An Explainable-AI Based Approach Towards Measuring Cognitive Reserve

Abstract

Cognitive Reserve (CR) refers to the brain's ability to compensate for brain damage or age-related changes, which can explain why some individuals show greater cognitive resilience to brain pathology despite damage or age-related changes. Understanding CR is crucial for identifying factors that contribute to cognitive decline among individuals. Currently, there is no direct, valid and widely accepted method for quantifying CR. To address this gap, we conducted a systematic review regarding approaches used by researchers and identified that Machine Learning (ML) based approaches offer promising potential for developing reliable, data-driven, and accessible methods to quantify CR. However, ML models have been known for their black-box nature due to their lack of transparency and interpretability which makes it difficult for clinicians to trust the decision making processes of these models. To address this limitation, a literature review was conducted using Google Scholar and 21 relevant papers were included in the final systematic review. Our review highlights that while ML-based approaches enhance CR mea-surement, the lack of standardized proxies variables and model transparency limits clinical adoption. Our reviewed approach will bring transparency and interpretability in measuring CR.

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