Neuroimaging Studies on Familiarity of Music in Children with Autism Spectrum Disorder
Bibliographic record
Abstract
The field of music neuroscience allows us to use music to investigate human cognition in vivo. Examining how brain processes familiar and unfamiliar music can elucidate underlying neural mechanisms of several cognitive processes. To date, familiarity in music listening and its neural correlates in typical adults have been investigated using a variety of neuroimaging techniques, yet the results are inconsistent. In addition, these correlates and respective functional connectivity related to music familiarity in typically developing (TD) children and children with autism spectrum disorder (ASD) are unknown. The present work consists of two studies. The first one reviews and qualitatively synthesizes relevant literature on the neural correlates of music familiarity, in healthy adult populations, using different neuroimaging methods. Then it estimates the brain areas most active when listening to familiar and unfamiliar musical excerpts using a coordinate-based meta-analyses technique of neuroimaging data. We established that motor brain structures were consistently active during familiar music listening. The activation of these motor-related areas could reflect audio-motor synchronization to elements of the music, such as rhythm and melody, so that one can tap, dance and “covert” sing along with a known song. Results from this research guided our second study. This work investigated the familiarity effect in music listening in both TD and ASD children, using magnetoencephalography (MEG). This technique enabled us to study brain connectivity and characterize the networks and frequency bands involved while listening to familiar and unfamiliar songs. TD children recruited a similar brain network as those in typical adults during familiar music listening, in the gamma frequency band. Compared to TD, children with ASD showed relatively intact processing of familiar songs but atypical processing of unfamiliar songs in theta and beta-bands. Atypical functional connectivity of other unfamiliar stimuli has been reported in ASD. Our findings reinforced that processing novelty is a challenge. Overall, this work contributes to the advancement of both fields of music neuroscience and brain connectivity in ASD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".