The Alzheimer's Disease Neuroimaging Initiative (ADNI), designed as a naturalistic longitudinal study to develop and validate magnetic resonance, positron emission tomography, cerebrospinal fluid, and genetic biomarkers for use in AD clinical trials, has made many impacts in the decade since its inception. The initial 5-year study, ADNI-1, enrolled cognitively normal, mild cognitive impairment (MCI) and AD subjects, and the subsequent studies (ADNI-GO and ADNI-2) added early- and late-MCI cohorts. The development of standardized methods allowed comparison of data gathered across multiple sites, and these data are available to qualified researchers without embargo. ADNI data have been used in >600 publications including those describing relationships between biomarkers, improved methods for disease diagnosis and the prediction of future decline, and identifying novel genetic AD risk loci. ADNI has provided a framework for similar initiatives worldwide.

Observations in animal models suggest that amyloid can cause network hypersynchrony in the early preclinical phase of Alzheimer's disease (AD). The aim of this study was (a) to obtain evidence of paroxysmal hypersynchrony in cognitively intact subjects (CN) with increased brain amyloid load from task-free fMRI exams using a dynamic analysis approach, (b) to investigate if and how hypersynchrony interferes with memory performance, and (c) to describe its relationship with gray and white matter connectivity. Florbetapir-F18 PET and task-free 3T functional and structural MRI were acquired in 47 CN (age = 70.6 ± 6.6), 17 were amyloid pos (florbetapir SUVR >1.11). A parcellation scheme encompassing 382 regions of interest was used to extract regional gray matter volumes, FA-weighted fiber tracts and regional BOLD signals. Graph analysis was used to characterize the gray matter atrophy profile and the white matter connectivity of each subject. The fMRI data was processed using a combination of sliding windows, graph and hierarchical cluster analysis. Each activity cluster was characterized by identifying strength dispersion (difference between pos and neg strength) their maximal and minimal pos and neg strength rois and by investigating their distribution and association with memory performance and gray and white matter connectivity using spearman rank correlations (FDR p < 0.05). The cluster analysis identified eight different activity clusters. Cluster 8 was characterized by the largest strength dispersion indicating hypersynchrony. Its duration/subject was positively correlated with amyloid load (r = 0.42, p = 0.03) and negatively with memory performance (CVLT delayed recall r = -0.39 p = 0.04). The assessment of the regional strength distribution indicated a functional disconnection between mesial temporal structures and the rest of the brain. White matter connectivity was increased in left lateral and mesial temporal lobe and was positively correlated with strength dispersion in the cross-modality analysis suggesting that it enables widespread hypersynchrony. In contrast, precuneus, gray matter connectivity was decreased in the right fusiform gyrus and negatively correlated with high degrees of strength dispersion suggesting that progressing gray matter atrophy could prevent the generation of paroxysmal hypersynchrony in later stages of the disease.

Over the past six months, we have been in discussion with you and the authors of a recent publication in NeuroImage: Clinical (Amyloid pathology fingerprint differentiates posttraumatic stress disorder and traumatic brain injury. Mohamed AZ, et al. Neuroimaging Clinical 2018 Jun 5;19:716-726). We raised a variety of methodological concerns with Mohamed and colleagues and discussion about these concerns led to their preparation of a corrigendum to be published in NeuroImage: Clinical. We appreciate that the authors were responsive to our concerns and made a number of changes as noted in the corrigendum. Ultimately, however, the corrigendum does not address the primary conclusions of the study, which we were unable to replicate. We are troubled by the publication of results that cannot be replicated using identical methods and participant data. This is a letter to the authors to address these concerns.

Objective

To identify a neuroimaging signature predictive of brain amyloidosis as a screening tool to identify individuals with mild cognitive impairment (MCI) that are most likely to have high levels of brain amyloidosis or to be amyloid-free.

Methods

The prediction model cohort included 62 MCI subjects screened with structural magnetic resonance imaging (MRI) and (11) C-labeled Pittsburgh compound B positron emission tomography (PET). We identified an anatomical shape variation-based neuroimaging predictor of brain amyloidosis and defined a structural MRI-based brain amyloidosis score (sMRI-BAS). Amyloid beta positivity (Aβ(+) ) predictive power of sMRI-BAS was validated on an independent cohort of 153 MCI patients with cerebrospinal fluid Aβ1-42 biomarker data but no amyloid PET scans. We compared the Aβ(+) predictive power of sMRI-BAS to those of apolipoprotein E (ApoE) genotype and hippocampal volume, the 2 most relevant candidate biomarkers for the prediction of brain amyloidosis.

Results

Anatomical shape variations predictive of brain amyloidosis in MCI embraced a characteristic spatial pattern known for high vulnerability to Alzheimer disease pathology, including the medial temporal lobe, temporal-parietal association cortices, posterior cingulate, precuneus, hippocampus, amygdala, caudate, and fornix/stria terminals. Aβ(+) prediction performance of sMRI-BAS and ApoE genotype jointly was significantly better than the performance of each predictor separately (area under the curve [AUC] = 0.88 vs AUC = 0.70 and AUC = 0.81, respectively) with >90% sensitivity and specificity at 20% false-positive rate and false-negative rate thresholds. Performance of hippocampal volume as an independent predictor of brain amyloidosis in MCI was only marginally better than random chance (AUC = 0.56).

Interpretation

As one of the first attempts to use an imaging technique that does not require amyloid-specific radioligands for identification of individuals with brain amyloidosis, our findings could lead to development of multidisciplinary/multimodality brain amyloidosis biomarkers that are reliable, minimally invasive, and widely available.

Introduction

Chronic cigarette smoking is associated with increased risk for Alzheimer's disease (AD). The goal of this study was to determine if smoking history moderated the associations of age and APOE genotype (the most robust risk factors for AD) on brain amyloid deposition, glucose metabolism, and neurocognition in cognitively-normal elders.

Methods

Participants (n = 264) were grouped according to their APOE ε4 carrier status (ε4 carrier: APOE4+; non-ε4 carrier: APOE4-) and smoking status (smokers: at least 1 year of smoking during lifetime; never-smokers: no history of smoking). Approximately 89% of the smoking sample was former-smokers. We specifically tested for interactions of smoking status with APOE ε4 carrier status and age on measures of cortical amyloid deposition, glucose metabolism, and neurocognition.

Results

(1) smoking status interacted with APOE ε4 carrier status, where smoker APOE4+ showed lower glucose metabolism and poorer auditory-verbal learning and memory than never-smoking APOE4-, never-smoking APOE4+, and smoking APOE4-; (2) smoking status interacted with age on measures of semantic fluency, processing speed/set-shifting and global neurocognition; smokers, irrespective of APOE ε4 carrier status, demonstrated poorer performance with increasing age than never-smokers; and (3) smoking APOE4+ and never-smoking APOE4+ showed greater cortical amyloid deposition than never-smoking APOE4- and smoking APOE4-.

Conclusions

The findings indicate consideration of smoking history is essential to both better understand the factors associated with neurobiological and neurocognitive abnormalities in elders, and the risk for development of AD-related neuropathology.

Objective

To examine the effect of education (a surrogate measure of cognitive reserve) on FDG-PET brain metabolism in elderly cognitively healthy (HC) subjects with preclinical Alzheimer disease (AD).

Methods

Fifty-two HC subjects (mean age 75 years) with FDG-PET and CSF measurement of Aβ1-42 were included from the prospective Alzheimer's Disease Neuroimaging Initiative biomarker study. HC subjects received a research classification of preclinical AD if CSF Aβ1-42 was <192 pg/mL (Aβ1-42 [+]) vs HC with normal Aβ (Aβ1-42 [-]). In regression analyses, we tested the interaction effect between education and CSF Aβ1-42 status (Aβ1-42 [+] vs Aβ1-42 [-]) on FDG-PET metabolism in regions of interest (ROIs) (posterior cingulate, angular gyrus, inferior/middle temporal gyrus) and the whole brain (voxel-based).

Results

An interaction between education and CSF Aβ1-42 status was observed for FDG-PET in the posterior cingulate (p < 0.001) and angular gyrus ROIs (p = 0.03), but was not significant for the inferior/middle temporal gyrus ROI (p = 0.06), controlled for age, sex, and global cognitive ability (Alzheimer's Disease Assessment Scale-cognitive subscale). The interaction effect was such that higher education was associated with lower FDG-PET in the Aβ1-42 (+) group, but with higher FDG-PET in the Aβ1-42 (-) group. Voxel-based analysis showed that this interaction effect was primarily restricted to temporo-parietal and ventral prefrontal brain areas.

Conclusions

Higher education was associated with lower FDG-PET in preclinical AD (Aβ1-42 [+]), suggesting that cognitive reserve had a compensatory function to sustain cognitive ability in presence of early AD pathology that alters FDG-PET metabolism.

Magnetic resonance (MR) technology produces data on brain structure and activity without relying on radiation or invasive surgery. Magnetic resonance imaging (MRI) creates images, and magnetic resonance spectroscopy (MRS) produces spectra based on the ability of atomic nuclei in tissues to absorb and release pulses of energy. MRS studies of alcohol in the brain reveal that only a portion of the alcohol in the brain can be detected by MR technology, suggesting that alcohol there exists in multiple pools. The pools not visible using MRS is hypothesized to be bound to cell membranes. Indirect evidence from MR studies of chronic alcohol abusers suggests that tolerance to alcohol's effects results in an increased rigidity of cell membranes that forces more alcohol to remain in the MR-visible pool (i.e., the pool not bound to membranes) compared with alcohol in the brains of nontolerant people.

Background

Recent studies have demonstrated that arterial spin labeling magnetic resonance imaging (ASL-MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) identify similar regional abnormalities and have comparable diagnostic accuracy in Alzheimer's disease (AD). The agreement between these modalities in the AD continuum, which is an important concept for early detection and disease monitoring, is yet unclear.

Objective

We aimed to assess the ability of the cerebral blood flow (CBF) measures from ASL-MRI and cerebral metabolic rate for glucose (CMRgl) measures from FDG-PET to distinguish amyloid-β-positive (Aβ+) subjects in the AD continuum from healthy controls.

Methods

The study included asymptomatic, cognitively normal (CN) controls and patients with early mild cognitive impairment (MCI), late MCI, and AD, all with significant levels of cortical Aβ based on their florbetapir PET scans to restrict the study to patients truly in the AD continuum. The discrimination power of each modality was based on the whole-brain patterns of CBF and CMRgl changes identified by partial least squares logistic regression, a multivariate analysis technique.

Results

While CBF changes in the posterior inferior aspects of the brain and a pattern of CMRgl changes in the superior aspects of the brain including frontal and parietal regions best discriminated the Aβ+ subjects in the early disease stages from the Aβ- CN subjects, there was a greater agreement in the whole-brain patterns of CBF and CMRgl changes that best discriminated the Aβ+ subjects from the Aβ- CN subjects in the later disease stages. Despite the differences in the whole-brain patterns of CBF and CMRgl changes, the discriminative powers of both modalities were similar with statistically nonsignificant performance differences in sensitivity and specificity.

Conclusion

The results comparing measurements of CBF to CMRgl add to previous reports that MRI-measured CBF has a similar diagnostic ability to detect AD as has FDG-PET. Our findings that CBF and CMRgl changes occur in different brain regions in Aβ+ subjects across the AD continuum compared with Aβ- CN subjects may be the result of methodological differences. Alternatively, these findings may signal alterations in neurovascular coupling which alter relationships between brain perfusion and glucose metabolism in the AD continuum.

Background

Cigarette smoking has been linked with both increased and decreased risk for Alzheimer's disease (AD). This is relevant for the US military because the prevalence of smoking in the military is approximately 11% higher than in civilians.

Methods

A systematic review of published studies on the association between smoking and increased risk for AD and preclinical and human literature on the relationships between smoking, nicotine exposure, and AD-related neuropathology was conducted. Original data from comparisons of smoking and never-smoking cognitively normal elders on in vivo amyloid imaging are also presented.

Results

Overall, literature indicates that former/active smoking is related to a significantly increased risk for AD. Cigarette smoke/smoking is associated with AD neuropathology in preclinical models and humans. Smoking-related cerebral oxidative stress is a potential mechanism promoting AD pathology and increased risk for AD.

Conclusions

A reduction in the incidence of smoking will likely reduce the future prevalence of AD.

Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities, such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its reduced resolution limits its power for detecting specific disease effects on perfusion patterns. This reduced resolution is compounded by artifacts such as partial volume effects, Gibbs ringing, and aliasing, which are caused by necessarily limited k-space sampling and the subsequent use of discrete Fourier transform (DFT) reconstruction. In this study, a Bayesian modeling procedure (K-Bayes) is developed for the reconstruction of perfusion MRI. The K-Bayes approach (described in detail in Part II: Modeling and Technical Development) combines a process model for the MRI signal in k-space with a Markov random field prior distribution that incorporates high-resolution segmented structural MRI information. A simulation study was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT. The improvements were validated using in vivo perfusion MRI data of the human brain. The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT.