40 ratio by LC-MS/MS in Alzheimer's disease assessment

INTRODUCTION

Plasma Aβ42/40 ratio can be used to help predict amyloid PET status, but its clinical utility in Alzheimer's disease (AD) assessment is unclear.

METHODS

Aβ42/40 ratio was measured by LC-MS/MS in 250 specimens with associated amyloid PET imaging, diagnosis, and demographic data, and 6,192 consecutive clinical specimens submitted for Aβ42/40 testing.

RESULTS

High diagnostic sensitivity and negative predictive value (NPV) for Aβ-PET positivity were observed, consistent with the clinical performance of other plasma LC-MS/MS assays, but with greater separation between Aβ42/40 values for individuals with positive vs negative Aβ-PET results. Assuming a moderate prevalence of Aβ-PET positivity, a cutpoint was identified with 99% NPV, which could help predict that AD is likely not the cause of patients' cognitive impairment and help reduce PET evaluation by about 40%.

DISCUSSION

Using high-throughput plasma Aβ42/40 LC-MS/MS assays can help reduce PET evaluations in patients with low likelihood of AD pathology, allowing for cost savings.

Association of Cognitive Impairment With Free Water in the Nucleus Basalis of Meynert and Locus Coeruleus to Transentorhinal Cortex Tract

BACKGROUND AND OBJECTIVES

The goal of this work was to determine the relationship between diffusion microstructure and early changes in Alzheimer disease (AD) severity as assessed by clinical diagnosis, cognitive performance, dementia severity, and plasma concentrations of neurofilament light chain.

METHODS

Diffusion MRI scans were collected on cognitively normal participants (CN) and patients with early mild cognitive impairment (EMCI), late mild cognitive impairment, and AD. Free water (FW) and FW-corrected fractional anisotropy were calculated in the locus coeruleus to transentorhinal cortex tract, 4 magnocellular regions of the basal forebrain (e.g., nucleus basalis of Meynert), entorhinal cortex, and hippocampus. All patients underwent a battery of cognitive assessments; neurofilament light chain levels were measured in plasma samples.

RESULTS

FW was significantly higher in patients with EMCI compared to CN in the locus coeruleus to transentorhinal cortex tract, nucleus basalis of Meynert, and hippocampus (mean Cohen d = 0.54; p fdr < 0.05). FW was significantly higher in those with AD compared to CN in all the examined regions (mean Cohen d = 1.41; p fdr < 0.01). In addition, FW in the hippocampus, entorhinal cortex, nucleus basalis of Meynert, and locus coeruleus to transentorhinal cortex tract positively correlated with all 5 cognitive impairment metrics and neurofilament light chain levels (mean r 2 = 0.10; p fdr < 0.05).

DISCUSSION

These results show that higher FW is associated with greater clinical diagnosis severity, cognitive impairment, and neurofilament light chain. They also suggest that FW elevation occurs in the locus coeruleus to transentorhinal cortex tract, nucleus basalis of Meynert, and hippocampus in the transition from CN to EMCI, while other basal forebrain regions and the entorhinal cortex are not affected until a later stage of AD. FW is a clinically relevant and noninvasive early marker of structural changes related to cognitive impairment.

Baseline Neuroimaging Predicts Decline to Dementia From Amnestic Mild Cognitive Impairment

Background and Objectives: Prediction of decline to dementia using objective biomarkers in high-risk patients with amnestic mild cognitive impairment (aMCI) has immense utility. Our objective was to use multimodal MRI to (1) determine whether accurate and precise prediction of dementia conversion could be achieved using baseline data alone, and (2) generate a map of the brain regions implicated in longitudinal decline to dementia. Methods: Participants meeting criteria for aMCI at baseline (N = 55) were classified at follow-up as remaining stable/improved in their diagnosis (N = 41) or declined to dementia (N = 14). Baseline T1 structural MRI and resting-state fMRI (rsfMRI) were combined and a semi-supervised support vector machine (SVM) which separated stable participants from those who decline at follow-up with maximal margin. Cross-validated model performance metrics and MRI feature weights were calculated to include the strength of each brain voxel in its ability to distinguish the two groups. Results: Total model accuracy for predicting diagnostic change at follow-up was 92.7% using baseline T1 imaging alone, 83.5% using rsfMRI alone, and 94.5% when combining T1 and rsfMRI modalities. Feature weights that survived the p < 0.01 threshold for separation of the two groups revealed the strongest margin in the combined structural and functional regions underlying the medial temporal lobes in the limbic system. Discussion: An MRI-driven SVM model demonstrates accurate and precise prediction of later dementia conversion in aMCI patients. The multi-modal regions driving this prediction were the strongest in the medial temporal regions of the limbic system, consistent with literature on the progression of Alzheimer's disease.

Mild behavioral impairment as a predictor of cognitive functioning in older adults

OBJECTIVE

To assess the influence of mild behavioral impairment (MBI) on the cognitive performance of older adults who are cognitively healthy or have mild cognitive impairment (MCI).

METHODS

Secondary data analysis of a sample (n = 497) of older adults from the Florida Alzheimer's Disease Research Center who were either cognitively healthy (n = 285) or diagnosed with MCI (n = 212). Over half of the sample (n = 255) met the operationalized diagnostic criteria for MBI. Cognitive domains of executive function, attention, short-term memory, and episodic memory were assessed using a battery of neuropsychological tests.

RESULTS

Older adults with MBI performed worse on tasks of executive function, attention, and episodic memory compared to those without MBI. A significant interaction revealed that persons with MBI and MCI performed worse on tasks of episodic memory compared to individuals with only MCI, but no significant differences were found in performance in cognitively healthy older adults with or without MBI on this cognitive domain. As expected, cognitively healthy older adults performed better than individuals with MCI on every domain of cognition.

CONCLUSIONS

The present study found evidence that independent of cognitive status, individuals with MBI performed worse on tests of executive function, attention, and episodic memory than individuals without MBI. Additionally, those with MCI and MBI perform significantly worse on episodic memory tasks than individuals with only MCI. These results provide support for a unique cognitive phenotype associated with MBI and highlight the necessity for assessing both cognitive and behavioral symptoms.

Implications of the Use of Algorithmic Diagnoses or Medicare Claims to Ascertain Dementia

INTRODUCTION

Formal dementia ascertainment with research criteria is resource-intensive, prompting the growing use of alternative approaches. Our objective was to illustrate the potential bias and implications for study conclusions introduced through the use of alternate dementia ascertainment approaches.

METHODS

We compared dementia prevalence and risk factor associations obtained using criterion-standard dementia diagnoses to those obtained using algorithmic or Medicare-based dementia ascertainment in participants of the baseline visit of the Aging, Demographics, and Memory Study (ADAMS), a Health and Retirement Study (HRS) sub-study.

RESULTS

Estimates of dementia prevalence derived using algorithmic or Medicare-based ascertainment differ substantially from those obtained using criterion-standard ascertainment. Use of algorithmic or Medicare-based dementia ascertainment can, but does not always, lead to risk factor associations that substantially differ from those obtained using criterion-standard ascertainment.

DISCUSSION/CONCLUSIONS

Absolute estimates of dementia prevalence should rely on samples with formal dementia ascertainment. The use of multiple algorithms is recommended for risk factor studies when formal dementia ascertainment is not available.

Neuropsychiatric symptoms as a distinguishing factor between memory diagnoses

OBJECTIVES

To determine whether neuropsychiatric symptoms (NPS) are able to differentiate those with mild cognitive impairment (MCI) and dementia from persons who are cognitively healthy.

METHODS

Multinomial and binary logistic regressions were used to assess secondary data of a sample (n = 613) of older adults with NPS. Analyses evaluated the ability to differentiate between diagnoses, as well as the influence of these symptoms for individuals with amnestic MCI (MCI-A), non-amnestic MCI (MCI-NA), and dementia compared with those who are cognitively healthy.

RESULTS

Persons with MCI were more likely to have anxiety, apathy, and appetite changes compared with cognitively healthy individuals. Persons with dementia were more likely to have aberrant motor behaviors, anxiety, apathy, appetite changes, and delusions compared with those who were cognitively healthy. Individuals with any type of cognitive impairment were more likely to have anxiety, apathy, appetite changes, and delusions. Specifically, anxiety, apathy, appetite changes, and disinhibition were predictors of MCI-A; agitation and apathy were predictors of MCI-NA; and aberrant motor behaviors, anxiety, apathy, appetite changes, and delusions were predictors of dementia. Finally, nighttime behavior disorders were less likely in individuals with dementia.

CONCLUSIONS

The present study's results demonstrate that specific NPS are differentially represented among types of cognitive impairment and establish the predictive value for one of these cognitive impairment diagnoses.

A comparison of resting state EEG and structural MRI for classifying Alzheimer's disease and mild cognitive impairment

Alzheimer's disease (AD) is the leading cause of dementia, accounting for 70% of cases worldwide. By 2050, dementia prevalence will have tripled, with most new cases occurring in low- and middle-income countries. Mild cognitive impairment (MCI) is a stage between healthy aging and dementia, marked by cognitive deficits that do not impair daily living. People with MCI are at increased risk of dementia, with an average progression rate of 39% within 5 years. There is urgent need for low-cost, accessible and objective methods to facilitate early dementia detection. Electroencephalography (EEG) has potential to address this need due to its low cost and portability. Here, we collected resting state EEG, structural MRI (sMRI) and rich neuropsychological data from older adults (55+ years) with AD, amnestic MCI (aMCI) and healthy controls (~60 per group). We evaluated a range of candidate EEG markers (i.e., frequency band power and functional connectivity) for AD and aMCI classification and compared their performance with sMRI. We also tested a combined EEG and cognitive classification model (using Mini-Mental State Examination; MMSE). sMRI outperformed resting state EEG at classifying AD (AUCs ​= ​1.00 vs 0.76, respectively). However, both EEG and sMRI were only moderately good at distinguishing aMCI from healthy aging (AUCs ​= ​0.67-0.73), and neither method achieved sensitivity above 70%. The addition of EEG to MMSE scores had no added benefit relative to MMSE scores alone. This is the first direct comparison of EEG and sMRI for classification of AD and aMCI.

Evaluation of a research diagnostic algorithm for DSM-5 neurocognitive disorders in a population-based cohort of older adults

BACKGROUND

There is little information on the application and impact of revised criteria for diagnosing dementia and mild cognitive impairment (MCI), now termed major and mild neurocognitive disorders (NCDs) in the DSM-5. We evaluate a psychometric algorithm for diagnosing DSM-5 NCDs in a community-dwelling sample, and characterize the neuropsychological and functional profile of expert-diagnosed DSM-5 NCDs relative to DSM-IV dementia and International Working Group criteria for MCI.

METHODS

A population-based sample of 1644 adults aged 72-78 years was assessed. Algorithmic diagnostic criteria used detailed neuropsychological data, medical history, longitudinal cognitive performance, and informant interview. Those meeting all criteria for at least one diagnosis had data reviewed by a neurologist (expert diagnosis) who achieved consensus with a psychiatrist for complex cases.

RESULTS

The algorithm accurately classified DSM-5 major NCD (area under the curve (AUC) = 0.95, 95% confidence interval (CI) 0.92-0.97), DSM-IV dementia (AUC = 0.91, 95% CI 0.85-0.97), DSM-5 mild NCD (AUC = 0.75, 95% CI 0.70-0.80), and MCI (AUC = 0.76, 95% CI 0.72-0.81) when compared to expert diagnosis. Expert diagnosis of dementia using DSM-5 criteria overlapped with 90% of DSM-IV dementia cases, but resulted in a 127% increase in diagnosis relative to DSM-IV. Additional cases had less severe memory, language impairment, and instrumental activities of daily living (IADL) impairments compared to cases meeting DSM-IV criteria for dementia. DSM-5 mild NCD overlapped with 83% of MCI cases and resulted in a 19% increase in diagnosis. These additional cases had a subtly different neurocognitive profile to MCI cases, including poorer social cognition.

CONCLUSION

DSM-5 NCD criteria can be operationalized in a psychometric algorithm in a population setting. Expert diagnosis using DSM-5 NCD criteria captured most cases with DSM-IV dementia and MCI in our sample, but included many additional cases suggesting that DSM-5 criteria are broader in their categorization.

Prognostic Significance of Mild Cognitive Impairment Subtypes for Dementia and Mortality: Data from the NEDICES Cohort

BACKGROUND

The predictive value of diverse subtypes of mild cognitive impairment (MCI) for dementia and death is highly variable.

OBJECTIVE

To compare the predictive value of several MCI subtypes in progression to dementia and/or mortality in the NEDICES (Neurological Disorders in Central Spain) elderly cohort.

METHODS

Retrospect algorithmic MCI subgroups were established in a non-dementia baseline NEDICES cohort using Spanish adaptations of the original Mini-Mental State Examination (MMSE-37) and Pfeffer's Functional Activities Questionnaire (Pfeffer-11). The presence of MCI was defined according two cognitive criteria: using two cut-offs points on the total MMSE-37 score. Five cognitive domains were used to establish the MCI subtypes. Functional capacity (Pfeffer-11) was preserved or minimally impaired in all MCI participants. The incident dementia diagnoses were established by specialists and the mortality data obtained from Spanish official registries.

RESULTS

3,411 participants without dementia were assessed in 1994-5. The baseline prevalence of MCI varied according to the MCI definition (4.3%-31.8%). The follow-up was a mean of 3.2 years (1997-8). The dementia incidence varied between 14.9 and 71.8 per 1,000/person-years. The dementia conversion rate was increased in almost all MCI subgroups (p >  0.01), and mortality rate was raised only in four MCI subtypes. The amnestic-multi-domain MCI (aMd-MCI) had the best dementia predictive accuracy (highest positive likelihood ratio and highest clinical utility when negative).

CONCLUSIONS

Those with aMd-MCI were at greatest risk of progression to dementia, as in other surveys and might be explored with increased attention in MCI research and in dementia preventive trials.

Identifying biomarkers of dementia prevalent among amnestic mild cognitively impaired ethnic female patients

Background

There is a need to investigate biomarkers that are indicative of the progression of dementia in ethnic patient populations. The disparity of information in these populations has been the focus of many clinical and academic centers, including ours, to contribute to a higher success rate in clinical trials. In this study, we have investigated plasma biomarkers in amnestic mild cognitively impaired (aMCI) female patient cohorts in the context of ethnicity and cognitive status.

Method

A panel of 12 biomarkers involved in the progression of brain pathology, inflammation, and cardiovascular disorders were investigated in female cohorts of African American, Hispanic, and White aMCI patients. Both biochemical and algorithmic analyses were applied to correlate biomarker levels measured during the early stages of the disease for each ethnicity.

Results

We report elevated plasma Aβ₄₀, Aβ₄₂, YKL-40, and cystatin C levels in the Hispanic cohort at early aMCI status. In addition, elevated plasma Aβ₄₀ levels were associated with the aMCI status in both White and African American patient cohorts by the decision tree algorithm. Eotaxin-1 levels, as determined by the decision tree algorithm and biochemically measured total tau levels, were associated with the aMCI status in the African American cohort.

Conclusions

Overall, our data displayed novel differences in the plasma biomarkers of the aMCI female cohorts where the plasma levels of several biomarkers distinguished between each ethnicity at an early aMCI stage. Identification of these plasma biomarkers encourages new areas of investigation among aMCI ethnic populations, including larger patient cohorts and longitudinal study designs.

Electronic supplementary material

The online version of this article (doi:10.1186/s13195-016-0211-0) contains supplementary material, which is available to authorized users.

Estimating Intracranial Volume in Brain Research: An Evaluation of Methods

Intracranial volume (ICV) is a standard measure often used in morphometric analyses to correct for head size in brain studies. Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation across different subject groups in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and type of software most suitable for use in estimating the ICV measure. Four groups of 53 subjects are considered, including adult controls (AC, adults with Alzheimer's disease (AD), pediatric controls (PC) and group of pediatric epilepsy subjects (PE). Reference measurements were calculated for each subject by manually tracing intracranial cavity without sub-sampling. The reliability of reference measurements were assured through intra- and inter- variation analyses. Three publicly well-known software packages (FreeSurfer Ver. 5.3.0, FSL Ver. 5.0, SPM8 and SPM12) were examined in their ability to automatically estimate ICV across the groups. Results on sub-sampling studies with a 95 % confidence showed that in order to keep the accuracy of the inter-leaved slice sampling protocol above 99 %, sampling period cannot exceed 20 mm for AC, 25 mm for PC, 15 mm for AD and 17 mm for the PE groups. The study assumes a priori knowledge about the population under study into the automated ICV estimation. Tuning of the parameters in FSL and the use of proper atlas in SPM showed significant reduction in the systematic bias and the error in ICV estimation via these automated tools. SPM12 with the use of pediatric template is found to be a more suitable candidate for PE group. SPM12 and FSL subjected to tuning are the more appropriate tools for the PC group. The random error is minimized for FS in AD group and SPM8 showed less systematic bias. Across the AC group, both SPM12 and FS performed well but SPM12 reported lesser amount of systematic bias.

Latent Classes of Neuropsychiatric Symptoms in NACC Controls and Conversion to Mild Cognitive Impairment or Dementia

BACKGROUND

A number of studies have linked neuropsychiatric symptoms to increase risk of dementia.

OBJECTIVE

To determine if risk of conversion to mild cognitive impairment or dementia among healthy controls varied as a function of their pattern of neuropsychiatric symptoms.

METHOD

We studied individuals in the National Alzheimer Coordinating Center dataset collected from 34 Alzheimer Disease Centers between 2005 and 2013. The analysis included 4,517 volunteers who were ≥60 years old, cognitively normal, and had complete Neuropsychiatric Inventory data at their baseline visit, and had at least one follow-up. We used latent class analysis to identify four classes based on patterns of NPI symptoms. We used a Cox proportional hazards model to determine if time to MCI or dementia varied by baseline latent class membership.

RESULTS

We identified four latent classes of neuropsychiatric symptoms: irritable, depressed, complex (depression, apathy, irritability, and nighttime behaviors) and asymptomatic. 873 participants converted to MCI or dementia. Hazard ratios for conversion by class were 1.76 (95% CI: 1.34, 2.33) for the irritable class, 3.20 (95% CI: 2.24, 4.58) for the complex class, and 1.90 (95% CI: 1.49, 2.43) for the depressed class, with the asymptomatic class as the reference.

CONCLUSIONS

Membership in all three symptomatic classes was associated with greater risk of conversion to MCI or dementia; the complex class had the greatest risk. Different patterns of neuropsychiatric symptoms may represent different underlying neuropathological pathways to dementia. Further work imaging and pathology research is necessary to determine if this is the case.

The accuracy of the Edinburgh diplopia diagnostic algorithm

PurposeTo assess the diagnostic accuracy of the Edinburgh diplopia diagnostic algorithm.MethodsThis was a prospective study. Details of consecutive patients referred to ophthalmology clinics at Falkirk Community Hospital and Princess Alexandra Eye Pavilion, Edinburgh, with double vision were collected by the clinician first seeing the patient and passed to the investigators. The investigators then assessed the patient using the algorithm. An assessment of the degree of concordance between the 'algorithm assisted' diagnosis and the 'gold standard' diagnosis, made by a consultant ophthalmologist was then carried out. The accuracy of the pre-algorithm diagnosis made by the referrer was also noted.ResultsAll patients referred with diplopia were eligible for inclusion. Fifty-one patients were assessed; six were excluded. The pre-algorithm accuracy of referrers was 24% (10/41). The algorithm assisted diagnosis was correct 82% (37/45) of the time. It correctly diagnosed: cranial nerve (CN) III palsy in 6/6, CN IV palsy in 7/8, CN VI palsy in 12/12, internuclear ophthalmoplegia in 4/4, restrictive myopathy in 4/4, media opacity in 1/1, and blurred vision in 3/3. The algorithm assisted diagnosis was wrong in 18% (8/45) of the patients.ConclusionsThe baseline diagnostic accuracy of non-ophthalmologists rose from 24 to 82% when patients were assessed using the algorithm. The improvement in the diagnostic accuracy resulting from the use of the algorithm would, hopefully, result in more accurate triage of patients with diplopia that are referred to the hospital eye service. We hope we have demonstrated its potential as a learning tool for inexperienced clinicians.

MRI visual rating scales in the diagnosis of dementia: evaluation in 184 post-mortem confirmed cases

Accurately distinguishing between different degenerative dementias during life is challenging but increasingly important with the prospect of disease-modifying therapies. Molecular biomarkers of dementia pathology are becoming available, but are not widely used in clinical practice. Conversely, structural neuroimaging is recommended in the evaluation of cognitive impairment. Visual assessment remains the primary method of scan interpretation, but in the absence of a structured approach, diagnostically relevant information may be under-utilized. This definitive, multi-centre study uses post-mortem confirmed cases as the gold standard to: (i) assess the reliability of six visual rating scales; (ii) determine their associated pattern of atrophy; (iii) compare their diagnostic value with expert scan assessment; and (iv) assess the accuracy of a machine learning approach based on multiple rating scales to predict underlying pathology. The study includes T ₁ -weighted images acquired in three European centres from 184 individuals with histopathologically confirmed dementia (101 patients with Alzheimer’s disease, 28 patients with dementia with Lewy bodies, 55 patients with frontotemporal lobar degeneration), and scans from 73 healthy controls. Six visual rating scales (medial temporal, posterior, anterior temporal, orbito-frontal, anterior cingulate and fronto-insula) were applied to 257 scans (two raters), and to a subset of 80 scans (three raters). Six experts also provided a diagnosis based on unstructured assessment of the 80-scan subset. The reliability and time taken to apply each scale was evaluated. Voxel-based morphometry was used to explore the relationship between each rating scale and the pattern of grey matter volume loss. Additionally, the performance of each scale to predict dementia pathology both individually and in combination was evaluated using a support vector classifier, which was compared with expert scan assessment to estimate clinical value. Reliability of scan assessment was generally good (intraclass correlation coefficient > 0.7), and average time to apply all six scales was <3 min. There was a very close association between the pattern of grey matter loss and the regions of interest each scale was designed to assess. Using automated classification based on all six rating scales, the accuracy (estimated using the area under the receiver-operator curves) for distinguishing each pathological group from controls ranged from 0.86–0.97; and from one another, 0.75–0.92. These results were substantially better than the accuracy of any single scale, at least as good as expert reads, and comparable to previous studies using molecular biomarkers. Visual rating scores from magnetic resonance images routinely acquired as part of the investigation of dementias, offer a practical, inexpensive means of improving diagnostic accuracy.

Patterns of Neuropsychiatric Symptoms in Mild Cognitive Impairment and Risk of Dementia

OBJECTIVE

To identify clusters of patients with incident mild cognitive impairment (MCI) based on their neuropsychiatric symptoms (NPS) and to examine the risk of progression to dementia based on these clusters.

METHODS

In this cohort study with a median of 2 years of follow-up from the National Alzheimer's Coordinating Center, 540 patients with MCI at least 60 years old with complete data and follow-up were studied. Latent class analysis was used to identify clusters of patients based on their NPS, and Cox proportional hazards models were used to examine risk of progression to dementia based on clusters. Incident MCI was defined as a participant having MCI at a current visit but having been cognitively normal at his or her previous (yearly) visit. The Neuropsychiatric Inventory Questionnaire assessed the presence of 12 neuropsychiatric behavioral domains.

RESULTS

Three clusters were identified: a severe cluster (agitation, anxiety, apathy, nighttime behaviors, inhibition), an affective cluster (depression, anxiety, irritability, nighttime behaviors), and an asymptomatic cluster. The prevalence of each class was 56% for the asymptomatic class followed by the affective class (37%) and finally the severe class (7%). Compared with the asymptomatic class, the severe class had more than twice the hazard of progression to dementia (2.69; 95% CI: 1.12-2.70) and the affective class had over 1.5 times the hazard of progression to dementia (1.79; 95% CI: 1.12-2.70).

CONCLUSION

Among persons with incident MCI, patterns of NPS may increase the likelihood of progression to dementia. Implications for early detection and treatment are discussed.

Using visual rating to diagnose dementia: a critical evaluation of MRI atrophy scales

Visual rating scales, developed to assess atrophy in patients with cognitive impairment, offer a cost-effective diagnostic tool that is ideally suited for implementation in clinical practice. By focusing attention on brain regions susceptible to change in dementia and enforcing structured reporting of these findings, visual rating can improve the sensitivity, reliability and diagnostic value of radiological image interpretation. Brain imaging is recommended in all current diagnostic guidelines relating to dementia, and recent guidelines have also recommended the application of medial temporal lobe atrophy rating. Despite these recommendations, and the ease with which rating scales can be applied, there is still relatively low uptake in routine clinical assessments. Careful consideration of atrophy rating scales is needed to verify their diagnostic potential and encourage uptake among clinicians. Determining the added value of combining scores from visual rating in different brain regions may also increase the diagnostic value of these tools.

Dynamic Factor Analysis for Multivariate Time Series: An Application to Cognitive Trajectories

We propose a dynamic factor model appropriate for large epidemiological studies and develop an estimation algorithm which can handle datasets with large number of subjects and short temporal information. The algorithm uses a two cycle iterative approach for parameter estimation in such a large dataset. Each iteration consists of two distinct cycles, both following an EM algorithm approach. This iterative process will continue until convergence is achieved. We utilized a dataset from the National Alzheimer Coordinating Center (NACC) to estimate underlying measures of cognition based on a battery of observed neuropsychological tests. We assess the goodness of fit and the precision of the dynamic factor model estimators and compare it with a non-dynamic version in which temporal information is not used. The dynamic factor model is superior to a non-dynamic version with respect to fit statistics shown in simulation experiments. Moreover, it has increased power to detect differences in the rate of decline for a given sample size.

The accuracy of the Edinburgh visual loss diagnostic algorithm

PURPOSE

To assess the diagnostic accuracy of the Edinburgh visual loss algorithm.

METHODS

This was a prospective study. Patients referred to the Edinburgh Eye Pavilion with visual loss were assessed using the Edinburgh Visual Loss Algorithm by either a medical student, an inexperienced ophthalmology trainee or an optometrist in the Lothian Optometry Treat and Teach clinic. Accuracy of this 'algorithm-assisted' diagnosis was then compared with the 'gold-standard' diagnosis, made by an experienced ophthalmologist. Accuracy of the pre-algorithm diagnosis, made by the referrer, was also compared with the algorithm-assisted diagnosis.

RESULTS

All patients referred with visual loss were eligible for inclusion. Seventy patients were assessed; two were excluded. Pre-algorithm accuracy of referral of patients with visual loss was 51% (30/59). Overall, the algorithm-assisted diagnosis was correct 84% (57/68) of the time. The algorithm correctly diagnosed: retina in 71% of cases (5/7), macula in 86% (25/29), peripheral retina in 100% (2/2), optic nerve in 71% (5/7), media opacity in 89% (16/18), post chiasmal in 100% (4/4), and refractive error in 0% (0/1). Accuracy of diagnosis was similar for each algorithm user; medical student 81%, inexperienced ophthalmology trainee 84% and optometrist 92%.

DISCUSSION

The baseline diagnostic accuracy of clinicians who are inexperienced in ophthalmology rose from 51 to 84% when patients were assessed using the algorithm. This algorithm significantly improves the diagnostic accuracy of referrals to the hospital eye service, regardless of the user's previous ophthalmic experience. We hope we have demonstrated its potential as a learning tool for inexperienced clinicians.

Inclusion of Neuropsychological Scores in Atrophy Models Improves Diagnostic Classification of Alzheimer's Disease and Mild Cognitive Impairment

Brain atrophy in mild cognitive impairment (MCI) and Alzheimer's disease (AD) are difficult to demarcate to assess the progression of AD. This study presents a statistical framework on the basis of MRI volumes and neuropsychological scores. A feature selection technique using backward stepwise linear regression together with linear discriminant analysis is designed to classify cognitive normal (CN) subjects, early MCI (EMCI), late MCI (LMCI), and AD subjects in an exhaustive two-group classification process. Results show a dominance of the neuropsychological parameters like MMSE and RAVLT. Cortical volumetric measures of the temporal, parietal, and cingulate regions are found to be significant classification factors. Moreover, an asymmetrical distribution of the volumetric measures across hemispheres is seen for CN versus EMCI and EMCI versus AD, showing dominance of the right hemisphere; whereas CN versus LMCI and EMCI versus LMCI show dominance of the left hemisphere. A 2-fold cross-validation showed an average accuracy of 93.9%, 90.8%, and 94.5%, for the CN versus AD, CN versus LMCI, and EMCI versus AD, respectively. The accuracy for groups that are difficult to differentiate like EMCI versus LMCI was 73.6%. With the inclusion of the neuropsychological scores, a significant improvement (24.59%) was obtained over using MRI measures alone.

A practical guideline for intracranial volume estimation in patients with Alzheimer's disease

Background

Intracranial volume (ICV) is an important normalization measure used in morphometric analyses to correct for head size in studies of Alzheimer Disease (AD). Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation in patients with Alzheimer disease in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and the type of software most suitable for use in estimating the ICV measure.

Methods

Two groups of 22 subjects are considered, including adult controls (AC) and patients with Alzheimer Disease (AD). Reference measurements were calculated for each subject by manually tracing intracranial cavity by the means of visual inspection. The reliability of reference measurements were assured through intra- and inter- variation analyses. Three publicly well-known software packages (Freesurfer, FSL, and SPM) were examined in their ability to automatically estimate ICV across the groups.

Results

Analysis of the results supported the significant effect of estimation method, gender, cognitive condition of the subject and the interaction among method and cognitive condition factors in the measured ICV. Results on sub-sampling studies with a 95% confidence showed that in order to keep the accuracy of the interleaved slice sampling protocol above 99%, the sampling period cannot exceed 20 millimeters for AC and 15 millimeters for AD. Freesurfer showed promising estimates for both adult groups. However SPM showed more consistency in its ICV estimation over the different phases of the study.

Conclusions

This study emphasized the importance in selecting the appropriate protocol, the choice of the sampling period in the manual estimation of ICV and selection of suitable software for the automated estimation of ICV. The current study serves as an initial framework for establishing an appropriate protocol in both manual and automatic ICV estimations with different subject populations.

The accuracy of the Edinburgh Red Eye Diagnostic Algorithm

PURPOSE

To assess the diagnostic accuracy of the Edinburgh Red Eye Algorithm.

METHODS

This was a prospective study. A questionnaire was designed and made available to clinicians referring patients to the acute ophthalmology service within Edinburgh. The questionnaire involved them using the algorithm to reach a diagnosis in patients presenting with red eye(s). Patients were then referred to the emergency eye clinic and the questionnaire faxed to the clinic or sent with the patients. Patients were then examined by an experienced ophthalmologist (not blinded) to reach a 'gold standard' diagnosis. The concordance between the 'algorithm assisted' diagnosis and the 'gold standard' was then assessed.

RESULTS

All patients presenting with red eye(s) were eligible for inclusion. Forty-one questionnaires were completed, two were excluded. The algorithm assisted diagnosis was correct 72% (28/39) of the time. It correctly diagnosed: acute angle closure glaucoma in 100% of cases (4/4); iritis in 82% (9/11); stromal keratitis in 63% (5/8); epithelial keratitis in 70% (7/10); and infective conjunctivitis in 50% (3/6).

DISCUSSION

The diagnostic accuracy of The Edinburgh Red Eye Diagnostic Algorithm is 72, rising to 76% when only the most serious red eye(s) causes are included. The diagnostic accuracy of non-ophthalmologists when assessing patients presenting with red eye(s) is greater when the algorithm is used. We hope that the use of this algorithm will prevent delayed presentations of certain serious eye conditions and reduce the morbidity from delayed treatment.

Significance of normalization on anatomical MRI measures in predicting Alzheimer's disease

This study establishes a new approach for combining neuroimaging and neuropsychological measures for an optimal decisional space to classify subjects with Alzheimer's disease (AD). This approach relies on a multivariate feature selection method with different MRI normalization techniques. Subcortical volume, cortical thickness, and surface area measures are obtained using MRIs from 189 participants (129 normal controls and 60 AD patients). Statistically significant variables were selected for each combination model to construct a multidimensional space for classification. Different normalization approaches were explored to gauge the effect on classification performance using a support vector machine classifier. Results indicate that the Mini-mental state examination (MMSE) measure is most discriminative among single-measure models, while subcortical volume combined with MMSE is the most effective multivariate model for AD classification. The study demonstrates that subcortical volumes need not be normalized, whereas cortical thickness should be normalized either by intracranial volume or mean thickness, and surface area is a weak indicator of AD with and without normalization. On the significant brain regions, a nearly perfect symmetry is observed for subcortical volumes and cortical thickness, and a significant reduction in thickness is particularly seen in the temporal lobe, which is associated with brain deficits characterizing AD.

The utility of age-specific cut-offs for visual rating of medial temporal atrophy in classifying Alzheimer's disease, MCI and cognitively normal elderly subjects

Background: New research criteria for diagnosing Alzheimer's disease (AD) in the mild cognitive impairment stage (MCI-AD) incorporate biomarkers to assign a level of certainty to the diagnosis. Structural MRI is widely available but greatly under-utilized for assessing atrophy of structures affected in early AD, such as the hippocampus (HP), because the quantification of HP volumes (HP-v) requires special expertise, and normative values have not been established.

Methods: Elderly subjects (n =273) from the Florida ADRC were classified as having no cognitive impairment (cognitively normal, CN), amnestic mild cognitive impairment (aMCI) or AD. Volumes for the hippocampus (HP-v) were measured on structural MRI scans. A validated visual rating system for measuring medial temporal atrophy (VRS-MTA), including hippocampal, entorhinal cortex and perirhinal cortex atrophy was employed. The participants were subdivided into younger (less than or equal to 75 years of age) and older (greater than 75 years of age) subgroups.

Results: Volumetric and VRS-MTA measures were equivalent in predicting classification of CN vs. aMCI for older (area under the receiver operator curves [aROC]: 0.652 vs. 0.723) and younger subjects (aROC: 0.764 vs. 0.736). However, for younger AD subjects, aROC values were significantly higher for VRS-MTA measures (0.920) than for volumetric measures (0.847). Relative to HP-v, VRS-MTA score was significantly more correlated to impairment on a range of memory tests and was more associated with progression of aMCI to AD than HP-v.

Conclusion: Structural MRI with VRS-MTA assessment can serve as a biomarker for supporting the diagnosis of MCI-AD. Age-adjusted VRS-MTA scores are at least as effective as HP-v for distinguishing aMCI and AD from CN and for predicting progression from aMCI to AD. VRS-MTA is convenient for use in the clinic as well as for clinical trials and can readily be incorporated into a standardized radiological report.

Comparing new templates and atlas-based segmentations in the volumetric analysis of brain magnetic resonance images for diagnosing Alzheimer's disease

BACKGROUND

The segmentation of brain structures on magnetic resonance imaging scans for calculating regional brain volumes, using automated anatomic labeling, requires the use of both brain atlases and templates (template sets). This study aims to improve the accuracy of volumetric analysis of hippocampus (HP) and amygdala (AMG) in the assessment of early Alzheimer's disease (AD) by developing template sets that correspond more closely to the brains of elderly individuals.

METHODS

Total intracranial volume and HP and AMG volumes were calculated for elderly subjects with no cognitive impairment (n = 103), with amnestic mild cognitive impairment (n = 68), or with probable AD (n = 46) using the following: (1) a template set consisting of a standard atlas (atlas S), drawn on a young adult male brain, and the widely used Montreal Neurological Institute template (MNI template set); (2) a template set (template S set) in which the template is based on smoothing the image from which atlas S is derived; and (3) a new template set (template E set) in which the template is based on an atlas (atlas E) created from the brain of an elderly individual.

RESULTS

Correspondence to HP and AMG volumes derived from manual segmentation was highest with automated segmentation by template E set, intermediate with template S set, and lowest with the MNI template set. The areas under the receiver operating curve for distinguishing elderly subjects with no cognitive impairment from elderly subjects with amnestic mild cognitive impairment or probable AD and the correlations between HP and AMG volumes and cognitive and functional scores were highest for template E set, intermediate for template S set, and lowest for the MNI template set.

CONCLUSIONS

The accuracy of automated anatomic labeling and the diagnostic value of the derived volumes are improved with template sets based on brain atlases closely resembling the anatomy of the to-be-segmented brain magnetic resonance imaging scans.

Amyloid positron emission tomography with (18)F-flutemetamol and structural magnetic resonance imaging in the classification of mild cognitive impairment and Alzheimer's disease

OBJECTIVE

To evaluate the contributions of amyloid-positive (Am+) and medial temporal atrophy-positive (MTA+) scans to the diagnostic classification of prodromal and probable Alzheimer's disease (AD).

METHODS

(18)F-flutemetamol-labeled amyloid positron emission tomography (PET) and magnetic resonance imaging (MRI) were used to classify 10 young normal, 15 elderly normal, 20 amnestic mild cognitive impairment (aMCI), and 27 AD subjects. MTA+ status was determined using a cut point derived from a previous study, and Am+ status was determined using a conservative and liberal cut point.

RESULTS

The rates of MRI scans with positive results among young normal, elderly normal, aMCI, and AD subjects were 0%, 20%, 75%, and 82%, respectively. Using conservative cut points, the rates of Am+ scans for these same groups of subjects were 0%, 7%, 50%, and 93%, respectively, with the aMCI group showing the largest discrepancy between Am+ and MTA+ scans. Among aMCI cases, 80% of Am+ subjects were also MTA+, and 70% of amyloid-negative (Am-) subjects were MTA+. The combination of amyloid PET and MTA data was additive, with an overall correct classification rate for aMCI of 86%, when a liberal cut point (standard uptake value ratio = 1.4) was used for amyloid positivity.

INTERPRETATION

(18)F-flutemetamol PET and structural MRI provided additive information in the diagnostic classification of aMCI subjects, suggesting an amyloid-independent neurodegenerative component among aMCI subjects in this sample.

Volumetric and visual rating of magnetic resonance imaging scans in the diagnosis of amnestic mild cognitive impairment and Alzheimer's disease

BACKGROUND

In the diagnosis of Alzheimer's disease (AD), structural magnetic resonance imaging (MRI) scans have been used primarily to exclude non-Alzheimer's causes of dementia. However, the pattern and the extent of medial temporal atrophy on structural MRI scans, which correlate strongly with the pathological severity of AD, can be used to support the diagnosis of a degenerative dementia, especially AD, even in its early predementia stage.

METHODS

Elderly subjects (n = 224) were diagnosed with either no cognitive impairment (NCI), amnestic mild cognitive impairment (aMCI), or AD. Hippocampal and hemispheric gray matter volumes were measured on structural MRI scans, and a new visual rating system was used to score the severity of medial temporal atrophy (VRS-MTA) of the hippocampus (HPC), entorhinal cortex, and perirhinal cortex on a coronal image intersecting the mammillary bodies.

RESULTS

Although both VRS-MTA scores and HPC volumes distinguished between subjects with NCI, aMCI, and AD, subjects with aMCI and NCI could be better distinguished using right VRS-MTA scores, in comparison with right HPC volumes. VRS-MTA scores were more highly correlated with episodic memory and Clinical Dementia Rating scores. A combination of left sided VRS-MTA scores and left sided hippocampal volume was the most predictive measure of diagnostic classification.

CONCLUSION

VRS-MTA is a clinically convenient method or distinguishing aMCI or AD from NCI. As compared with volumetric measures, it provides better discriminatory power and correlates more strongly with memory and functional scores.

Pre-MCI and MCI: neuropsychological, clinical, and imaging features and progression rates

OBJECTIVE

To compare clinical, imaging, and neuropsychological characteristics and longitudinal course of subjects with pre-mild cognitive impairment (pre-MCI), who exhibit features of MCI on clinical examination but lack impairment on neuropsychological examination, to subjects with no cognitive impairment (NCI), nonamnestic MCI (naMCI), amnestic MCI (aMCI), and mild dementia.

METHODS

For 369 subjects, clinical dementia rating sum of boxes (CDR-SB), ApoE genotyping, cardiovascular risk factors, parkinsonism (UPDRS) scores, structural brain MRIs, and neuropsychological testing were obtained at baseline, whereas 275 of these subjects received an annual follow-up for 2-3 years.

RESULTS

At baseline, pre-MCI subjects showed impairment on tests of executive function and language, higher apathy scores, and lower left hippocampal volumes (HPCV) in comparison to NCI subjects. Pre-MCI subjects showed less impairment on at least one memory measure, CDR-SB and UPDRS scores, in comparison to naMCI, aMCI and mild dementia subjects. Follow-up over 2-3 years showed 28.6% of pre-MCI subjects, but less than 5% of NCI subjects progressed to MCI or dementia. Progression rates to dementia were equivalent between naMCI (22.2%) and aMCI (34.5%) groups, but greater than for the pre-MCI group (2.4%). Progression to dementia was best predicted by the CDR-SB, a list learning and executive function test.

CONCLUSION

This study demonstrates that clinically defined pre-MCI has cognitive, functional, motor, behavioral and imaging features that are intermediate between NCI and MCI states at baseline. Pre-MCI subjects showed accelerated rates of progression to MCI as compared to NCI subjects, but slower rates of progression to dementia than MCI subjects.

FDG- and amyloid-PET in Alzheimer's disease: is the whole greater than the sum of the parts?

The development of prevention therapies for Alzheimer's disease (AD) would greatly benefit from biomarkers that are sensitive to subtle brain changes occurring prior to the onset of clinical symptoms, when the potential for preservation of function is at the greatest. In vivo brain imaging is a promising tool for the early detection of AD through visualization of abnormalities in brain structure, function and histopathology. Currently, positron emission tomography (PET) imaging with amyloid-beta (Aβ) tracers and 2-[(18)F]fluoro-2-Deoxy-D-glucose (FDG) is largely utilized in the diagnosis of AD. This paper reviews brain Aβ- and FDG-PET studies in AD patients as well as in non-demented individuals at risk for AD. We then discuss the potential of combining symptoms-sensitive FDG-PET measures with pathology-specific Aβ-PET to improve the early detection of AD.

Minimal atrophy of the entorhinal cortex and hippocampus: progression of cognitive impairment

BACKGROUND

In Alzheimer's disease, neurodegenerative atrophy progresses from the entorhinal cortex (ERC) to the hippocampus (HP), limbic system and neocortex. The significance of very mild atrophy of the ERC and HP on MRI scans among elderly subjects is unknown.

METHODS

A validated visual rating system on coronal MRI scans was used to identify no atrophy of the HP or ERC (HP(0); ERC(0)), or minimal atrophy of the HP or ERC (HP(ma); ERC(ma)), among 414 participants. Subjects fell into the following groups: (1) ERC(0)/HP(0), (2) ERC(ma)/HP(0), (3) ERC(0)/HP(ma), and (4) ERC(ma)/HP(ma). HP volume was independently measured using volumetric methods.

RESULTS

In comparison to ERC(0)/HP(0) subjects, those with ERC(0)/HP(ma) had impairment on 1 memory test, ERC(ma)/HP(0) subjects had impairment on 2 memory tests and the Mini Mental State Examination (MMSE), while ERC(ma)/HP(ma) subjects had impairment on 3 memory tests, the MMSE and Clinical Dementia Rating. Progression rates of cognitive and functional impairment were significantly greater among subjects with ERC(ma).

CONCLUSION

Minimal atrophy of the ERC results in greater impairment than minimal atrophy of the HP, and the combination is additive when measured by cognitive and functional tests. Rates of progression to greater impairment were higher among ERC(ma) subjects.

Reducing case ascertainment costs in U.S. population studies of Alzheimer's disease, dementia, and cognitive impairment-Part 1

Establishing methods for ascertainment of dementia and cognitive impairment that are accurate and also cost-effective is a challenging enterprise. Large population-based studies often using administrative data sets offer relatively inexpensive and reliable estimates of severe conditions including moderate to advanced dementia that are useful for public health planning, but they can miss less severe cognitive impairment which may be the most effective point for intervention. Clinical and epidemiological cohorts, intensively assessed, provide more sensitive detection of less severe cognitive impairment but are often costly. In this article, several approaches to ascertainment are evaluated for validity, reliability, and cost. In particular, the methods of ascertainment from the Health and Retirement Study are described briefly, along with those of the Aging, Demographics, and Memory Study (ADAMS). ADAMS, a resource-intense sub-study of the Health and Retirement Study, was designed to provide diagnostic accuracy among persons with more advanced dementia. A proposal to streamline future ADAMS assessments is offered. Also considered are algorithmic and Web-based approaches to diagnosis that can reduce the expense of clinical expertise and, in some contexts, can reduce the extent of data collection. These approaches are intended for intensively assessed epidemiological cohorts where goal is valid and reliable case detection with efficient and cost-effective tools.