The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Distinct Cortical Pathways for Music and Speech Revealed by Hypothesis-Free Voxel Decomposition
Open accessAbstract
SUMMARY The organization of human auditory cortex remains unresolved, due in part to the small stimulus sets common to fMRI studies and the overlap of neural populations within voxels. To address these challenges, we measured fMRI responses to 165 natural sounds and inferred canonical response profiles (“components”) whose weighted combinations explained voxel responses throughout auditory cortex. This analysis revealed six components, each with interpretable response characteristics despite being unconstrained by prior functional hypotheses. Four components embodied selectivity for particular acoustic features (frequency, spectrotemporal modulation, pitch). Two others exhibited pronounced selectivity for music and speech, respectively, and were not explainable by standard acoustic features. Anatomically, music and speech selectivity concentrated in distinct regions of non-primary auditory cortex. However, music selectivity was weak in raw voxel responses, and its detection required a decomposition method. Voxel decomposition identifies primary dimensions of response variation across natural sounds, revealing distinct cortical pathways for music and speech.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
The Jackknife, the Bootstrap and Other Resampling Plans
Efron, B. (1982) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970319 · Cited by 8,120
A global optimisation method for robust affine registration of brain images
Jenkinson, M., & Smith, S. (2001) · Medical Image Analysis · doi:10.1016/s1361-8415(01)00036-6 · Cited by 6,872
Nonparametric permutation tests for functional neuroimaging: A primer with examples
Nichols, T. E., & Holmes, A. P. (2002) · Human Brain Mapping · doi:10.1002/hbm.1058 · Cited by 6,256
Fast and robust fixed-point algorithms for independent component analysis
Hyvarinen, A. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.761722 · Cited by 6,246
The cortical organization of speech processing
Hickok, G., & Poeppel, D. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2113 · Cited by 5,495
Sparse coding with an overcomplete basis set: A strategy employed by V1?
Olshausen, B. A., & Field, D. J. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00169-7 · Cited by 3,642
Accurate and robust brain image alignment using boundary-based registration
Greve, D. N., & Fischl, B. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.06.060 · Cited by 3,596
Cited by
Audio Set: An ontology and human-labeled dataset for audio events
Gemmeke, J. F., Ellis, D. P. W., Freedman, D., Jansen, A., Lawrence, W., Moore, R. C., Plakal, M., & Ritter, M. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2017.7952261 · Cited by 3,134
Kell, A. J. E., Yamins, D. L. K., Shook, E. N., Norman-Haignere, S. V., & McDermott, J. H. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.044 · Cited by 544
Self-citationTemporal modulations in speech and music
Ding, N., Patel, A. D., Chen, L., Butler, H., Luo, C., & Poeppel, D. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.02.011 · Cited by 502
Distinct sensitivity to spectrotemporal modulation supports brain asymmetry for speech and melody
Albouy, P., Benjamin, L., Morillon, B., & Zatorre, R. J. (2020) · Science · doi:10.1126/science.aaz3468 · Cited by 298
The language network as a natural kind within the broader landscape of the human brain
Fedorenko, E., Ivanova, A. A., & Regev, T. I. (2024) · Nature Reviews Neuroscience · doi:10.1038/s41583-024-00802-4 · Cited by 293
Metadata
- Authors
- Norman-Haignere, S., Kanwisher, N. G., & McDermott, J. H.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2015-12
- Volume
- 88
- Issue
- 6
- Pages
- 1281-1296
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W2210407171
- Cited by
- 465
- References
- 60
- FWCI
- 15.49
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Distinct Cortical Pathways for Music and Speech Revealed by Hypothesis-Free Voxel Decomposition. https://app.cochat.ai/papers/10.1016/j.neuron.2015.11.035
- The paper
- Norman-Haignere, S., Kanwisher, N. G., & McDermott, J. H. (2015). Distinct Cortical Pathways for Music and Speech Revealed by Hypothesis-Free Voxel Decomposition. Neuron. https://doi.org/10.1016/j.neuron.2015.11.035
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
