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Updated: Mar 2, 2026
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
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Cognitive modeling as an interface between brain and behavior: Measuring the semantic decline in mild cognitive
Brendan T Johns1, Vanessa Taler2, David B Pisoni3
1Department of Communicative Disorders and Sciences, University at Buffalo.
Summary
Analyzing verbal fluency semantic paths can detect early cognitive changes in mild cognitive impairment (MCI). This method offers a promising approach for identifying individuals at risk for Alzheimer's disease.
Area of Science:
- Cognitive Neuroscience
- Computational Psychiatry
Background:
- Mild cognitive impairment (MCI) is a precursor to Alzheimer's disease, characterized by memory deficits without dementia.
- Verbal fluency tasks are standard diagnostic tools, but traditional analysis (item count) is limited.
Purpose of the Study:
- To develop and validate a cognitive model for analyzing verbal fluency semantic paths.
- To investigate if semantic path analysis can detect early changes in memory search processes associated with MCI.
Main Methods:
- A cognitive model integrating lexical information and a memory search process (Luce choice rule).
- Utilized a semantic space model for representing word associations.
- Applied the model to analyze verbal fluency data from individuals with and without MCI.
Main Results:
- The cognitive model successfully differentiated memory search processes between individuals developing MCI and controls.
- Semantic path analysis revealed distinct patterns in individuals with MCI.
Conclusions:
- Formal analysis of verbal fluency semantic paths is a promising method for detecting early cognitive changes in MCI.
- This approach may aid in the early identification of individuals at risk for Alzheimer's disease.
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