A robust brain signature region approach for episodic memory performance in older adults

Subjective cognitive complaints and future risk of dementia and cognitive impairment, which matters most

Many older adults report subjective cognitive decline (SCD); however, the specific types of complaints most strongly associated with early disease detection remain unclear. This study examines which complaints from the Everyday Cognition Scales (ECog) are associated with progression from normal cognition to mild cognitive impairment (MCI)/dementia. 415 older adults were monitored annually for 5 years, on average. Cox proportional hazards models assessed associations between ECog complaints and progression to MCI/dementia. Follow-up models included depression as a covariate. Numerous Memory (5 items), Language (3 items), Visuospatial (1 item), Planning (2 items), and Organization (1 item) complaints were associated with diagnostic progression. After covarying for depression, remembering appointments and understanding spoken instructions remained significant predictors of diagnostic progression. While previous work has focused largely on memory-based SCD complaints, the current findings support a wider assessment of complaints may be useful in identifying those at risk for a neurodegenerative disease.

A data-driven, multi-domain brain gray matter signature as a powerful biomarker associated with several clinical outcomes

INTRODUCTION

Characterizing pathological changes in the brain that underlie cognitive impairment, including Alzheimer's disease and related disorders, is central to clinical concerns of prevention, diagnosis, and treatment.

METHODS

We describe the properties of a brain gray matter region ("Union Signature") that is derived from four behavior-specific, data-driven signatures in a discovery cohort.

RESULTS

In a separate validation set, the Union Signature demonstrates clinically relevant properties. Its associations with episodic memory, executive function, and Clinical Dementia Rating Sum of Boxes are stronger than those of several standardly accepted brain measures (e.g., hippocampal volume, cortical gray matter) and other previously developed brain signatures. The ability of the Union Signature to classify clinical syndromes among normal, mild cognitive impairment, and dementia exceeds that of the other measures.

DISCUSSION

The Union Signature is a powerful, multipurpose correlate of clinically relevant outcomes and a strong classifier of clinical syndromes.

Highlights

Data-driven brain signatures are potentially valuable in models of cognitive aging.In previous work, we outlined rigorous validation of signatures for memory.This work demonstrates a signature predicting multiple clinical measures.This could be useful in models of interventions for brain support of cognition.

Development of a machine learning algorithm to predict the residual cognitive reserve index

Elucidating the mechanisms by which late-life neurodegeneration causes cognitive decline requires understanding why some individuals are more resilient than others to the effects of brain change on cognition (cognitive reserve). Currently, there is no way of measuring cognitive reserve that is valid (e.g. capable of moderating brain-cognition associations), widely accessible (e.g. does not require neuroimaging and large sample sizes), and able to provide insight into resilience-promoting mechanisms. To address these limitations, this study sought to determine whether a machine learning approach to combining standard clinical variables could (i) predict a residual-based cognitive reserve criterion standard and (ii) prospectively moderate brain-cognition associations. In a training sample combining data from the University of California (UC) Davis and the Alzheimer's Disease Neuroimaging Initiative-2 (ADNI-2) cohort (N = 1665), we operationalized cognitive reserve using an MRI-based residual approach. An eXtreme Gradient Boosting machine learning algorithm was trained to predict this residual reserve index (RRI) using three models: Minimal (basic clinical data, such as age, education, anthropometrics, and blood pressure), Extended (Minimal model plus cognitive screening, word reading, and depression measures), and Full [Extended model plus Clinical Dementia Rating (CDR) and Everyday Cognition (ECog) scale]. External validation was performed in an independent sample of ADNI 1/3/GO participants (N = 1640), which examined whether the effects of brain change on cognitive change were moderated by the machine learning models' cognitive reserve estimates. The three machine learning models differed in their accuracy and validity. The Minimal model did not correlate strongly with the criterion standard (r = 0.23) and did not moderate the effects of brain change on cognitive change. In contrast, the Extended and Full models were modestly correlated with the criterion standard (r = 0.49 and 0.54, respectively) and prospectively moderated longitudinal brain-cognition associations, outperforming other cognitive reserve proxies (education, word reading). The primary difference between the Minimal model-which did not perform well as a measure of cognitive reserve-and the Extended and Full models-which demonstrated good accuracy and validity-is the lack of cognitive performance and informant-report data in the Minimal model. This suggests that basic clinical variables like anthropometrics, vital signs, and demographics are not sufficient for estimating cognitive reserve. Rather, the most accurate and valid estimates of cognitive reserve were obtained when cognitive performance data-ideally augmented by informant-reported functioning-was used. These results indicate that a dynamic and accessible proxy for cognitive reserve can be generated for individuals without neuroimaging data and gives some insight into factors that may promote resilience.

A novel neuroimaging signature for ADRD risk stratification in the community

INTRODUCTION

Early risk stratification for clinical dementia could lead to preventive therapies. We identified and validated a magnetic resonance imaging (MRI) signature for Alzheimer's disease (AD) and related dementias (ARDR).

METHODS

An MRI ADRD signature was derived from cortical thickness maps in Framingham Heart Study (FHS) participants with AD dementia and matched controls. The signature was related to the risk of ADRD and cognitive function in FHS. Results were replicated in the University of California Davis Alzheimer's Disease Research Center (UCD-ADRC) cohort.

RESULTS

Participants in the bottom quartile of the signature had more than three times increased risk for ADRD compared to those in the upper three quartiles (P < 0.001). Greater thickness in the signature was related to better general cognition (P < 0.01) and episodic memory (P = 0.01). Results replicated in UCD-ADRC.

DISCUSSION

We identified a robust neuroimaging biomarker for persons at increased risk of ADRD. Other cohorts will further test the validity of this biomarker.

The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022

The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.

A new accuracy metric under three classes when subclasses are involved and its confidence interval estimation

"Compound multi-class classification" refers to the setting where three or more main classes are involved and at least one of the main classes have multiple subclasses. A common practice in evaluating biomarker performance under "compound multi-class classification" is "subclasses pooling." In this article, we first explore the downsides of accuracy metrics based on pooled data. Then we propose a new accuracy measure proper for "compound multi-class classification" with three ordinal main classes, namely "volume under compoundR O C $$ ROC $$ surface (V U S C $$ VU{S}_C $$ )." The proposedV U S C $$ VU{S}_C $$ evaluates the accuracy of a biomarker appropriately by identifying main classes without requiring specification of an ordering for marker values of subclasses within each main class. For confidence interval estimation ofV U S C $$ VU{S}_C $$ , both parametric and nonparametric methods are studied, and simulation studies are carried out to assess coverage probabilities. A subset of Alzheimer's Disease Neuroimaging Initiative study dataset is analyzed.

Toward a statistical validation of brain signatures as robust measures of behavioral substrates

The "brain signature of cognition" concept has garnered interest as a data-driven, exploratory approach to better understand key brain regions involved in specific cognitive functions, with the potential to maximally characterize brain substrates of behavioral outcomes. Previously we presented a method for computing signatures of episodic memory. However, to be a robust brain measure, the signature approach requires a rigorous validation of model performance across a variety of cohorts. Here we report validation results and provide an example of extending it to a second behavioral domain. In each of two discovery data cohorts, we derived regional brain gray matter thickness associations for two domains: neuropsychological and everyday cognition memory. We computed regional association to outcome in 40 randomly selected discovery subsets of size 400 in each cohort. We generated spatial overlap frequency maps and defined high-frequency regions as "consensus" signature masks. Using separate validation datasets, we evaluated replicability of cohort-based consensus model fits and explanatory power by comparing signature model fits with each other and with competing theory-based models. Spatial replications produced convergent consensus signature regions. Consensus signature model fits were highly correlated in 50 random subsets of each validation cohort, indicating high replicability. In comparisons over each full cohort, signature models outperformed other models. In this validation study, we produced signature models that replicated model fits to outcome and outperformed other commonly used measures. Signatures in two memory domains suggested strongly shared brain substrates. Robust brain signatures may therefore be achievable, yielding reliable and useful measures for modeling substrates of behavioral domains.

P-tau and neurodegeneration mediate the effect of β-amyloid on cognition in non-demented elders

BACKGROUND

There are many pathological changes in the brains of Alzheimer's disease (AD) patients. For many years, the mainstream view on the pathogenesis of AD believes that β-amyloid (Aβ) usually acts independently in addition to triggering functions. However, the evidence now accumulating indicates another case that these pathological types have synergies. The objective of this study was to investigate whether effects of Aβ pathology on cognition were mediated by AD pathologies, including tau-related pathology (p-tau), neurodegeneration (t-tau, MRI measurements), axonal injury (NFL), synaptic dysfunction (neurogranin), and neuroinflammation (sTREM2, YKL-40).

METHODS

Three hundred seventy normal controls (CN) and 623 MCI patients from the ADNI (Alzheimer's Disease Neuroimaging Initiative) database were recruited in this research. Linear mixed-effects models were used to evaluate the associations of baseline Aβ with cognitive decline and biomarkers of several pathophysiological pathways. Causal mediation analyses with 10,000 bootstrapped iterations were conducted to explore the mediation effects of AD pathologies on cognition.

RESULTS

Tau-related pathology, neurodegeneration, neuroinflammation are correlated with the concentration of Aβ, even in CN participants. The results show that age, gender, and APOE ε4 carrier status have a moderating influence on some of these relationships. There is a stronger association of Aβ with biomarkers and cognitive changes in the elderly and females. In CN group, Aβ pathology is directly related to poor cognition and has no mediating effect (p < 0.05). In mild cognitive impairment, tau-related pathology (26.15% of total effect) and neurodegeneration (14.8% to 47.0% of total effect) mediate the impact of Aβ on cognition.

CONCLUSIONS

In conclusion, early Aβ accumulation has an independent effect on cognitive decline in CN and a tau, neurodegeneration-dependent effect in the subsequent cognitive decline in MCI patients.

A robust brain signature region approach for episodic memory performance in older adults

This scientific commentary refers to ‘A robust brain signature region approach for episodic memory performance in older adults’ by Fletcher et al. (doi:10.1093/brain/awab007).

Functional reserve: The residual variance in instrumental activities of daily living not explained by brain structure, cognition, and demographics

OBJECTIVE

Cognitive reserve is a concept that explains individual differences in resilience to brain pathology and susceptibility to poor late-life cognitive outcomes. We evaluate the analogous concept of "Functional Reserve," defined as the difference between observed functional abilities and those predicted by brain structure, cognitive performance, and demographics. This study aims to validate the construct of functional reserve by testing its utility in predicting clinical outcomes and exploring its predictors.

METHOD

Longitudinal data collected annually for up to 7 years from 1,084 older adults (ndementia = 163; nMCI = 333; nCN = 523) were analyzed. Functional reserve was operationalized as the residual variance in the Lawton-Brody Instrumental Activities of Daily Living (IADL) Scale after accounting for demographics (sex/gender, race, ethnicity, education), neuropathology (gray matter, hippocampal, and white matter hyperintensity volumes), and cognition (executive function, verbal episodic memory, semantic memory, and spatial function). Structural equation models estimated (a) functional reserve's associations with 7-year changes in clinical diagnosis and disease severity and (b) predictors of functional reserve.

RESULTS

Functional reserve was lower in dementia versus cognitively normal individuals. Higher baseline functional reserve was associated with lower concurrent dementia severity and slower clinical progression and attenuated the association of cognition with concurrent dementia severity. Physical function and apathy were the strongest predictors of functional reserve.

CONCLUSIONS

Results provide preliminary validation of functional reserve for explaining individual differences in susceptibility to IADL dysfunction independent of neuropathology, cognition, and demographics. Physical functioning and apathy are promising modifiable intervention targets to enhance functional reserve in the context of brain atrophy and cognitive decline. (PsycInfo Database Record (c) 2021 APA, all rights reserved).