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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Are empirically-derived subtypes of mild cognitive impairment consistent with conventional subtypes?

Lindsay R Clark1, Lisa Delano-Wood, David J Libon

  • 1San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA.

Journal of the International Neuropsychological Society : JINS
|April 5, 2013
PubMed

Insights

Diagnosing mild cognitive impairment (MCI) using different criteria yields varied subtypes. One method identified distinct Dysexecutive and Visuospatial subtypes, unlike conventional approaches.

Area of Science:

  • Neuroscience
  • Psychology
  • Gerontology

Background:

  • Identifying early signs of dementia, known as prodromes, is crucial for developing effective treatments.
  • Mild cognitive impairment (MCI) is a transitional stage between normal aging and dementia.
  • Standardized diagnostic criteria for MCI are essential for consistent research and clinical practice.

Purpose of the Study:

  • To compare two distinct diagnostic methods for identifying mild cognitive impairment (MCI).
  • To determine the consistency of empirically-derived MCI subtypes between these methods and conventional subtypes.
  • To investigate whether different MCI diagnostic criteria yield comparable or distinct patient profiles.

Main Methods:

  • Participants were diagnosed with MCI using either conventional Petersen/Winblad criteria or comprehensive neuropsychological criteria (Jak et al., 2009).
  • Hierarchical cluster and discriminant function analyses were employed to examine the resulting MCI samples.
  • Empirically-derived MCI subtypes were compared with conventional subtypes (amnestic, non-amnestic, single-domain, multi-domain).

Main Results:

  • Neuropsychological profiles differed significantly based on the diagnostic criteria used for MCI.
  • Both criteria identified an Amnestic MCI subtype, potentially indicating prodromal Alzheimer's disease (AD), and a Mixed subtype.
  • Comprehensive criteria uniquely identified Dysexecutive and Visuospatial MCI subtypes; conventional criteria yielded a subtype with normal performance, suggesting potential false positives.

Conclusions:

  • The choice of diagnostic criteria influences the identification of MCI subtypes.
  • Comprehensive criteria may offer a more nuanced classification of MCI, revealing subtypes like Dysexecutive and Visuospatial.
  • Further research is needed to validate these empirically-derived MCI subtypes and their association with neuropathology and dementia progression.

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