Population receptive field mapping and tractography in people with absolute pitch.
Bibliographic record
Abstract
Introduction As the spatial resolution of functional magnetic resonance imaging (fMRI) has advanced, recent studies have been able to establish tonotopic mapping in the human auditory cortex. However, there still remains a debate of where the exact orientation of primary gradients occur in Heshl's gyrus leading to various interpretations. In our study we used fMRI to measure the population receptive fields (pRFs) in the auditory cortex to investigate underlying differences in sensory processing in absolute pitch (AP) possessors compared to age and gender matched controls. Tractography was also performed using diffusion MRI to investigate differences in connectivity in the auditory and visual structures. Methods Each participant had their cortex scanned at 1.5 ' 1.5 ' 2 mm3 resolution using a Siemens Trio 3T MRI scanner and 32-channel head coil. Our stimulus consisted of pure tone logarithmic chirps that enabled tonotopic and tuning width mapping of cortical regions. We analyzed the data using an adaptation of the population receptive field (pRF) technique developed by Dumoulin and Wandell (2008), used initially for retinotopic mapping of the visual cortex. Our model treated the pRF underlying each voxel's response as a one-dimensional Gaussian function of frequency providing an estimated sensitivity function for each voxel with a preferred frequency and tuning bandwidth. Diffusion tensor imaging (DTI) scans were acquired with 64 diffusion directions and tractography was performed. Results Both centre frequency and tuning width information was derived from the 1D pRF Gaussian models and plotted on the unfolded cortical surface for each hemisphere in each subject. We were able to obtain reliable tonotopic and tuning bandwidth maps as well as find differences in connectivity in humans with AP compared to controls. Conclusions Our data has helped reveal the variability and consistencies of multi-sensory processing pathways in people with AP compared to normal controls. Meeting abstract presented at VSS 2016
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".