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More than a feeling: Expressive style influences cortical speech tracking in subjective cognitive decline

Matthew King-Hang Ma, Yun Feng, Cloris Pui-Hang Li, Manson Cheuk-Man Fong

TL;DR

The CTS of higher-level linguistic features while listening to prosodically flat speech may serve as a potential biomarker for early-stage cognitive decline.

Abstract

Subjective cognitive decline (SCD) doubles dementia risk. This study investigates how self-perceived cognitive worsening manifests in neural dynamics during naturalistic speech perception. EEG was collected from 60 cognitively normal older adults while they listened to speech varied in prosodic contexts, categorized by expressive styles (scrambled, descriptive, dialogue, exciting). Encoding models mapping three speech representations -- acoustic, subsyllabic segmentation and phonotactic features -- to the ongoing EEG signals were built. Cortical tracking strength (CTS) showed that models fitted with linguistic features outperformed acoustic ones. Crucially, a greater degree of SCD was associated with weaker CTS of (1) higher-level linguistic but not acoustic features, and (2) prosodically flat speech (scrambled and descriptive). Thus, the CTS of higher-level linguistic features while listening to prosodically flat speech may serve as a potential biomarker for early-stage cognitive decline.

More than a feeling: Expressive style influences cortical speech tracking in subjective cognitive decline

TL;DR

The CTS of higher-level linguistic features while listening to prosodically flat speech may serve as a potential biomarker for early-stage cognitive decline.

Abstract

Subjective cognitive decline (SCD) doubles dementia risk. This study investigates how self-perceived cognitive worsening manifests in neural dynamics during naturalistic speech perception. EEG was collected from 60 cognitively normal older adults while they listened to speech varied in prosodic contexts, categorized by expressive styles (scrambled, descriptive, dialogue, exciting). Encoding models mapping three speech representations -- acoustic, subsyllabic segmentation and phonotactic features -- to the ongoing EEG signals were built. Cortical tracking strength (CTS) showed that models fitted with linguistic features outperformed acoustic ones. Crucially, a greater degree of SCD was associated with weaker CTS of (1) higher-level linguistic but not acoustic features, and (2) prosodically flat speech (scrambled and descriptive). Thus, the CTS of higher-level linguistic features while listening to prosodically flat speech may serve as a potential biomarker for early-stage cognitive decline.

Paper Structure

This paper contains 18 sections, 3 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Materials and Methods
  3. Participants
  4. Stimuli and experimental procedures
  5. Data acquisition and preprocessing
  6. Feature extraction
  7. Acoustic features
  8. Subsyllabic segmentation and phonotactic features
  9. Multivariate temporal response function (mTRF)
  10. Statistical analyses
  11. Results
  12. Subsyllabic models outperform the acoustic model
  13. SCD modulates CTS in linguistic (not acoustic) models
  14. SCD modulates CTS in rich (not flat) styles
  15. Discussion
  16. ...and 3 more sections

Figures (2)

  • Figure 1: Methodology. A. Three different set of stimulus features (Aco: Acoustic, Seg: Segmentation, Pho: Phonotactic); B. Training and testing of mTRF models; and the derivation of cortical tracking strength. C. The grouping of 64 electrodes into 6 sites.
  • Figure 2: A. CTS across models; B. SCDS $\times$ Model interaction. C. SCDS $\times$ ExpressStyle interaction. Y-axis: estimated marginal means; ribbons: 95% CIs.