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Neural encoding of phonetic features relates to phonological processing in PPA

Heather R. Dial1, 2*, Benjamin D. Zinszer1, 2, Bharath Chandrasekaran1, 2, 3, 4, 5, 6 and Maya Henry1, 2
  • 1 University of Texas at Austin, Communication Sciences and Disorders, United States
  • 2 University of Texas at Austin, Multimodal Neuroimaging Initiative, United States
  • 3 University of Texas at Austin, Psychology, United States
  • 4 University of Texas at Austin, Linguistics, United States
  • 5 University of Texas at Austin, Institute of Mental Health Research, United States
  • 6 University of Texas at Austin, Institute for Neuroscience, United States

Introduction Primary progressive aphasia (PPA) is a disorder characterized by a gradual loss of speech and language functions resulting from neurodegenerative disease. Three PPA subtypes have been defined, each with a unique neuroanatomical/behavioral profile (Gorno-Tempini et al., 2011). The nonfluent/agrammatic variant (nfvPPA) is characterized by fronto-insular atrophy, syntactic processing deficits and motor speech impairment. The semantic variant (svPPA) presents with anterior temporal lobe atrophy and loss of core semantic knowledge. The logopenic variant (lvPPA) is characterized by temporo-parietal atrophy and phonological processing deficits. Whereas there is research investigating language production, single word comprehension and syntactic comprehension in PPA, evidence is lacking in regard to the perception of continuous speech, which is more akin to everyday communication. In the current study, we examined continuous speech perception in PPA by relating EEG signals to a continuous speech stream using multivariate temporal response function (mTRF) modeling, a recent innovation in EEG analysis that directly compares continuously-varying aspects of a stimulus with fluctuations in a participant’s EEG signal (DiLiberto et al., 2015; Crosse et al., 2016). Recent research demonstrated TRF model changes associated with language impairment (dyslexia, DiLiberto et al., 2018). Therefore, this approach has the potential to inform a mechanistic account of changes to speech perception in PPA. Method Seven participants with PPA diagnosis (1 lvPPA, 1 svPPA, 5 nfvPPA; Mini-Mental State Exam scores ≥ 17), consistent with diagnostic criteria, listened to 15 one-minute segments of the audiobook Alice’s Adventures in Wonderland while EEG responses were continuously measured using a 32-channel electrode cap. We modeled phonetic information in the speech stream with a 19-dimension phonetic feature space (Mesgarani et al., 2014). Phonetic encoding and temporal alignment of this model with the speech stream was completed by expert linguists. We compared the phonetic feature model to EEG responses using the mTRF. Estimated EEG data predicted by the mTRF were compared to observed EEG data via Pearson’s correlation, providing a measure of model fit (r) in each participant. Correlation coefficients were transformed using Fisher’s r-to-z transformation, and compared to behavioral measures of linguistic processing: three involving phonological input (WAB Repetition, digit span, WAB Auditory Comprehension) and one without phonological input (Northwestern Anagram Test; NAT, a test of grammar). We predicted that higher z-scores in the mTRF would be related to better performance on tasks involving phonological input, but would not be related to NAT performance. Results/Discussion Strong correlations were observed between performance on measures involving phonological processing and mTRF fit (r’s ≥ .67, p’s ≤ .099), though statistical significance was only observed for one measure (Figure 1a-c). MTRF fit was not correlated with the NAT (r = .08, p = .866; Figure 1d). Together, these data suggest that neural encoding of phonetic features is a valid measure of phonetic processing and that EEG responses may encode a continuous speech stream differently depending on underlying linguistic deficits. In ongoing research we will apply higher-level linguistic models (e.g., Broderick et al., 2018) to investigate whether deficits in other domains (e.g., semantics) are also reflected by the mTRF.

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Acknowledgements

This work was supported by NIH/NIDCD R01DC016291 (MH), NIH/NIDCD R03DC013403 (MH), Darrell K Royal Research Fund Fund for Alzheimer’s Disease (MH), NIH/NIDCD R01DC015504 (BC), NIH/NIDCD R01DC013315 (BC) and Texas Speech-Language-Hearing Foundation Lear Ashmore Research Fund (HD).

References

Broderick, M. P., Anderson, A. J., Di Liberto, G. M., Crosse, M. J., & Lalor, E. C. (2018). Electrophysiological correlates of semantic dissimilarity reflect the comprehension of natural, narrative speech. Current Biology, 28, 1-7

Crosse, M. J., Di Liberto, G. M., Bednar, A., & Lalor, E. C. (2016). The multivariate temporal response function (mTRF) toolbox: a MATLAB toolbox for relating neural signals to continuous stimuli. Frontiers in Human Neuroscience, 10, 604.

DiLiberto, G., O’Sullivan, J., & Lalor, E. (2015). Low-frequency cortical entrainment to speech reflects phoneme-level processing. Current Biology, 25, 2457-2465.

Di Liberto, G. M., Peter, V., Kalashnikova, M., Goswami, U., Burnham, D., & Lalor, E. C. (2018). Atypical cortical entrainment to speech in the right hemisphere underpins phonemic deficits in dyslexia. NeuroImage, 175, 70-79.

Gorno-Tempini, M., Hillis, A., Weintraub, S., Kertesz, A., Mendez, M., Cappa, S., … & Manes,
F. (2011). Classification of primary progressive aphasia and its variants. Neurology, 76, 1006-1014.

Keywords: multivariate temporal response function, primary progressive aphasia, phonetic feature encoding, phonological processing, Speech Perception, continuous speech processing

Conference: Academy of Aphasia 56th Annual Meeting, Montreal, Canada, 21 Oct - 23 Oct, 2018.

Presentation Type: poster presentation

Topic: Eligible for a student award

Citation: Dial HR, Zinszer BD, Chandrasekaran B and Henry M (2019). Neural encoding of phonetic features relates to phonological processing in PPA. Conference Abstract: Academy of Aphasia 56th Annual Meeting. doi: 10.3389/conf.fnhum.2018.228.00017

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Received: 30 Apr 2018; Published Online: 22 Jan 2019.

* Correspondence: Dr. Heather R Dial, University of Texas at Austin, Communication Sciences and Disorders, Austin, United States, heather.raye.dial@gmail.com