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Detecting Mild Cognitive Impairment (MCI) is improved by analyzing temporal language markers from conversations. A novel harmonization method enhances prediction accuracy by mitigating individual speaking style differences in early Alzheimer's disease detection.

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Area of Science:

  • Neuroscience
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Mild Cognitive Impairment (MCI) is a precursor to Alzheimer's disease, necessitating early detection for timely intervention.
  • Linguistic markers in conversation offer a promising, cost-effective method for MCI identification.
  • Analyzing the temporal dynamics of language, rather than aggregated features, may reveal crucial cognitive information.

Purpose of the Study:

  • To investigate the effectiveness of using temporal sequences of linguistic markers for MCI detection.
  • To develop and evaluate a novel temporal harmonization method to address subject-specific speaking style variations.
  • To enhance the accuracy of MCI prediction by leveraging finer-grained conversational data.

Main Methods:

  • Utilized 6,771 conversations from 74 participants in the I-CONECT clinical trial.
  • Extracted a 99-dimensional feature set from 30-minute conversation sessions, including linguistic features, syntactic complexity, and lexical diversity.
  • Developed a temporal harmonization method using adversarial training (Seq2Seq Harmonization, Subject Classifier, Cognitive Classifier) to mitigate subject-specific linguistic variations.

Main Results:

  • Temporal sequences of linguistic markers improved MCI detection performance compared to aggregated single-conversation outputs.
  • The proposed temporal harmonization method significantly increased subject classification performance, achieving an AUC of 0.720 compared to 0.647 without harmonization.
  • The study demonstrated improved MCI detection by analyzing temporal linguistic features with harmonization.

Conclusions:

  • Temporal sequences of language markers provide valuable insights for detecting Mild Cognitive Impairment (MCI).
  • Temporal harmonization effectively removes subject-specific linguistic variations, leading to enhanced cognitive detection.
  • This approach offers a robust method for early MCI detection using semi-structured conversational data.