Alzheimer's Disease Neuroimaging Initiative

BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer’s Disease

Alzheimer’s disease (AD), the most common form of dementia, shares many aspects of abnormal brain aging. We present a novel magnetic resonance imaging (MRI)-based biomarker that predicts the individual progression of mild cognitive impairment (MCI) to AD on the basis of pathological brain aging patterns. By employing kernel regression methods, the expression of normal brain-aging patterns forms the basis to estimate the brain age of a given new subject. If the estimated age is higher than the chronological age, a positive brain age gap estimation (BrainAGE) score indicates accelerated atrophy and is considered a risk factor for conversion to AD. Here, the BrainAGE framework was applied to predict the individual brain ages of 195 subjects with MCI at baseline, of which a total of 133 developed AD during 36 months of follow-up (corresponding to a pre-test probability of 68%). The ability of the BrainAGE framework to correctly identify MCI-converters was compared with the performance of commonly used cognitive scales, hippocampus volume, and state-of-the-art biomarkers derived from cerebrospinal fluid (CSF). With accuracy rates of up to 81%, BrainAGE outperformed all cognitive scales and CSF biomarkers in predicting conversion of MCI to AD within 3 years of follow-up. Each additional year in the BrainAGE score was associated with a 10% greater risk of developing AD (hazard rate: 1.10 [CI: 1.07–1.13]). Furthermore, the post-test probability was increased to 90% when using baseline BrainAGE scores to predict conversion to AD. The presented framework allows an accurate prediction even with multicenter data. Its fast and fully automated nature facilitates the integration into the clinical workflow. It can be exploited as a tool for screening as well as for monitoring treatment options.

The effects of intracranial volume adjustment approaches on multiple regional MRI volumes in healthy aging and Alzheimer's disease

In neurodegeneration research, normalization of regional volumes by intracranial volume (ICV) is important to estimate the extent of disease-driven atrophy. There is little agreement as to whether raw volumes, volume-to-ICV fractions or regional volumes from which the ICV factor has been regressed out should be used for volumetric brain imaging studies. Using multiple regional cortical and subcortical volumetric measures generated by Freesurfer (51 in total), the main aim of this study was to elucidate the implications of these adjustment approaches. Magnetic resonance imaging (MRI) data were analyzed from two large cohorts, the population-based PIVUS cohort (N = 406, all subjects age 75) and the Alzheimer disease Neuroimaging Initiative (ADNI) cohort (N = 724). Further, we studied whether the chosen ICV normalization approach influenced the relationship between hippocampus and cognition in the three diagnostic groups of the ADNI cohort (Alzheimers disease, mild cognitive impairment, and healthy individuals). The ability of raw vs. adjusted hippocampal volumes to predict diagnostic status was also assessed. In both cohorts raw volumes correlate positively with ICV, but do not scale directly proportionally with it. The correlation direction is reversed for all volume-to-ICV fractions, except the lateral and third ventricles. Most gray matter fractions are larger in females, while lateral ventricle fractions are greater in males. Residual correction effectively eliminated the correlation between the regional volumes and ICV and removed gender differences. The association between hippocampal volumes and cognition was not altered by ICV normalization. Comparing prediction of diagnostic status using the different approaches, small but significant differences were found. The choice of normalization approach should be carefully considered when designing a volumetric brain imaging study.

Fine mapping of genetic variants in BIN1, CLU, CR1 and PICALM for association with cerebrospinal fluid biomarkers for Alzheimer's disease.

Recent genome-wide association studies of Alzheimers disease (AD) have identified variants in BIN1, CLU, CR1 and PICALM that show replicable association with risk for disease. We have thoroughly sampled common variation in these genes, genotyping 355 variants in over 600 individuals for whom measurements of two AD biomarkers, cerebrospinal fluid (CSF) 42 amino acid amyloid beta fragments (Aβ42) and tau phosphorylated at threonine 181 (ptau181), have been obtained. Association analyses were performed to determine whether variants in BIN1, CLU, CR1 or PICALM are associated with changes in the CSF levels of these biomarkers. Despite adequate power to detect effects as small as a 1.05 fold difference, we have failed to detect evidence for association between SNPs in these genes and CSF Aβ42 or ptau181 levels in our sample. Our results suggest that these variants do not affect risk via a mechanism that results in a strong additive effect on CSF levels of Aβ42 or ptau181.

Association between anticholinergic medication use and cognition, brain metabolism, and brain atrophy in cognitively normal older adults

IMPORTANCE The use of anticholinergic (AC) medication is linked to cognitive impairment and an increased risk of dementia. To our knowledge, this is the first study to investigate the association between AC medication use and neuroimaging biomarkers of brain metabolism and atrophy as a proxy for understanding the underlying biology of the clinical effects of AC medications. OBJECTIVE To assess the association between AC medication use and cognition, glucose metabolism, and brain atrophy in cognitively normal older adults from the Alzheimers Disease Neuroimaging Initiative (ADNI) and the Indiana Memory and Aging Study (IMAS). DESIGN, SETTING, AND PARTICIPANTS The ADNI and IMAS are longitudinal studies with cognitive, neuroimaging, and other data collected at regular intervals in clinical and academic research settings. For the participants in the ADNI, visits are repeated 3, 6, and 12 months after the baseline visit and then annually. For the participants in the IMAS, visits are repeated every 18 months after the baseline visit (402 cognitively normal older adults in the ADNI and 49 cognitively normal older adults in the IMAS were included in the present analysis). Participants were either taking (hereafter referred to as the AC+ participants [52 from the ADNI and 8 from the IMAS]) or not taking (hereafter referred to as the AC- participants [350 from the ADNI and 41 from the IMAS]) at least 1 medication with medium or high AC activity. Data analysis for this study was performed in November 2015. MAIN OUTCOMES AND MEASURES Cognitive scores, mean fludeoxyglucose F 18 standardized uptake value ratio (participants from the ADNI only), and brain atrophy measures from structural magnetic resonance imaging were compared between AC+ participants and AC- participants after adjusting for potential confounders. The total AC burden score was calculated and was related to target measures. The association of AC use and longitudinal clinical decline (mean [SD] follow-up period, 32.1 [24.7] months [range, 6-108 months]) was examined using Cox regression. RESULTS The 52 AC+ participants (mean [SD] age, 73.3 [6.6] years) from the ADNI showed lower mean scores on Weschler Memory Scale-Revised Logical Memory Immediate Recall (raw mean scores: 13.27 for AC+ participants and 14.16 for AC- participants; P = .04) and the Trail Making Test Part B (raw mean scores: 97.85 seconds for AC+ participants and 82.61 seconds for AC- participants; P = .04) and a lower executive function composite score (raw mean scores: 0.58 for AC+ participants and 0.78 for AC- participants; P = .04) than the 350 AC- participants (mean [SD] age, 73.3 [5.8] years) from the ADNI. Reduced total cortical volume and temporal lobe cortical thickness and greater lateral ventricle and inferior lateral ventricle volumes were seen in the AC+ participants relative to the AC- participants. CONCLUSIONS AND RELEVANCE The use of AC medication was associated with increased brain atrophy and dysfunction and clinical decline. Thus, use of AC medication among older adults should likely be discouraged if alternative therapies are available.

Validating predicted biological effects of Alzheimer’s disease associated SNPs using CSF biomarker levels

Recent large-scale genetic studies of late-onset Alzheimers disease have identified risk variants in CALHM1, GAB2, and SORL1. The mechanisms by which these genes might modulate risk are not definitively known. CALHM1 and SORL1 may alter amyloid-β (Aβ) levels and GAB2 may influence phosphorylation of the tau protein. In this study we have analyzed disease associated genetic variants in each of these genes for association with cerebrospinal fluid (CSF) Aβ or tau levels in 602 samples from two independent CSF series. We failed to detect association between CSF Aβ42 levels and single nucleotide polymorphisms in SORL1 despite substantial statistical power to detect association. While we also failed to detect association between variants in GAB2 and CSF tau levels, power to detect this association was limited. Finally, our data suggest that the minor allele of rs2986017, in CALHM1, is marginally associated with CSF Aβ42 levels. This association is consistent with previous reports that this non-synonymous coding substitution results in increased Aβ levels in vitro and provides support for an Aβ-related mechanism for modulating risk for Alzheimers disease.

Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification

Background Individuals with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimers disease (AD). In this study, we developed a multivariate prognostic model for predicting MCI-to-dementia progression at the individual patient level. Methods Using baseline data from 259 MCI patients and a probabilistic, kernel-based pattern classification approach, we trained a classifier to distinguish between patients who progressed to AD-type dementia (n = 139) and those who did not (n = 120) during a three-year follow-up period. More than 750 variables across four data sources were considered as potential predictors of progression. These data sources included risk factors, cognitive and functional assessments, structural magnetic resonance imaging (MRI) data, and plasma proteomic data. Predictive utility was assessed using a rigorous cross-validation framework. Results Cognitive and functional markers were most predictive of progression, while plasma proteomic markers had limited predictive utility. The best performing model incorporated a combination of cognitive/functional markers and morphometric MRI measures and predicted progression with 80% accuracy (83% sensitivity, 76% specificity, AUC = 0.87). Predictors of progression included scores on the Alzheimers Disease Assessment Scale, Rey Auditory Verbal Learning Test, and Functional Activities Questionnaire, as well as volume/cortical thickness of three brain regions (left hippocampus, middle temporal gyrus, and inferior parietal cortex). Calibration analysis revealed that the model is capable of generating probabilistic predictions that reliably reflect the actual risk of progression. Finally, we found that the predictive accuracy of the model varied with patient demographic, genetic, and clinical characteristics and could be further improved by taking into account the confidence of the predictions. Conclusions We developed an accurate prognostic model for predicting MCI-to-dementia progression over a three-year period. The model utilizes widely available, cost-effective, non-invasive markers and can be used to improve patient selection in clinical trials and identify high-risk MCI patients for early treatment.

Cortical atrophy in patients with cerebral amyloid angiopathy: a case-control study

BACKGROUND Loss of cortical grey matter is a diagnostic marker of many neurodegenerative diseases, and is a key mediator of cognitive impairment. We postulated that cerebral amyloid angiopathy (CAA), characterised by cortical vascular amyloid deposits, is associated with cortical tissue loss independent of parenchymal Alzheimers disease pathology. We tested this hypothesis in patients with hereditary cerebral haemorrhage with amyloidosis-Dutch type (HCHWA-D), a monogenetic disease with minimal or no concomitant Alzheimers disease pathology, as well as in patients with sporadic CAA and healthy and Alzheimers disease controls. METHODS In this observational case-control study, we included six groups of participants: patients diagnosed with HCHWA-D using genetic testing; healthy controls age-matched to the HCHWA-D group; patients with probable sporadic CAA without dementia; two independent cohorts of healthy controls age-matched to the CAA group; and patients with Alzheimers disease age-matched to the CAA group. De-identified (but unmasked) demographic, clinical, radiological, and genetic data were collected at Massachusetts General Hospital (Boston, MA, USA), at Leiden University (Leiden, Netherlands), and at sites contributing to Alzheimers Disease Neuroimaging Initiative (ADNI). The primary outcome measure was cortical thickness. The correlations between cortical thickness and structural lesions, and blood-oxygen-level-dependent time-to-peak (BOLD-TTP; a physiological measure of vascular dysfunction) were analysed to understand the potential mechanistic link between vascular amyloid and cortical thickness. The radiological variables of interest were quantified using previously validated computer-assisted tools, and all results were visually reviewed to ensure their accuracy. RESULTS Between March 15, 2006, and Dec 1, 2014, we recruited 369 individuals (26 patients with HCHWA-D and 28 age-matched, healthy controls; 63 patients with sporadic CAA without dementia; two healthy control cohorts with 63 and 126 individuals; and 63 patients with Alzheimers disease). The 26 patients with HCHWA-D had thinner cortices (2·31 mm [SD 0·18]) than the 28 healthy controls (mean difference -0·112 mm, 95% CI -0·190 to -0·034, p=0·006). The 63 patients with sporadic CAA without dementia had thinner cortices (2·17 mm [SD 0·11]) than the two healthy control cohorts (n=63, mean difference -0·14 mm, 95% CI -0·17 to -0·10, p<0·0001; and n=126, -0·10, -0·13 to -0·06, p<0·0001). All differences remained independent in multivariable analyses. The 63 patients with Alzheimers disease displayed more severe atrophy than the patients with sporadic CAA (2·1 mm [SD 0·14], difference 0·07 mm, 95% CI 0·11 to 0·02, p=0·005). We found strong associations between cortical thickness and vascular dysfunction in the patients with HCHWA-D (ρ=-0·58, p=0·003) or sporadic CAA (r=-0·4, p=0·015), but not in controls. Vascular dysfunction was identified as a mediator of the effect of hereditary CAA on cortical atrophy, accounting for 63% of the total effect. INTERPRETATION The appearance of cortical thinning in patients with HCHWA-D indicates that vascular amyloid is an independent contributor to cortical atrophy. These results were reproduced in patients with the more common sporadic CAA. Our findings also suggest that CAA-related cortical atrophy is at least partly mediated by vascular dysfunction. Our results also support the view that small vessel diseases such as CAA can cause cortical atrophy even in the absence of Alzheimers disease, a conclusion that can help radiologists, neurologists, and other clinicians who diagnose these common geriatric conditions. FUNDING National Institutes of Health.

Apathy as a feature of prodromal Alzheimer's disease: an FDG-PET ADNI study†

The goal of this study is to evaluate brain metabolism in mild cognitive impairment (MCI) patients with and without apathy (as determined by the Neuropsychiatric Inventory Questionnaire).

Biomarkers and cognitive endpoints to optimize trials in Alzheimer's disease

To find the combination of candidate biomarkers and cognitive endpoints to maximize statistical power and minimize cost of clinical trials of healthy elders at risk for cognitive decline due to Alzheimers disease.

Neuropsychiatric symptoms predict hypometabolism in preclinical Alzheimer disease

Objective: To identify regional brain metabolic dysfunctions associated with neuropsychiatric symptoms (NPS) in preclinical Alzheimer disease (AD). Methods: We stratified 115 cognitively normal individuals into preclinical AD (both amyloid and tau pathologies present), asymptomatic at risk for AD (either amyloid or tau pathology present), or healthy controls (no amyloid or tau pathology present) using [18F]florbetapir PET and CSF phosphorylated tau biomarkers. Regression and voxel-based regression models evaluated the relationships between baseline NPS measured by the Neuropsychiatric Inventory (NPI) and baseline and 2-year change in metabolism measured by [18F]fluorodeoxyglucose (FDG) PET. Results: Individuals with preclinical AD with higher NPI scores had higher [18F]FDG uptake in the posterior cingulate cortex (PCC), ventromedial prefrontal cortex, and right anterior insula at baseline. High NPI scores predicted subsequent hypometabolism in the PCC over 2 years only in individuals with preclinical AD. Sleep/nighttime behavior disorders and irritability and lability were the components of the NPI that drove this metabolic dysfunction. Conclusions: The magnitude of NPS in preclinical cases, driven by sleep behavior and irritability domains, is linked to transitory metabolic dysfunctions within limbic networks vulnerable to the AD process and predicts subsequent PCC hypometabolism. These findings support an emerging conceptual framework in which NPS constitute an early clinical manifestation of AD pathophysiology.