# Predicting cognitive change using functional, structural, and neuropsychological predictors

## TL;DR

This study explores how brain activation, along with structural and neuropsychological measures, can predict cognitive decline in people at risk for Alzheimer's disease.

## Contribution

The study demonstrates that brain activation is a strong predictor of cognitive decline and improves prediction when combined with other measures.

## Key findings

- Functional activation predicted cognitive change with 87.6% accuracy and 98.7% specificity.
- Multimodal models combining functional, structural, and neuropsychological data improved sensitivity and explanatory power.
- Hyperactivation may serve as an early marker for Alzheimer's disease progression.

## Abstract

To effectively address Alzheimer’s disease, it is crucial to understand its earliest manifestations, underlying mechanisms and early markers of progression. Recent findings of very early brain activation anomalies highlight their potential for early disease characterization and predicting future cognitive decline. Our objective was to evaluate the value of brain activation—both individually and in combination with structural and neuropsychological measures—for predicting cognitive change. The study included 105 individuals from the Consortium for the Early Identification of Alzheimer’s Disease–Quebec cohort who exhibited subjective cognitive decline or mild cognitive impairment. Cognitive decline was assessed by calculating the slope of Montreal Cognitive Assessment scores using regression models across successive assessments, and individuals were characterized as either decliners or stable based on clinically reliable change. We evaluated cognitive decline predictions using unimodal models for each class of predictors and multimodal models that combined these predictors. Functional activation emerged as a strong predictor of cognitive change (R²=52.5%), with 87.6% accuracy and 98.7% specificity, performing comparably to structural and neuropsychological measures. Although the unimodal functional model exhibited high specificity, indicating that functional abnormalities frequently predict future decline, it had low sensitivity (60%), meaning that the absence of abnormalities does not rule out future decline. Multimodal models provided greater explanatory power than unimodal models and greater sensitivity than the functional model. These findings highlight the potential role of early brain activation anomalies in the early detection of future cognitive changes, offering valuable insights for clinicians and researchers in assessing cognitive decline risk and refining clinical trial criteria.

Décarie-Labbé et al. suggest hyperactivation could serve as an early marker of Alzheimer’s disease. Their multimodal study identified brain activation as a strong predictor of cognitive decline in at-risk individuals, with excellent specificity but lower sensitivity. Predictive power and sensitivity increased when activation was combined with structural and neuropsychological measures.

Graphical Abstract