Rapid cortical mapping with cross-participant encoding models.

Code ↔ Paper

4 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

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

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The paper is loaded by your browser, with JavaScript: read it at doi.org or on Europe PMC.

The authors' code

Python · 1 line · 6.8 KB · no license · 2 matches

english1000words = sorted(set([w.lower() for w in """a, about, above, across, act, active, activity, add, afraid, after, again, age, ago, agree, air, all, alone, along, already, always, am, amount, an, and, angry, another, answer, any, anyone, anything, appear, apple, are, area, arm, army, around, arrive, art, as, ask, at, attack, aunt, autumn, away, baby, base, back, bad, bag, ball, bank, basket, bath, be, bear, beautiful, beer, bed, bedroom, behave, before, begin, behind, bell, below, besides, best, better, between, big, bird, birth, birthday, bit, bite, black, block, blood, blow, blue, board, boat, body, boil, bone, book, border, born, borrow, both, bottle, bottom, bowl, box, boy, branch, brave, bread, break, breakfast, breathe, bridge, bright, bring, brother, brown, brush, build, burn, business, bus, busy, but, buy, by, cake, call, can, candle, cap, car, card, care, careful, careless, carry, case, cat, catch, central, century, certain, chair, chance, change, chase, cheap, cheese, 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english1000.py at commit c7de9fa, no license · at the source

Overview

Authors: Jerry Tang¹, Alexander G Huth^2,3

ORCID iDs: Jerry Tang

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

Institutions: The University of Texas at Austin (United States); University of California, Berkeley (United States)

Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1349

Dates: received 21 November 2025; accepted 29 July 2026; published online 3 September 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1162/imag.a.1349 · PMID 42699512 · PMCID PMC13543446 · OpenAlex W7202213433

Open access: diamond, a free copy (OpenAlex)

Status: code verified

Categories: fMRI (modality), human (organism), cognitive (subfield)

Methods: Connectivity, Statistics, Machine learning, Spectral & time-frequency

Keywords: fMRI, semantic mapping, language mapping, functional alignment, language, vision

MeSH: Brain Mapping*, Cerebral Cortex*, Magnetic Resonance Imaging*, Models, Neurological*, Humans, Image Processing, Computer-Assisted, Language, Semantics (* major topic)

Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: NIDCD NIH HHS (F32 DC022178, R01 DC020088)

Citations: not cited yet (Europe PMC); 73 references in the paper

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

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

HuthLab/rapid-cortical-mapping

License: none: the authors keep all their rights

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: c7de9fa685c9f34af1f12cd123542785804ba2c6, 25 July 2026

Languages: Python (22), Jupyter (6)

Size: 30 files, 28 scripts

Software Heritage: not archived

Found in: “Data and Code Availability”

Holds: README, 6 notebooks

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: NumPy (23 files), SciPy (8 files), Matplotlib (6 files), pycortex (1 file)

Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

29 files

consts.py, Python, 7 lines
demo/0_motion_correction.ipynb, Jupyter, 28 lines
demo/1_model_fitting.ipynb, Jupyter, 61 lines
demo/2_semantic_mapping.ipynb, Jupyter, 116 lines
demo/3_reference_scaling.ipynb, Jupyter, 224 lines
demo/4_movie_mapping.ipynb, Jupyter, 116 lines
demo/5_auditory_mapping.ipynb, Jupyter, 126 lines
demo/dconsts.py, Python, 18 lines
demo/dpaths.py, Python, 14 lines
demo/eval_prediction.py, Python, 64 lines
demo/eval_selectivity.py, Python, 86 lines
demo/utils_viz.py, Python, 39 lines
em/DataSequence.py, Python, 120 lines
em/SemanticModel.py, Python, 326 lines
em/detrend.py, Python, 85 lines, 1 match
em/dsutils.py, Python, 30 lines
em/english1000.py, Python, 1 line, 2 matches
em/interpdata.py, Python, 233 lines
em/npp.py, Python, 26 lines
em/permutation.py, Python, 64 lines
em/ridge.py, Python, 275 lines, 1 match
em/stimulus_utils.py, Python, 67 lines
em/textgrid.py, Python, 653 lines
em/util.py, Python, 379 lines
em/utils.py, Python, 219 lines
paths.py, Python, 17 lines
train.py, Python, 86 lines
utils_cm.py, Python, 38 lines
README.md, Text, 22 lines

At the source: github.com/HuthLab/rapid-cortical-mapping

The paper's code and data availability statement is in the Data section.

Tracing map

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1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
28 scripts, each with its path and the digest of its content;
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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://openneuro.org/datasets/ds003020 and https://openneuro.org/datasets/ds005717. All code used in the analysis are publicly available at https://github.com/HuthLab/rapid-cortical-mapping

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

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://doi.org/10.1162/imag.a.1349

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/imag.a.1349}, url = {https://doi.org/10.1162/imag.a.1349}, 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 - 2026/09/03 VL - 4 SP - IMAG.a.1349 SN - 2837-6056 PB - MIT Press DO - 10.1162/imag.a.1349 UR - https://doi.org/10.1162/imag.a.1349 LA - en ER -

CSL-JSON

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