OSCR

Binding items to contexts through conjunctive neural representations with the method of loci.

Code ↔ Paper

6 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 6 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › fMRI preprocessing › Functional data preprocessing ↔ post_fmriprep.ipynb, lines 56–199 · score 0.95 · framewise displacement, motion outliers, preprocessed BOLD, brain mask, CompCor, aseg
  2. [2] § Methods › Activity pattern extraction ↔ post_fmriprep.ipynb, lines 56–199 · score 0.82 · motion outlier, linear regression, zero, cosine, derivatives, signals
  3. [3] § Methods › Relating the similarity of encoding residuals to the similarity of stories (Fig. 3) ↔ scripts/rsa.ipynb, lines 487–521 · score 0.82 · neural semantic correlation, semantic correlation matrices, Spearman correlation, lower triangles, encoding residual, permutation
  4. [4] § Results › Encoding residuals track idiosyncratic semantic combinations of loci and items ↔ scripts/rsa.ipynb, lines 487–521 · score 0.66 · Spearman correlation, lower triangles, neural representations, encoding residual, semantic, cross
  5. [5] § Methods › fMRI preprocessing › Functional data preprocessing ↔ runSingularity.sh, the whole file · a weak match · score 0.62 · MNI152NLin2009cAsym, singular, fMRIPrep, fsaverage6, space, variable
  6. [6] § Results › Relationship between conjunctive representation in the DMN and training and behavior ↔ scripts/hipp_sem.ipynb, lines 328–370 · score 0.55 · univariate activity, story deviation, duration, ROIs, neural, AG

Paper

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The authors' code

Jupyter notebook · 200 lines · 8.3 KB · no license · 2 matches

  1. # %%
  2. import nibabel as nib
  3. import os
  4. import h5py
  5. from sklearn import linear_model
  6. from scipy import stats
  7. import numpy as np
  8. import json
  9. import glob
  10. #path to data (change to your path)
  11. fmripreppath = '/data/MoL_clean/fmriprep/'
  13. #path to output (change to your path)
  14. prepath = '/data/MoL_clean/preprocessed/'
  16. #list of sessions
  17. sesslist=['ses-01','ses-02','ses-03']
  19. #list of tasks/#runs in each session
  20. tasklist = [[('Loci', 2),('Item',2),('Encode',1),('Retrieve',1)],
  21. [('Loci', 2),('Item',2),('Encode',1),('Retrieve',1)],
  22. [('Loci', 2),('Item',2),('Encode',1),('Retrieve',1)]]
  24. #list of subjects
  25. subs= ['sub-%02d' % i for i in range(1,26)]
  28. # %%
  29. os.path.exists(os.path.exists(sess_prefix + '_task-' + task[0] + ('_run-%.2d' % offset) + '_desc-confounds_timeseries.tsv'))
  32. # %%
  33. # identify group masks for hippocampus
  34. regional_mask_filenames = sorted([f for f in glob.glob(fmripreppath+'*/ses-01/func/*_ses-01_task-Loci_run-01_space-MNI152NLin2009cAsym_desc-aseg_dseg.nii.gz') ])
  35. regional_mask_filenames = regional_mask_filenames[:10]+regional_mask_filenames[16:]
  36. regional_masks = []
  37. hippo_masks = []
  38. for regional_mask_fname in regional_mask_filenames:
  39. regional_mask = nib.load(regional_mask_fname).get_fdata()
  40. regional_masks.append(regional_mask)
  41. hippo_masks.append(np.where(np.logical_or(regional_mask == 17, regional_mask == 53)))
  43. hippo_group_mask_template = np.zeros(regional_mask.shape)
  44. hippo_group_mask = np.zeros(regional_mask.shape)
  45. for hippo_mask in hippo_masks:
  46. hippo_group_mask_template[hippo_mask] += 1
  47. hippo_group_mask[hippo_group_mask_template==25] = 1
  49. anterior_group,posterior_group = hippo_group_mask.copy(),hippo_group_mask.copy()
  50. anterior_group[:,55:,:] = False
  51. posterior_group[:,:55,:] = False
  52. anterior_group = anterior_group.astype(bool)
  53. posterior_group = posterior_group.astype(bool)
  54. hippo_group_mask = hippo_group_mask.astype(bool)
  56. # %%
  58. for sub in subs:
  59. print('Processing subject:', sub)
  61. for sess_ind, sess in enumerate(sesslist):
  62. print('session:', sess)
  64. if sess == '':
  65. sess_prefix = os.path.join(fmripreppath, sub, sess, 'func', sub)
  66. else:
  67. sess_prefix = os.path.join(fmripreppath, sub, sess, 'func', sub + '_' + sess)
  69. sess_tasknames = []
  70. for task in tasklist[sess_ind]:
  71. if task[1] == 1:
  72. sess_tasknames.append(task[0]+'_run-01')
  73. else:
  74. # For run numbers that don't start at zero
  75. offset = 0
  76. while not os.path.exists(sess_prefix + '_task-' + task[0] + ('_run-%.2d' % offset) + '_desc-confounds_timeseries.tsv'):
  77. print('looping')
  78. offset += 1
  79. if offset>10:
  80. print("something is wrong")
  81. break
  82. for r in range(task[1]):
  83. sess_tasknames.append('%s_run-%.2d' % (task[0], r+offset))
  84. for task_name in sess_tasknames:
  85. print('task:', task_name)
  87. task_prefix = sess_prefix + '_task-' + task_name
  89. D = dict()
  90. for hem in ['L', 'R', 'Vol']:
  91. if hem == 'Vol':
  92. # Load all timecourses in the 3d brain mask
  94. fname = task_prefix + '_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
  95. print(' Loading ', fname)
  97. nii_4d = nib.load(fname).get_fdata()
  99. mask_fname = task_prefix + '_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz'
  100. mask_3d = nib.load(mask_fname).get_fdata().astype(bool)
  102. regional_mask_fname = task_prefix + '_space-MNI152NLin2009cAsym_desc-aseg_dseg.nii.gz'
  103. regional_mask = nib.load(regional_mask_fname).get_fdata()
  104. hippo_mask = np.logical_or(regional_mask == 17, regional_mask == 53)
  105. anterior,posterior = hippo_mask.copy(),hippo_mask.copy()
  106. anterior[:,55:,:] = False
  107. posterior[:,:55,:] = False
  108. D['anterior_hipp'] = nii_4d[anterior]
  109. D['posterior_hipp'] = nii_4d[posterior]
  110. D['hippo'] = nii_4d[hippo_mask]
  111. D['anterior_hipp_group'] = nii_4d[anterior_group]
  112. D['posterior_hipp_group'] = nii_4d[posterior_group]
  113. D['hippo_group'] = nii_4d[hippo_group_mask]
  114. D[hem] = nii_4d[mask_3d]
  116. else:
  117. # Load all timecourses for one cortical hemisphere
  119. fname = task_prefix +'_hemi-'+hem+'_space-fsaverage6_bold.func.gii'
  120. print(' Loading ', fname)
  122. gi = nib.load(fname)
  123. D[hem] = np.column_stack([gi.darrays[t].data for t in range(len(gi.darrays))])
  126. # Load confound regressors
  127. conf = np.genfromtxt(task_prefix + '_desc-confounds_timeseries.tsv', names=True)
  129. conf_json = json.load(open(task_prefix + '_desc-confounds_timeseries.json'))
  131. # Find first combined compcor regressor
  132. first_cc = 0
  133. while True:
  134. if conf_json['a_comp_cor_%02d' % first_cc]['Mask'] == 'combined':
  135. break
  136. first_cc += 1
  138. reg = np.column_stack((
  140. # Motion and motion derivatives
  141. conf['trans_x'],
  142. conf['trans_x_derivative1'],
  143. conf['trans_y'],
  144. conf['trans_y_derivative1'],
  145. conf['trans_z'],
  146. conf['trans_z_derivative1'],
  147. conf['rot_x'],
  148. conf['rot_x_derivative1'],
  149. conf['rot_y'],
  150. conf['rot_y_derivative1'],
  151. conf['rot_z'],
  152. conf['rot_z_derivative1'],
  153. conf['framewise_displacement'],
  155. # First six compcor components (white matter + CSF signals)
  156. conf['a_comp_cor_%02d' % first_cc],
  157. conf['a_comp_cor_%02d' % (first_cc+1)],
  158. conf['a_comp_cor_%02d' % (first_cc+2)],
  159. conf['a_comp_cor_%02d' % (first_cc+3)],
  160. conf['a_comp_cor_%02d' % (first_cc+4)],
  161. conf['a_comp_cor_%02d' % (first_cc+5)],
  163. # Cosine (drift) and motion spikes
  164. np.column_stack([conf[k] for k in conf.dtype.names if ('cosine' in k) or ('motion_outlier' in k)])))
  166. # Remove nans, e.g. from framewise_displacement
  167. reg = np.nan_to_num(reg)
  169. print(' Cleaning and zscoring')
  170. for hem in ['L', 'R', 'Vol', 'anterior_hipp','posterior_hipp','hippo','anterior_hipp_group','posterior_hipp_group','hippo_group']:
  171. # Regress out confounds from data
  172. regr = linear_model.LinearRegression()
  173. regr.fit(reg, D[hem].T)
  174. D[hem] = D[hem] - np.dot(regr.coef_, reg.T) - regr.intercept_[:, np.newaxis]
  175. # Note 8% of values on cortical surface are NaNs, and the following will therefore throw an error
  176. D[hem] = stats.zscore(D[hem], axis=1)
  178. # Save hdf5 file
  179. if sess == '':
  180. savepath = os.path.join(prepath, sub + '_' + task_name + '.h5')
  181. else:
  182. savepath = os.path.join(prepath, sub + '_' + sess + '_' + task_name + '.h5')
  183. with h5py.File(savepath,'w') as hf:
  184. grp = hf.create_group(task_name)
  185. grp.create_dataset('L', data=D['L'])
  186. grp.create_dataset('R', data=D['R'])
  187. grp.create_dataset('Vol', data=D['Vol'])
  188. grp.create_dataset('anterior_hipp', data=D['anterior_hipp'])
  189. grp.create_dataset('posterior_hipp', data=D['posterior_hipp'])
  190. grp.create_dataset('hippo', data=D['hippo'])
  191. grp.create_dataset('anterior_hipp_group', data=D['anterior_hipp_group'])
  192. grp.create_dataset('posterior_hipp_group', data=D['posterior_hipp_group'])
  193. grp.create_dataset('hippo_group', data=D['hippo_group'])
  195. grp.create_dataset('reg',data=reg)
  196. grp.create_dataset('mask', data=mask_3d)
  197. grp.create_dataset('hippo_mask', data=hippo_mask)
  199. print(' saved hdf5 file')

post_fmriprep.ipynb at commit 1cf2ce5, no license · at the source

Overview

Authors: Jiawen Huang1, Akshay Manglik1, Nick Dutra1, Hannah Tarder-Stoll1,2,3, Taylor Chamberlain1, Robert Ajemian4,5, Qiong Zhang6,7, Kenneth A Norman8,9, Christopher Baldassano1
ORCID iDs: Jiawen Huang, Akshay Manglik, Qiong Zhang, Kenneth A Norman, Christopher Baldassano
  1. Department of Psychology, Columbia University, New York, NY USA
  2. Baycrest Health Sciences, Rotman Research Institute, Toronto, ON Canada
  3. Department of Psychology, Glendon Campus, York University, Toronto, ON, Canada
  4. Department of Brain and Cognitive Sciences, MIT, Cambridge, MA USA
  5. McGovern Institute for Brain Research, MIT, Cambridge, MA USA
  6. Department of Psychology, Rutgers University, New Brunswick, NJ USA
  7. Department of Computer Science, Rutgers University, New Brunswick, NJ USA
  8. Princeton Neuroscience Institute, Princeton University, Princeton, NJ USA
  9. Department of Psychology, Princeton University, Princeton, NJ USA
Institutions: Columbia University (United States); York University (Canada); Baycrest Academy for Research and Education (Canada); Rotman Research Institute (Canada); McGovern Institute for Brain Research (United States); Massachusetts Institute of Technology (United States); Rutgers, The State University of New Jersey (United States); Princeton University (United States)
Journal: Nature communications, volume 17, issue 1, article 5347
Dates: received 28 March 2025; accepted 11 March 2026; published online 17 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71428-6 · PMID 41997927 · PMCID PMC13273176 · OpenAlex W4405966708
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.12.19.629352
Status: code verified
Categories: human (organism), systems (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging, Machine learning
Keywords: Language, Long-term memory
MeSH: Memory, Episodic*, Prefrontal Cortex*, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Mental Recall, Young Adult (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Science Foundation (2024587)
Citations: not cited yet (Europe PMC); 105 references in the paper
Research resources: RRID:SCR_001362, RRID:SCR_00184791, RRID:SCR_002438, 87 RRID:SCR_002502, RRID:SCR_00282390, distributed with ANTs 2.3.389 RRID:SCR_004757, RRID:SCR_008796, 85 RRID:SCR_016216

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.

Repositories

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dpmlab/MoL_code

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 1cf2ce5193614e9146343e217b2273a4ad94b49c, 4 August 2025
Languages: Jupyter (7), Shell (1), R (1), Python (1)
Size: 549 files, 10 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 8 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (8 files), SciPy (7 files), h5py (6 files), Matplotlib (6 files), pandas (6 files), scikit-learn (6 files), statsmodels (4 files), seaborn (3 files), fMRIPrep (1 file), ggplot2 (1 file), lme4 (1 file), lmerTest (1 file), NiBabel (1 file), Nilearn (1 file), Plotly (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
11 files
At the source: github.com/dpmlab/MoL_code

Zenodo 17703712

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (8 files), SciPy (7 files), h5py (6 files), Matplotlib (6 files), pandas (6 files), scikit-learn (6 files), statsmodels (4 files), seaborn (3 files), fMRIPrep (1 file), ggplot2 (1 file), lme4 (1 file), lmerTest (1 file), NiBabel (1 file), Nilearn (1 file), Plotly (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
11 files
At the source:

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Read it in the paper: doi.org/10.1038/s41467-026-71428-6.

Tracing map

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  • 6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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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/10.1038/s41467-026-71428-6.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 2 keywords, 10 MeSH terms, 1 funder, 85 references, 8 RRIDs.

Cite

This paper

Huang, J., Manglik, A., Dutra, N., Tarder-Stoll, H., Chamberlain, T., Ajemian, R., Zhang, Q., Norman, K. A., & Baldassano, C. (2026). Binding items to contexts through conjunctive neural representations with the method of loci. Nature communications, 17(1), 5347. https://doi.org/10.1038/s41467-026-71428-6

BibTeX

@article{huang2026binding,
author = {Huang, Jiawen and Manglik, Akshay and Dutra, Nick and Tarder-Stoll, Hannah and Chamberlain, Taylor and Ajemian, Robert and Zhang, Qiong and Norman, Kenneth A and Baldassano, Christopher},
title = {{Binding items to contexts through conjunctive neural representations with the method of loci}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5347},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71428-6},
url = {https://doi.org/10.1038/s41467-026-71428-6},
pmid = {41997927},
pmcid = {PMC13273176}
}

RIS

TY - JOUR
AU - Huang, Jiawen
AU - Manglik, Akshay
AU - Dutra, Nick
AU - Tarder-Stoll, Hannah
AU - Chamberlain, Taylor
AU - Ajemian, Robert
AU - Zhang, Qiong
AU - Norman, Kenneth A
AU - Baldassano, Christopher
TI - Binding items to contexts through conjunctive neural representations with the method of loci
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/17
VL - 17
IS - 1
SP - 5347
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71428-6
UR - https://doi.org/10.1038/s41467-026-71428-6
LA - en
ER -

CSL-JSON

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