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Visual Word Form Area demonstrates individual and task-agnostic consistency but inter-individual variability.

Code ↔ Paper

5 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 5 matches
  1. [1] § Methods › Functional regions of interest ↔ extract_ROI_PSC.ipynb, lines 129–139 · score 0.69 · kFFA, rFFA, kVWFA, rVWFA, template ROIs, FFAs
  2. [2] § Methods › Functional regions of interest ↔ freeview_label_screenshots.ipynb, lines 196–218 · score 0.69 · kFFA, rFFA, kVWFA, rVWFA, template ROIs, FFAs
  3. [3] § Methods › Functional regions of interest ↔ plotting.ipynb, lines 201–235 · score 0.67 · aFFA, cFFA, aVWFA, cVWFA, ROI
  4. [4] § Methods › Functional regions of interest ↔ extract_ROI_PSC.ipynb, lines 129–139 · score 0.67 · aFFA, cFFA, aVWFA, cVWFA, map, ROI
  5. [5] § Results › Group and template VWFAs obscure text-selective results due to individual differences ↔ plotting.ipynb, lines 201–235 · score 0.65 · aVWFA, cVWFA, kVWFA, rVWFA, pseudofonts, limbs

Paper

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

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The authors' code

Jupyter notebook · 178 lines · 5.2 KB · MIT · 2 matches

  1. # %% [markdown]
  2. # # Set up
  4. # %%
  5. #import packages
  7. import pandas as pd
  8. from nilearn import surface
  9. import numpy as np
  10. import os
  11. import glob
  12. import usefulFunctions as uf
  14. # %%
  15. # flag to save csv
  16. save_csv = True
  18. #set paths
  19. base_dir = f'{os.path.dirname(os.getcwd())}/'
  20. roi_dir = f'{base_dir}data/labels/'
  21. psc_dir = f'{base_dir}data/psc_maps/'
  22. csv_dir = f'{base_dir}analysis/CSVs/'
  24. #define variable list
  25. subs = uf.subject_list(f'{psc_dir}native/allData/')
  26. ROIs = ['VWFA','FFA']
  27. spaces = ['native','average']
  28. data_types = ['allData','sepTask']
  29. categories = ['T','FF','F','O','L','noText']
  30. map_tasks = ['oneback','fixation']
  31. roi_tasks = ['oneback','fixation']
  33. # %% [markdown]
  34. # # allData ROIs
  36. # %%
  37. # Collect all data
  38. data_records = []
  40. # loop through subs, rois, spaces, and categories to extract mean psc
  41. for sub in subs:
  42. for roi in ROIs:
  43. for space in spaces:
  44. # check in an roi exists
  45. ROI_paths = glob.glob(f'{roi_dir}{space}/allData/{sub}/{sub}_*-v-ALL_{roi}*_LH.label')
  46. if len(ROI_paths)>0:
  47. # load roi lable
  48. roi_indices = uf.read_in_rois(ROI_paths)
  50. for category in categories:
  52. #load psc map
  53. psc_map = surface.load_surf_data(f'{psc_dir}{space}/allData/{sub}/{sub}_{category}_LH.curv')
  55. # calculate mean psc for each existing roi
  56. if psc_map is not None:
  57. mean_psc = np.mean(psc_map[roi_indices])
  58. else: # assign nan if roi doesn't exist
  59. mean_psc = np.nan
  61. # Store the results
  62. data_records.append({
  63. 'sub': sub,
  64. 'roi': roi,
  65. 'space': space,
  66. 'category': category,
  67. 'mean_psc': mean_psc
  68. })
  70. # Create DataFrame
  71. df = pd.DataFrame(data_records)
  73. # save df as csv
  74. df_path = f'{csv_dir}allDataROIs_allDataMaps_meanPSC.csv'
  75. if save_csv:
  76. df.to_csv(df_path, index=False)
  78. # %% [markdown]
  79. # # sepTask ROIs
  81. # %%
  82. # Collect all data
  83. data_records = []
  85. # loop through subs, rois, spaces, task, and categories to extract mean psc
  86. for sub in subs:
  87. for roi in ROIs:
  88. for space in spaces:
  89. for roi_task in roi_tasks:
  90. # check in an roi exists
  91. ROI_paths = glob.glob(f'{roi_dir}{space}/sepTask/{sub}/{sub}_task-{roi_task}_*-v-ALL_{roi}*_LH.label')
  92. if len(ROI_paths)>0:
  93. # load roi lable
  94. roi_indices = uf.read_in_rois(ROI_paths)
  96. for category in categories:
  97. for map_task in map_tasks:
  99. #load psc map
  100. psc_map = surface.load_surf_data(f'{psc_dir}{space}/sepTask/{sub}/{sub}_task-{map_task}_{category}_LH.curv')
  102. # calculate mean psc for each existing roi
  103. if psc_map is not None:
  104. mean_psc = np.mean(psc_map[roi_indices])
  105. else: # assign nan if roi doesn't exist
  106. mean_psc = np.nan
  108. # Store the results
  109. data_records.append({
  110. 'sub': sub,
  111. 'roi': roi,
  112. 'space': space,
  113. 'roi_task': roi_task,
  114. 'category': category,
  115. 'map_task': map_task,
  116. 'mean_psc': mean_psc
  117. })
  120. # Create DataFrame
  121. df = pd.DataFrame(data_records)
  123. # save df as csv
  124. df_path = f'{csv_dir}sepTaskROIs_sepTaskMaps_meanPSC.csv'
  125. if save_csv:
  126. df.to_csv(df_path, index=False)
  129. # %% [markdown]
  130. # # Group/Template ROIs
  132. # %%
  133. # set directory
  134. roi_dir = f'{base_dir}data/labels/group_labels/'
  135. psc_dir = f'{base_dir}data/psc_maps/average/allData/'
  137. # define variables
  138. categories = ['T','FF','F','O','L','noText']
  139. ROIs = ['aVWFA','cVWFA','kVWFA','rVWFA','aFFA','cFFA','kFFA','rFFA']
  141. # %%
  142. # Collect all data
  143. data_records = []
  145. # loop through subs, rois, and categories to extract mean psc
  147. for roi in ROIs:
  148. # load the ROI
  149. roi_indices = uf.read_in_rois(glob.glob(f'{roi_dir}{roi}*_LH.label'))
  150. for sub in subs:
  151. for category in categories:
  153. #load psc map
  154. psc_map = surface.load_surf_data(f'{psc_dir}{sub}/{sub}_{category}_LH.curv')
  156. # calculate mean psc for each existing roi
  157. if psc_map is not None:
  158. mean_psc = np.mean(psc_map[roi_indices])
  159. else: # assign nan if roi doesn't exist
  160. mean_psc = np.nan
  162. # Store the results
  163. data_records.append({
  164. 'sub': sub,
  165. 'roi': roi,
  166. 'category': category,
  167. 'mean_psc': mean_psc
  168. })
  171. # Create DataFrame
  172. df = pd.DataFrame(data_records)
  174. # save df as csv
  175. df_path = f'{csv_dir}groupROIs_allDataMaps_meanPSC.csv'
  176. if save_csv:
  177. df.to_csv(df_path, index=False)

extract_ROI_PSC.ipynb at commit d2dd277, under MIT · at the source

Overview

Authors: Jamie L. Mitchell1,2, Mia Jimenez1, Hannah L. Stone3, Maya Yablonski1,4, Jason D. Yeatman1,2,4
ORCID iDs: Jamie L. Mitchell, Jason D. Yeatman
  1. Graduate School of Education, Stanford University, Stanford, CA, USA
  2. Department of Psychology, Stanford University, Stanford, CA, USA
  3. Department of Psychological & Brain Sciences, University of California, Santa Baraba, CA, USA
  4. Division of Developmental-Behavioral Pediatrics, Department of Pediatrics,Stanford University School of Medicine, Stanford, CA, USA
Institutions: Stanford University (United States); University of California, Santa Barbara (United States); Stanford Medicine (United States)
Journal: Developmental cognitive neuroscience, volume 79, article 101703
Dates: received 16 August 2025; accepted 2 March 2026; published online 3 March 2026; in print June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.dcn.2026.101703 · PMID 41791266 · PMCID PMC12993163 · OpenAlex W7133297411
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism)
Methods: Connectivity, Statistics, fMRI & imaging
Keywords: Visual word form area, Individual differences, FMRI, Functional organization, Ventral occipitotemporal cortex, Category selectivity
MeSH: Occipital Lobe*, Pattern Recognition, Visual*, Reading*, Temporal Lobe*, Adolescent, Adult, Brain Mapping, Child, Female, Humans, Individuality, Magnetic Resonance Imaging, Male, Photic Stimulation, Young Adult (* major topic)
Topic: Neurobiology of Language and Bilingualism (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute of Child Health and Human Development (R01-HD095861)
Citations: not cited yet (Europe PMC); 53 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

jamielmitchell/Mitchell_DCN2026

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: d2dd277917d8924a11ea2920b44e1e2bec52818c, 3 March 2026
Languages: Jupyter (7), R (1), Python (1)
Size: 12 files, 9 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, 8 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (8 files), Nilearn (6 files), pandas (5 files), Matplotlib (3 files), FreeSurfer (2 files), NiBabel (2 files), SciPy (2 files), lme4 (1 file), lmerTest (1 file), seaborn (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
11 files
At the source: github.com/jamielmitchell/Mitchell_DCN2026

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 9 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1016/j.dcn.2026.101703.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 15 MeSH terms, 1 funder, 48 references.

Cite

This paper

Mitchell, J. L., Jimenez, M., Stone, H. L., Yablonski, M., & Yeatman, J. D. (2026). Visual Word Form Area demonstrates individual and task-agnostic consistency but inter-individual variability. Developmental cognitive neuroscience, 79, 101703. https://doi.org/10.1016/j.dcn.2026.101703

BibTeX

@article{mitchell2026visual,
author = {Mitchell, Jamie L. and Jimenez, Mia and Stone, Hannah L. and Yablonski, Maya and Yeatman, Jason D.},
title = {{Visual Word Form Area demonstrates individual and task-agnostic consistency but inter-individual variability}},
journal = {Developmental cognitive neuroscience},
year = {2026},
month = mar,
volume = {79},
pages = {101703},
publisher = {Elsevier},
issn = {1878-9293},
doi = {10.1016/j.dcn.2026.101703},
url = {https://doi.org/10.1016/j.dcn.2026.101703},
pmid = {41791266},
pmcid = {PMC12993163}
}

RIS

TY - JOUR
AU - Mitchell, Jamie L.
AU - Jimenez, Mia
AU - Stone, Hannah L.
AU - Yablonski, Maya
AU - Yeatman, Jason D.
TI - Visual Word Form Area demonstrates individual and task-agnostic consistency but inter-individual variability
T2 - Developmental cognitive neuroscience
J2 - Dev Cogn Neurosci
PY - 2026
DA - 2026/03/03
VL - 79
SP - 101703
SN - 1878-9293
PB - Elsevier
DO - 10.1016/j.dcn.2026.101703
UR - https://doi.org/10.1016/j.dcn.2026.101703
LA - en
ER -

CSL-JSON

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