Citations

Background: Amyloid-β (Aβ) plaques and tau protein tangles in the brain are the defining ‘A’ and ‘T’ hallmarks of Alzheimer’s disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative (‘N’) biomarkers comprise the “ATN framework” of AD. Current methods to detect Aβ/tau pathology include cerebrospinal fluid (invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (promising but mainly still in development). Objective: To develop a non-invasive and widely available structural MRI-based framework to quantitatively predict the amyloid and tau measurements. Methods: With MRI-based hippocampal multivariate morphometry statistics (MMS) features, we apply our Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) method combined with the ridge regression model to individual amyloid/tau measure prediction. Results: We evaluate our framework on amyloid PET/MRI and tau PET/MRI datasets from the Alzheimer’s Disease Neuroimaging Initiative. Each subject has one pair consisting of a PET image and MRI scan, collected at about the same time. Experimental results suggest that amyloid/tau measurements predicted with our PASCP-MP representations are closer to the real values than the measures derived from other approaches, such as hippocampal surface area, volume, and shape morphometry features based on spherical harmonics. Conclusion: The MMS-based PASCP-MP is an efficient tool that can bridge hippocampal atrophy with amyloid and tau pathology and thus help assess disease burden, progression, and treatment effects.

AbstractIntroductionMultiple positron emission tomography (PET) tracers are available for amyloid imaging, posing a significant challenge to consensus interpretation and quantitative analysis. We accordingly developed and validated a deep learning model as a harmonization strategy.MethodA Residual Inception Encoder‐Decoder Neural Network was developed to harmonize images between amyloid PET image pairs made with Pittsburgh Compound‐B and florbetapir tracers. The model was trained using a dataset with 92 subjects with 10‐fold cross validation and its generalizability was further examined using an independent external dataset of 46 subjects.ResultsSignificantly stronger between‐tracer correlations (P < .001) were observed after harmonization for both global amyloid burden indices and voxel‐wise measurements in the training cohort and the external testing cohort.DiscussionWe proposed and validated a novel encoder‐decoder based deep model to harmonize amyloid PET imaging data from different tracers. Further investigation is ongoing to improve the model and apply to additional tracers.

AbstractBackgroundAPOE4 gene dose, the number of apolipoprotein E ‐ɛ4 (APOE4) ɛ4 alleles in a person’s genotype, is associated with higher Alzheimer’s disease (AD) risk and younger median age at dementia onset. We previously found relationships between PET, plasma, and CSF measurements of amyloid‐β (Aβ) pathophysiology in APOE4 gene dose. Here, we characterize the core AD CSF biomarkers (Aβ42/40, pTau181, tTau), the neurodegeneration marker neurofilament light chain (NfL) and the glial biomarkers soluble TREM2 (sTREM2), and glial fibrillary acidic protein (GFAP) measurements in age‐matched 48‐70 year‐old cognitively unimpaired (CU) APOE4 homozygotes (HMs), heterozygotes (HTs) and non‐carriers (NCs) from the Arizona cohort.MethodCSF biomarker measurements were performed at the University of Gothenburg using Lumipulse and ELISA immunoassays. CSF biomarker measurements were characterized and compared using linear‐tend ANOVAs in 12 HMs, 17 HTs, and 20 NCs who were CU, 48‐70 years old and did not differ significantly in their age, sex, or educational level.ResultAs expected, CSF Aβ42/40 ratios were inversely associated with APOE4 gene dose (linear trend, HM<HT<NC, p<0.05), and tTau/Aβ42 and pTau181/Aβ42 ratios were directly associated with APOE4 gene dose (HM>HT>NC p<0.05). Non‐significant trends suggested that CSF sTREM2 and sTREM2/pTau181 measurements were inversely associated with APOE4 gene dose (HM<HT≤NC, (0.05<p≤0.06). We failed to detect significant differences or linear trends in CSF pTau181, tTau, GFAP, or NfL measurements among these small subject groups.ConclusionAPOE4 gene dose is associated with measures of CSF Aβ42/40 and Aβ‐related tau pathophysiology, reflecting higher Aβ plaque burden in CU subjects close to their estimated ages at clinical onset. Additional studies are needed to clarify relationships between different Aβ, tau, neurodegenerative, inflammatory, and other CSF, or blood‐based biomarkers, and APOE4 gene dose in larger subject groups and at other ages.

Background: A univariate neurodegeneration biomarker (UNB) based on MRI with strong statistical discrimination power would be highly desirable for studying hippocampal surface morphological changes associated with APOE ɛ4 genetic risk for AD in the cognitively unimpaired (CU) population. However, existing UNB work either fails to model large group variances or does not capture AD induced changes. Objective: We proposed a subspace decomposition method capable of exploiting a UNB to represent the hippocampal morphological changes related to the APOE ɛ4 dose effects among the longitudinal APOE ɛ4 homozygotes (HM, N = 30), heterozygotes (HT, N = 49) and non-carriers (NC, N = 61). Methods: Rank minimization mechanism combined with sparse constraint considering the local continuity of the hippocampal atrophy regions is used to extract group common structures. Based on the group common structures of amyloid-β (Aβ) positive AD patients and Aβ negative CU subjects, we identified the regions-of-interest (ROI), which reflect significant morphometry changes caused by the AD development. Then univariate morphometry index (UMI) is constructed from these ROIs. Results: The proposed UMI demonstrates a more substantial statistical discrimination power to distinguish the longitudinal groups with different APOE ɛ4 genotypes than the hippocampal volume measurements. And different APOE ɛ4 allele load affects the shrinkage rate of the hippocampus, i.e., HM genotype will cause the largest atrophy rate, followed by HT, and the smallest is NC. Conclusion: The UMIs may capture the APOE ɛ4 risk allele-induced brain morphometry abnormalities and reveal the dose effects of APOE ɛ4 on the hippocampal morphology in cognitively normal individuals.

Background: Altered cerebral glucose metabolism, especially prominent in APOE ɛ4 carriers, occurs years prior to symptoms in Alzheimer’s disease (AD). We recently found an association between a higher ratio of plasma apolipoprotein E4 (apoE4) over apoE3, and cerebral glucose hypometabolism in cognitively healthy APOE ɛ3/ɛ4 subjects. Plasma apoE does not cross the blood-brain barrier, hence we speculate that apoE is linked to peripheral glucose metabolism which is known to affect glucose metabolism in the brain. Objective: Explore potential associations between levels of plasma insulin and glucose with previously acquired plasma apoE, cerebral metabolic rate of glucose (CMRgl), gray matter volume, and neuropsychological test scores. Methods: Plasma insulin and glucose levels were determined by ELISA and a glucose oxidase assay whereas apoE levels were earlier quantified by mass-spectrometry in 128 cognitively healthy APOE ɛ3/ɛ4 subjects. Twenty-five study subjects had previously undergone FDG-PET and structural MRI. Results: Lower plasma apoE3 associated with higher plasma glucose but not insulin in male subjects and subjects with a body mass index above 25. Negative correlations were found between plasma glucose and CMRgl in the left prefrontal and bilateral occipital regions. These associations may have functional implications since glucose levels in turn were negatively associated with neuropsychological test scores. Conclusion: Plasma apoE3 but not apoE4 may be involved in insulin-independent processes governing plasma glucose levels. Higher plasma glucose, which negatively affects brain glucose metabolism, was associated with lower plasma apoE levels in APOE ɛ3/ɛ4 subjects. High plasma glucose and low apoE levels may be a hazardous combination leading to an increased risk of AD.

Background: Besides their other roles, brain imaging and other biomarkers of Alzheimer’s disease (AD) have the potential to inform a cognitively unimpaired (CU) person’s likelihood of progression to mild cognitive impairment (MCI) and benefit subject selection when evaluating promising prevention therapies. We previously described that among baseline FDG-PET and MRI measures known to be preferentially affected in the preclinical and clinical stages of AD, hippocampal volume was the best predictor of incident MCI within 2 years (79%sensitivity/78%specificity), using standard automated MRI volumetric algorithmic programs, binary logistic regression, and leave-one-out procedures. Objective: To improve the same prediction by using different hippocampal features and machine learning methods, cross-validated via two independent and prospective cohorts (Arizona and ADNI). Methods: Patch-based sparse coding algorithms were applied to hippocampal surface features of baseline TI-MRIs from 78 CU adults who subsequently progressed to amnestic MCI in approximately 2 years (“progressors”) and 80 matched adults who remained CU for at least 4 years (“nonprogressors”). Nonprogressors and progressors were matched for age, sex, education, and apolipoprotein E4 allele dose. We did not include amyloid or tau biomarkers in defining MCI. Results: We achieved 92%prediction accuracy in the Arizona cohort, 92%prediction accuracy in the ADNI cohort, and 90%prediction accuracy when combining the two demographically distinct cohorts, as compared to 79%(Arizona) and 72%(ADNI) prediction accuracy using hippocampal volume. Conclusion: Surface multivariate morphometry and sparse coding, applied to individual MRIs, may accurately predict imminent progression to MCI even in the absence of other AD biomarkers.

Background: Whether brain-derived neurotrophic factor (BDNF) Met carriage impacts the risk or progression of Alzheimer’s disease (AD) is unknown. Objective: To evaluate the interaction of BDNF Met and APOE4 carriage on cerebral metabolic rate for glucose (CMRgl), amyloid burden, hippocampus volume, and cognitive decline among cognitively unimpaired (CU) adults enrolled in the Arizona APOE cohort study. Methods: 114 CU adults (mean age 56.85 years, 38% male) with longitudinal FDG PET, magnetic resonance imaging, and cognitive measures were BDNF and APOE genotyped. A subgroup of 58 individuals also had Pittsburgh B (PiB) PET imaging. We examined baseline CMRgl, PiB PET amyloid burden, CMRgl, and hippocampus volume change over time, and rate of change in cognition over an average of 15 years. Results: Among APOE4 carriers, BDNF Met carriers had significantly increased amyloid deposition and accelerated CMRgl decline in regions typically affected by AD, but without accompanying acceleration of cognitive decline or hippocampal volume changes and with higher baseline frontal CMRgl and slower frontal decline relative to the Val/Val group. The BDNF effects were not found among APOE4 non-carriers. Conclusion: Our preliminary studies suggest that there is a weak interaction between BDNF Met and APOE4 on amyloid-β plaque burden and longitudinal PET measurements of AD-related CMRgl decline in cognitively unimpaired late-middle-aged and older adults, but with no apparent effect upon rate of cognitive decline. We suggest that cognitive effects of BDNF variants may be mitigated by compensatory increases in frontal brain activity—findings that would need to be confirmed in larger studies.

AbstractIntroductionWe previously characterized associations between brain imaging measurements of amyloid‐β (Aβ) plaque burden and apolipoprotein E (APOE) ε4 gene dose in a small number of cognitively unimpaired late‐middle‐aged APOE ε4 homozygotes (HMs), heterozygotes (HTs), and noncarriers (NCs). We now characterize cross‐sectional Aβ plaque, tau tangle, and cortical atrophy (neurodegeneration) measurements, classifications, and associations with age in a larger number of unimpaired HMs, HTs, and NCs over a wider age range.MethodsWe analyzed 11C Pittsburgh compound B (Aβ) positron emission tomography (PET), flortaucipir (tau) PET, and volumetric magnetic resonance imaging data from 164 study participants of age 47–86 years, including 26 APOE ε4 HMs, 48 HTs, and 90 NCs matched for age and sex.ResultsAβ PET measurements rose, plateaued at the respective ages of 68 and 76, and then declined with age in unimpaired HM and HT groups. Compared with NCs, these two groups began to have significantly higher Aβ PET measurements at ages 62 and 70, respectively, and no longer had significantly higher measurements by ages 71 and 78, respectively. They began to have significantly higher entorhinal cortex tau PET measurements at ages 66 and 70, respectively, and no longer had significantly higher measurements by ages 74 and 78, respectively. Brain atrophy measurements tended to decline slowly with age in all three genetic groups. Their elevated tau PET measurements were attributable to those with positive Aβ PET scans. 41.0%, 18.0%, and 5.0% of the 47‐ to 70‐year‐old HMs, HTs, and NCs and 25.0%, 79.0%, and 38.0% of the 71‐ to 86‐year‐old HMs, HTs, and NCs had positive Aβ PET scans, and the long‐term recall memory scores are significantly higher in the older HMs than in HT and NC groups, suggesting resistance to Aβ deposition in those HMs who remained unimpaired at older ages.ConclusionsThis study provides information about Aβ plaque burden, tau tangle burden, and neurodegeneration in cognitively unimpaired persons at three levels of genetic risk for AD. Unimpaired APOE ε4 HMs can be studied before their 70s to evaluate the understanding of factors, processes, and interventions involved in the predisposition to and prevention of AD, and after their 70s, to discover factors, processes, and interventions involved in the resilience or resistance to and prevention of AD.