CogState computerized testing is associated with amyloid and FDG-PET: Implications for secondary preventive trials

Criteria for mild cognitive impairment (MCI) consider impairment in instrumental activities of daily living (IADL) as exclusionary, but cross-sectional studies suggest that some high-level functional deficits are present in MCI. This longitudinal study examines informant-rated IADL in MCI, compared with cognitively normal (CN) older individuals, and explores whether functional abilities, particularly those with high cognitive demand, are predictors of MCI and dementia over a 2-year period in individuals who were CN at baseline. A sample of 602 non-demented community dwelling individuals (375 CN and 227 with MCI) aged 70-90 years underwent baseline and 24-month assessments that included cognitive and medical assessments and an interview with a knowledgeable informant on functional abilities with the Bayer Activities of Daily Living Scale. Significantly more deficits in informant-reported IADL with high cognitive demand were present in MCI compared with CN individuals at baseline and 2-year follow-up. Functional ability in CN individuals at baseline, particularly in activities with high cognitive demand, predicted MCI and dementia at follow-up. Difficulties with highly cognitively demanding activities specifically predicted amnestic MCI but not non-amnestic MCI whereas those with low cognitive demand did not predict MCI or dementia. Age, depressive symptoms, cardiovascular risk factors and the sex of the informant did not contribute to the prediction. IADL are affected in individuals with MCI, and IADL with a high cognitive demand show impairment predating the diagnosis of MCI. Subtle cognitive impairment is therefore likely to be a major hidden burden in society.

Alzheimer's disease (AD), the most common disabling impairment in older adults, takes an unacceptable toll on patients and families. With the growing number of people living to older ages, it is projected to afflict more than 100 million people around the world by 2050. While there is an urgent need, it takes too many cognitively normal (CN) individuals and too many years to evaluate presymptomatic AD treatments using traditional clinical endpoints. We and our colleagues have been using imaging techniques to detect the brain changes associated with the predisposition to AD in CN individuals with 2, 1 and 0 copies of the apolipoprotein E (APOE) e4 allele, the major late-onset AD susceptibility gene, and in CN presenilin 1 (PSEN1) mutation carriers and non-carriers from the world’s largest autosomal dominant early-onset AD kindred in Antioquia, Colombia; we developed several image-analysis techniques and a composite cognitive measure with improved power; and we estimated the sample sizes needed when using these endpoints. We recently proposed the Alzheimer’s Prevention Initiative (API) to help in the effort to launch an era in AD prevention research. The API will include preclinical AD treatment/biomarker development trials in individuals at increased genetic risk for early-onset and late-onset AD, an extremely large Colombian early-onset AD Prevention Registry; and an extremely large North American Alzheimer’s Prevention Registry to support several preclinical AD trials. The API’s first trial will test the anti-amyloid immunization therapy crenezumab in CN PSEN1 mutation carriers using the best established brain imaging and cerebrospinal fluid measurements and our composite cognitive measure as the primary clinical endpoint. It is intended to test an anti-amyloid agent in the preclinical treatment of AD, help determine the extent to which a treatment’s biomarker effects predict a clinical outcome, provide a better test of the amyloid hypothesis, and provide raw data and biological samples to the research community. With support from the U.S. National Institutes of Health, philanthropic funds from the Banner Alzheimer’s Institute, and Genentech, it is also intended to help provide a new paradigm for research collaboration in clinical trials. In this presentation, we will briefly introduce the crucial role of flourodeoxyglucose positron emission tomography (PET), amyloid PET, structural and functional MRI in the preclinical detection, tracking, and treatment of AD. We will present the novel image processing methods we have developed to help in this endeavor, as well as their implementation in the API’s preclinical treatment/biomarker development programs.

Secondary prevention trials in subjects with preclinical Alzheimer disease may require documentation of brain amyloidosis. The identification of inexpensive and noninvasive screening variables that can identify individuals who have significant amyloid accumulation would reduce screening costs. A total of 483 cognitively normal (CN) individuals, aged 70-92 years, from the population-based Mayo Clinic Study of Aging, underwent Pittsburgh compound B (PiB)-PET imaging. Logistic regression determined whether age, sex, APOE genotype, family history, or cognitive performance was associated with odds of a PiB retention ratio >1.4 and >1.5. Area under the receiver operating characteristic curve (AUROC) evaluated the discrimination between PiB-positive and -negative subjects. For each characteristic, we determined the number needed to screen in each age group (70-79 and 80-89) to identify 100 participants with PiB >1.4 or >1.5. A total of 211 (44%) individuals had PiB >1.4 and 151 (31%) >1.5. In univariate and multivariate models, discrimination was modest (AUROC ∼0.6-0.7). Multivariately, age and APOE best predicted odds of PiB >1.4 and >1.5. Subjective memory complaints were similar to cognitive test performance in predicting PiB >1.5. Indicators of PiB positivity varied with age. Screening APOE ε4 carriers alone reduced the number needed to screen to enroll 100 subjects with PIB >1.5 by 48% in persons aged 70-79 and 33% in those aged 80-89. Age and APOE genotype are useful predictors of the likelihood of significant amyloid accumulation, but discrimination is modest. Nonetheless, these results suggest that inexpensive and noninvasive measures could significantly reduce the number of CN individuals needed to screen to enroll a given number of amyloid-positive subjects.

Background: Recent studies have shown that decreases in both letter fluency and category fluency may be present in addition to memory impairment in single-domain amnestic mild cognitive impairment (aMCI). However, the clinical utility of these fluency measures is unclear. The aim of this study was to determine what, if any, diagnostic value letter and category fluency provide in differentiating single-domain aMCI from normal cognition. Methods: Data from 66 individuals [33 cognitively normal (CN) and 33 aMCI] between the ages of 66 and 87 years participating in the Florida Alzheimer’s Disease Research Center were compared on the Controlled Oral Word Association Test (COWAT)-FAS and Category Fluency test, both in terms of raw and scaled scores. Results: Participants were matched on age, education and sex. Two-tailed independent sample t-tests found statistically significant differences between the CN and aMCI groups for both raw and scaled scores of COWAT-FAS and Category Fluency (p < 0.001). Logistic regression analyses found that COWAT-FAS and Category Fluency did not significantly improve diagnostic accuracy when combined with the Hopkins Verbal Learning Test-Revised delayed recall. Conclusion: Although decreased COWAT-FAS and Category Fluency performance may be present in single-domain aMCI, these tests do not improve the ability of the Hopkins Verbal Learning Test-Revised delayed recall to differentiate aMCI from CN individuals.

Alzheimer disease, cerebral vascular brain injury, and isocortical Lewy body disease (LBD) are the major contributors to dementia in community- and population-based studies. To estimate the prevalence of clinically silent forms of these diseases in cognitively normal (CN) adults. Autopsy study. Community- and population based. A total of 1672 brain autopsies from the Adult Changes in Thought study, Honolulu-Asia Aging Study, Nun Study, and Oregon Brain Aging Study, of which 424 met the criteria for CN. Of these, 336 cases had a comprehensive neuropathologic examination of neuritic plaque density, Braak stage for neurofibrillary tangles, LB distribution, and number of cerebral microinfarcts. Forty-seven percent of CN cases had moderate or frequent neuritic plaque density; of these, 6% also had Braak stage V or VI for neurofibrillary tangles. Fifteen percent of CN cases had medullary LBD; 8% also had nigral and 4% isocortical LBD. The presence of any cerebral microinfarcts was identified in 33% and of high-level cerebral microinfarcts in 10% of CN individuals. Overall, the burden of lesions in each individual and their comorbidity varied widely within each study but were similar across studies. These data show an individually varying complex convergence of subclinical diseases in the brain of older CN adults. Appreciating this ecology should help guide future biomarker and neuroimaging studies and clinical trials that focus on community- and population-based cohorts.

Since Alzheimer disease (AD) neuropathology is thought to develop years before dementia, it may be possible to detect subtle AD-related atrophy in preclinical AD. Here we hypothesized that the "disease signature" of AD-related cortical thinning, previously identified in patients with mild AD dementia, would be useful as a biomarker to detect anatomic abnormalities consistent with AD in cognitively normal (CN) adults who develop AD dementia after longitudinal follow-up. We studied 2 independent samples of adults who were CN when scanned. In sample 1, 8 individuals developing AD dementia (CN-AD converters) after an average of 11.1 years were compared to 25 individuals who remained CN (CN-stable). In sample 2, 7 CN-AD converters (average follow-up 7.1 years) were compared to 25 CN-stable individuals. AD-signature cortical thinning in CN-AD converters in both samples was remarkably similar, about 0.2 mm (p < 0.05). Despite this small absolute difference, Cohen d effect sizes for these differences were very large (> 1). Of the 11 CN individuals with baseline low AD-signature thickness (≥ 1 SD below cohort mean), 55% developed AD dementia over nearly the next decade, while none of the 9 high AD-signature thickness individuals (≥ 1 SD above mean) developed dementia. This marker predicted time to diagnosis of dementia (hazard ratio = 3.4, p < 0.0005); 1 SD of thinning increased dementia risk by 3.4. By focusing on cortical regions known to be affected in AD dementia, subtle but reliable atrophy is identifiable in asymptomatic individuals nearly a decade before dementia, making this measure a potentially important imaging biomarker of early neurodegeneration.

To cross-sectionally compare the regional white matter fractional anisotropy (FA) of cognitively normal (CN) older individuals and patients with mild cognitive impairment (MCI) and Alzheimer disease (AD), separately focusing on the normal-appearing white matter (NAWM) and white matter hyperintensities (WMH), and to test the independent effects of presumed degenerative and vascular process on FA differences. Forty-seven patients with AD, 73 patients with MCI, and 95 CN subjects received diffusion tensor imaging and vascular risk evaluation. To properly control normal regional variability of FA, we divided cerebral white matter into 4 strata as measured from a series of young healthy individuals (H1 = highest; H2 = intermediate high; H3 = intermediate low; H4 = lowest anisotropy stratum). For overall cerebral white matter, patients with AD had significantly lower FA than CN individuals or patients with MCI in the regions with higher baseline anisotropy (H1, H2, and H3), corresponding to long corticocortical association fibers, but not in H4, which mostly includes heterogeneously oriented fibers. Vascular risk showed significant independent effects on FA in all strata except H1, which corresponds to the genu and splenium of the corpus callosum. Similar results were found within NAWM. FA in WMH was significantly lower than NAWM across all strata but was not associated with diagnosis or vascular risk. Both vascular and Alzheimer disease degenerative process contribute to microstructural injury of cerebral white matter across the spectrum of cognitive ability and have different region-specific injury patterns.

A challenge in developing informative neuroimaging biomarkers for early diagnosis of Alzheimer's disease is the need to identify biomarkers that are evident before the onset of clinical symptoms, and which have sufficient sensitivity and specificity on an individual patient basis. Recent literature suggests that spatial patterns of brain atrophy discriminate amongst Alzheimer's disease, mild cognitive impairment (MCI) and cognitively normal (CN) older adults with high accuracy on an individual basis, thereby offering promise that subtle brain changes can be detected during prodromal Alzheimer's disease stages. Here, we investigate whether these spatial patterns of brain atrophy can be detected in CN and MCI individuals and whether they are associated with cognitive decline. Images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to construct a pattern classifier that recognizes spatial patterns of brain atrophy which best distinguish Alzheimer's disease patients from CN on an individual person basis. This classifier was subsequently applied to longitudinal magnetic resonance imaging scans of CN and MCI participants in the Baltimore Longitudinal Study of Aging (BLSA) neuroimaging study. The degree to which Alzheimer's disease-like patterns were present in CN and MCI subjects was evaluated longitudinally in relation to cognitive performance. The oldest BLSA CN individuals showed progressively increasing Alzheimer's disease-like patterns of atrophy, and individuals with these patterns had reduced cognitive performance. MCI was associated with steeper longitudinal increases of Alzheimer's disease-like patterns of atrophy, which separated them from CN (receiver operating characteristic area under the curve equal to 0.89). Our results suggest that imaging-based spatial patterns of brain atrophy of Alzheimer's disease, evaluated with sophisticated pattern analysis and recognition methods, may be useful in discriminating among CN individuals who are likely to be stable versus those who will show cognitive decline. Future prospective studies will elucidate the temporal dynamics of spatial atrophy patterns and the emergence of clinical symptoms.

IC-P2-091: Integration of MRI measures of atrophy and protein aggregation with CSF biomarkers of AD pathology in individuals with mild cognitive impairment

Quantitative assessment of possible age-related change in synaptic numbers in the human frontal cortex.

Basal ganglia volume in adults with Down syndrome

Hippocampal atrophy: an early brain marker for Alzheimer's disease (AD) and a correlate of HPA axis dysfunction