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Bilateral Ventral Pathways Support Phonological Awareness at Reading Onset in Spanish-Speaking Children.

Code ↔ Paper

3 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 3 matches
  1. [1] § METHODS AND MATERIALS › Tractography and Tract Extraction ↔ pyAFQ_explained_EarlyReading_project.ipynb, lines 70–112 · score 0.81 · constrained spherical deconvolution, Fiber orientation, bundle segmentation, pipeline, seeds, tracking
  2. [2] § RESULTS › White Matter Structures Associated with Phonological Awareness Abilities ↔ pyAFQ_explained_EarlyReading_project.ipynb, lines 31–52 · score 0.64 · inferior fronto occipital, inferior longitudinal, superior longitudinal, arcuate, uncinate
  3. [3] § METHODS AND MATERIALS › Tractography and Tract Extraction ↔ pyAFQ_explained_EarlyReading_project.ipynb, lines 70–112 · score 0.63 · equidistant nodes, Diffusion kurtosis, endpoints, Mahalanobis, resampled, FA

Paper

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The authors' code

Jupyter notebook · 130 lines · 5.1 KB · no license · 3 matches

  1. # %% [markdown]
  2. # # pyAFQ tractography pipeline (adapted for Moramay Ramos-Flores)
  3. #
  4. # This notebook explains **step by step** what each import, function, and parameter does.
  5. # All explanations are included directly above each code cell.
  6. # You must have:
  7. # - pyAFQ installed (`pip install pyafq`)
  8. # - Your diffusion data organized in **BIDS format**
  10. # %% [markdown]
  11. # ## 1. Import libraries
  12. #
  13. # Here we import all the modules needed. Each one has a specific role.
  14. #
  15. # - `AFQ.api.bundle_dict`: lets you load default bundles or create custom ones.
  16. # - `AFQ.data.fetch`: contains the datasets (e.g., `stanford_hardi`).
  17. # - `GroupAFQ`: main class to run pyAFQ on a whole BIDS dataset.
  18. # - `ImageFile` and `RoiImage`: used to define masks, ROIs, and their logic (include/exclude).
  19. # - `os` and `os.path`: used to build file paths safely.
  21. # %%
  22. import AFQ.api.bundle_dict as abd
  23. import AFQ.data.fetch as afd
  24. from AFQ.api.group import GroupAFQ
  25. import AFQ.utils.streamlines as aus
  26. from AFQ.definitions.image import ImageFile, RoiImage
  27. import AFQ.definitions.image as afm
  28. import os
  29. import os.path as op
  31. # %% [markdown]
  32. # ## 2. Select the bundles to analyze
  33. #
  34. # Here we select **a subset** of the 18 default bundles provided by pyAFQ.
  35. #
  36. # The list `other_bundles` contains the names exactly as pyAFQ defines them. These names must match the keys in the `default18_bd()` dictionary.
  37. #
  38. # `abd.default18_bd()` → returns *all* 18 bundles defined by pyAFQ.
  39. #
  40. # `abd.default18_bd()[other_bundles]` → extracts only the bundles we want.
  42. # %%
  43. other_bundles = [
  44. "Left Arcuate", "Right Arcuate",
  45. "Left Inferior Fronto-occipital", "Right Inferior Fronto-occipital",
  46. "Left Inferior Longitudinal", "Right Inferior Longitudinal",
  47. "Left Superior Longitudinal", "Right Superior Longitudinal",
  48. "Left Uncinate", "Right Uncinate"
  49. ]
  51. bundles = abd.default18_bd()[other_bundles]
  52. bundles.bundle_names
  54. # %% [markdown]
  55. # ## 3. Define brain mask logic
  56. #
  57. # pyAFQ needs a **brain mask** for tractography.
  58. #
  59. # - `suffix='mask'` tells pyAFQ to search for files ending in `*mask.nii.gz`
  60. # - `filters={'scope': 'freesurfer'}` restricts the search to masks generated by FreeSurfer.
  61. # - `exclusive_labels=[0]` tells pyAFQ to treat label 0 (background) as excluded.
  63. # %%
  64. brain_mask_definition = afm.LabelledImageFile(
  65. suffix='mask',
  66. filters={'scope': 'freesurfer'},
  67. exclusive_labels=[0]
  68. )
  70. # %% [markdown]
  71. # ## 4. Initialize GroupAFQ
  72. #
  73. # This is the **core object** that runs the entire bundle extraction pipeline.
  74. #
  75. # ### Explanation of parameters:
  76. # - `op.join(afd.afq_home,'stanford_hardi')` → loads the Stanford HARDI the dataset.
  77. # - `bundle_info=bundles` → the subset of bundles we selected.
  78. # - `preproc_pipeline='vistasoft'` → tells pyAFQ how the dataset the DWI images was preprocessed.
  79. # - `scalars=["dki_fa"]` → pyAFQ extracts FA along the tract.
  80. # - `tracking_params` → controls tractography:
  81. # - `n_seeds=1000000`: how many seeds per subject.
  82. # - `random_seeds=True`: seeds are randomly distributed inside the seed mask.
  83. # - `seed_mask=RoiImage(...)`: defines where streamlines can begin.
  84. #
  85. # pyAFQ automatically identifies all DWI and anatomical files following the BIDS specification. No manual file selection is required.
  86. #
  87. # Because the analysis uses dki_fa, pyAFQ automatically fits a Diffusion Kurtosis Imaging (DKI) model using weighted least-squares (default DIPY implementation). Additional DKI-derived metrics (mean, axial, and radial kurtosis) are computed implicitly.
  88. # Registration follows pyAFQ’s default two-step pipeline: affine alignment of the subject’s DKI-FA map to the template, followed by SyN non-linear registration.
  89. #
  90. # Fiber orientation reconstruction is performed using pyAFQ’s default single-tissue Constrained Spherical Deconvolution model (CSD; response function estimated using the Tournier method, SH order l=8).
  91. #
  92. # Streamlines were generated using probabilistic CSD tracking with pyAFQ’s default settings (0.5 mm step size, 30° curvature threshold, 30–250 mm allowed lengths).
  93. # 1,000,000 random seeds were used, and waypoint/endpoints masks were enforced.
  94. #
  95. # Bundle segmentation follows the default pyAFQ logic, combining waypoint-based inclusion/exclusion, probabilistic atlas matching, and automatic removal of anatomically inconsistent streamlines.
  96. #
  97. # Tract cleaning applies the standard pyAFQ procedure: length-based trimming (5%), Mahalanobis distance thresholding (5), and resampling of streamlines to 100 equidistant nodes.
  98. #
  100. # %%
  101. myafq = GroupAFQ(
  102. op.join(afd.afq_home, 'stanford_hardi'),
  103. bundle_info=bundles,
  104. brain_mask_definition=brain_mask_definition,
  105. preproc_pipeline='vistasoft',
  106. scalars=["dki_fa"],
  107. tracking_params={
  108. "n_seeds": 1000000,
  109. "random_seeds": True,
  110. "seed_mask": RoiImage(use_waypoints=True, use_endpoints=True)
  111. }
  112. )
  114. # %% [markdown]
  115. # ## 5. Export results
  116. #
  117. # `export_all()` generates outputs including:
  118. # - Tract profiles (.csv with tractID, nodeID, dki_fa, subjetID,sessionID)
  119. # - Streamlines
  120. # - Bundle segmentations
  121. #
  122. # Here we disable visualizations to speed up processing.
  124. # %%
  125. myafq.export_all(
  126. viz=False,
  127. afqbrowser=False,
  128. xforms=False,
  129. indiv=False
  130. )

pyAFQ_explained_EarlyReading_project.ipynb at commit b43b3c8, no license · at the source

Overview

Authors: Moramay Ramos-Flores1, Rebeca Hernandez Soto1, Liliana Sanchez-Zepeda1, Fernando Lizcano-Cortés1, Luis Concha1, M Florencia Assaneo1
ORCID iDs: Moramay Ramos-Flores, M Florencia Assaneo
  1. Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, México
Institutions: Universidad Nacional Autónoma de México (Mexico)
Journal: Neurobiology of language (Cambridge, Mass.), volume 7, article NOL.a.246
Dates: received 26 June 2025; accepted 12 February 2026; published online 23 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/nol.a.246 · PMID 42088906 · PMCID PMC13137884 · OpenAlex W7131088069
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Physiology & signal measures, fMRI & imaging
Keywords: phonological awareness, reading development, Spanish-speaking children, ventral and dorsal pathways, white matter tracts
Topic: Reading and Literacy Development (Developmental and Educational Psychology, Psychology), according to OpenAlex
Funding: Google (Gift 2021); DGAPA of the Universidad Nacional Autónoma de México
Citations: not cited yet (Europe PMC); 57 references in the paper

Abstract

Reading is a fundamental human skill that has been widely studied. While substantial progress has been made in identifying the white matter pathways supporting reading in adults, less is known about the neural substrates underlying reading acquisition in children. Moreover, existing evidence primarily focuses on a small set of languages, thereby raising questions about the generalizability of these findings. In this study, we address this gap by examining the white matter correlates of phonological awareness—a well-established precursor of reading development—in a cohort of monolingual Mexican Spanish–speaking children. Contrary to the classical view that left-lateralized dorsal pathways support phonological awareness, our results reveal that fractional anisotropy in bilateral ventral tracts, but not dorsal tracts, correlates with phonological awareness in this population. These findings challenge the traditional dichotomy between dorsal and ventral stream functions, instead highlighting the flexible and language-dependent nature of the neural mechanisms that support early reading development in children.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

openneuro:ds007398

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Code and Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
At the source: openneuro.org/datasets/ds007398/versions/1.0.2

moramay-rf/EarlyReading

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: b43b3c88087beb180feab7d618a7a6e69d0ebc96, 10 December 2025
Languages: Jupyter (1)
Size: 2 files, 1 script
Software Heritage: not archived
Found in: “Code and Data Availability Statement”
Holds: 1 notebook
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
1 file
At the source: github.com/moramay-rf/EarlyReading

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • 3 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 preprocessed data and all analysis scripts are publicly available on https://openneuro.org/datasets/ds007398/versions/1.0.2 and https://github.com/moramay-rf/EarlyReading, respectively.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 5 keywords, 2 funders, 53 references.

Cite

This paper

Ramos-Flores, M., Soto, R. H., Sanchez-Zepeda, L., Lizcano-Cortés, F., Concha, L., & Assaneo, M. F. (2026). Bilateral Ventral Pathways Support Phonological Awareness at Reading Onset in Spanish-Speaking Children. Neurobiology of language (Cambridge, Mass.), 7, NOL.a.246. https://doi.org/10.1162/nol.a.246

BibTeX

@article{ramosflores2026bilateral,
author = {Ramos-Flores, Moramay and Soto, Rebeca Hernandez and Sanchez-Zepeda, Liliana and Lizcano-Cortés, Fernando and Concha, Luis and Assaneo, M Florencia},
title = {{Bilateral Ventral Pathways Support Phonological Awareness at Reading Onset in Spanish-Speaking Children}},
journal = {Neurobiology of language (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {7},
pages = {NOL.a.246},
publisher = {MIT Press},
issn = {2641-4368},
doi = {10.1162/nol.a.246},
url = {https://doi.org/10.1162/nol.a.246},
pmid = {42088906},
pmcid = {PMC13137884}
}

RIS

TY - JOUR
AU - Ramos-Flores, Moramay
AU - Soto, Rebeca Hernandez
AU - Sanchez-Zepeda, Liliana
AU - Lizcano-Cortés, Fernando
AU - Concha, Luis
AU - Assaneo, M Florencia
TI - Bilateral Ventral Pathways Support Phonological Awareness at Reading Onset in Spanish-Speaking Children
T2 - Neurobiology of language (Cambridge, Mass.)
J2 - Neurobiol Lang (Camb)
PY - 2026
DA - 2026/04/23
VL - 7
SP - NOL.a.246
SN - 2641-4368
PB - MIT Press
DO - 10.1162/nol.a.246
UR - https://doi.org/10.1162/nol.a.246
LA - en
ER -

CSL-JSON

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