Identification of Patterns of Cognitive Impairment for Early Detection of Dementia

TL;DR

This paper tackles early dementia detection by identifying individual-specific patterns of cognitive impairment rather than relying solely on binary normal/MCI classification. It introduces a two-stage method: ensemble wrapper feature selection to derive a compact, multi-domain feature set and an unsupervised clustering step using $t$-SNE and region-growing segmentation to discover impairment patterns that map to MCI subtypes. The results show three clusters corresponding to amnestic and non-amnestic multi-domain MCIs, with normals distributed across clusters, enabling potential pre-symptomatic routing and personalized, shorter cognitive batteries for periodic assessment. Using 24,000 NACC subjects, the approach demonstrates heterogeneity in MCI and offers a scalable framework for early detection and longitudinal monitoring with practical implications for population screening.

Abstract

Early detection of dementia is crucial to devise effective interventions. Comprehensive cognitive tests, while being the most accurate means of diagnosis, are long and tedious, thus limiting their applicability to a large population, especially when periodic assessments are needed. The problem is compounded by the fact that people have differing patterns of cognitive impairment as they progress to different forms of dementia. This paper presents a novel scheme by which individual-specific patterns of impairment can be identified and used to devise personalized tests for periodic follow-up. Patterns of cognitive impairment are initially learned from a population cluster of combined normals and MCIs, using a set of standardized cognitive tests. Impairment patterns in the population are identified using a 2-step procedure involving an ensemble wrapper feature selection followed by cluster identification and analysis. These patterns have been shown to correspond to clinically accepted variants of MCI, a prodrome of dementia. The learned clusters of patterns can subsequently be used to identify the most likely route of cognitive impairment, even for pre-symptomatic and apparently normal people. Baseline data of 24,000 subjects from the NACC database was used for the study.

Figures (2)

• Figure 1: 2-D visualization of the dataset using t-SNE
• Figure 2: Feature variations of CN and MCI populations within each subgroup