Rapid cortical mapping with cross-participant encoding models.
The 4 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
- [1] § Methods › Cross-participant modeling framework ↔ em/ridge.py, lines 64–188 · score 0.78 · ridge regression, log spaced, predict responses, linear, weights, voxels
- [2] § Methods › MRI experiments ↔ em/english1000.py, the whole file · a weak match · score 0.64 · Love, Modern, Radio, Smoke, ear, watched
- [3] § Methods › MRI preprocessing ↔ em/detrend.py, lines 17–85 · score 0.54 · Golay filter, polynomial, signal, window
- [4] § Methods › MRI data collection ↔ em/english1000.py, the whole file · a weak match · score 0.52 · age, female, hearing, Board, healthy
Paper
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The authors' code
Python · 1 line · 6.8 KB · no license · 2 matches
english1000.py at commit c7de9fa, no license · at the source
Overview
- Department of Speech, Language, and Hearing Sciences, The University of Texas at Austin, Austin, TX, United States
- Department of Statistics, University of California, Berkeley, CA, United States
- Department of Neuroscience, University of California, Berkeley, CA, United States
Abstract
Voxelwise encoding models trained on functional MRI data can produce detailed maps of cortical organization. However, voxelwise encoding models must be trained on many hours of brain responses from each participant, limiting clinical applications. In this study, we introduce a cross-participant modeling framework for rapid cortical mapping. In this framework, voxelwise encoding models are trained on many hours of brain responses from previously scanned reference participants, and then transferred to a new participant by aligning brain responses using a small set of stimuli. We evaluated cross-participant encoding models on linguistic semantic mapping, non-linguistic semantic mapping, and auditory mapping. In each case, we found that cross-participant encoding models had more accurate selectivity estimates and prediction performance than within-participant encoding models trained on the same amount of data from the new participant. We also found that cross-participant encoding models improved with the amount of data from each reference participant and the number of reference participants. These results demonstrate that cross-participant modeling can substantially reduce the amount of data required for detailed cortical mapping, which may facilitate new clinical applications of functional neuroimaging.
Reproduced under the paper's license (CC BY), from the paper cited above.
Repository
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HuthLab/rapid-cortical-mapping
c7de9fa685c9f34af1f12cd123542785804ba2c6, 25 July 2026Availability: 1 check, the latest on 26 September 2026: the link answers
- 26 September 2026: the link answers
29 files, not copied: shown from their source
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- consts.py — Python, 7 lines, shown from its source
- demo/
0_motion_correction.ipyn — Jupyter, 28 lines, shown from its sourceb - demo/
1_model_fitting.ipynb — Jupyter, 61 lines, shown from its source - demo/
2_semantic_mapping.ipynb — Jupyter, 116 lines, shown from its source - demo/
3_reference_scaling.ipyn — Jupyter, 224 lines, shown from its sourceb - demo/
4_movie_mapping.ipynb — Jupyter, 116 lines, shown from its source - demo/
5_auditory_mapping.ipynb — Jupyter, 126 lines, shown from its source - demo/
dconsts.py — Python, 18 lines, shown from its source - demo/
dpaths.py — Python, 14 lines, shown from its source - demo/
eval_prediction.py — Python, 64 lines, shown from its source - demo/
eval_selectivity.py — Python, 86 lines, shown from its source - demo/
utils_viz.py — Python, 39 lines, shown from its source - em/
DataSequence.py — Python, 120 lines, shown from its source - em/
SemanticModel.py — Python, 326 lines, shown from its source - em/
detrend.py — Python, 85 lines, 1 match, shown from its source - em/
dsutils.py — Python, 30 lines, shown from its source - em/
english1000.py — Python, 1 line, 2 matches, shown from its source - em/
interpdata.py — Python, 233 lines, shown from its source - em/
npp.py — Python, 26 lines, shown from its source - em/
permutation.py — Python, 64 lines, shown from its source - em/
ridge.py — Python, 275 lines, 1 match, shown from its source - em/
stimulus_utils.py — Python, 67 lines, shown from its source - em/
textgrid.py — Python, 653 lines, shown from its source - em/
util.py — Python, 379 lines, shown from its source - em/
utils.py — Python, 219 lines, shown from its source - paths.py — Python, 17 lines, shown from its source
- train.py — Python, 86 lines, shown from its source
- utils_cm.py — Python, 38 lines, shown from its source
- README.md — Text, 22 lines, shown from its source
The paper's code and data availability statement is in the Data section.
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Data
Datasets cited
- openneuro:ds003020 — at OpenNeuro; found in “Data and Code Availability”
- openneuro:ds005717 — at OpenNeuro; found in “Data and Code Availability”
Data and Code Availability
All data used in the analysis are publicly available at https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 2 authors, 6 keywords, 8 MeSH terms, 1 funder, 73 references.
Cite
This paper
Tang, J., & Huth, A. G. (2026). Rapid cortical mapping with cross-participant encoding models. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1349. https://
BibTeX
@article{tang2026rapid,
author = {Tang, Jerry and Huth, Alexander G},
title = {{Rapid cortical mapping with cross-participant encoding models}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = sep,
volume = {4},
pages = {IMAG.a.1349},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/
url = {https://
pmid = {42699512},
pmcid = {PMC13543446}
}
RIS
TY - JOUR
AU - Tang, Jerry
AU - Huth, Alexander G
TI - Rapid cortical mapping with cross-participant encoding models
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/
VL - 4
SP - IMAG.a.1349
SN - 2837-6056
PB - MIT Press
DO - 10.1162/
UR - https://
LA - en
ER -
CSL-JSON
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