Lim, Yen Ying

AbstractBackgroundThe residual approach has found wide application in researching cognitive resilience, a phenomenon conceptually understood as cognitive performance being better‐than‐typical for an individual, despite apparent AD pathology. The standard residual approach extracts information about an individual’s resilience from the residuals of a linear model predicting cognition. This approach is subject to several limiting assumptions. Here, we explore the implications of these assumptions and propose an alternative predictive modeling approach which circumvents them.MethodsOur alternative approach relies on training a machine learning model on a sample of participants who follow a “typical” clinical progression (Fig. 1A). Residuals are then extracted for a heterogeneous dataset that potentially includes resilient participants by comparing observed cognitive outcomes to those predicted by the “typical” model (Fig. 1B). Additionally, our estimate can be refined by effectively estimating and removing model error from the extracted residual by leveraging a second predictive model (Fig. 1C). Using simulated data, we explore the assumptions made by the standard approach and the consequences of their violation. We then contrast this against our approach.ResultsOur simulations demonstrated that the standard residual approach only extracted accurate resilience estimates if the predictors used in the model explained cognition sufficiently well, while being unrelated to resilience itself. That is, as the predictors explain more of the resilience, the residuals became increasingly determined by model error (Fig. 2). We found that residuals achieved with our approach are more precise and provide individualized estimates that are unaffected by the aforementioned trade‐off (Fig. 3).ConclusionsOur approach holds several advantages over the standard approach: The definition of resilience can be freely adjusted and enforced via different definitions of “typical”, it allows for incorporating complex, non‐linear models without removing relevant information from the residual, the error component of the residual can be estimated through cross‐validation, and all predictors that potentially explain cognition can be included into the model without losing resilience information from the residual. Our approach can be used to measure any phenomenon that can be operationalized as a deviation from a typical outcome and, thus, is also applicable for researching resistance to pathology.

AbstractIntroductionAlthough some members of families with autosomal dominant Alzheimer's disease mutations learn their mutation status, most do not. How knowledge of mutation status affects clinical disease progression is unknown. This study quantifies the influence of mutation awareness on clinical symptoms, cognition, and biomarkers.MethodsMutation carriers and non‐carriers from the Dominantly Inherited Alzheimer Network (DIAN) were stratified based on knowledge of mutation status. Rates of change on standard clinical, cognitive, and neuroimaging outcomes were examined.ResultsMutation knowledge had no associations with cognitive decline, clinical progression, amyloid deposition, hippocampal volume, or depression in either carriers or non‐carriers. Carriers who learned their status mid‐study had slightly higher levels of depression and lower cognitive scores.DiscussionKnowledge of mutation status does not affect rates of change on any measured outcome. Learning of status mid‐study may confer short‐term changes in cognitive functioning, or changes in cognition may influence the determination of mutation status.

Abstract Several concepts, which in the aggregate get might be used to account for “resilience” against age‐ and disease‐related changes, have been the subject of much research. These include brain reserve, cognitive reserve, and brain maintenance. However, different investigators have use these terms in different ways, and there has never been an attempt to arrive at consensus on the definition of these concepts. Furthermore, there has been confusion regarding the measurement of these constructs and the appropriate ways to apply them to research. Therefore the reserve, resilience, and protective factors professional interest area, established under the auspices of the Alzheimer's Association, established a whitepaper workgroup to develop consensus definitions for cognitive reserve, brain reserve, and brain maintenance. The workgroup also evaluated measures that have been used to implement these concepts in research settings and developed guidelines for research that explores or utilizes these concepts. The workgroup hopes that this whitepaper will form a reference point for researchers in this area and facilitate research by supplying a common language.

ObjectivePrevious studies suggest that the brain‐derived neurotrophic factor (BDNF) Val66Met (rs6265) polymorphism may influence symptom onset in Alzheimer's disease (AD). Our recent cross‐sectional findings suggest that Met66 may influence clinical expression in dominantly inherited AD (DIAD) through its effects on tau. However, it remains unclear whether carriage of Met66 in DIAD results in faster increases in cerebrospinal fluid (CSF) tau and ptau181, and whether these increases are associated with accelerated brain volume loss and memory decline.MethodsA total of 211 subjects (101 mutation noncarriers, 110 mutation carriers), who were cognitively normal, as defined by a Clinical Dementia Rating global score of 0, completed assessments of cognitive function, neuroimaging, and CSF sampling over 3.5 years as part of the Dominantly Inherited Alzheimer's Network.ResultsIn mutation carriers, Met66 carriers showed faster memory decline (4×), hippocampal volume loss (16×), and CSF tau and ptau181increases (6×) than Val66 homozygotes.BDNFdid not influence rates of cortical β‐amyloid accumulation or change in CSF Aβ42levels in mutation carriers. In mutation noncarriers,BDNFgenotype had no effect on change in cognition, brain volume, cortical β‐amyloid accumulation, or change in any CSF measures of tau, ptau181, and CSF Aβ42.InterpretationAs in sporadic AD, the deleterious effects of β‐amyloid on cognitive function, brain volume loss, and CSF tau in DIAD mutation carriers are less in Val66 homozygotes. TheBDNFVal66Met polymorphism should be considered as a potential moderator of clinical trial outcomes in current treatment and prevention trials in DIAD and sporadic AD. Ann Neurol 2018;84:424–435