Periodic sound encoding in the human auditory system: variability and plasticity
Notice bibliographique
Résumé
The human auditory system is made up of a network of processing centres in the brainstem, thalamus, and cortex, which in turn interact with higher-level functions and the sensory and motor systems.Although the coordinated activity of the entire ensemble is responsible for human auditory perception and related behaviour, including language and music, it has been suggested that the fidelity with which important features of sound are encoded and processed in early auditory areas may place limitations on system performance on auditory tasks.In this thesis, we address a set of research questions within the theme of relationships between early sound encoding and higher-level cognitive function, and their respective neural correlates.Throughout these studies, our primary focus is on temporal encoding of periodic sound, as measured using the frequency following response (FFR), an evoked response that has typically been studied using electroencephalography (EEG).The FFR has been related to individual differences in perception and pathology of the auditory system, is malleable to musical and linguistic training, and can be modulated by top-down factors like attention, making it a valuable tool for studying interactions between basic sound processes and higherlevel cognition.However, due to limitations imposed by the methodology of its measurement, gaps exist in our knowledge of its neural origins that limit the interpretation of results.To better inform our cognitive research questions, we therefore have also ventured into FFR methodological testing and development.This dissertation comprises four studies.In the first study, we recorded FFR using magnetoencephalography (MEG) for the first time.After confirming its equivalence to the scalp-recorded EEG, we used source modelling to clarify its generators.In addition to confirming sources in brainstem nuclei and thalamus, we found a right-lateralized contribution to the FFR from the auditory cortex, which proved to be behaviourally relevant as it was significantly related to musicianship and fine pitch discrimination skills.The results from this study potentially affect the interpretation of existing literature, as it raises the possibility that previously identified FFR enhancements and deficits might originate at the cortical level or throughout the auditory system (including the cortex) rather than only in the brainstem.In this work, we developed and validated MEG-FFR, which will facilitate the study of sound encoding in humans by allowing for spatial separation of contributing neural sources.In the second study, we used functional magnetic resonance imaging (fMRI) to examine the neural correlates of FFR encoding strength in the cortex.We found that FFR strength across individuals was related to hemodynamic response strength in the right auditory cortex, close to the FFR source generators observed in the first study using MEG.fMRI detects Ann Coffey, whose feats include single-handedly moving us and looking after Alice when we were dealing with Kye's untimely arrival as well the less heroic but nonetheless much appreciated acts of cleaning things and feeding people when we fall sick, or before deadlines.Credit is also due to Alice and Kye, who have encouraged me to develop excellent time management skills, and Dennis and Joan Coffey for remote perspective and encouragement.I am thankful to be a part of such a friendly and collegial research community, including my many lab-mates past and present (many of whom are good friends), our technicians, my students (Stephanie Scala, Oles Chepesiuk, and Emilia Colagrosso), and also randoms from cognitive neuroscience and related fields.Thanks is also due to Stephanie Sabbagh and Emilia Colagrosso for the abstract translation.I have found everyone from the technical experts to senior researchers willing to humour requests, provide me with resources, look at my data, and sit down for enlightening and useful discussions.This kind of interaction helps me believe in science as a global collaborative endeavour of humanity.In case I don't have another opportunity to do so formally, I would also like to acknowledge my long history of mentors: Shona Pentland, teacher, for encouragement during a critical period.Adam Fogo, with whom I have crashed an airplane and who is equal parts pilot, musician, and teacher.Glen Lynch, businessman and pilot, who helped me realise that I did not belong in his world (although that was undoubtedly not his goal).Pieter Goltstein, for daily first-hand introduction to science.I try to 'be like Pieter' when faced with problems that seem overwhelmingly complex -and just get on with it.Josephine Nalbantoglu and Dave Ragsdale helped match me up with Robert and supported my move, which worked out rather well.I have also been guided and offered considerable freedom by George Fouriezos (
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».