Periodic sound encoding in the human auditory system: variability and plasticity
Bibliographic record
Abstract
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 (
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".