The Relation of Home Literacy Environment to Brain Specialization and Sensitivity for Phonological and Semantic Processing of Spoken Words.

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] § Method › Data Analysis › Preprocessing ↔ preproc/main_just_for_movement.m, lines 1 to 43 · score 0.91 · skull stripped anatomical, global signal intensity, anatomical image, Art Repair, cerebrospinal, products · Report a mismatch
[2] § Method › Data Analysis › Preprocessing ↔ preproc/main_children_mb.m, lines 1 to 38 · score 0.89 · skull stripped anatomical, global signal intensity, anatomical image, Art Repair, cerebrospinal, template · Report a mismatch
[3] § Method › Deviation From Preregistration ↔ preproc/main_just_for_movement.m, lines 1 to 43 · score 0.50 · global signal intensity, deviations, movement, repaired, volumes · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: 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

MATLAB · 107 lines · 5.3 KB · no license · 2 matches

%% This script will be used for the first time screening of the data (only give you movement info)
%This script was created by Professor Baxter Rogeres (VUIIS), but is heavily modified based on our lab pipeline by Jin Wang 3/7/2019
%(1) realignment to mean, reslice the mean.
%(2) segment anatomical image to TPM template. We get a deformation file "y_filename" and this is used in normalisation step to normalize all the
% functional data and the mean functional data.
%(3) Then we make a skull-striped anatomical T1 (based on segmentation) and coregister mean functional data (and all other functional data) to the anatomical T1.
%(4) Smoothing.
%(5) Art_global. It calls the realignmentfile (the rp_*.txt) to do the interpolation. This step identifies the bad volumes(by setting scan-to-scan movement
% mv_thresh =1.5mm and global signal intensity deviation Percent_thresh= 4 percent, any volumes movement to reference volume, which is the mean, >5mm) and repair
% them with interpolation. This step uses art-repair art_global.m function (the subfunctions within it are art_repairvol, which does repairment, and art_climvmnt, which identifies volumes movment to reference.
%(6) We use check_reg.m to see how well the meanfunctional data was normalized to template by visual check.
%(7) collapse all the 3d files into 4d files for saving space. You can decide whether you want to delete the product of not later.
%Before you run this script, you should make sure that your data structure is as expected.
global CCN;
addpath(genpath('/dors/booth/JBooth-Lab/BDL/jinwang/SemPhon_7_8/scripts')); %This is the code path
spm_path='/dors/booth/JBooth-Lab/BDL/LabCode/typical_data_analysis/spm12_elp'; %This is your spm path
%tpm='/dors/gpc/JamesBooth/JBooth-Lab/BDL/LabCode/typical_data_analysis/templates_cerebroMatic/mw_com_prior_Age_0081.nii'; %This is your template path
addpath(genpath(spm_path));
root='/dors/booth/JBooth-Lab/BDL/jinwang/SemPhon_7_8'; %This is your project folder
test_subjects={'sub-5003'};
run_script=1; % 1 is to run test_subjects, 2 is to run all the rest of the subjects in the preprocessed folder that's not specified in test_subjects.
%if user did not fully specify the subjects list, then it will read in all the
%data except the ones specified in the subjects list
listing=dir([root '/' CCN.preprocessed]);
all_list=extractfield(listing,'name');
index=strfind(all_list,'sub');
idx=find(not(cellfun('isempty',index)));
subjects_all=all_list(idx);
if run_script ==1
subjects=test_subjects;
else
subjects=subjects_all(~ismember(subjects_all,test_subjects));
end
CCN.preprocessed_folder='preprocessed'; %This is your data folder needs to be preprocessed
CCN.session='ses-7'; %This is the time point you want to analyze. If you have two time points, do the preprocessing one time point at a time by changing ses-T1 to ses-T2.
CCN.func_folder='sub*'; % This is your functional folder name
CCN.func_pattern='sub*.nii'; %This is your functional data name
%CCN.anat_pattern='ses-T1_T1w*.nii'; %This is your anat data name
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%shouldn't be modified below%%%%%%%%%%%%%%%
% Initialize
%addpath(spm_path);
spm('defaults','fmri');
spm_jobman('initcfg');
spm_figure('Create','Graphics','Graphics');
% Dependency and sanity checks
if verLessThan('matlab','R2013a')
error('Matlab version is %s but R2013a or higher is required',version)
end
req_spm_ver = 'SPM12 (6225)';
spm_ver = spm('version');
if ~strcmp( spm_ver,req_spm_ver )
error('SPM version is %s but %s is required',spm_ver,req_spm_ver)
end
try
%Start to preprocess data from here
for i=1:length(subjects)
fprintf('work on subject %s\n', subjects{i});
CCN.subj_folder=[root '/' CCN.preprocessed_folder '/' subjects{i}];
out_path=[CCN.subj_folder '/' CCN.session];
CCN.func_f='[subj_folder]/[session]/func/[func_folder]/';
func_f=expand_path(CCN.func_f);
func_file=[];
for m=1:length(func_f)
func_file{m}=expand_path([func_f{m} '[func_pattern]']);
end
Percent_thresh= 4; %global signal intensity change
mv_thresh =1.5; % scan-to-scan movement
MVMTTHRESHOLD=5; % movement to reference,see in art_clipmvmt
%expand 4d functional data to 3d data
func_vols=cell(length(func_file),1);
for x=1:length(func_file)
hdr = load_nii_hdr( char(func_file{x}) );
nscan = hdr.dime.dim(5);
expand_nii_scan(char(func_file{x}),1:nscan)
[func_p,func_n] = fileparts(char(func_file{x}));
func_vols{x}=cellstr(spm_select('ExtFPList',func_p, [func_n '_0.*\.nii$']));
[rfunc_file, rp_file] = realignment_byrun(func_vols{x}, out_path);%this will run the realignment by run by run
for jj=1:length(rfunc_file)
swfunc_list{jj}=erase(rfunc_file{jj},',1');
end
art_global_jin(char(swfunc_list),rp_file,4,1,Percent_thresh,mv_thresh,MVMTTHRESHOLD);
for ii=1:length(swfunc_list)
delete(swfunc_list{ii});
[p,n,ext]=fileparts(swfunc_list{ii});
delete([p '/v' n ext]);
end
[p,n,ext]=fileparts(swfunc_list{1});
delete([p '/mean' n ext]);
delete([p '/rp_' n '.txt']);
end
end
catch e
rethrow(e)
%display the errors
end

main_just_for_movement.m at commit b02cdaf, no license · at the source

Overview

Authors: Alisha B. Compton¹, Anna Banaszkiewicz^1,2, Jin Wang³, James R. Booth¹

ORCID iDs: Alisha B. Compton, Anna Banaszkiewicz

Institutions: Vanderbilt University (United States); Instytut Biologii Doświadczalnej im. Marcelego Nenckiego (Poland); Polish Academy of Sciences (Poland); University of California, Los Angeles (United States)

Journal: Journal of speech, language, and hearing research : JSLHR, volume 69, issue 4, pages 1686-1705

Dates: received 9 June 2025; accepted 8 December 2025; published online 26 March 2026; in print April 2026

Type: Research article · Language: English

License: CC BY-NC

Identifiers: DOI 10.1044/2025_jslhr-25-00448 · PMID 41885550 · PMCID PMC13086200 · OpenAlex W7140527794

Open access: hybrid, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: human (organism), cognitive (subfield)

Methods: Connectivity, Statistics, fMRI & imaging

MeSH: Brain*, Language Development*, Literacy*, Phonetics*, Semantics*, Speech Perception*, Child, Child, Preschool, Female, Humans, Magnetic Resonance Imaging, Male, Reading (* major topic)

Journal subjects: Language

Topic: Reading and Literacy Development (Developmental and Educational Psychology, Psychology), according to OpenAlex

Funding: National Institute on Deafness and Other Communication Disorders (R01DC013274)

Citations: cited by 2 papers (Europe PMC); 55 references in the paper

Abstract

Purpose: Neural specialization is a developmental phenomenon across multiple domains of language processing. The home literacy environment (HLE) is observed to relate to brain activation during language and reading tasks; however, whether HLE relates to phonological and semantic functional specialization and sensitivity remains unknown.

Method: Using an open-source data set, this study examined thirty-three 5- to 6-year-olds and seventy-six 7- to 8-year-olds. Data from functional magnetic resonance imaging sound and meaning judgment tasks were used to examine phonological and semantic functional specialization (contrasting tasks) and sensitivity (comparing conditions within a task). Then, voxel-wise regression analyses were used to test correlations between those brain indexes and HLE (i.e., family-to-child reading, child independent reading) measured using a parent survey.

Results: We observed weak evidence of phonological specialization at 5 years old and weak evidence of semantic specialization at 7 years old associated with family-to-child reading. We also observed weak evidence of phonological sensitivity at 5 years old and strong evidence of semantic sensitivity at 7 years old associated with family-to-child reading. Across the cohorts, a progression from temporal to frontal brain regions was observed in those relations, in line with prior literature on language specialization and sensitivity across development.

Conclusions: Overall, our results suggest that HLE is linked to functional specialization and sensitivity, with family-to-child reading showing a weak relation to sound structure processing at 5 years old but a stronger relation to meaning processing at 7 years old. This finding supports the interactive specialization theory, which emphasizes the role of environmental inputs in neural specialization.

Supplemental Material: https://doi.org/10.23641/asha.31606621

Reproduced under the paper's license (CC BY-NC), 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.

comptoab/HLE_PhonSem_Specialization

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: 24fe9014c9039f39fb3e916756f381323c390174, 12 November 2024

Languages: MATLAB (74)

Size: 81 files, 74 scripts

Software Heritage: not archived

Found in: “Data Availability Statement”

Holds: README

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

Tools: SPM (22 files), Tools for NIfTI and ANALYZE image (MATLAB) (8 files), Image Processing Toolbox (2 files)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

75 files

firstlevel/contrast_f.m, MATLAB, 14 lines
firstlevel/expand_path.m, MATLAB, 168 lines
firstlevel/firstlevel_4d.m, MATLAB, 69 lines
firstlevel/firstlevel_generate_bids_ELP_ses5.m, MATLAB, 194 lines
firstlevel/firstlevel_generate_bids_ELP_ses7.m, MATLAB, 194 lines
fmri_preproc_generic_mni/call.m, MATLAB, 119 lines
fmri_preproc_generic_mni/canny/Contents.m, MATLAB, 11 lines
fmri_preproc_generic_mni/canny/canny.m, MATLAB, 325 lines
fmri_preproc_generic_mni/canny/checkattributes.m, MATLAB, 20 lines
fmri_preproc_generic_mni/canny/exindex.m, MATLAB, 277 lines
fmri_preproc_generic_mni/canny/fractile.m, MATLAB, 34 lines
fmri_preproc_generic_mni/canny/gradients_n.m, MATLAB, 208 lines
fmri_preproc_generic_mni/canny/gsmoothn.m, MATLAB, 195 lines
fmri_preproc_generic_mni/canny/hystThresh.m, MATLAB, 40 lines
fmri_preproc_generic_mni/canny/nonmaxSuppress.m, MATLAB, 142 lines
fmri_preproc_generic_mni/coreg_check.m, MATLAB, 53 lines
fmri_preproc_generic_mni/coregister.m, MATLAB, 82 lines
fmri_preproc_generic_mni/drop_volumes.m, MATLAB, 22 lines
fmri_preproc_generic_mni/expand_path.m, MATLAB, 168 lines
fmri_preproc_generic_mni/mkmask.m, MATLAB, 17 lines
fmri_preproc_generic_mni/niftitools/affine.m, MATLAB, 554 lines
fmri_preproc_generic_mni/niftitools/bipolar.m, MATLAB, 94 lines
fmri_preproc_generic_mni/niftitools/bresenham_line3d.m, MATLAB, 189 lines
fmri_preproc_generic_mni/niftitools/collapse_nii_scan.m, MATLAB, 225 lines
fmri_preproc_generic_mni/niftitools/expand_nii_scan.m, MATLAB, 25 lines
fmri_preproc_generic_mni/niftitools/extra_nii_hdr.m, MATLAB, 255 lines
fmri_preproc_generic_mni/niftitools/flip_lr.m, MATLAB, 84 lines
fmri_preproc_generic_mni/niftitools/get_nii_frame.m, MATLAB, 108 lines
fmri_preproc_generic_mni/niftitools/load_nii.m, MATLAB, 138 lines
fmri_preproc_generic_mni/niftitools/load_nii_ext.m, MATLAB, 148 lines
fmri_preproc_generic_mni/niftitools/load_nii_hdr.m, MATLAB, 320 lines
fmri_preproc_generic_mni/niftitools/load_nii_hdr_jin.m, MATLAB, 321 lines
fmri_preproc_generic_mni/niftitools/load_nii_img.m, MATLAB, 386 lines
fmri_preproc_generic_mni/niftitools/load_untouch0_nii_hdr.m, MATLAB, 200 lines
fmri_preproc_generic_mni/niftitools/load_untouch_nii.m, MATLAB, 128 lines
fmri_preproc_generic_mni/niftitools/load_untouch_nii_hdr.m, MATLAB, 217 lines
fmri_preproc_generic_mni/niftitools/load_untouch_nii_img.m, MATLAB, 425 lines
fmri_preproc_generic_mni/niftitools/make_ana.m, MATLAB, 226 lines
fmri_preproc_generic_mni/niftitools/make_nii.m, MATLAB, 243 lines
fmri_preproc_generic_mni/niftitools/mat_into_hdr.m, MATLAB, 83 lines
fmri_preproc_generic_mni/niftitools/reslice_nii.m, MATLAB, 261 lines
fmri_preproc_generic_mni/niftitools/rri_file_menu.m, MATLAB, 179 lines
fmri_preproc_generic_mni/niftitools/rri_orient.m, MATLAB, 95 lines
fmri_preproc_generic_mni/niftitools/rri_orient_ui.m, MATLAB, 251 lines
fmri_preproc_generic_mni/niftitools/rri_xhair.m, MATLAB, 92 lines
fmri_preproc_generic_mni/niftitools/rri_zoom_menu.m, MATLAB, 33 lines
fmri_preproc_generic_mni/niftitools/save_nii.m, MATLAB, 233 lines
fmri_preproc_generic_mni/niftitools/save_nii_ext.m, MATLAB, 38 lines
fmri_preproc_generic_mni/niftitools/save_nii_hdr.m, MATLAB, 239 lines
fmri_preproc_generic_mni/niftitools/save_untouch0_nii_hdr.m, MATLAB, 219 lines
fmri_preproc_generic_mni/niftitools/save_untouch_nii.m, MATLAB, 197 lines
fmri_preproc_generic_mni/niftitools/save_untouch_nii_hdr.m, MATLAB, 207 lines
fmri_preproc_generic_mni/niftitools/unxform_nii.m, MATLAB, 40 lines
fmri_preproc_generic_mni/niftitools/verify_nii_ext.m, MATLAB, 45 lines
fmri_preproc_generic_mni/niftitools/view_nii.m, MATLAB, 4,876 lines
fmri_preproc_generic_mni/niftitools/view_nii_menu.m, MATLAB, 317 lines
fmri_preproc_generic_mni/niftitools/xform_nii.m, MATLAB, 519 lines
fmri_preproc_generic_mni/no_skull.m, MATLAB, 20 lines
fmri_preproc_generic_mni/normalise.m, MATLAB, 46 lines
fmri_preproc_generic_mni/realignment.m, MATLAB, 48 lines
fmri_preproc_generic_mni/realignment_byrun.m, MATLAB, 37 lines
fmri_preproc_generic_mni/segmentation.m, MATLAB, 58 lines
fmri_preproc_generic_mni/slice_timing_correction.m, MATLAB, 66 lines
fmri_preproc_generic_mni/smoothing.m, MATLAB, 29 lines
preproc/count_repaired.m, MATLAB, 67 lines
preproc/expand_path.m, MATLAB, 168 lines
preproc/main_children_mb.m, MATLAB, 134 lines, 1 match
preproc/main_just_for_movement.m, MATLAB, 107 lines
secondlevel/expand_path.m, MATLAB, 168 lines
secondlevel/multiple_regression.m, MATLAB, 53 lines
secondlevel/onesample_t.m, MATLAB, 55 lines
secondlevel/secondlevel_generate_regression_noSES.m, MATLAB, 129 lines
secondlevel/secondlevel_generate_regression_ses5.m, MATLAB, 128 lines
secondlevel/secondlevel_generate_regression_ses7.m, MATLAB, 128 lines
README.md, Text, 1 line

At the source: github.com/comptoab/HLE_PhonSem_Specialization

wangjinvandy/PhonSem_Specialization_7-8

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: b02cdafda0e2507fb26ac14ca9af6bdc71b462d0, 9 March 2021

Languages: MATLAB (70)

Size: 77 files, 70 scripts

Software Heritage: not archived

Found in: the text, “First-Level (Within-Subject) Analysis”

Holds: README

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

Tools: SPM (21 files), Tools for NIfTI and ANALYZE image (MATLAB) (10 files), Image Processing Toolbox (2 files), AFNI (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

71 files

batch_SmoothingEstim_spm12.m, MATLAB, 95 lines
firstlevel/contrast.m, MATLAB, 14 lines
firstlevel/firstlevel.m, MATLAB, 66 lines
firstlevel/firstlevel_generate_bids_ELP.m, MATLAB, 196 lines
fmri_preproc_generic_mni/call.m, MATLAB, 119 lines
fmri_preproc_generic_mni/canny/Contents.m, MATLAB, 11 lines
fmri_preproc_generic_mni/canny/canny.m, MATLAB, 325 lines
fmri_preproc_generic_mni/canny/checkattributes.m, MATLAB, 20 lines
fmri_preproc_generic_mni/canny/exindex.m, MATLAB, 277 lines
fmri_preproc_generic_mni/canny/fractile.m, MATLAB, 34 lines
fmri_preproc_generic_mni/canny/gradients_n.m, MATLAB, 208 lines
fmri_preproc_generic_mni/canny/gsmoothn.m, MATLAB, 195 lines
fmri_preproc_generic_mni/canny/hystThresh.m, MATLAB, 40 lines
fmri_preproc_generic_mni/canny/nonmaxSuppress.m, MATLAB, 142 lines
fmri_preproc_generic_mni/coreg_check.m, MATLAB, 53 lines
fmri_preproc_generic_mni/coregister.m, MATLAB, 82 lines
fmri_preproc_generic_mni/drop_volumes.m, MATLAB, 22 lines
fmri_preproc_generic_mni/expand_path.m, MATLAB, 168 lines
fmri_preproc_generic_mni/mkmask.m, MATLAB, 17 lines
fmri_preproc_generic_mni/niftitools/affine.m, MATLAB, 554 lines
fmri_preproc_generic_mni/niftitools/bipolar.m, MATLAB, 94 lines
fmri_preproc_generic_mni/niftitools/bresenham_line3d.m, MATLAB, 189 lines
fmri_preproc_generic_mni/niftitools/collapse_nii_scan.m, MATLAB, 225 lines
fmri_preproc_generic_mni/niftitools/expand_nii_scan.m, MATLAB, 25 lines
fmri_preproc_generic_mni/niftitools/extra_nii_hdr.m, MATLAB, 255 lines
fmri_preproc_generic_mni/niftitools/flip_lr.m, MATLAB, 84 lines
fmri_preproc_generic_mni/niftitools/get_nii_frame.m, MATLAB, 108 lines
fmri_preproc_generic_mni/niftitools/load_nii.m, MATLAB, 138 lines
fmri_preproc_generic_mni/niftitools/load_nii_ext.m, MATLAB, 148 lines
fmri_preproc_generic_mni/niftitools/load_nii_hdr.m, MATLAB, 320 lines
fmri_preproc_generic_mni/niftitools/load_nii_hdr_jin.m, MATLAB, 321 lines
fmri_preproc_generic_mni/niftitools/load_nii_img.m, MATLAB, 386 lines
fmri_preproc_generic_mni/niftitools/load_untouch0_nii_hdr.m, MATLAB, 200 lines
fmri_preproc_generic_mni/niftitools/load_untouch_nii.m, MATLAB, 128 lines
fmri_preproc_generic_mni/niftitools/load_untouch_nii_hdr.m, MATLAB, 217 lines
fmri_preproc_generic_mni/niftitools/load_untouch_nii_img.m, MATLAB, 425 lines
fmri_preproc_generic_mni/niftitools/make_ana.m, MATLAB, 226 lines
fmri_preproc_generic_mni/niftitools/make_nii.m, MATLAB, 243 lines
fmri_preproc_generic_mni/niftitools/mat_into_hdr.m, MATLAB, 83 lines
fmri_preproc_generic_mni/niftitools/reslice_nii.m, MATLAB, 261 lines
fmri_preproc_generic_mni/niftitools/rri_file_menu.m, MATLAB, 179 lines
fmri_preproc_generic_mni/niftitools/rri_orient.m, MATLAB, 95 lines
fmri_preproc_generic_mni/niftitools/rri_orient_ui.m, MATLAB, 251 lines
fmri_preproc_generic_mni/niftitools/rri_xhair.m, MATLAB, 92 lines
fmri_preproc_generic_mni/niftitools/rri_zoom_menu.m, MATLAB, 33 lines
fmri_preproc_generic_mni/niftitools/save_nii.m, MATLAB, 233 lines
fmri_preproc_generic_mni/niftitools/save_nii_ext.m, MATLAB, 38 lines
fmri_preproc_generic_mni/niftitools/save_nii_hdr.m, MATLAB, 239 lines
fmri_preproc_generic_mni/niftitools/save_untouch0_nii_hdr.m, MATLAB, 219 lines
fmri_preproc_generic_mni/niftitools/save_untouch_nii.m, MATLAB, 197 lines
fmri_preproc_generic_mni/niftitools/save_untouch_nii_hdr.m, MATLAB, 207 lines
fmri_preproc_generic_mni/niftitools/unxform_nii.m, MATLAB, 40 lines
fmri_preproc_generic_mni/niftitools/verify_nii_ext.m, MATLAB, 45 lines
fmri_preproc_generic_mni/niftitools/view_nii.m, MATLAB, 4,876 lines
fmri_preproc_generic_mni/niftitools/view_nii_menu.m, MATLAB, 317 lines
fmri_preproc_generic_mni/niftitools/xform_nii.m, MATLAB, 519 lines
fmri_preproc_generic_mni/no_skull.m, MATLAB, 20 lines
fmri_preproc_generic_mni/normalise.m, MATLAB, 46 lines
fmri_preproc_generic_mni/realignment.m, MATLAB, 48 lines
fmri_preproc_generic_mni/realignment_byrun.m, MATLAB, 37 lines
fmri_preproc_generic_mni/segmentation.m, MATLAB, 58 lines
fmri_preproc_generic_mni/slice_timing_correction.m, MATLAB, 66 lines
fmri_preproc_generic_mni/smoothing.m, MATLAB, 29 lines
preproc/count_repaired_acc.m, MATLAB, 130 lines
preproc/main_children_mb.m, MATLAB, 128 lines
preproc/main_just_for_movement.m, MATLAB, 107 lines, 2 matches
secondlevel/multiple_regression.m, MATLAB, 53 lines
secondlevel/onesample_t.m, MATLAB, 55 lines
secondlevel/secondlevel_generate_onesamplet.m, MATLAB, 139 lines
secondlevel/secondlevel_generate_regression.m, MATLAB, 138 lines
README.md, Text, 2 lines

At the source: github.com/wangjinvandy/PhonSem_Specialization_7-8

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;
144 scripts, 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

Datasets cited

doi:10.18112/openneuro.ds003604.v1.0.7, at OpenNeuro; found in the references
openneuro:ds003604, at OpenNeuro; found in “Data Availability Statement”
osf:d9cxm, at OSF; found in “Data Availability Statement”
osf:xrgtu, at OSF; found in “Data Availability Statement”

Data Availability Statement

The data set is publicly available on OpenNeuro.org (Wang et al., 2022; https://openneuro.org/datasets/ds003604). A copy of the exact codes used for this project can be found on GitHub (https://github.com/comptoab/HLE_PhonSem_Specialization). The research questions, hypotheses, and analytical plan were preregistered through the Open Science Framework prior to beginning the data analyses (see https://osf.io/d9cxm/ and https://osf.io/xrgtu).

Reproduced under the paper's license (CC BY-NC), 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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 13 MeSH terms, 1 funder, 47 references.

Cite

This paper

Compton, A. B., Banaszkiewicz, A., Wang, J., & Booth, J. R. (2026). The Relation of Home Literacy Environment to Brain Specialization and Sensitivity for Phonological and Semantic Processing of Spoken Words. Journal of speech, language, and hearing research : JSLHR, 69(4), 1686-1705. https://doi.org/10.1044/2025_jslhr-25-00448

BibTeX

@article{compton2026relation, author = {Compton, Alisha B. and Banaszkiewicz, Anna and Wang, Jin and Booth, James R.}, title = {{The Relation of Home Literacy Environment to Brain Specialization and Sensitivity for Phonological and Semantic Processing of Spoken Words}}, journal = {Journal of speech, language, and hearing research : JSLHR}, year = {2026}, month = mar, volume = {69}, number = {4}, pages = {1686--1705}, publisher = {American Speech-Language-Hearing Association}, issn = {1092-4388}, doi = {10.1044/2025_jslhr-25-00448}, url = {https://doi.org/10.1044/2025_jslhr-25-00448}, pmid = {41885550}, pmcid = {PMC13086200} }

RIS

TY - JOUR AU - Compton, Alisha B. AU - Banaszkiewicz, Anna AU - Wang, Jin AU - Booth, James R. TI - The Relation of Home Literacy Environment to Brain Specialization and Sensitivity for Phonological and Semantic Processing of Spoken Words T2 - Journal of speech, language, and hearing research : JSLHR J2 - J Speech Lang Hear Res PY - 2026 DA - 2026/03/26 VL - 69 IS - 4 SP - 1686 EP - 1705 SN - 1092-4388 PB - American Speech-Language-Hearing Association DO - 10.1044/2025_jslhr-25-00448 UR - https://doi.org/10.1044/2025_jslhr-25-00448 LA - en ER -

CSL-JSON

{ "id": "10.1044/2025_jslhr-25-00448", "type": "article-journal", "title": "The Relation of Home Literacy Environment to Brain Specialization and Sensitivity for Phonological and Semantic Processing of Spoken Words", "container-title": "Journal of speech, language, and hearing research : JSLHR", "author": [ { "family": "Compton", "given": "Alisha B." }, { "family": "Banaszkiewicz", "given": "Anna" }, { "family": "Wang", "given": "Jin" }, { "family": "Booth", "given": "James R." } ], "container-title-short": "J Speech Lang Hear Res", "volume": "69", "issue": "4", "page": "1686-1705", "DOI": "10.1044/2025_jslhr-25-00448", "PMID": "41885550", "PMCID": "PMC13086200", "ISSN": "1092-4388", "publisher": "American Speech-Language-Hearing Association", "URL": "https://doi.org/10.1044/2025_jslhr-25-00448", "language": "en", "issued": { "date-parts": [ [ 2026, 3, 26 ] ] } }

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