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Foreign Language Learning in Older Adults Modifies Resting-State Functional Connectivity Between the Subcortical Structures and the Cortex.

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  1. [1] § Results › Changes of rsFC of Subcortical Regions Induced by FLL ↔ notebooks/fig2d_neurosynth_islands.ipynb, lines 64–96 · score 0.51 · PCC, anterior, mPFC, insula, posterior, prefrontal

Paper

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The authors' code

Jupyter notebook · 171 lines · 5.3 KB · MIT · 1 match

  1. # %%
  2. #neurosynth term island annotations for fig 2d
  4. # %%
  5. import scripts.neurosynth_tools as nt
  6. import numpy as np
  7. import nibabel as nb
  8. import os
  9. import matplotlib.pyplot as plt
  11. # %%
  12. n_perm=1000
  13. spins= np.load(f'spin_dir/spins_{n_perm}.npy')
  14. base_dir = '/data1/allen_surfaces/'
  15. w_dir= '/data1/bigbrain/phate_testing/'
  17. cortex=nb.load(os.path.join(base_dir,'hcp_surfs','fs_LR32k','Glasser_2016.32k.L.label.gii'))
  18. cortex=cortex.darrays[0].data>0
  20. # %%
  21. import json
  22. with open('weighted_island_vectors/glasser_rois.json') as f:
  23. d=json.load(f)
  24. rois=[d['{}'.format(x)][0][2:-4] for x in np.arange(6)+1]
  26. islands = np.loadtxt(os.path.join(w_dir,'weighted_island_vectors','clustered_islands.txt'))
  28. # %%
  29. from importlib import reload
  30. reload(nt)
  32. # %%
  33. fig = plt.figure(figsize=(15,12))
  34. gs = fig.add_gridspec(2,3, wspace=0.1,width_ratios=[1,1,1],height_ratios=[1,1])
  35. axs = gs.subplots(sharex=False, sharey=False)
  36. axes=axs.ravel()
  37. terms = np.zeros((len(rois),34),dtype=object)
  38. t_stats = np.zeros((len(rois),34))
  39. pvals_all = np.zeros((len(rois),34))
  40. top_term = []
  41. for island,roi in enumerate(rois):
  43. map1=(islands==island)[cortex]
  44. t_stats[island], pvals, terms[island],perm_t=nt.neurosynth_binary_annotation(map1,
  45. spins,test=nt.dice)
  46. nt.plot_neurosynth(t_stats[island],pvals,terms[island],title=f'{roi}',
  47. test='dice',ax=axes[island])
  48. print(terms[island],pvals)
  49. top_term.append( terms[island][np.argsort(pvals)[0]])
  50. pvals_all[island] = pvals
  52. # %%
  53. top_term
  55. # %%
  56. n_per=2
  57. bool_include = np.zeros_like(t_stats,dtype=bool)
  58. for island,roi in enumerate(rois):
  59. bool_include[island,np.argsort(t_stats[island])[-n_per:]]=1
  61. # %%
  62. import pandas as pd
  64. # %%
  65. reduced_t=t_stats[:,np.any(bool_include,axis=0)]
  67. reduced_p = pvals_all[:,np.any(bool_include,axis=0)]
  68. reduced_terms = terms[:,np.any(bool_include,axis=0)][0]
  69. unwanted_terms = ['cortex','cerebellar','anterior','cingulate',
  70. 'prefrontal','pfc','insula','insular','acc','anterior',
  71. 'dorsal','posterior','anterior','orbitofrontal','medial','dacc','dorsolateral','dlpfc',
  72. 'cerebellum','basal','ganglia','nucleus','thalamus','caudate', 'putamen',
  73. 'striatal', 'thalamic','nuclei', 'structures','cerebral', 'brainstem','lateral','ventral','amygdala',
  74. 'emotion','magnetic','resonance','pcc','rostral','frontal','ai','mpfc','action']
  75. terms_sheet=pd.read_excel('neurosynth/NeuroSynthV5Topic50List.xlsx',skiprows=2)
  77. for k,t in enumerate(reduced_terms):
  78. single_terms=reduced_terms[k].split('_')
  79. #find matching row
  80. scores=np.zeros(len(terms_sheet['Top terms']))
  81. for s_t in single_terms:
  82. for fl,full_list in enumerate(terms_sheet['Top terms']):
  83. if s_t in full_list.split(', ')[:n_per]:
  84. scores[fl]+=1
  85. term_index = np.argmax(scores)
  86. sc=0
  87. new_single_terms = single_terms.copy()
  88. for s_t in single_terms:
  89. if s_t in unwanted_terms:
  90. sc+=1
  91. new_single_terms.remove(s_t)
  92. while terms_sheet['Top terms'][term_index].split(', ')[2+sc] in unwanted_terms:
  93. sc+=1
  94. new_single_terms.append(terms_sheet['Top terms'][term_index].split(', ')[2+sc])
  95. print(new_single_terms)
  96. reduced_terms[k] = '\n'.join(new_single_terms)
  99. # %%
  100. def reorder_modules_from_similarity(matrix):
  101. from scipy import cluster
  102. import seaborn as sns
  103. clustergrid = sns.clustermap(np.nan_to_num(np.corrcoef(matrix)));
  104. new_order = np.array(clustergrid.dendrogram_row.reordered_ind)
  105. plt.close('all')
  107. return new_order
  109. # %%
  111. new_order = reorder_modules_from_similarity(1-np.corrcoef(reduced_t.T))
  112. #new_order = (len(new_order)-1-new_order)
  116. # %%
  117. new_order = np.roll(new_order,5)
  119. # %%
  120. #reorder terms
  121. #new_order = np.array([1,11,8,10,9,2,6,0,4,3,5,7])
  122. #rois=np.array(rois)[new_order]
  123. reduced_t = np.array(reduced_t)[:,new_order]
  124. reduced_p = np.array(reduced_p)[:,new_order]
  125. reduced_terms=np.array(reduced_terms)[new_order]
  127. # %%
  128. np.save('weighted_island_vectors/neurosynth_top.npy',reduced_terms[np.argmax(reduced_t,axis=1)])
  131. # %%
  132. cmap = plt.get_cmap("tab20")
  133. colors = cmap(np.arange(20))
  135. # %%
  136. #colors=plt.rcParams['axes.prop_cycle'].by_key()['color']
  137. colors=colors[np.round(np.linspace(0,19,7)).astype(int)]
  139. # %%
  140. plt.figure(figsize=(6,6))
  141. plt.rcParams.update({'font.size': 14})
  143. ax = plt.axes(polar=True)
  144. ax.spines['polar'].set_visible(False)
  145. for k,t in enumerate(rois):
  146. #vals=np.clip(0,np.max(reduced_t[k]),reduced_t[k])
  147. vals = reduced_t[k]
  148. vals = np.hstack([vals,vals[0]])
  149. vals=vals/np.max(vals)
  150. offset=0
  151. if t in ['2','4']:
  152. offset = [0.02,-0.02][['2','4'].index(t)]
  154. ax.plot(np.linspace(np.pi*2,0,len(reduced_terms)+1)+offset,vals,
  155. linewidth=4,label=t,c=colors[k+1])
  156. ax.set_ylim([0,1.3])
  157. ax.set_xticks(np.linspace(np.pi*2,0,len(reduced_terms)+1)[:-1])
  158. ax.set_xticklabels(reduced_terms,zorder=0,size=20,ha='center')
  159. #ax.xaxis.labelpad = 50
  160. #ax.set_rlabel_position(-22.5)
  161. # pos=ax.get_theta_label_position()
  162. # ax.set_th
  163. ax.set_yticks([])
  164. ax.set_theta_zero_location("N")
  166. #ax.xaxis.grid(False)
  167. #ax.xaxis.set_visible(False)
  168. plt.tight_layout()
  169. #ax.set_yticklabels(['','','','','',''])
  170. plt.legend(loc=[1.12,0.8])
  171. plt.savefig('neurosynth/neurosynth_island_annotations.pdf')

fig2d_neurosynth_islands.ipynb at commit b611a0c, under MIT · at the source

Overview

Authors: Giovanna Bubbico1,2, Federica Tomaiuolo2,3, Carlo Sestieri1,2, Golnoush Akhlaghipour4, Alberto Granzotto1,5, Antonio Ferretti1,2,6, Mauro Gianni Perrucci1,2, Stefano L Sensi1,2,5,7, Stefano Delli Pizzi1,2,5
ORCID iDs: Stefano Delli Pizzi
  1. Department of Neuroscience, Imaging and Clinical Sciences, G. D'annunzio University of Chieti‐Pescara, Chieti, Italy
  2. Institute for Advanced Biomedical Technologies (ITAB), G. D'annunzio University of Chieti‐Pescara, Chieti, Italy
  3. Department of Engineering and Geology, G. D'annunzio University of Chieti‐Pescara, Chieti, Italy
  4. Department of Neurology, Montefiore Einstein Saul R. Korey, Bronx, New York, USA
  5. Molecular Neurology Unit, Centre for Advanced Studies and Technology (CAST), G. D'annunzio University of Chieti‐Pescara, Chieti, Italy
  6. UdA‐TechLab, Research Center, University G. D'annunzio of Chieti‐Pescara, Chieti, Italy
  7. Institute of Neurology, SS Annunziata University Hospital, University G. D'annunzio of Chieti‐Pescara, Chieti, Italy
Institutions: University of Chieti-Pescara (Italy); Albert Einstein College of Medicine (United States); Ospedale SS. Annunziata (Italy)
Journal: Aging medicine (Milton (N.S.W)), volume 9, issue 2, pages 99-110
Dates: received 9 September 2025; accepted 13 April 2026; published online 23 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1002/agm2.70073 · PMID 42130753 · PMCID PMC13163939 · OpenAlex W7155363777
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism)
Methods: Spectral & time-frequency, Statistics, Preprocessing, Connectivity, fMRI & imaging
Keywords: aging, CB1 receptor, NTRK2 gene expression, resting‐state functional connectivity, semantic memory, subcortical networks
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Commission (D73C22001930006, D73C22000840006)
Citations: not cited yet (Europe PMC); 65 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

kwagstyl/MAGICC

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: b611a0c96ef17fe1cadea974fb7a54b2b4128d30, 8 February 2024
Languages: Python (18), Jupyter (10)
Size: 31 files, 28 scripts
Software Heritage: not archived
Found in: the text, “Spatial Correlation With Receptor and Gene Densi”
Holds: README, license file, 10 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (26 files), pandas (23 files), NiBabel (15 files), Matplotlib (14 files), SciPy (10 files), seaborn (7 files), scikit-learn (4 files), Pillow (3 files), statsmodels (3 files), Connectome Workbench (2 files), FreeSurfer (1 file), h5py (1 file), netneurotools (1 file), Nilearn (1 file), PyTorch (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
30 files
At the source: github.com/kwagstyl/MAGICC

netneurolab/neuromaps

License: CC-BY-NC-SA-4.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: ffcc2e0f657943ce00a1b6a968396f32250e495c, 4 June 2026
Languages: Python (45), Shell (3)
Size: 90 files, 48 scripts
Software Heritage: archived
Found in: the text, “Spatial Correlation With Receptor and Gene Densi”
Holds: README, license file, environment (Dockerfile, pyproject.toml, requirements.txt, setup.py, docs/requirements.txt), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: neuromaps (29 files), NumPy (19 files), NiBabel (8 files), SciPy (7 files), Nilearn (5 files), scikit-learn (3 files), Matplotlib (2 files), BrainSMASH (1 file), BrainSpace (1 file), pandas (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
50 files
At the source: github.com/netneurolab/neuromaps

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;
  • 76 scripts, each with its path and the digest of its content;
  • 1 match 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

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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, issue, pages, dates, 9 authors, 6 keywords, 1 funder, 64 references.

Cite

This paper

Bubbico, G., Tomaiuolo, F., Sestieri, C., Akhlaghipour, G., Granzotto, A., Ferretti, A., Perrucci, M. G., Sensi, S. L., & Delli Pizzi, S. (2026). Foreign Language Learning in Older Adults Modifies Resting-State Functional Connectivity Between the Subcortical Structures and the Cortex. Aging medicine (Milton (N.S.W)), 9(2), 99-110. https://doi.org/10.1002/agm2.70073

BibTeX

@article{bubbico2026foreign,
author = {Bubbico, Giovanna and Tomaiuolo, Federica and Sestieri, Carlo and Akhlaghipour, Golnoush and Granzotto, Alberto and Ferretti, Antonio and Perrucci, Mauro Gianni and Sensi, Stefano L and Delli Pizzi, Stefano},
title = {{Foreign Language Learning in Older Adults Modifies Resting-State Functional Connectivity Between the Subcortical Structures and the Cortex}},
journal = {Aging medicine (Milton (N.S.W))},
year = {2026},
month = apr,
volume = {9},
number = {2},
pages = {99--110},
publisher = {Wiley},
issn = {2475-0360},
doi = {10.1002/agm2.70073},
url = {https://doi.org/10.1002/agm2.70073},
pmid = {42130753},
pmcid = {PMC13163939}
}

RIS

TY - JOUR
AU - Bubbico, Giovanna
AU - Tomaiuolo, Federica
AU - Sestieri, Carlo
AU - Akhlaghipour, Golnoush
AU - Granzotto, Alberto
AU - Ferretti, Antonio
AU - Perrucci, Mauro Gianni
AU - Sensi, Stefano L
AU - Delli Pizzi, Stefano
TI - Foreign Language Learning in Older Adults Modifies Resting-State Functional Connectivity Between the Subcortical Structures and the Cortex
T2 - Aging medicine (Milton (N.S.W))
J2 - Aging Med (Milton)
PY - 2026
DA - 2026/04/23
VL - 9
IS - 2
SP - 99
EP - 110
SN - 2475-0360
PB - Wiley
DO - 10.1002/agm2.70073
UR - https://doi.org/10.1002/agm2.70073
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Foreign Language Learning in Older Adults Modifies Resting-State Functional Connectivity Between the Subcortical Structures and the Cortex",
"container-title": "Aging medicine (Milton (N.S.W))",
"author": [
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"family": "Bubbico",
"given": "Giovanna"
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{
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{
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{
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{
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{
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{
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{
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"page": "99-110",
"DOI": "10.1002/agm2.70073",
"PMID": "42130753",
"PMCID": "PMC13163939",
"ISSN": "2475-0360",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/agm2.70073",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
23
]
]
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}

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