Identification of Patterns of Cognitive Impairment for Early Detection of Dementia

TL;DR

This paper introduces a novel method for identifying individual-specific cognitive impairment patterns to enable early, personalized detection of dementia, using large-scale population data and clustering techniques.

Contribution

It presents a new approach to detect personalized impairment patterns for early dementia diagnosis, leveraging ensemble feature selection and clustering on extensive baseline data.

Findings

Patterns correspond to clinically accepted MCI variants

Clusters can predict likely impairment routes

Method applied to 24,000 subjects from NACC

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.