ObjectiveCognitive practice effects (PEs) can delay detection of progression from cognitively unimpaired to mild cognitive impairment (MCI). They also reduce diagnostic accuracy as suggested by biomarker positivity data. Even among those who decline, PEs can mask steeper declines by inflating cognitive scores. Within MCI samples, PEs may increase reversion rates and thus impede detection of further impairment. Within an MCI sample at baseline, we evaluated how PEs impact prevalence, reversion rates, and dementia progression after 1 year.MethodsWe examined 329 baseline Alzheimer’s Disease Neuroimaging Initiative MCI participants (mean age = 73.1; SD = 7.4). We identified test-naïve participants who were demographically matched to returnees at their 1-year follow-up. Since the only major difference between groups was that one completed testing once and the other twice, comparison of scores in each group yielded PEs. PEs were subtracted from each test to yield PE-adjusted scores. Biomarkers included cerebrospinal fluid phosphorylated tau and amyloid beta. Cox proportional models predicted time until first dementia diagnosis using PE-unadjusted and PE-adjusted diagnoses.ResultsAccounting for PEs increased MCI prevalence at follow-up by 9.2% (272 vs. 249 MCI), and reduced reversion to normal by 28.8% (57 vs. 80 reverters). PEs also increased stability of single-domain MCI by 12.0% (164 vs. 147). Compared to PE-unadjusted diagnoses, use of PE-adjusted follow-up diagnoses led to a twofold increase in hazard ratios for incident dementia. We classified individuals as false reverters if they reverted to cognitively unimpaired status based on PE-unadjusted scores, but remained classified as MCI cases after accounting for PEs. When amyloid and tau positivity were examined together, 72.2% of these false reverters were positive for at least one biomarker.InterpretationEven when PEs are small, they can meaningfully change whether some individuals with MCI retain the diagnosis at a 1-year follow-up. Accounting for PEs resulted in increased MCI prevalence and altered stability/reversion rates. This improved diagnostic accuracy also increased the dementia-predicting ability of MCI diagnoses.

Abstract Introduction Practice effects (PEs) on cognitive tests obscure decline, thereby delaying detection of mild cognitive impairment (MCI). Importantly, PEs may be present even when there are performance declines, if scores would have been even lower without prior test exposure. We assessed how accounting for PEs using a replacement‐participants method impacts incident MCI diagnosis. Methods Of 889 baseline cognitively normal (CN) Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, 722 returned 1 year later (mean age = 74.9 ± 6.8 at baseline). The scores of test‐naïve demographically matched “replacement” participants who took tests for the first time were compared to returnee scores at follow‐up. PEs—calculated as the difference between returnee follow‐up scores and replacement participants scores—were subtracted from follow‐up scores of returnees. PE‐adjusted cognitive scores were then used to determine if individuals were below the impairment threshold for MCI. Cerebrospinal fluid amyloid beta, phosphorylated tau, and total tau were used for criterion validation. In addition, based on screening and recruitment numbers from a clinical trial of amyloid‐positive individuals, we estimated the effect of earlier detection of MCI by accounting for cognitive PEs on a hypothetical clinical trial in which the key outcome was progression to MCI. Results In the ADNI sample, PE‐adjusted scores increased MCI incidence by 19% (P < .001), increased proportion of amyloid‐positive MCI cases (+12%), and reduced proportion of amyloid‐positive CNs (–5%; P’s < .04). Additional calculations showed that the earlier detection and increased MCI incidence would also substantially reduce necessary sample size and study duration for a clinical trial of progression to MCI. Cost savings were estimated at ≈$5.41 million. Discussion Detecting MCI as early as possible is of obvious importance. Accounting for cognitive PEs with the replacement‐participants method leads to earlier detection of MCI, improved diagnostic accuracy, and can lead to multi‐million‐dollar cost reductions for clinical trials.

Operationalizations of mild cognitive impairment (MCI) are inconsistent. Actuarial criteria have been proposed to improve accuracy. We investigated dementia and Alzheimer’s disease (AD) conversion for conventional versus actuarial MCI criteria in a community-based prospective cohort study. 1413 non-demented participants were administered measures from memory, language, and attention/executive function domains. We used published norms to generate demographically-adjusted z-scores. Conventional MCI criteria were met for scores >1.5sd below the mean on any measure. Actuarial MCI criteria were met for 2 scores >1.0sd below the mean in one domain or a score >1.0sd below the mean in all three domains. Participants were followed biennially to capture dementia incidence. A consensus conference used DSM-IV dementia criteria and NINDS-ADRDA AD criteria for diagnosis. We focused on 6-year outcomes following MCI categorization. At baseline, n=875 (62%) met neither conventional nor actuarial MCI criteria, n=16 (1%) met only actuarial MCI criteria, n=383 (27%) met only conventional MCI criteria, and n=139 (10%) met both. Demographic characteristics are in Table 1. Education differed across criteria (p<0.0001); more people with lower education met only conventional MCI criteria. During follow up (7090 person-years, mean=5 years), there were 175 incident dementia cases, of whom 110 (63%) met conventional and 53 (30%) met actuarial MCI criteria at their baseline neuropsychological evaluation. In a Cox model adjusting for age, sex, education, and race/ethnicity, the conventional MCI dementia hazard ratio (HR) was 2.93 (95% CI 2.15, 4.01, p<0.001), and the actuarial MCI HR was 3.62 (95% CI 2.60, 5.03; p<0.001). There were 154 incident AD cases; 101 (66%) met conventional and 49 (32%) met actuarial criteria. The adjusted conventional MCI AD HR was 3.25 (95% CI 2.32, 4.56, p<0.001), and the actuarial MCI AD HR was 3.81 (95% CI 2.69, 5.38, p<0.001). Both conventional and actuarial MCI criteria identified individuals at higher risk of developing dementia and AD over 6 years of follow-up. Conventional criteria flag a higher proportion of those who ultimately developed dementia and AD compared to actuarial criteria. However, hazard ratios for progression to dementia and AD were slightly higher for the actuarial criteria.

Although type 2 diabetes is a well-known risk factor for Alzheimer's disease (AD), little is known about how its precursor—prediabetes—impacts neuropsychological function and brain health. Thus, we examined the relationship between prediabetes and AD-related biological and cognitive/clinical markers in a well-characterized sample drawn from the Alzheimer's Disease Neuroimaging Initiative. Additionally, because women show higher rates of AD and generally more atherogenic lipid profiles than men, particularly in the context of diabetes, we examined whether sex moderates any observed associations. The total sample of 911 nondemented and non-diabetic participants [normal control = 540; mild cognitive impairment (MCI) = 371] included 391 prediabetic (fasting blood glucose: 100–125 mg/dL) and 520 normoglycemic individuals (age range: 55–91). Linear mixed effects models, adjusted for demographics and vascular and AD risk factors, examined the independent and interactive effects of prediabetes and sex on 2–6 year trajectories of FDG-PET measured cerebral metabolic glucose rate (CMRglu), hippocampal/intracranial volume ratio (HV/IV), cerebrospinal fluid phosphorylated tau- 181 /amyloid-β 1−42 ratio (p-tau 181 /Aβ 1−42 ), cognitive function (executive function, language, and episodic memory) and the development of dementia. Analyses were repeated in the MCI subsample. In the total sample, prediabetic status had an adverse effect on CMRglu across time regardless of sex, whereas prediabetes had an adverse effect on executive function across time in women only. Within the MCI subsample, prediabetic status was associated with lower CMRglu and poorer executive function and language performance across time within women, whereas these associations were not seen within men. In the total sample and MCI subsample, prediabetes did not relate to HV/IV, p-tau 181 /Aβ 1−42 , memory function or dementia risk regardless of sex; however, among incident dementia cases, prediabetic status related to earlier age of dementia onset in women but not in men. Results suggest that prediabetes may affect cognition through altered brain metabolism, and that women may be more vulnerable to the negative effects of glucose intolerance.

AbstractBackgroundPractice effects (PEs) mask true cognitive decline. Even with declines at follow‐up, PEs can still obscure even steeper declines. Accounting for PEs means that impairment cutoffs are reached earlier. Importantly, mild cognitive impairment (MCI) would be detected earlier. If these diagnoses are valid, there should be more biomarker‐positive MCI cases and fewer biomarker‐positive cognitively normal subjects (CNs). We tested this hypothesis using a replacement‐subjects method.MethodWe examined 722 Alzheimer’s Disease Neuroimaging Initiative (ADNI) subjects who were CN at baseline (mean age=74.9±6.8). MCI at follow‐up was diagnosed by Jak‐Bondi and Petersen criteria. We identified “pseudo‐replacements” whose baseline age was matched to the age of returnees at their 1‐year follow‐up. Education and estimated premorbid IQ were also matched. Difference scores were calculated at Time 2 between returnees on their second testing and the demographically‐matched pseudo‐replacements on their first testing. Attrition effects were estimated by comparing returnee baseline scores with the full baseline sample. PEs = difference scores – attrition effects. PEs are then subtracted from follow‐up scores, with the resultant scores used in diagnosing MCI. CSF Aβ42 and phosphorylated (p)‐tau were measured 6‐12 months prior to retesting.ResultThere were significant PEs on 5 of 6 tests (average Cohen’s d=.27). For Jak‐Bondi diagnoses, accounting for PEs at follow‐up resulted in a 24.8% increase in the proportion of MCI cases (p<.000001). There were increased proportions of Aβ42‐positive (18.8%) and p‐tau‐positive cases (29.8%), and reduced proportions of Aβ42‐positive (5.8%) and p‐tau‐positive (7.9%) CNs (McNemar χ2: Aβ42: p<.0014, p‐tau: p<.028; Figure 1). Results were similar for Petersen diagnoses. Of 26 false negatives (i.e., CNs whose diagnosis changed to MCI after accounting for PEs), 9 (35%) were MCI without PE adjustment at 2‐year follow‐up.ConclusionAccounting for PEs led to earlier detection of MCI at 1‐year follow‐up. Biomarker data support these being more accurate diagnoses because more cases were biomarker‐positive and more CNs were biomarker‐negative. Longer‐term follow‐up indicated that MCI cases may be detected a year or more earlier if performance is adjusted for PEs. Accounting for PEs has meaningful, real‐world implications for early identification, which may, in turn, improve opportunities to slow disease progression.

OBJECTIVE:The current study examined the interactive effect of type 2 diabetes and Alzheimer disease (AD) risk factors on the rate of functional decline in cognitively normal participants from the Alzheimer's Disease Neuroimaging Initiative.METHODS:Participants underwent annual assessments that included the Functional Activities Questionnaire, an informant-rated measure of everyday functioning. Multilevel modeling, controlling for demographic variables and ischemic risk, examined the interactive effects of diabetes status (diabetes, n=69; no diabetes, n=744) and AD risk factors in the prediction of 5-year longitudinal change in everyday functioning. One model was run for each AD risk factor, including: objectively-defined subtle cognitive decline (Obj-SCD), and genetic susceptibility [apolipoprotein E ε4 (APOE ε4) as well as cerebrospinal fluid β-amyloid (Aβ), total tau (tau), and hyperphosphorylated tau (p-tau).RESULTS:The 3-way diabetes×AD risk factor×time interaction predicted increased rates of functional decline in models that examined Obj-SCD, APOE ε4, tau, and p-tau positivity, but not Aβ positivity.CONCLUSIONS:Participants with both diabetes and at least 1 AD risk factor (ie, Obj-SCD, APOE ε4, tau, and p-tau positivity) demonstrated faster functional decline compared with those without both risk factors (diabetes or AD). These findings have implications for early identification of, and perhaps earlier intervention for, diabetic individuals at risk for future functional difficulty.

Mild cognitive impairment (MCI) has long been conceptualized as a transitional stage between normal aging and Alzheimer’s disease (AD) and other dementia subtypes, thus providing a potential window for early intervention in at-risk older adults. Nonetheless, effective intervention requires accurate identification of prodromal dementia, and the criteria for MCI have undergone significant evolution over the last 30 years to improve diagnostic sensitivity and specificity. There is increasing recognition of heterogeneous neuropsychological presentations in MCI. When traditional diagnostic criteria for the diagnosis of MCI have been applied a body of research employing statistical algorithms consistently demonstrated the existences of 3 to 4 unique MCI subtypes. Importantly, one subgroup that has emerged across studies involves a sizeable minority of participants that are cognitively normal on comprehensive neuropsychological assessment. These false-positives have highlighted the susceptibility of traditional Petersen/Winblad MCI criteria to diagnostic errors and demonstrated the need for developing of neuropsychological paradigms for MCI classification, and a new set of comprehensive MCI criteria using actuarial methods has been proposed. Accumulating evidence indicates that these comprehensive criteria pose an advantage over conventional criteria in the accurate identification of prodromal dementia and capture the neuropsychological and biological heterogeneity of MCI. Future research should expand on early work examining the association between empirical MCI subtypes and biological, neuroimaging, and genetic markers of AD.

INTRODUCTION:The Alzheimer's Disease Neuroimaging Initiative (ADNI) separates "early" and "late" mild cognitive impairment (MCI) based on a single memory test. We compared ADNI's MCI classifications to our neuropsychological approach, which more broadly assesses cognitive abilities.METHODS:Three hundred thirty-six ADNI-2 participants were classified as "early" or "late" MCI. Cluster analysis was performed on neuropsychological test data, and participants were reclassified based on cluster results. These two staging approaches were compared on progression rates, cerebrospinal fluid biomarkers, and cortical thickness profiles.RESULTS:There was little correspondence between the two staging methods. ADNI's early MCI group included a large proportion of false-positive diagnostic errors. The reclassified neuropsychological MCI groups showed steeper survival curves and more abnormal biomarkers.CONCLUSIONS:Our novel neuropsychological approach improved the staging of MCI by (1) capturing individuals at an early symptomatic stage, (2) minimizing false-positive cases, and (3) identifying a late MCI group further along the disease trajectory.

Previous research has shown considerable heterogeneity within MCI samples diagnosed via conventional diagnostic criteria. Using statistical cluster-analytic techniques, we previously identified four cognitive subtypes within the Alzheimer's Disease Neuroimaging Initiative (ADNI) MCI cohort (n=825; see Table 1): amnestic MCI (35%), dysnomic MCI (19%), dysexecutive/mixed MCI (12%), and a “false positive” group (34%) characterized by intact neuropsychological performance at baseline despite their MCI diagnosis. For this study, we examined the longitudinal cognitive trajectories of these MCI subtypes. ADNI participants completed neuropsychological testing at baseline and annually for up to 4 years, including measures of language (animal fluency; Boston Naming Test), attention/executive function (Trail Making Test, Parts A and B), and memory (Rey Auditory Verbal Learning Test, Delayed Recall and Recognition). Raw scores were converted to demographically-adjusted z-scores at each time point. Linear mixed effects models examined cognitive performance, and survival analysis examined progression to probable Alzheimer's disease (AD) over the 4-year period. The false positive group performed within normal limits on all six measures at all time points and showed the lowest rate of progression (p<.001). Dysnomic MCI demonstrated a steeper rate of decline on a measure of executive function relative to amnestic MCI (p=.03); these two groups otherwise showed similar trajectories of decline and similar progression rates. Dysexecutive/mixed MCI demonstrated a steeper rate of decline on all measures relative to amnestic MCI (p<.001 to .03). In comparison to dysnomic MCI, the dysexecutive/mixed MCI group showed a steeper rate of decline in language and attention/executive function (p<.001 to .02), but similar trajectories on memory testing. Dysexecutive/mixed MCI showed the highest rate of progression (p<.001).

Mild cognitive impairment (MCI) and abnormal profiles of amyloid, tau, and neurodegeneration biomarkers are recommended for early identification of individuals at-risk of Alzheimer's disease (AD) dementia. We evaluated the independent and combined prediction of progression to dementia by different MCI operationalizations and mixture modeling-based profiles of cerebrospinal fluid levels (CSF) of core AD biomarkers. At baseline, 950 ADNI participants were diagnosed as cognitively normal or MCI according to conventional ADNI or actuarial neuropsychological criteria. Participants were classified into biomarker profile subgroups based on CSF levels of amyloid (Aβ1-42), phosphorylated tau (pTau181), and neurodegeneration (tTau), as well as APOE-ε4 allele counts using mixture modeling. Dementia status, as classified by ADNI, was evaluated at 6-, 12-, 24-, 36-, and 48-months. Mixture modeling yielded four biomarker profile groups, which were characterized by (1) low Aβ1-42, high pTau181/tTau, and 77% APOE-ε4-positivity (Biomarker AD); (2) low Aβ1-42, modest pTau181/tTau, and 59% APOE-ε4 positivity (Biomarker Abeta); (3) very high Aβ1-42, modest pTau181/tTau, and 15% APOE-ε4 positivity (Biomarker Mild Tau); and (4) high Aβ1-42, low pTau181/tTau, and 17% APOE-ε4-positivity (Biomarker Normal). Discrete-time survival analysis revealed that Conventional MCI (HR = 32.28, 95% CI = 11.99 – 86.90), Actuarial MCI (HR = 10.00, 95% CI = 6.60 – 15.14), Biomarker AD (HR = 19.61, 95% CI = 10.52 – 36.56), and Biomarker Abeta (HR = 8.68, 95% CI = 4.91 – 15.36) increased dementia risk. Considered independently, Actuarial MCI (AIC = 1276.3) was a stronger predictor of dementia risk than conventional MCI criteria (AIC = 1295.4) or biomarker profile classification (AIC = 1286.6). Dementia prediction was optimized using both actuarial MCI and biomarker profile classification predictors, with participants jointly classified as Actuarial MCI/Biomarker AD (HR = 56.61) and Actuarial MCI/Biomarker Abeta (HR = 36.63) at the highest risk of dementia conversion. Mixture modeling-based CSF biomarker profiles characterized by abnormal amyloid levels increase dementia risk. MCI classification using an actuarial neuropsychological approach provides unique information that improves the specificity and resultant clinical prediction of biomarker profile-based classification. Use of both biomarker profiles and actuarial neuropsychological criteria can improve dementia prediction in clinical practice and sample selection in clinical trials.

INTRODUCTION:We examined reasons for low mild cognitive impairment (MCI)-to-cognitively normal (CN) reversion rates in the Alzheimer's Disease Neuroimaging Initiative (ADNI).METHODS:CN and MCI participants were identified as remaining stable, progressing, or reverting at 1-year of follow-up (Year 1). Application of ADNI's MCI criteria at Year 1 in addition to Alzheimer's disease biomarkers by group were examined.RESULTS:The MCI-to-CN reversion rate was 3.0%. When specific components were examined, 22.5% of stable MCI participants had normal memory performance at Year 1 and their Alzheimer's disease biomarkers were consistent with the stable CN group. At Year 1, when all MCI criteria were not met, the more subjective Clinical Dementia Rating rather than objective memory measure appeared to drive continuation of the MCI diagnosis.DISCUSSION:Results demonstrate an artificially low 1-year MCI-to-CN reversion rate in ADNI-diagnosed participants. If the Logical Memory cutoffs had been consistently applied, the reversion rate would have been at least 21.8%.

OBJECTIVE:Intraindividual cognitive variability (IIV), a measure of within-person variability across cognitive measures at a single time point, is associated with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Little is known regarding brain changes underlying IIV, or the relationship between IIV and functional ability. Therefore, we investigated the association between IIV and cerebral atrophy in AD-vulnerable regions and everyday functioning in nondemented older adults.METHOD:736 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants (285 cognitively normal [CN]; 451 MCI) underwent neuropsychological testing and serial MRI over 2 years. Linear mixed effects models examined the association between baseline IIV and change in entorhinal cortex thickness, hippocampal volume, and everyday functioning.RESULTS:Adjusting for age, sex, apolipoprotein E genotype, amyloid-β positivity, and mean level of cognitive performance, higher baseline IIV predicted faster rates of entorhinal and hippocampal atrophy, as well as functional decline. Higher IIV was associated with both entorhinal and hippocampal atrophy among MCI participants but selective vulnerability of the entorhinal cortex among CN individuals.CONCLUSIONS:IIV was associated with more widespread medial temporal lobe (MTL) atrophy in individuals with MCI relative to CN, suggesting that IIV may be tracking advancing MTL pathologic changes across the continuum of aging, MCI, and dementia. Findings suggest that cognitive dispersion may be a sensitive marker of neurodegeneration and functional decline in nondemented older adults. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

INTRODUCTION:The low mild cognitive impairment (MCI) to cognitively normal (CN) reversion rate in the Alzheimer's Disease Neuroimaging Initiative (2-3%) suggests the need to examine reversion by other means. We applied comprehensive neuropsychological criteria (NP criteria) to determine the resulting MCI to CN reversion rate.METHODS:Participants with CN (n = 641) or MCI (n = 569) were classified at baseline and year 1 using NP criteria. Demographic, neuropsychological, and Alzheimer's disease biomarker variables as well as progression to dementia were examined across stable CN, reversion, and stable MCI groups.RESULTS:NP criteria produced a one-year reversion rate of 15.8%. Reverters had demographics, Alzheimer's disease biomarkers, and risk-of-progression most similar to the stable CN group and showed the most improvement on neuropsychological measures from baseline to year 1.DISCUSSION:NP criteria produced a reversion rate that is consistent with, albeit modestly improved from, reversion rates in meta-analyses. Reverters' biomarker profiles and progression rates suggest that NP criteria accurately tracked with underlying pathophysiologic status.

Background/Aims: Mild cognitive impairment (MCI) lacks a “gold standard” operational definition. The Jak/Bondi actuarial neuropsychological criteria for MCI are associated with improved diagnostic stability and prediction of progression to dementia compared to conventional MCI diagnostic approaches, although its utility in diagnosing MCI in old-old individuals (age 75+) is unknown. Therefore, we investigated the applicability of neuropsychological MCI criteria among old-old from the Framingham Heart Study. Methods: A total of 347 adults (ages 79–102) were classified as cognitively normal or MCI via Jak/Bondi and conventional Petersen/Winblad criteria, which differ on cutoffs for cognitive impairment and number of impaired scores required for a diagnosis. Cox models examined MCI status in predicting risk of progression to dementia. Results: MCI diagnosed by both the Jak/Bondi and Petersen/Winblad criteria was associated with incident dementia; however, when both criteria were included in the regression model together, only the Jak/Bondi criteria remained statistically significant. At follow-up, the Jak/Bondi criteria had a lower MCI-to-normal reversion rate than the Petersen/Winblad criteria. Conclusions: Our findings are consistent with previous research on the Jak/Bondi criteria and support the use of a comprehensive neuropsychological diagnostic approach for MCI among old-old individuals.

Type II diabetes mellitus (DM) has been shown to increase risk for dementia, including Alzheimer's disease (AD), accelerate cognitive decline, and increase the rate of progression to dementia in older adults with mild cognitive impairment (MCI). The current study aimed to extend this work by examining the interactive effect of DM and AD risk factors on rate of functional decline in older adults without a neurocognitive disorder. Participants without MCI or dementia from the Alzheimer's Disease Neuroimaging Initiative underwent annual assessments that included the Functional Activities Questionnaire (FAQ), an informant-rated measure of everyday functioning in which higher scores indicate more functional difficulty. Multilevel modeling, controlling for demographics and ischemic risk, examined the interactive effects of baseline DM (DM positive status [+] n=68; DM negative status [-] n=739) and AD risk factors in the prediction of four-year longitudinal change in everyday functioning. One MLM was run for each AD risk factor, including: subtle cognitive decline (SCD+ n=256; SCD- n=492), genetic susceptibility (APOE ε4+ n=269; APOE ε4- n=535), cerebrospinal fluid Aβ (Aβ+ n=263; Aβ- n=323), t-tau (t-tau+ n=120; t-tau- n=461), and p-tau (p-tau+ n=366; p-tau- n=220). Compared to DM-, DM+ participants were more likely to be male and had higher ischemic risk. The three-way DM x AD risk factor x Visit interaction was significant for models that examined SCD (p=.031; Fig. 1), APOE ε4 (p=.001; Fig. 2), t-tau (p=.022; Fig. 3), and p-tau (p=.041; Fig. 4), but not Aβ (Fig. 5). DM moderated the relationship between AD risk factors (except Aβ) and longitudinal functional trajectories such that having DM increased the rate of functional decline for those who were positive for each of these risk factors. FAQ trajectories by Diabetes and Subtle Cognitive Decline status. Error bars represent 95% confidence interval. FAQ trajectories by Diabetes and APOE ε4 status. Error bars represent 95% confidence interval. FAQ trajectories by Diabetes and CSF t-tau status. Error bars represent 95% confidence interval. FAQ trajectories by Diabetes and CSF p-tau status. Error bars represent 95% confidence interval. FAQ trajectories by Diabetes and CSF Aβ status. Error bars represent 95% confidence interval. Participants who were positive for both DM and an AD risk factor demonstrated the fastest rate of functional decline, and only these participants reached an impaired level of functional difficulty within four years (FAQ score >5). DM+ status did not modify the relationship between Aβ and functional decline. Future work should identify potential mechanisms for the modifying effect of DM on SCD, APOE ε4, and CSF biomarkers (t-tau and p-tau) across the aging spectrum.

Objective: Preclinical Alzheimer's disease (AD) defined by a positive AD biomarker in the presence of normal cognition is presumed to precede mild cognitive impairment (MCI). Subtle cognitive deficits and cognitive inefficiencies in preclinical AD may be detected through process and error scores on neuropsychological tests in those at risk for progression to MCI. Method: Cognitively normal participants (n = 525) from the Alzheimer's Disease Neuroimaging Initiative were followed for up to 5 years and classified as either stable normal (n = 305) or progressed to MCI (n = 220). Cox regressions were used to determine whether baseline process scores on the Rey Auditory Verbal Learning Test (AVLT; intrusion errors, learning slope, proactive interference, retroactive interference) predicted progression to MCI and a Clinical Dementia Rating (CDR) score of 1 after considering demographic characteristics, apolipoprotein E epsilon 4 status, cerebrospinal fluid AD biomarkers, ischemia risk, mood, functional difficulty, and standard neuropsychological total test scores for the model. Results: Baseline AVLT intrusion errors predicted progression to MCI (hazard ratio = 1.04, 95% confidence interval 1.01-1.07, p = .008) and improved model fit after the other valuable predictors were already in the model, chi(2)(df = 1) = 6.330, p =.012. AVLT intrusion errors also predicted progression to CDR = 1 (hazard ratio = 1.10, 95% confidence interval 1.02-1.18, p =.016) and again improved model fit, chi(2)(df = 1) = 4.682, p =.030. Conclusions: Intrusion errors on the AVLT contribute unique value for predicting progression from normal cognition to MCI and normal cognition to mild dementia (CDR = 1). Intrusion errors appear to reflect subtle change and inefficiencies in cognition that precede impairment detected by neuropsychological total scores.

OBJECTIVES:Although subjective cognitive complaints (SCC) are an integral component of the diagnostic criteria for mild cognitive impairment (MCI), previous findings indicate they may not accurately reflect cognitive ability. Within the Alzheimer's Disease Neuroimaging Initiative, we investigated longitudinal change in the discrepancy between self- and informant-reported SCC across empirically derived subtypes of MCI and normal control (NC) participants. METHODS:Data were obtained for 353 MCI participants and 122 "robust" NC participants. Participants were classified into three subtypes at baseline via cluster analysis: amnestic MCI, mixed MCI, and cluster-derived normal (CDN), a presumptive false-positive group who performed within normal limits on neuropsychological testing. SCC at baseline and two annual follow-up visits were assessed via the Everyday Cognition Questionnaire (ECog), and discrepancy scores between self- and informant-report were calculated. Analysis of change was conducted using analysis of covariance. RESULTS:The amnestic and mixed MCI subtypes demonstrated increasing ECog discrepancy scores over time. This was driven by an increase in informant-reported SCC, which corresponded to participants' objective cognitive decline, despite stable self-reported SCC. Increasing unawareness was associated with cerebrospinal fluid Alzheimer's disease biomarker positivity and progression to Alzheimer's disease. In contrast, CDN and NC groups over-reported cognitive difficulty and demonstrated normal cognition at all time points. CONCLUSIONS:MCI participants' discrepancy scores indicate progressive underappreciation of their evolving cognitive deficits. Consistent over-reporting in the CDN and NC groups despite normal objective cognition suggests that self-reported SCC do not predict impending cognitive decline. Results demonstrate that self-reported SCC become increasingly misleading as objective cognitive impairment becomes more pronounced. (JINS, 2018, 24, 842-853).

Neuropathologic studies have identified the locus coeruleus (LC) as the earliest site for formation of tau pathology1,2, and LC degeneration has been observed the context of Alzheimer's disease (AD)3. Despite these findings, little research has explored potential mechanisms by which LC degeneration may influence AD-related pathogenic processes. Animal studies have suggested that the LC modulates neurovascular coupling mechanisms4, although no studies to our knowledge have investigated the link between LC integrity and markers of cerebrovascular pathology in humans. 17 nondemented older adults (13 cognitively normal; 4 MCI) received T1 fast spin-echo and fluid-attenuated inversion-recovery MRI scans to assess LC integrity and white matter hyperintensities (WMH), respectively. Neuromelanin-related signal intensity relative to a reference region was used to derive bilateral contrast ratios (CRs) as a proxy of LC integrity, consistent with prior work.3 Lobar distribution of WMH volumes were measured with in-house developed software.5 Associations between LC-CRs and lobar WMH volumes were assessed using Pearson partial correlations adjusting for age. Large negative correlations were observed between LC-CRs and frontal (r = -.51, p = .05) and temporal (r = -.52, p = .04) WMH volumes. There was also a moderate-to-large nonsignificant correlation between LC-CRs and parietal WMH volumes (r = -.43, p = .09) but no significant association between LC-CRs and occipital WMH volumes (r = -.10, p = .73). Age was not associated with LC-CRs (p = .85) or WMH volumes (all ps > .15).

We have previously shown that staging early and late mild cognitive impairment (MCI) with comprehensive neuropsychological assessment better predicted progression to Alzheimer's disease (AD) compared to the staging method used by the Alzheimer's Disease Neuroimaging Initiative (ADNI), which is based on performance on a single memory test. We compared the two staging approaches on the resulting cortical thickness profiles of early and late MCI. 172 ADNI subjects were originally classified as either early MCI (ADNI-EMCI; n=95) or late MCI (ADNI-LMCI; n=77) based on ADNI's cutoffs on a story memory test. A alternative staging using cluster analysis of participants’ performance across six neuropsychological (NP) measures was performed, and the same participants were reclassified based on the resulting cluster groups (Figure 1): (1) early MCI with primary memory impairment (NP-EMCI; n=70), (2) late MCI with multi-domain deficits (NP-LMCI; n=26), and (3) false positive MCI diagnoses with normal cognitive performance (n=76); this latter group has previously been shown to have normal biomarker profiles and a low rate of progression to AD. MANOVAs with Bonferroni-correction and statistical t-value group maps compared cortical thickness in regions of interest to robust normal control participants (NCs; n=82). Neuropsychological performance for cluster-derived groups. Error bars denote standard error of the mean. The horizontal dotted line indicates the typical cutoff for impairment (-1.5 SDs). BNT=Boston Naming Test; TMT=Trail Making Test; RAVLT=Rey Auditory Verbal Learning Test; NP=neuropsychological; MCI=mild cognitive impairment. There was little correspondence between the two staging methods (Figure 2). Cortical thickness did not differ between “false positives” and NCs (Figure 3). NP-EMCI participants demonstrated cortical thinning primarily in bilateral medial temporal regions relative to NCs, whereas the NP-LMCI group showed thinning in temporal, frontal, parietal, and cingulate regions. In contrast, ADNI-EMCI participants demonstrated cortical thickness that was similar to NCs; mild medial temporal thinning became non-significant with Bonferroni-correction. ADNI-LMCI participants showed thinning primarily in bilateral medial temporal regions. Number of participants in the early MCI (EMCI) and late MCI (LMCI) groups by staging method. T-value maps showing regional cortical thickness on the left and right lateral and medial pial surfaces for each group relative to the robust normal controls (NCs). The cyan/blue shades represent areas where the MCI subgroup has thinner cortex than NCs. Neuropsychological staging of MCI resulted in stronger associations with neuroimaging biomarkers of cortical thickness relative to ADNI's staging approach. Neuropsychological tests assessing several cognitive domains improved staging of MCI by (1) capturing an EMCI group comprised of memory-impaired individuals in an early symptomatic stage of AD, rather than a large number of false positive MCI cases, and (2) identifying a smaller LMCI group that is further along the disease trajectory given their multi-domain cognitive impairment and more extensive cortical thinning.

BACKGROUND:We previously operationally-defined subtle cognitive decline (SCD) in preclinical Alzheimer's disease (AD) using total scores on neuropsychological (NP) tests. NP process scores (i.e., provide information about how a total NP score was achieved) may be a useful tool for identifying early cognitive inefficiencies prior to objective impairment seen in mild cognitive impairment (MCI) and dementia. OBJECTIVE:We aimed to integrate process scores into the SCD definition to identify stages of SCD and improve early detection of those at risk for decline. METHODS:Cognitively "normal" participants from the Alzheimer's Disease Neuroimaging Initiative were classified as "early" SCD (E-SCD; >1 SD below norm-adjusted mean on 2 process scores or on 1 process score plus 1 NP total score), "late" SCD (L-SCD; existing SCD criteria of >1 SD below norm-adjusted mean on 2 NP total scores in different domains), or "no SCD" (NC). Process scores considered in the SCD criteria were word-list intrusion errors, retroactive interference, and learning slope. Cerebrospinal fluid AD biomarkers were used to examine pathologic burden across groups. RESULTS:E-SCD and L-SCD progressed to MCI 2.5-3.4 times faster than the NC group. Survival curves for E-SCD and L-SCD converged at 7-8 years after baseline. The combined (E-SCD+L-SCD) group had improved sensitivity to detect progression to MCI relative to L-SCD only. AD biomarker positivity increased across NC, SCD, and MCI groups. CONCLUSIONS:Process scores can be integrated into the SCD criteria to allow for increased sensitivity and earlier identification of cognitively normal older adults at risk for decline prior to frank impairment on NP total scores.