Parcellation of Human and Monkey Core Auditory Cortex with fMRI Pattern Classification and Objective Detection of Tonotopic Gradient Reversals
Bibliographic record
Abstract
Auditory cortex (AC) contains several primary-like, or "core," fields, which receive thalamic input and project to non-primary "belt" fields. In humans, the organization and layout of core and belt auditory fields are still poorly understood, and most auditory neuroimaging studies rely on macroanatomical criteria, rather than functional localization of distinct fields. A myeloarchitectonic method has been suggested recently for distinguishing between core and belt fields in humans (Dick F, Tierney AT, Lutti A, Josephs O, Sereno MI, Weiskopf N. 2012. In vivo functional and myeloarchitectonic mapping of human primary auditory areas. J Neurosci. 32:16095-16105). We propose a marker for core AC based directly on functional magnetic resonance imaging (fMRI) data and pattern classification. We show that a portion of AC in Heschl's gyrus classifies sound frequency more accurately than other regions in AC. Using fMRI data from macaques, we validate that the region where frequency classification performance is significantly above chance overlaps core auditory fields, predominantly A1. Within this region, we measure tonotopic gradients and estimate the locations of the human homologues of the core auditory subfields A1 and R. Our results provide a functional rather than anatomical localizer for core AC. We posit that inter-individual variability in the layout of core AC might explain disagreements between results from previous neuroimaging and cytological studies.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".