A Visual Interpretation Algorithm for Assessing Brain Tauopathy with 18F-MK-6240 PET.

In vivo characterization of pathologic deposition of tau protein in the human brain by PET imaging is a promising tool in drug development trials of Alzheimer disease (AD). 6-(fluoro-18F)-3-(1H-pyrrolo[2,3-c]pyridin-1-yl)isoquinolin-5-amine (18F-MK-6240) is a radiotracer with high selectivity and subnanomolar affinity for neurofibrillary tangles that shows favorable nonspecific brain penetration and excellent kinetic properties. The purpose of the present investigation was to develop a visual assessment method that provides both an overall assessment of brain tauopathy and regional characterization of abnormal tau deposition. Methods: 18F-MK-6240 scans from 102 participants (including cognitively normal volunteers and patients with AD or other neurodegenerative disorders) were reviewed by an expert nuclear medicine physician masked to each participant's diagnosis to identify common patterns of brain uptake. This initial visual read method was field-tested in a separate, nonoverlapping cohort of 102 participants, with 2 additional naïve readers trained on the method. Visual read outcomes were compared with semiquantitative assessments using volume-of-interest SUV ratio. Results: For the visual read, the readers assessed 8 gray-matter regions per hemisphere as negative (no abnormal uptake) or positive (1%-25% of the region involved, 25%-75% involvement, or >75% involvement) and then characterized the tau binding pattern as positive or negative for evidence of tau and, if positive, whether brain uptake was in an AD pattern. The readers demonstrated agreement 94% of the time for overall positivity or negativity. Concordance on the determination of regional binary outcomes (negative or positive) showed agreement of 74.3% and a Fleiss κ of 0.912. Using clinical diagnosis as the ground truth, the readers demonstrated a sensitivity of 73%-79% and specificity of 91%-93%, with a combined reader-concordance sensitivity of 80% and specificity of 93%. The average SUV ratio in cortical regions showed a robust correlation with visually derived ratings of regional involvement (r = 0.73, P < 0.0001). Conclusion: We developed a visual read algorithm for 18F-MK-6240 PET offering determination of both scan positivity and the regional degree of cortical involvement. These cross-sectional results show strong interreader concordance on both binary and regional assessments of tau deposition, as well as good sensitivity and excellent specificity supporting use as a tool for clinical trials.

Association of the top 20 Alzheimer's disease risk genes with [18F]flortaucipir PET.

Introduction: We previously reported genetic associations of the top Alzheimer's disease (AD) risk alleles with amyloid deposition and neurodegeneration. Here, we report the association of these variants with [18F]flortaucipir standardized uptake value ratio (SUVR). Methods: We analyzed the [18F]flortaucipir scans of 352 cognitively normal (CN), 160 mild cognitive impairment (MCI), and 54 dementia (DEM) participants from Alzheimer's Disease Neuroimaging Initiative (ADNI)2 and 3. We ran step-wise regression with log-transformed [18F]flortaucipir meta-region of interest SUVR as the outcome measure and genetic variants, age, sex, and apolipoprotein E (APOE) ε4 as predictors. The results were visualized using parametric mapping at familywise error cluster-level-corrected P < .05. Results: APOE ε4 showed significant (P < .05) associations with tau deposition across all disease stages. Other significantly associated genes include variants in ABCA7 in CN, CR1 in MCI, BIN1 and CASS4 in MCI and dementia participants. Discussion: We found significant associations to tau deposition for ABCA7, BIN1, CASS4, and CR1, in addition to APOE ε4. These four variants have been previously associated with tau metabolism through model systems.

Associations between Cortical Thickness and Metamemory in Alzheimer's Disease.

Metacognitive deficits affect Alzheimer's disease (AD) patient safety and increase caregiver burden. The brain areas that support metacognition are not well understood. 112 participants from the Imaging and Genetic Biomarkers for AD (ImaGene) study underwent comprehensive cognitive testing and brain magnetic resonance imaging. A performance-prediction paradigm was used to evaluate metacognitive abilities for California Verbal Learning Test-II learning (CVLT-II 1-5) and delayed recall (CVLT-II DR); Visual Reproduction-I immediate recall (VR-I Copy) and Visual Reproduction-II delayed recall (VR-II DR); Rey-Osterrieth Complex Figure Copy (Rey-O Copy) and delayed recall (Rey-O DR). Vertex-wise multivariable regression of cortical thickness was performed using metacognitive scores as predictors while controlling for age, sex, education, and intracranial volume. Subjects who overestimated CVLT-II DR in prediction showed cortical atrophy, most pronounced in the bilateral temporal and left greater than right (L > R) frontal cortices. Overestimation of CVLT-II 1-5 prediction and DR performance in postdiction showed L > R associations with medial, inferior and lateral temporal and left posterior cingulate cortical atrophy. Overconfident prediction of VR-I Copy performance was associated with right greater than left medial, inferior and lateral temporal, lateral parietal, anterior and posterior cingulate and lateral frontal cortical atrophy. Underestimation of Rey-O Copy performance in prediction was associated with atrophy localizing to the temporal and cingulate areas, and in postdiction, with diffuse cortical atrophy. Impaired metacognition was associated to cortical atrophy. Our results indicate that poor insight into one's cognitive abilities is a pervasive neurodegenerative feature associated with AD across the cognitive spectrum.

Prescribing cholinesterase inhibitors in mild cognitive impairment-Observations from the Alzheimer's Disease Neuroimaging Initiative.

INTRODUCTION: Analyses of off-label use of acetylcholinesterase inhibitors (AChEIs) in mild cognitive impairment (MCI) has produced mixed results. Post hoc analyses of observational cohorts, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), have reported deleterious effects in AChEI-treated subjects (AChEI+). Here, we used neuroimaging biomarkers to determine whether AChEI+ subjects had a greater rate of neurodegeneration than untreated (AChEI-) subjects while accounting for baseline differences. METHODS: We selected 121 ADNI MCI AChEI+ subjects and 151 AChEI- subjects with a magnetic resonance imaging (MRI) scan; 82 AChEI+ and 110 AChEI- also had a fluorodeoxyglucose (FDG) scan. A subset (83 AChEI+ and 98 AChEI-) had cerebrospinal fluid (CSF) or amyloid positron emission tomography (PET) assessment for amyloid positivity. Linear regression models were used to compare the effect of treatment on changes in Mini-Mental State Examination and Clinical Dementia Rating-Sum of Boxes scores. We used standard regression in SPM (for baseline) and the SPM toolbox sandwich estimator, SwE (for longitudinal) for comparisons of AChEI+ and AChEI- FDG PET and MRI data. RESULTS: At baseline, the AChEI+ group had significantly reduced cortical gray matter density (GMD) and more hypometabolism than AChEI- subjects. The greater rate of atrophy and hypometabolic changes over time in AChEI+ compared to AChEI- subjects did not survive correction for baseline differences. AChEI+ participants were more likely to be amyloid-positive and have lower GMD and FDG standardized uptake value ratio than AChEI- at baseline. AChEI+ subjects showed greater atrophy over time, which remained significant after controlling for amyloid status. DISCUSSION: Our data suggest that the observed differences in rates of cognitive decline, atrophy, and hypometabolism are likely the result of significant baseline differences between the groups. Furthermore, the data indicate no treatment effect of AChEI (positive of negative), rather that physicians prescribe AChEI to subjects who present with more severe clinical impairment. This alone may account for the negative effect seen previously in the ADNI population of AChEI use among MCI subjects.

Neurodegenerative changes in early- and late-onset cognitive impairment with and without brain amyloidosis.

BACKGROUND: A substantial number of patients clinically diagnosed with Alzheimer's disease do not harbor amyloid pathology. We analyzed the presence and extent of tau deposition and neurodegeneration in amyloid-positive (AD) and amyloid-negative (nonAD) ADNI subjects while also taking into account age of onset (< or > 65 years) as we expected that the emerging patterns could vary by age and presence or absence of brain amyloidosis. METHODS: One hundred and ten early-onset AD (EOAD), 121 EOnonAD, 364 late-onset AD (LOAD), and 175 LOnonAD mild cognitive impairment (MCI) and dementia (DEM) subjects were compared to 291 ADNI amyloid-negative control subjects using voxel-wise regression in SPM12 with cluster-level family-wise error correction at pFWE < 0.05). A subset of these subjects also received 18F-flortaucipir scans and allowed for analysis of global tau burden. RESULTS: As expected, relative to LOAD, EOAD subjects showed more extensive neurodegeneration and tau deposition in AD-relevant regions. EOnonADMCI showed no significant neurodegeneration, while EOnonADDEM showed bilateral medial and lateral temporal, and temporoparietal hypometabolism. LOnonADMCI and LOnonADDEM showed diffuse brain atrophy and a fronto-temporo-parietal hypometabolic pattern. LOnonAD and EOnonAD subjects failed to show significant tau binding. CONCLUSIONS: LOnonAD subjects show a fronto-temporal neurodegenerative pattern in the absence of tau binding, which may represent underlying hippocampal sclerosis with TDP-43, also known as limbic-predominant age-related TDP-43 encephalopathy (LATE). The hypometabolic pattern observed in EOnonADDEM seems similar to the one observed in EOADMCI. Further investigation into the underlying etiology of EOnonAD is warranted.

Neurodegenerative Patterns of Cognitive Clusters of Early-Onset Alzheimer's Disease Subjects: Evidence for Disease Heterogeneity.

BACKGROUND/AIMS: Alzheimer's disease (AD) with onset before 65 (early-onset AD [EOAD]) occurs in approximately 6% of cases and can affect nonmemory domains. Here, we analyze patterns of impairment in amnestic EOAD individuals using data-driven statistical analyses. METHODS: Cognitive data of 146 EOAD subjects were Z-normalized to 395 cognitively normal (CN) individuals. Domain-averaged Z-scores were adjusted for age, sex, and education followed by Wald cluster analysis of residuals. Magnetic resonance imaging and positron emission tomography comparisons of EOAD clusters to age-matched CN were done using Statistic Parametric Mapping 8. Cluster-level-family-wise error (p < 0.05) correction was applied. Mixed-effect models were used to compute longitudinal change across clusters. RESULTS: Scree plot using the pseudo-T-squared suggested a 4-cluster solution. Cluster 1 (memory-predominant impairment) showed atrophy/hypometabolism in medial/lateral temporal, lateral parietal, and posterior cingulate regions. Cluster 2 (memory/visuospatial-predominant) showed atrophy/hypometabolism of medial temporal, temporoparietal, and frontal cortices. Cluster 3 (memory, language, and executive function) and Cluster 4 (globally impaired) manifested atrophy and hypometabolism throughout the brain. Longitudinally between-cluster differences in the visuospatial and language/executive domains were significant, suggesting phenotypic variation. CONCLUSION: We observed significant heterogeneity in cognitive presentation among amnestic EOAD subjects and patterns of atrophy/hypometabolism in each cluster in agreement with the observed cognitive phenotype.

Associations of the Top 20 Alzheimer Disease Risk Variants With Brain Amyloidosis.

Importance: Late-onset Alzheimer disease (AD) is highly heritable. Genome-wide association studies have identified more than 20 AD risk genes. The precise mechanism through which many of these genes are associated with AD remains unknown. Objective: To investigate the association of the top 20 AD risk variants with brain amyloidosis. Design, Setting, and Participants: This study analyzed the genetic and florbetapir F 18 data from 322 cognitively normal control individuals, 496 individuals with mild cognitive impairment, and 159 individuals with AD dementia who had genome-wide association studies and 18F-florbetapir positron emission tomographic data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a prospective, observational, multisite tertiary center clinical and biomarker study. This ongoing study began in 2005. Main Outcomes and Measures: The study tested the association of AD risk allele carrier status (exposure) with florbetapir mean standard uptake value ratio (outcome) using stepwise multivariable linear regression while controlling for age, sex, and apolipoprotein E ε4 genotype. The study also reports on an exploratory 3-dimensional stepwise regression model using an unbiased voxelwise approach in Statistical Parametric Mapping 8 with cluster and significance thresholds at 50 voxels and uncorrected P < .01. Results: This study included 977 participants (mean [SD] age, 74 [7.5] years; 535 [54.8%] male and 442 [45.2%] female) from the ADNI-1, ADNI-2, and ADNI-Grand Opportunity. The adenosine triphosphate-binding cassette subfamily A member 7 (ABCA7) gene had the strongest association with amyloid deposition (χ2 = 8.38, false discovery rate-corrected P < .001), after apolioprotein E ε4. Significant associations were found between ABCA7 in the asymptomatic and early symptomatic disease stages, suggesting an association with rapid amyloid accumulation. The fermitin family homolog 2 (FERMT2) gene had a stage-dependent association with brain amyloidosis (FERMT2 × diagnosis χ2 = 3.53, false discovery rate-corrected P = .05), which was most pronounced in the mild cognitive impairment stage. Conclusions and Relevance: This study found an association of several AD risk variants with brain amyloidosis. The data also suggest that AD genes might differentially regulate AD pathologic findings across the disease stages.

The effect of the top 20 Alzheimer disease risk genes on gray-matter density and FDG PET brain metabolism.

INTRODUCTION: We analyzed the effects of the top 20 Alzheimer disease (AD) risk genes on gray-matter density (GMD) and metabolism. METHODS: We ran stepwise linear regression analysis using posterior cingulate hypometabolism and medial temporal GMD as outcomes and all risk variants as predictors while controlling for age, gender, and APOE ε4 genotype. We explored the results in 3D using Statistical Parametric Mapping 8. RESULTS: Significant predictors of brain GMD were SLC24A4/RIN3 in the pooled and mild cognitive impairment (MCI); ZCWPW1 in the MCI; and ABCA7, EPHA1, and INPP5D in the AD groups. Significant predictors of hypometabolism were EPHA1 in the pooled, and SLC24A4/RIN3, NME8, and CD2AP in the normal control group. DISCUSSION: Multiple variants showed associations with GMD and brain metabolism. For most genes, the effects were limited to specific stages of the cognitive continuum, indicating that the genetic influences on brain metabolism and GMD in AD are complex and stage dependent.