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Coupled neural systems underlie the production and comprehension of naturalistic narrative speech

This study explores the neural systems underlying the production and comprehension of naturalistic narrative speech. It investigates the correlation between speech production and comprehension areas, using a novel neural coupling analysis. Results suggest the presence of coupled networks involved in speech processing.

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Coupled neural systems underlie the production and comprehension of naturalistic narrative speech

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  1. Coupled neural systems underlie the production and comprehension of naturalistic narrative speech Lauren J. Silbert, Christopher K. Honet, Erez Simony, David Poeppel, and Uri Hasson Nicco Reggente & Jeff Chiang Presented for Neuroimaging+ Journal Club on Thursday December 4th by

  2. Speech ProductionExperimental Setup / Design • 1 highly trained reference speaker (RS) • 2 Secondary Speakers (SS) • RS verbally told an autobiographical story 12 times in the scanner. • First time was “spontaneous” (previous study) • SSs told RS’s story • Control nonsense phrases “goo da gaba la la la fee foo fa” spoken by RS and SSs. • To the beat of a metronome

  3. Speech ProductionExperimental Setup / Design

  4. Speech ProductionPreprocessing • Head motions were <1mm • Rigid body alignment to first time point • Signal from “noise ROIs” in white matter and scalp were regressed out. • Physiological fluctuations + residual motion artifacts • White-matter (MPRAGE), scalp, eyeball

  5. Speech ProductionTime-Warp Analysis • Need to temporally align each retelling Time 1 Time 2

  6. Speech ProductionTime-Warp Analysis • Need the BOLD signal to follow Time 1 Time 2

  7. Speech ProductionIntra-subject Correlation • Assess the reliability of the neural time courses across retellings. • TC1 correlated with mean(TC2:TCN) • Voxel-by-Voxel, exhaustively Example: motor cortex

  8. Speech ProductionInter-subject Correlation • Same as Intra, but reference speaker compared to secondary speakers.

  9. Phase-Randomized Bootstrapping • Used to test for significance of correlations • Generated a null distribution by generating many timeseries from the same power spectrum by varying phase

  10. Speech ProductionIntra-subject Correlation (Results)

  11. Speech ProductionInter-subject Correlation (Results)

  12. Speech Production“Spontaneous” vs. Rehearsed (Results) • Of the same story. • When comparing original story (repeated 12 times) to a new, spontaneous story… • they get no significant reliable results from intra-correlations.

  13. Nonsense Speech ProductionIntra-subject Correlation (Results)

  14. Speech Production Network

  15. Speech Comprehension • 11 subjects listened to original, spontaneous story • Used TTL pulse to synchronize story and fMRI acquisition across subjects (basically each TR mapped across time across subjects) • Listened to nonsense phrases • Asked to freely recall story after the scan • These were evaluated by independent raters

  16. Nonsense Speech ComprehensionIntra-subject Correlation (Results)

  17. Intra-Subject Correlation maps

  18. Neural Coupling procedure • GLM of the form:

  19. Production-Comprehension areas using Neural Coupling

  20. Neural coupling compared to overlay

  21. Results Summary

  22. Discussion • PNAS Direct Submission • Spontaneous Listening Only? • Rote/boredom effects may have drowned out true “listening” signal. • Neural Coupling of Nonsense? • Include future TR in neural coupling equation?

  23. Discussion • They try to directly tackle the problem of “natural speech” • Alternate methods to existing ones (Phase randomized bootstrap, neural coupling) • Though they might be reinventing the wheel…

  24. Speech ProductionIntra-SC Lateralization Index • A way to quantify reliability of a hemisphere in an exploratory analysis.

  25. Lateralization Index • “Reliability” = average reliable responses (correlations) in ROIs. • What exactly is an average correlation?

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