Citations

AbstractIntroductionThe Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020.MethodsWe identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion.ResultsDisease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity.DiscussionADNI has had a profound impact in improving clinical trials for AD.

Abstract Introduction Asymptomatic and mildly symptomatic dominantly inherited Alzheimer's disease mutation carriers (DIAD‐MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD‐MC and could help predict risk for dementia during trial enrollment. Methods We created a dementia risk score by entering standardized gray‐matter volumes from 231 DIAD‐MC into a logistic regression to classify participants with and without dementia. The score's predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD‐MC followed longitudinally. Results Our risk score separated asymptomatic versus demented DIAD‐MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%–98.2%]) and improved prediction beyond established methods based on familial age of onset. Discussion Individualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD‐MC participants for prevention trials.

Abstract Cognitive resilience is an important modulating factor of cognitive decline in Alzheimer’s disease, but the functional brain mechanisms that support cognitive resilience remain elusive. Given previous findings in normal ageing, we tested the hypothesis that higher segregation of the brain’s connectome into distinct functional networks represents a functional mechanism underlying cognitive resilience in Alzheimer’s disease. Using resting-state functional MRI, we assessed both resting-state functional MRI global system segregation, i.e. the balance of between-network to within-network connectivity, and the alternate index of modularity Q as predictors of cognitive resilience. We performed all analyses in two independent samples for validation: (i) 108 individuals with autosomal dominantly inherited Alzheimer’s disease and 71 non-carrier controls; and (ii) 156 amyloid-PET-positive subjects across the spectrum of sporadic Alzheimer’s disease and 184 amyloid-negative controls. In the autosomal dominant Alzheimer’s disease sample, disease severity was assessed by estimated years from symptom onset. In the sporadic Alzheimer’s sample, disease stage was assessed by temporal lobe tau-PET (i.e. composite across Braak stage I and III regions). In both samples, we tested whether the effect of disease severity on cognition was attenuated at higher levels of functional network segregation. For autosomal dominant Alzheimer’s disease, we found higher functional MRI-assessed system segregation to be associated with an attenuated effect of estimated years from symptom onset on global cognition (P = 0.007). Similarly, for patients with sporadic Alzheimer’s disease, higher functional MRI-assessed system segregation was associated with less decrement in global cognition (P = 0.001) and episodic memory (P = 0.004) per unit increase of temporal lobe tau-PET. Confirmatory analyses using the alternate index of modularity Q revealed consistent results. In conclusion, higher segregation of functional connections into distinct large-scale networks supports cognitive resilience in Alzheimer’s disease.

Background: Cerebral amyloid angiopathy with related inflammation (CAA-ri) is a rare age-associated disorder characterized by an inflammatory response to amyloid in cerebral blood vessels. CAA-ri is often treated with corticosteroids, but response to treatment is variable. Objective: To assess the relationship between clinical and paraclinical measures and outcomes in patients with CAA-ri treated with high doses of methylprednisolone. Methods: Longitudinal clinical course, and results from serum and cerebrospinal fluid (CSF) testing, electroencephalography, and neuroimaging were reviewed from 11 prospectively-accrued CAA-ri patients diagnosed, treated, and followed at Barnes Jewish Hospital (St. Louis, MO, USA). Magnetic resonance imaging (MRI) changes were quantified using a scoring system validated in cases of amyloid related imaging abnormality (ARIA-E). Clinical outcomes were assessed as change in modified Rankin Scale (ΔmRS) from baseline to final assessment (median 175 days from treatment with high doses of methylprednisolone; range, 31–513). Results: Worse outcomes following methylprednisolone treatment were associated with requirement for intensive care unit admission (median ΔmRS, 5 versus 1.5; p = 0.048), CSF pleocytosis (median ΔmRS 4.5 versus 1; p = 0.04), or lower CSF Aβ40 at presentation (rho = –0.83; p = 0.02), and diffusion restriction (median ΔmRS 4 versus 1.5; p = 0.03) or higher late ARIA-E scores (rho = 0.70; p = 0.02) on MRI, but not preexisting cognitive decline (median ΔmRS 2 versus 2; p = 0.66). Conclusion: Clinical and paraclinical measures associated with outcomes may inform clinical counseling and treatment decisions in patients with CAA-ri. Baseline cognitive status was not associated with treatment responsiveness.

AbstractIntroductionMachine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.MethodsLongitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non‐carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status.ResultsThe Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2 = 0.95), fluorodeoxyglucose (R2 = 0.93), and atrophy (R2 = 0.95) in mutation carriers compared to non‐carriers.DiscussionResults suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.

AbstractBackgroundA reliable history is critical to establish accurate clinical diagnoses, prioritize investigations and select appropriate treatments; yet few studies have considered the factors that influence the reliability of medical histories. Recognizing this, we determined the accuracy of medical history in community‐dwelling older adults engaged in longitudinal studies of memory and aging.MethodThe association between participant‐specific factors and accurate reporting of stroke or diabetes were evaluated using multivariable logistic regression in 1,401 participants enrolled in studies at the Knight Alzheimer Disease Research Center (Saint Louis, Missouri), including 425 participants with dementia (30.3%). History was verified with a reliable collateral source in all participants. Stroke and diabetes were selected as index variables, as gold standard measures—brain MRI for stroke and hemoglobinA1c (HbA1c) for diabetes—were routinely obtained in participants.Result25/862 (2.9%) participants endorsed a history of stroke, which was confirmed on MRI in 8/25 (positive predictive value, 17.4%). Stroke was detected on MRI in 38/837 (4.5%) of those without reported stroke (negative predictive value, 95.5%). Older age (OR per decade=1.7, 95%CI: 1.2‐2.5) and use of an unrelated collateral source (OR=2.0, 95%CI: 1.03‐3.7) were associated with greater odds of inaccurate reporting of stroke history. 190/1322 (14.4%) participants reported a history of diabetes, which was confirmed with HbA1c in 105/190 (positive predictive value, 55.3%). HbA1c 6.5% was detected in 29/1132 (2.6%) of those who denied diabetes (negative predictive value, 97.4%). African Americans (OR=1.6, 95%CI: 1.02‐2.6) and those with hypertension (OR=2.6, 95%CI: 1.5‐4.2) or HbA1c compatible with impaired glucose tolerance (OR=4.2, 95%CI: 2.7‐6.4) had greater odds of reporting a history of diabetes. Cognitive impairment was not associated with inaccurate reporting of stroke or diabetes.ConclusionThe accuracy of reported history of diabetes and stroke was low in older community‐dwelling participants, independent of cognitive status. A history of diabetes or stroke should be objectively confirmed when relevant to decision making in clinical and research settings.