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Multi-level language deficits in behavioural frontotemporal degeneration and related disorders.

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The authors' code

Python · 80 lines · 4.1 KB · no license

  1. from os import listdir
  2. from os.path import isfile, join, basename
  3. import pandas as pd
  4. import csv
  6. excel_folder = "C:\\Users\\denev\\OneDrive\\Documents\\Travail\\These\\Etude_1\\Donnees_neuropsycho_ortho\\Donnees_GREMOTS\\Verification_orale_photos\\patients"
  7. excel_file_paths = []
  8. result_file_name = "verif_result_patients.csv"
  10. for file in listdir(excel_folder):
  11. if (
  12. isfile(join(excel_folder, file))
  13. and file.endswith(".xls")
  14. ):
  15. excel_file_paths.append(join(excel_folder, file))
  17. images = {
  18. "gland": {"noisette": "sem_proche", "gland": "cible", "bouée": "neutre", "fraise": "sem_loin"},
  19. "muguet": {"jacinthe": "sem_proche", "muguet": "cible", "ardoise": "neutre", "lavande": "sem_loin"},
  20. "hérisson": {"taupe": "sem_proche", "hérisson": "cible", "cadenas": "neutre", "sanglier": "sem_loin"},
  21. "seau": {"arrosoir": "sem_proche", "seau": "cible", "citron": "neutre", "brouette": "sem_loin"},
  22. "enclume": {"fer à cheval": "sem_proche", "enclume": "cible", "girafe": "neutre", "pioche": "sem_loin"},
  23. "eventail": {"ventilateur": "sem_proche", "eventail": "cible", "pélican": "neutre", "peigne": "sem_loin"},
  24. "ane": {"zèbre": "sem_proche", "ane": "cible", "pelle": "neutre", "pigeon": "sem_loin"},
  25. "canard": {"cygne": "sem_proche", "canard": "cible", "moto": "neutre", "lapin": "sem_loin"},
  26. "haricots": {"petit pois": "sem_proche", "haricots": "cible", "chaussure": "neutre", "carottes": "sem_loin"},
  27. "gants": {"bonnet": "sem_proche", "gants": "cible", "mouton": "neutre", "bottes": "sem_loin"},
  28. "marteau": {"tournevis": "sem_proche", "marteau": "cible", "cerise": "neutre", "hache": "sem_loin"},
  29. "canapé": {"fauteuil": "sem_proche", "canapé": "cible", "marguerite": "neutre", "tabouret": "sem_loin"},
  30. "chat": {"chien": "sem_proche", "chat": "cible", "livre": "neutre", "ours": "sem_loin"},
  31. "poisson": {"crabe": "sem_proche", "poisson": "cible", "ballon": "neutre", "poule": "sem_loin"},
  32. "avocat": {"mangue": "sem_proche", "avocat": "cible", "ordinateur": "neutre", "orange": "sem_loin"},
  33. "masque": {"déguisement": "sem_proche", "masque": "cible", "lion": "neutre", "lunettes": "sem_loin"},
  34. "bouteille": {"carafe": "sem_proche", "bouteille": "cible", "singe": "neutre", "poêle": "sem_loin"},
  35. "téléphone": {"cibie": "sem_proche", "téléphone": "cible", "aigle": "neutre", "télévision": "sem_loin"},
  36. }
  38. csv_result = [["identifiant", "score (/18)", "temps","cible","sémantique_proche","sémantique_loin","neutre"]]
  40. for excel_file_path in excel_file_paths:
  41. df = pd.read_excel(excel_file_path)
  42. identifiant = basename(excel_file_path)[17:][:-4]
  44. counter_score = df.iloc[483].iloc[4]
  45. counter_sem = {"sem_proche": 0, "cible": 0, "neutre": 0, "sem_loin": 0}
  47. counter_time = df.iloc[483].iloc[3][:-2].replace(",", ".")
  49. for i in range(391, 481):
  50. parsed_row = df.iloc[i]
  51. image_1 = parsed_row.iloc[3].strip().lower()
  52. image_2 = parsed_row.iloc[4].strip().lower()
  53. reponse = parsed_row.iloc[5].strip().lower()
  55. if image_1 in images:
  56. categorie = images[image_1][image_2]
  58. if image_1 == image_2:
  59. correcte = (reponse == "o")
  60. else:
  61. correcte = (reponse == "n")
  63. counter_sem[categorie] += (1 if correcte else 0)
  65. # 🟢 Ligne de vérification détaillée :
  66. print(f"{identifiant} | ligne {i} | image: {image_1} | mot: {image_2} | catégorie: {categorie} | réponse: {reponse} | correcte: {correcte}")
  68. # Résumé pour ce fichier
  69. print(f"\n== Résumé {identifiant} ==")
  70. print(f"Cible: {counter_sem['cible']}/18 | Sem proche: {counter_sem['sem_proche']}/18 | Sem loin: {counter_sem['sem_loin']}/18 | Neutre: {counter_sem['neutre']}/18\n")
  72. csv_result.append([
  73. identifiant, counter_score, counter_time,
  74. counter_sem["cible"], counter_sem["sem_proche"],
  75. counter_sem["sem_loin"], counter_sem["neutre"]
  76. ])
  78. with open(result_file_name, 'w', newline='') as file:
  79. writer = csv.writer(file)
  80. writer.writerows(csv_result)

script_comprehension_photos.py, no license · at the source

Overview

Authors: Agnès Denève1, Aurore Mahut Dubos1, Thi Mai Tran1, Antoine Renard2, Coline Carpentier1, Nathalie Forestier1, Grégory Kuchcinski1,3,4, Renaud Lopes1,3,5, Ali Amad1,6, Thibaud Lebouvier1, Maxime Bertoux1
ORCID iDs: Agnès Denève, Antoine Renard, Maxime Bertoux
  1. Lille Neuroscience & Cognition, Inserm U1172, Univ Lille, CHU Lille, Lille F-59000, France
  2. Liège Unité PsyNCog, Université de Liège, Liège 4000, Belgium
  3. Department of Nuclear Medicine, CHU Lille, Lille F-59000, France
  4. Department of Neuroradiology, CHU Lille, Lille F-59000, France
  5. CNRS, INSERM, CHU Lille, Institut Pasteur de Lille, Univ. Lille, US 41 - UAR 2014 - PLBS, Lille F-59000, France
  6. Department of Psychiatry, CHU Lille, Lille F-59000, France
Institutions: Inserm (France); Université de Lille (France); Centre Hospitalier Universitaire de Lille (France); Lille Neurosciences & Cognition (France); Centre National de la Recherche Scientifique (France); Institut Pasteur de Lille (France); Plateformes Lilloises en Biologie et Santé (France)
Journal: Brain communications, volume 8, issue 2, article fcag116
Dates: received 20 November 2025; accepted 31 March 2026; published online 1 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag116 · PMID 41982624 · PMCID PMC13075989 · OpenAlex W7147142039
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: structural MRI / diffusion (modality), human (organism), depression (population), bipolar (population)
Methods: Statistics, Machine learning, Connectivity, fMRI & imaging, Preprocessing
Keywords: frontotemporal degeneration, psychiatric disorders, major depressive disorder, bipolar disorder, fluency
Topic: Neurobiology of Language and Bilingualism (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Association France Alzheimer (6175); Fondation Vaincre Alzheimer (70202)
Citations: not cited yet (Europe PMC); 78 references in the paper

Abstract

Language is essential to social communication. Its complexity and hierarchical organization, from low-level operations to high-order integrative processes, may provide valuable diagnostic insights in neurodegeneration beyond classical aphasia syndromes. However, systematic investigations in these conditions, particularly in behavioural variant frontotemporal degeneration (bvFTD), remain scarce. To refine differential diagnosis, an exhaustive characterization of language functions is required.

We systematically compared multi-level language functioning across bvFTD, Alzheimer’s disease (AD) and primary psychiatric disorders (PPD), within an integrative neurolinguistic framework distinguishing lexical, syntactic and discursive levels, together with a cross-modal transposition/transcoding dimension. A total of 85 patients (including 34 with bvFTD, 30 with AD, 21 with PPD) and 40 matched healthy controls underwent an extensive language assessment using the GREMOTs battery. Composite, quantitative as well as qualitative indices were computed for each linguistic level. Structural MRI data were analysed using voxel-based morphometry (P < 0.05 corrected for multiple comparisons).

All clinical groups exhibited lexical impairments relative to controls, with bvFTD presenting the most severe and widespread deficits across fluency, naming and comprehension (partial eta-squared, ηp2, ranging from 0.18 to 0.49). AD and PPD showed milder lexical inefficiencies (ηp2 = 0.11–0.39 and ηp2 = 0.07–0.29, respectively). Syntactic processing was also more impaired in bvFTD (ηp2 = 0.03–0.27) than in AD and PPD (ηp2 = 0.02–0.07 and ηp2 = 0.00–0.15, respectively). At the discourse-level, bvFTD displayed key deficits (ηp2 = 0.05–0.29), with pervasive pragmatic breakdowns whereas AD and PPD showed milder integrative deficits with preserved global coherence (ηp2 = 0.00–0.17 and ηp2 = 0.02–0.13). Transcoding and transposing tasks revealed minor deficits, mainly in bvFTD (ηp2 = 0.01–0.25). A logistic regression identified that a subset of 12/23 tasks accurately classified 85.9% of bvFTD cases (sensitivity: 57.6%; specificity: 95.6%). The analysis of the types of responses (including errors) allowed to provide a more comprehensive group profiling. In bvFTD, the decrease of language performance related to widespread frontotemporal and posterior (including cerebellar) atrophy, whereas AD showed more restricted frontal and temporal involvement. PPD displayed smaller fronto-temporal, insular and precuneal associations.

In conclusion, these findings delineate a graded, multi-level linguistic profile across neurodegenerative and psychiatric conditions. bvFTD is mainly characterized by pervasive lexical and discursive–pragmatic impairments, alongside syntactic difficulties, while AD and PPD primarily show lexical inefficiencies with preserved syntax. Convergent neural evidence supports a distributed network model of language integrating frontal–insular control and temporal semantic systems. Embedding such multi-level assessments into clinical practice could enhance diagnostic precision and provide valid cognitive endpoints for future trials.

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

Repository

Its files are read in the Code ↔ Paper reader above.

OSF bychj

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: Python (4)
Size: 5 files, 4 scripts
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (3 files)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
4 files
At the source: osf.io/bychj

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;
  • 4 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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.

Data availability

Access to anonymized data could be granted on demand in accordance with ethical guidelines and subject to a data-sharing agreement with Lille University Hospital. Commercial use is not permitted. Scripts written to facilitate the extraction of qualitative indicators are freely available online (https://osf.io/bychj).51

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

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 5 keywords, 2 funders, 72 references.

Cite

This paper

Denève, A., Mahut Dubos, A., Tran, T. M., Renard, A., Carpentier, C., Forestier, N., Kuchcinski, G., Lopes, R., Amad, A., Lebouvier, T., & Bertoux, M. (2026). Multi-level language deficits in behavioural frontotemporal degeneration and related disorders. Brain communications, 8(2), fcag116. https://doi.org/10.1093/braincomms/fcag116

BibTeX

@article{deneve2026multi,
author = {Denève, Agnès and Mahut Dubos, Aurore and Tran, Thi Mai and Renard, Antoine and Carpentier, Coline and Forestier, Nathalie and Kuchcinski, Grégory and Lopes, Renaud and Amad, Ali and Lebouvier, Thibaud and Bertoux, Maxime},
title = {{Multi-level language deficits in behavioural frontotemporal degeneration and related disorders}},
journal = {Brain communications},
year = {2026},
month = apr,
volume = {8},
number = {2},
pages = {fcag116},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag116},
url = {https://doi.org/10.1093/braincomms/fcag116},
pmid = {41982624},
pmcid = {PMC13075989}
}

RIS

TY - JOUR
AU - Denève, Agnès
AU - Mahut Dubos, Aurore
AU - Tran, Thi Mai
AU - Renard, Antoine
AU - Carpentier, Coline
AU - Forestier, Nathalie
AU - Kuchcinski, Grégory
AU - Lopes, Renaud
AU - Amad, Ali
AU - Lebouvier, Thibaud
AU - Bertoux, Maxime
TI - Multi-level language deficits in behavioural frontotemporal degeneration and related disorders
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/04/01
VL - 8
IS - 2
SP - fcag116
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag116
UR - https://doi.org/10.1093/braincomms/fcag116
LA - en
ER -

CSL-JSON

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"id": "10.1093/braincomms/fcag116",
"type": "article-journal",
"title": "Multi-level language deficits in behavioural frontotemporal degeneration and related disorders",
"container-title": "Brain communications",
"author": [
{
"family": "Denève",
"given": "Agnès"
},
{
"family": "Mahut Dubos",
"given": "Aurore"
},
{
"family": "Tran",
"given": "Thi Mai"
},
{
"family": "Renard",
"given": "Antoine"
},
{
"family": "Carpentier",
"given": "Coline"
},
{
"family": "Forestier",
"given": "Nathalie"
},
{
"family": "Kuchcinski",
"given": "Grégory"
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{
"family": "Lopes",
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"container-title-short": "Brain Commun",
"volume": "8",
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"page": "fcag116",
"DOI": "10.1093/braincomms/fcag116",
"PMID": "41982624",
"PMCID": "PMC13075989",
"ISSN": "2632-1297",
"publisher": "Oxford University Press",
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"language": "en",
"issued": {
"date-parts": [
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1
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}

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