Disentangling consonants and vowels in auditory cortices using an oscillation paradigm
Notice bibliographique
Résumé
The auditory cortices contain tonotopic maps1. Phonemes may to some degree be organized similarly in phonotopic maps2,3. However, fMRI’s low temporal resolution challenges the localization of fast speed sounds. Here, we combined an oscillation-based protocol with continuous repetitive syllable presentation during fast fMRI acquisition to map phonemes in the brain. We aimed to disentangle the effects of vowels and consonants, despite being presented together in syllables, at two different oscillation frequencies4. We acquired fMRI scans of 23 healthy participants using a fast fMRI protocol at 3T (TR=371ms, multiband-EPI). Participants listened to continuous auditory stimuli with one 0.5s syllable (one consonant and one vowel) repeated twice per second (e.g., ba-ba-de-de-gi-gi), using two conditions (9v5c-9c5v). The 9v5c-condition combined nine Danish vowels with five consonants (45 unique syllables), with vowels (consonants) being repeated at every 9th (5th) trial, creating both new combinations and two highly predictable non-interfering oscillations. The 9c5v-condition combined nine consonants with five vowels. We ran 3 sessions of 18min with 6x4 blocks (6x9v5c-6x9c5v-6x9v5c-6x9c5v). To secure attention, participants had to respond to rare mismatches (two per 45s) (e.g., ba-ba-de-du-gi-gi). Preprocessing utilized the standard protocol in SPM12, including motion correction, normalization, and 4mm-FWHM smoothing. Oscillations in the data were modelled for each participant with sine and cosine waves at each presentation frequency (1/9Hz-1/5Hz). Using trigonometry, we converted the model’s beta estimates into amplitude maps for each condition and frequency, which indicated activation magnitude. A 2nd-level ANOVA-model across all subjects estimated the main effect of phoneme type, FWE-corrected (p<0.05). Highest amplitudes for the different conditions and frequencies localized to the auditory cortices. A main effect of phoneme type (consonants vs. vowels) was likewise observed in both auditory hemispheres. Consonants had a larger amplitude than vowels in both left [-42,-36,14] and right auditory cortex [56,-24,14], regardless of stimulus frequency, whereas vowels did not yield higher amplitude in any area. Perhaps because consonants cover a broader frequency spectrum and therefore activated a larger area. 1/9Hz oscillations yielded larger amplitudes than 1/5Hz across many brain areas, possibly because the natural rhythm of the BOLD signal is closer to 1/9Hz. We successfully differentiated between vowels and consonants despite the continuous stimulus with intermixed vowels and consonants. This oscillation-based method is a step towards faster fMRI protocols with more natural stimuli. However, several posterior areas were coincidentally activated at one of the chosen frequencies, making it imperative to control for oscillation power and frequency when comparing BOLD responses. References 1 Saenz, M., & Langers, D. R. (2014). Tonotopic mapping of human auditory cortex. Hearing Research, 307, 42-52, 10.1016/j.heares.2013.07.016. 2Formisano, E., De Martino, F., Bonte, M., & Goebel, R. (2008). “Who” is saying “what”? Brain-based decoding of human voice and speech. Science (New York, NY), 322, 970-973, 10.1126/science.1164318. 3Wallentin, M., Lund, T. E., Andersen, C. M., & Rocca, R. (2018). Fast phonotopic mapping with oscillation-based fMRI – Proof of concept In Society for the Neurobiology of Language. Quebec, 4Lewis, L. D., Setsompop, K., Rosen, B. R., & Polimeni, J. R. (2016). Fast fMRI can detect oscillatory neural activity in humans. Proceedings of the National Academy of Sciences of the United States of America, 113, E6679-E6685, 10.1073/pnas.1608117113.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».