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Related Experiment Video

Updated: Jan 22, 2026

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

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

Published on: January 11, 2020

Verbal fluency as a screening tool for mild cognitive impairment.

Michelle McDonnell¹, Lauren Dill², Stella Panos^3,4

¹Loma Linda VA Healthcare System, Loma Linda, CA, USA.

International Psychogeriatrics

|July 2, 2019

Summary

Semantic and phonemic fluency effectively distinguish normal aging, mild cognitive impairment, and Alzheimer's disease. Semantic fluency showed greater predictive power for accurate dementia screening.

Keywords:

Alzheimer’s disease dementia mild cognitive impairment screening

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Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

Area of Science:

Neuropsychology
Gerontology
Cognitive Science

Background:

Early and accurate diagnosis of dementia is crucial for effective patient management.
Distinguishing between normal aging, amnestic-Mild Cognitive Impairment (a-MCI), and Alzheimer's disease (AD) presents a clinical challenge.
Verbal fluency tests are commonly used in neuropsychological assessments.

Purpose of the Study:

To assess the diagnostic utility of semantic (e.g., animal naming) and phonemic (e.g., FAS) verbal fluency tasks.
To determine the ability of these fluency measures to discriminate between normal aging, a-MCI, and AD.
To identify which type of verbal fluency is a stronger predictor of diagnostic classification.

Main Methods:

Utilized binary logistic regressions, multinomial regressions, and discriminant analysis.
Evaluated 232 participants (99 normal aging, 90 a-MCI, 43 AD) with a mean age of 65.75 years.
Administered the Mini-Mental State Examination (MMSE) and the Controlled Oral Word Association Test.

Main Results:

Both semantic and phonemic fluency significantly predicted diagnostic classification.
Semantic fluency accounted for a larger proportion of the discriminant ability within the predictive model.
Verbal fluency measures demonstrated efficacy in differentiating cognitive statuses.

Conclusions:

Verbal fluency, especially semantic fluency, serves as a valuable tool for screening cognitive decline.
These findings suggest that verbal fluency tests can be efficient in identifying potential dementia cases in clinical settings.
Semantic fluency may offer a more robust indicator for early detection of neurodegenerative conditions.