OSCR

Music processing in behavioural variant frontotemporal dementia and Alzheimer's disease: a functional MRI study.

Code ↔ Paper

The paper beside its authors' code: matches between them have not been computed for this paper yet.

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 358 lines · 23 KB · no license

  1. %-----------------------------------------------------------------------
  2. % Job saved on 21-Dec-2023 16:22:28 by cfg_util (rev $Rev: 7345 $)
  3. % spm SPM - SPM12 (7771)
  4. % cfg_basicio BasicIO - Unknown
  5. %-----------------------------------------------------------------------
  6. %%
  9. subjects = [011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 054 055 056 057 058 059 060 062 063 064 065]; % Replace with a list of all of the subjects you wish to analyze
  10. %001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 054 055 056 057 058 059 060 062 063 064 065
  12. for subject=subjects
  14. subject = num2str(subject, '%03d'); % Zero-pads each number so that the subject ID is 3 characters long
  17. matlabbatch{1}.spm.spatial.realign.estwrite.data = {{
  18. ['/task_fMRI.nii,1']
  19. ['/task_fMRI.nii,2']
  20. ['/task_fMRI.nii,3']
  21. ['/task_fMRI.nii,4']
  22. ['/task_fMRI.nii,5']
  23. ['/task_fMRI.nii,6']
  24. ['/task_fMRI.nii,7']
  25. ['/task_fMRI.nii,8']
  26. ['/task_fMRI.nii,9']
  27. ['/task_fMRI.nii,10']
  28. ['/task_fMRI.nii,11']
  29. ['/task_fMRI.nii,12']
  30. ['/task_fMRI.nii,13']
  31. ['/task_fMRI.nii,14']
  32. ['/task_fMRI.nii,15']
  33. ['/task_fMRI.nii,16']
  34. ['/task_fMRI.nii,17']
  35. ['/task_fMRI.nii,18']
  36. ['/task_fMRI.nii,19']
  37. ['/task_fMRI.nii,20']
  38. ['/task_fMRI.nii,21']
  39. ['/task_fMRI.nii,22']
  40. ['/task_fMRI.nii,23']
  41. ['/task_fMRI.nii,24']
  42. ['/task_fMRI.nii,25']
  43. ['/task_fMRI.nii,26']
  44. ['/task_fMRI.nii,27']
  45. ['/task_fMRI.nii,28']
  46. ['/task_fMRI.nii,29']
  47. ['/task_fMRI.nii,30']
  48. ['/task_fMRI.nii,31']
  49. ['/task_fMRI.nii,32']
  50. ['/task_fMRI.nii,33']
  51. ['/task_fMRI.nii,34']
  52. ['/task_fMRI.nii,35']
  53. ['/task_fMRI.nii,36']
  54. ['/task_fMRI.nii,37']
  55. ['/task_fMRI.nii,38']
  56. ['/task_fMRI.nii,39']
  57. ['/task_fMRI.nii,40']
  58. ['/task_fMRI.nii,41']
  59. ['/task_fMRI.nii,42']
  60. ['/task_fMRI.nii,43']
  61. ['/task_fMRI.nii,44']
  62. ['/task_fMRI.nii,45']
  63. ['/task_fMRI.nii,46']
  64. ['/task_fMRI.nii,47']
  65. ['/task_fMRI.nii,48']
  66. ['/task_fMRI.nii,49']
  67. ['/task_fMRI.nii,50']
  68. ['/task_fMRI.nii,51']
  69. ['/task_fMRI.nii,52']
  70. ['/task_fMRI.nii,53']
  71. ['/task_fMRI.nii,54']
  72. ['/task_fMRI.nii,55']
  73. ['/task_fMRI.nii,56']
  74. ['/task_fMRI.nii,57']
  75. ['/task_fMRI.nii,58']
  76. ['/task_fMRI.nii,59']
  77. ['/task_fMRI.nii,60']
  78. ['/task_fMRI.nii,61']
  79. ['/task_fMRI.nii,62']
  80. ['/task_fMRI.nii,63']
  81. ['/task_fMRI.nii,64']
  82. ['/task_fMRI.nii,65']
  83. ['/task_fMRI.nii,66']
  84. ['/task_fMRI.nii,67']
  85. ['/task_fMRI.nii,68']
  86. ['/task_fMRI.nii,69']
  87. ['/task_fMRI.nii,70']
  88. ['/task_fMRI.nii,71']
  89. ['/task_fMRI.nii,72']
  90. ['/task_fMRI.nii,73']
  91. ['/task_fMRI.nii,74']
  92. ['/task_fMRI.nii,75']
  93. }}';
  94. %%
  95. matlabbatch{1}.spm.spatial.realign.estwrite.eoptions.quality = 0.9;
  96. matlabbatch{1}.spm.spatial.realign.estwrite.eoptions.sep = 4;
  97. matlabbatch{1}.spm.spatial.realign.estwrite.eoptions.fwhm = 5;
  98. matlabbatch{1}.spm.spatial.realign.estwrite.eoptions.rtm = 1;
  99. matlabbatch{1}.spm.spatial.realign.estwrite.eoptions.interp = 2;
  100. matlabbatch{1}.spm.spatial.realign.estwrite.eoptions.wrap = [0 0 0];
  101. matlabbatch{1}.spm.spatial.realign.estwrite.eoptions.weight = '';
  102. matlabbatch{1}.spm.spatial.realign.estwrite.roptions.which = [2 1];
  103. matlabbatch{1}.spm.spatial.realign.estwrite.roptions.interp = 4;
  104. matlabbatch{1}.spm.spatial.realign.estwrite.roptions.wrap = [0 0 0];
  105. matlabbatch{1}.spm.spatial.realign.estwrite.roptions.mask = 1;
  106. matlabbatch{1}.spm.spatial.realign.estwrite.roptions.prefix = 'r';
  107. matlabbatch{2}.spm.spatial.coreg.estwrite.ref(1) = cfg_dep('Realign: Estimate & Reslice: Mean Image', substruct('.','val', '{}',{1}, '.','val', '{}',{1}, '.','val', '{}',{1}, '.','val', '{}',{1}), substruct('.','rmean'));
  108. matlabbatch{2}.spm.spatial.coreg.estwrite.source = {['/data/ac/researchcloud-vo-alzheimer01/users/jjvanthooft/2HARMONIA/All_scans/ANALYSIS/sub-' subject '/ses-01/anat/sub-' subject '_ses-01_acq-WIPacqMP2RAGE1_rec-HARMONIA_run-1_T1w.nii,1']};
  109. matlabbatch{2}.spm.spatial.coreg.estwrite.other = {''};
  110. matlabbatch{2}.spm.spatial.coreg.estwrite.eoptions.cost_fun = 'nmi';
  111. matlabbatch{2}.spm.spatial.coreg.estwrite.eoptions.sep = [4 2];
  112. matlabbatch{2}.spm.spatial.coreg.estwrite.eoptions.tol = [0.02 0.02 0.02 0.001 0.001 0.001 0.01 0.01 0.01 0.001 0.001 0.001];
  113. matlabbatch{2}.spm.spatial.coreg.estwrite.eoptions.fwhm = [7 7];
  114. matlabbatch{2}.spm.spatial.coreg.estwrite.roptions.interp = 4;
  115. matlabbatch{2}.spm.spatial.coreg.estwrite.roptions.wrap = [0 0 0];
  116. matlabbatch{2}.spm.spatial.coreg.estwrite.roptions.mask = 0;
  117. matlabbatch{2}.spm.spatial.coreg.estwrite.roptions.prefix = 'r';
  118. matlabbatch{3}.spm.spatial.preproc.channel.vols(1) = cfg_dep('Coregister: Estimate & Reslice: Coregistered Images', substruct('.','val', '{}',{2}, '.','val', '{}',{1}, '.','val', '{}',{1}, '.','val', '{}',{1}), substruct('.','cfiles'));
  119. matlabbatch{3}.spm.spatial.preproc.channel.biasreg = 0.001;
  120. matlabbatch{3}.spm.spatial.preproc.channel.biasfwhm = 60;
  121. matlabbatch{3}.spm.spatial.preproc.channel.write = [0 1];
  122. matlabbatch{3}.spm.spatial.preproc.tissue(1).tpm = {['/home/ac/jjvanthooft/spm12/tpm/TPM.nii,1']};
  123. matlabbatch{3}.spm.spatial.preproc.tissue(1).ngaus = 1;
  124. matlabbatch{3}.spm.spatial.preproc.tissue(1).native = [1 0];
  125. matlabbatch{3}.spm.spatial.preproc.tissue(1).warped = [0 0];
  126. matlabbatch{3}.spm.spatial.preproc.tissue(2).tpm = {['/home/ac/jjvanthooft/spm12/tpm/TPM.nii,2']};
  127. matlabbatch{3}.spm.spatial.preproc.tissue(2).ngaus = 1;
  128. matlabbatch{3}.spm.spatial.preproc.tissue(2).native = [1 0];
  129. matlabbatch{3}.spm.spatial.preproc.tissue(2).warped = [0 0];
  130. matlabbatch{3}.spm.spatial.preproc.tissue(3).tpm = {['/home/ac/jjvanthooft/spm12/tpm/TPM.nii,3']};
  131. matlabbatch{3}.spm.spatial.preproc.tissue(3).ngaus = 2;
  132. matlabbatch{3}.spm.spatial.preproc.tissue(3).native = [1 0];
  133. matlabbatch{3}.spm.spatial.preproc.tissue(3).warped = [0 0];
  134. matlabbatch{3}.spm.spatial.preproc.tissue(4).tpm = {['/home/ac/jjvanthooft/spm12/tpm/TPM.nii,4']};
  135. matlabbatch{3}.spm.spatial.preproc.tissue(4).ngaus = 3;
  136. matlabbatch{3}.spm.spatial.preproc.tissue(4).native = [1 0];
  137. matlabbatch{3}.spm.spatial.preproc.tissue(4).warped = [0 0];
  138. matlabbatch{3}.spm.spatial.preproc.tissue(5).tpm = {['/home/ac/jjvanthooft/spm12/tpm/TPM.nii,5']};
  139. matlabbatch{3}.spm.spatial.preproc.tissue(5).ngaus = 4;
  140. matlabbatch{3}.spm.spatial.preproc.tissue(5).native = [1 0];
  141. matlabbatch{3}.spm.spatial.preproc.tissue(5).warped = [0 0];
  142. matlabbatch{3}.spm.spatial.preproc.tissue(6).tpm = {['/home/ac/jjvanthooft/spm12/tpm/TPM.nii,6']};
  143. matlabbatch{3}.spm.spatial.preproc.tissue(6).ngaus = 2;
  144. matlabbatch{3}.spm.spatial.preproc.tissue(6).native = [0 0];
  145. matlabbatch{3}.spm.spatial.preproc.tissue(6).warped = [0 0];
  146. matlabbatch{3}.spm.spatial.preproc.warp.mrf = 1;
  147. matlabbatch{3}.spm.spatial.preproc.warp.cleanup = 1;
  148. matlabbatch{3}.spm.spatial.preproc.warp.reg = [0 0.001 0.5 0.05 0.2];
  149. matlabbatch{3}.spm.spatial.preproc.warp.affreg = 'mni';
  150. matlabbatch{3}.spm.spatial.preproc.warp.fwhm = 0;
  151. matlabbatch{3}.spm.spatial.preproc.warp.samp = 3;
  152. matlabbatch{3}.spm.spatial.preproc.warp.write = [0 1];
  153. matlabbatch{3}.spm.spatial.preproc.warp.vox = NaN;
  154. matlabbatch{3}.spm.spatial.preproc.warp.bb = [NaN NaN NaN
  155. NaN NaN NaN];
  156. matlabbatch{4}.spm.spatial.normalise.write.subj.def(1) = cfg_dep('Segment: Forward Deformations', substruct('.','val', '{}',{3}, '.','val', '{}',{1}, '.','val', '{}',{1}), substruct('.','fordef', '()',{':'}));
  157. matlabbatch{4}.spm.spatial.normalise.write.subj.resample(1) = cfg_dep('Realign: Estimate & Reslice: Resliced Images (Sess 1)', substruct('.','val', '{}',{1}, '.','val', '{}',{1}, '.','val', '{}',{1}, '.','val', '{}',{1}), substruct('.','sess', '()',{1}, '.','rfiles'));
  158. matlabbatch{4}.spm.spatial.normalise.write.woptions.bb = [-78 -112 -70
  159. 78 76 85];
  160. matlabbatch{4}.spm.spatial.normalise.write.woptions.vox = [2 2 2];
  161. matlabbatch{4}.spm.spatial.normalise.write.woptions.interp = 4;
  162. matlabbatch{4}.spm.spatial.normalise.write.woptions.prefix = 'w';
  163. matlabbatch{5}.spm.spatial.smooth.data(1) = cfg_dep('Normalise: Write: Normalised Images (Subj 1)', substruct('.','val', '{}',{4}, '.','val', '{}',{1}, '.','val', '{}',{1}, '.','val', '{}',{1}), substruct('()',{1}, '.','files'));
  164. matlabbatch{5}.spm.spatial.smooth.fwhm = [6 6 6];
  165. matlabbatch{5}.spm.spatial.smooth.dtype = 0;
  166. matlabbatch{5}.spm.spatial.smooth.im = 0;
  167. matlabbatch{5}.spm.spatial.smooth.prefix = 's';
  168. matlabbatch{6}.spm.stats.fmri_spec.dir = {['/data/ac/researchcloud-vo-alzheimer01/users/jjvanthooft/2HARMONIA/All_scans/ANALYSIS/sub-' subject '/ses-01/1stLvl']};
  169. matlabbatch{6}.spm.stats.fmri_spec.timing.units = 'secs';
  170. matlabbatch{6}.spm.stats.fmri_spec.timing.RT = 10;
  171. matlabbatch{6}.spm.stats.fmri_spec.timing.fmri_t = 16;
  172. matlabbatch{6}.spm.stats.fmri_spec.timing.fmri_t0 = 8;
  173. matlabbatch{6}.spm.stats.fmri_spec.sess.scans(1) = cfg_dep('Smooth: Smoothed Images', substruct('.','val', '{}',{5}, '.','val', '{}',{1}, '.','val', '{}',{1}), substruct('.','files'));
  174. matlabbatch{6}.spm.stats.fmri_spec.sess.cond = struct('name', {}, 'onset', {}, 'duration', {}, 'tmod', {}, 'pmod', {}, 'orth', {});
  175. matlabbatch{6}.spm.stats.fmri_spec.sess.multi = {''};
  176. matlabbatch{6}.spm.stats.fmri_spec.sess.regress(1).name = 'Favorite';
  177. %%
  178. matlabbatch{6}.spm.stats.fmri_spec.sess.regress(1).val = [0
  179. 0
  180. 0
  181. 1
  182. 0
  183. 0
  184. 0
  185. 1
  186. 0
  187. 1
  188. 0
  189. 0
  190. 0
  191. 1
  192. 0
  193. 1
  194. 0
  195. 0
  196. 0
  197. 1
  198. 0
  199. 1
  200. 0
  201. 0
  202. 0
  203. 1
  204. 0
  205. 1
  206. 0
  207. 0
  208. 0
  209. 1
  210. 0
  211. 1
  212. 0
  213. 0
  214. 0
  215. 1
  216. 0
  217. 1
  218. 0
  219. 0
  220. 0
  221. 1
  222. 0
  223. 1
  224. 0
  225. 0
  226. 0
  227. 1
  228. 0
  229. 1
  230. 0
  231. 0
  232. 0
  233. 1
  234. 0
  235. 1
  236. 0
  237. 0
  238. 0
  239. 1
  240. 0
  241. 1
  242. 0
  243. 0
  244. 0
  245. 1
  246. 0
  247. 1
  248. 0
  249. 0
  250. 0
  251. 1
  252. 0];
  253. %%
  254. matlabbatch{6}.spm.stats.fmri_spec.sess.regress(2).name = 'Neutral';
  255. %%
  256. matlabbatch{6}.spm.stats.fmri_spec.sess.regress(2).val = [0
  257. 0
  258. 0
  259. 0
  260. 1
  261. 0
  262. 1
  263. 0
  264. 0
  265. 0
  266. 1
  267. 0
  268. 1
  269. 0
  270. 0
  271. 0
  272. 1
  273. 0
  274. 1
  275. 0
  276. 0
  277. 0
  278. 1
  279. 0
  280. 1
  281. 0
  282. 0
  283. 0
  284. 1
  285. 0
  286. 1
  287. 0
  288. 0
  289. 0
  290. 1
  291. 0
  292. 1
  293. 0
  294. 0
  295. 0
  296. 1
  297. 0
  298. 1
  299. 0
  300. 0
  301. 0
  302. 1
  303. 0
  304. 1
  305. 0
  306. 0
  307. 0
  308. 1
  309. 0
  310. 1
  311. 0
  312. 0
  313. 0
  314. 1
  315. 0
  316. 1
  317. 0
  318. 0
  319. 0
  320. 1
  321. 0
  322. 1
  323. 0
  324. 0
  325. 0
  326. 1
  327. 0
  328. 1
  329. 0
  330. 0];
  331. %%
  332. matlabbatch{6}.spm.stats.fmri_spec.sess.multi_reg = {''};
  333. matlabbatch{6}.spm.stats.fmri_spec.sess.hpf = 256;
  334. matlabbatch{6}.spm.stats.fmri_spec.fact = struct('name', {}, 'levels', {});
  335. matlabbatch{6}.spm.stats.fmri_spec.bases.hrf.derivs = [0 0];
  336. matlabbatch{6}.spm.stats.fmri_spec.volt = 1;
  337. matlabbatch{6}.spm.stats.fmri_spec.global = 'None';
  338. matlabbatch{6}.spm.stats.fmri_spec.mthresh = 0.8;
  339. matlabbatch{6}.spm.stats.fmri_spec.mask = {''};
  340. matlabbatch{6}.spm.stats.fmri_spec.cvi = 'AR(1)';
  341. matlabbatch{7}.spm.stats.fmri_est.spmmat(1) = cfg_dep('fMRI model specification: SPM.mat File', substruct('.','val', '{}',{6}, '.','val', '{}',{1}, '.','val', '{}',{1}), substruct('.','spmmat'));
  342. matlabbatch{7}.spm.stats.fmri_est.write_residuals = 0;
  343. matlabbatch{7}.spm.stats.fmri_est.method.Classical = 1;
  344. matlabbatch{8}.spm.stats.con.spmmat(1) = cfg_dep('Model estimation: SPM.mat File', substruct('.','val', '{}',{7}, '.','val', '{}',{1}, '.','val', '{}',{1}), substruct('.','spmmat'));
  345. matlabbatch{8}.spm.stats.con.consess{1}.tcon.name = 'Favorite';
  346. matlabbatch{8}.spm.stats.con.consess{1}.tcon.weights = [1 0 0];
  347. matlabbatch{8}.spm.stats.con.consess{1}.tcon.sessrep = 'replsc';
  348. matlabbatch{8}.spm.stats.con.consess{2}.tcon.name = 'Neutral';
  349. matlabbatch{8}.spm.stats.con.consess{2}.tcon.weights = [0 1 0];
  350. matlabbatch{8}.spm.stats.con.consess{2}.tcon.sessrep = 'replsc';
  351. matlabbatch{8}.spm.stats.con.consess{3}.tcon.name = 'Favorite-Neutral';
  352. matlabbatch{8}.spm.stats.con.consess{3}.tcon.weights = [1 -1 0];
  353. matlabbatch{8}.spm.stats.con.consess{3}.tcon.sessrep = 'replsc';
  354. matlabbatch{8}.spm.stats.con.delete = 0;
  356. spm_jobman('run', matlabbatch);
  358. end

HARMONIA_preprocessing.m at commit 544a8bd, no license · at the source

Overview

Authors: Jochum J van ‘t Hooft1,2, Wietske van der Zwaag3,4, Alle Meije Wink5, Frederik Barkhof5,6, Rebecca S Schaefer7,8, Jason D Warren9, Yolande A L Pijnenburg1,2, Betty M Tijms1,2
ORCID iDs: Jochum J van ‘t Hooft, Alle Meije Wink, Frederik Barkhof, Jason D Warren, Betty M Tijms
  1. Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, 1081 HZ Amsterdam, The Netherlands
  2. Amsterdam Neuroscience, Neurodegeneration, 1081 BT Amsterdam, The Netherlands
  3. Spinoza Centre for Neuroimaging, Netherlands Academy for Arts and Sciences, 1105 BK Amsterdam, The Netherlands
  4. Computational Cognitive Neuroscience and Neuroimaging, Netherlands Institute for Neuroscience, 1105 BA Amsterdam, The Netherlands
  5. Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, 1081 HV Amsterdam, the Netherlands
  6. UCL Institutes of Neurology and Healthcare Engineering, University College London, London WC1E 6BT, United Kingdom
  7. Health, Medical and Neuropsychology Unit, Institute of Psychology, Leiden University, 2300 RB Leiden, The Netherlands
  8. Academy for Creative and Performing Arts, Leiden University, 2300 RA Leiden, The Netherlands
  9. Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London WC1N 1AR, United Kingdom
Institutions: Amsterdam Neuroscience (Netherlands); Vrije Universiteit Amsterdam (Netherlands); Amsterdam UMC Location Vrije Universiteit Amsterdam (Netherlands); Netherlands Institute for Neuroscience (Netherlands); Spinoza Centre for Neuroimaging (Netherlands); Amsterdam University Medical Centers (Netherlands); University College London (United Kingdom); Leiden University (Netherlands); UCL Queen Square Institute of Neurology (United Kingdom)
Journal: Brain communications, volume 8, issue 3, article fcag196
Dates: received 10 June 2025; accepted 29 May 2026; published online 1 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag196 · PMID 42293318 · PMCID PMC13263052 · OpenAlex W7163140263
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), Alzheimer's / dementia (population), cognitive (subfield)
Methods: Statistics, Preprocessing, fMRI & imaging
Keywords: music perception, musicality, salience network, musicophilia, supplementary motor area
Topic: Dementia and Cognitive Impairment Research (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Alzheimer's Society; National Institute for Health and Care Research; University College London; Alzheimer’s Research UK; University College London Hospitals NHS Foundation Trust; Alzheimer Nederland; Centro Singular de Investigación de Galicia; Amsterdam Neuroscience; Royal National Institute for Deaf People
Citations: not cited yet (Europe PMC); 53 references in the paper

Abstract

Music engages brain regions involved in perceptual, socio-emotional and cognitive functions that may be relatively preserved in individuals with Alzheimer's disease but seem affected early in behavioural variant frontotemporal dementia. The effects of music in dementia are often assessed through observational studies, leaving the neurophysiological underpinnings of music processing in these dementia types unclear. Improved understanding of these mechanisms is relevant because the effectiveness of music therapy may depend on dementia types. In this study we investigated whether patients with behavioural variant frontotemporal dementia and Alzheimer’s disease differ in music processing compared with healthy controls. We studied 60 participants (n = 35 female; aged 52–81), including 13 patients with behavioural variant frontotemporal dementia, 22 patients Alzheimer's disease, and 25 healthy controls. We designed a novel functional MRI paradigm based on passive listening to self-selected favourite music and experimenter-selected unfamiliar musical pieces using a sparse-sampling design. Activation patterns of favourite music listening (favourite > silence), unfamiliar music listening (unfamiliar > silence), and favourite music more than unfamiliar music (favourite > unfamiliar) were determined for each participant. Next, we compared activation patterns across groups for each contrast. Finally, associations between activation patterns and disease severity were investigated in behavioural variant frontotemporal dementia and Alzheimer’s disease separately. The patient groups exhibited typical neuropsychological, socio-emotional and structural anatomical changes associated with Alzheimer’s disease and behavioural variant frontotemporal dementia. Patients with behavioural variant frontotemporal dementia showed overall less activation during favourite music listening compared with Alzheimer’s disease and healthy controls. When contrasting favourite and unfamiliar music, we found that patients with behavioural variant frontotemporal dementia showed reduced activation in the supplementary motor area, a region that has previously been implicated as an important region for semantic musical memory. Increased connectivity of the auditory cortices was observed in behavioural variant frontotemporal dementia compared with controls, potentially indicating network immaturity. Only patients with Alzheimer’s disease exhibited activation in the caudate nucleus during unfamiliar music, a region associated with musical reward processing. Disease severity in Alzheimer’s disease and behavioural variant frontotemporal dementia were associated with distinct patterns of functional activation. Our results confirm and expand the observation that music is processed differently in patients with behavioural variant frontotemporal dementia and Alzheimer's disease. The reduced activation in the supplementary motor area may explain altered music processing in behavioural variant frontotemporal dementia. These differences in music processing could have clinical implications in the selection of music therapy.

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

Repository

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

Jochumvanthooft/HARMONIA_braincommunications

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 544a8bd912fa355cb47927ad30a0999d959d6299, 12 December 2025
Languages: MATLAB (1)
Size: 1 file, 1 script
Software Heritage: not archived
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
1 file
At the source: github.com/Jochumvanthooft/HARMONIA_braincommunications

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;
  • 1 script, 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

Data and stimuli generated that support the findings of this study are available from the corresponding author, upon reasonable request. The MATLAB codes are available at https://github.com/Jochumvanthooft/HARMONIA_braincommunications.

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 2, 28 September 2026

  • Funding: added Alzheimer's Society; National Institute for Health and Care Research; University College London; Alzheimer’s Research UK; University College London Hospitals NHS Foundation Trust; Alzheimer Nederland; Centro Singular de Investigación de Galicia; Amsterdam Neuroscience; Royal National Institute for Deaf People

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 5 keywords, 53 references.

Cite

This paper

van ‘t Hooft, J. J., van der Zwaag, W., Wink, A. M., Barkhof, F., Schaefer, R. S., Warren, J. D., Pijnenburg, Y. A. L., & Tijms, B. M. (2026). Music processing in behavioural variant frontotemporal dementia and Alzheimer's disease: a functional MRI study. Brain communications, 8(3), fcag196. https://doi.org/10.1093/braincomms/fcag196

BibTeX

@article{vanthooft2026music,
author = {van ‘t Hooft, Jochum J and van der Zwaag, Wietske and Wink, Alle Meije and Barkhof, Frederik and Schaefer, Rebecca S and Warren, Jason D and Pijnenburg, Yolande A L and Tijms, Betty M},
title = {{Music processing in behavioural variant frontotemporal dementia and Alzheimer's disease: a functional MRI study}},
journal = {Brain communications},
year = {2026},
month = jun,
volume = {8},
number = {3},
pages = {fcag196},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag196},
url = {https://doi.org/10.1093/braincomms/fcag196},
pmid = {42293318},
pmcid = {PMC13263052}
}

RIS

TY - JOUR
AU - van ‘t Hooft, Jochum J
AU - van der Zwaag, Wietske
AU - Wink, Alle Meije
AU - Barkhof, Frederik
AU - Schaefer, Rebecca S
AU - Warren, Jason D
AU - Pijnenburg, Yolande A L
AU - Tijms, Betty M
TI - Music processing in behavioural variant frontotemporal dementia and Alzheimer's disease: a functional MRI study
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/06/01
VL - 8
IS - 3
SP - fcag196
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag196
UR - https://doi.org/10.1093/braincomms/fcag196
LA - en
ER -

CSL-JSON

{
"id": "10.1093/braincomms/fcag196",
"type": "article-journal",
"title": "Music processing in behavioural variant frontotemporal dementia and Alzheimer's disease: a functional MRI study",
"container-title": "Brain communications",
"author": [
{
"family": "van ‘t Hooft",
"given": "Jochum J"
},
{
"family": "van der Zwaag",
"given": "Wietske"
},
{
"family": "Wink",
"given": "Alle Meije"
},
{
"family": "Barkhof",
"given": "Frederik"
},
{
"family": "Schaefer",
"given": "Rebecca S"
},
{
"family": "Warren",
"given": "Jason D"
},
{
"family": "Pijnenburg",
"given": "Yolande A L"
},
{
"family": "Tijms",
"given": "Betty M"
}
],
"container-title-short": "Brain Commun",
"volume": "8",
"issue": "3",
"page": "fcag196",
"DOI": "10.1093/braincomms/fcag196",
"PMID": "42293318",
"PMCID": "PMC13263052",
"ISSN": "2632-1297",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/braincomms/fcag196",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
1
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1093/braincomms/fcag161 [code]
Functional neuroanatomy of musical object processing in Alzheimer's disease and frontotemporal dementia.
Journal: Brain communications
In common: fMRI, Alzheimer's / dementia, cognitive, 15 references, author Jason D Warren
[2] doi:10.1093/braincomms/fcag257 [code]
Neuroplasticity and immune system are related to altered grey matter networks: a cohort study in sporadic Alzheimer's disease.
Journal: Brain communications
In common: SPM, Alzheimer's / dementia, 3 references, 2 authors
[3] doi:10.1038/s43856-026-01707-2 [code]
Decreased amyloid-related structure-function coupling in preclinical Alzheimer's disease.
Journal: Communications medicine
In common: SPM, Alzheimer's / dementia, 1 reference, 2 authors
[4] doi:10.1002/alz.71609
Cortical gray-white matter contrast alterations precede amyloid-β positivity and macrostructural changes in older adults without dementia.
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association
In common: Alzheimer's / dementia, 2 authors
[5] doi:10.1093/brain/awag032 [code]
A rostral prefrontal mediolateral gradient predicts creativity in frontotemporal dementia.
Journal: Brain : a journal of neurology
In common: SPM, fMRI, Alzheimer's / dementia, 3 references
[6] doi:10.64898/2026.06.03.26354793 [code]
Bayesian networks to estimate prognosis in vascular cognitive impairment and small vessel disease: integrated analyses of interdependent contributors to multiple outcomes
Journal: medRxiv (preprint)
In common: Alzheimer's / dementia, author Betty M Tijms
[7] doi:10.1038/s41467-026-71682-8 [code]
GWAS meta-analysis of cerebrospinal fluid Alzheimer's biomarkers reveals loci regulating lipids, brain volume and autophagy.
Journal: Nature communications
In common: Alzheimer's / dementia, author Betty M Tijms
[8] doi:10.1162/netn.a.545 [code]
Structure-function multilayer network integration and cognition in multiple sclerosis.
Journal: Network neuroscience (Cambridge, Mass.)
In common: fMRI, cognitive, author Frederik Barkhof
[9] doi:10.1007/s00415-026-13876-0 [code]
Functional connectivity dynamics of monoaminergic circuits related to fatigue in multiple sclerosis.
Journal: Journal of neurology
In common: fMRI, author Frederik Barkhof
[10] doi:10.1177/20552173261448282 [code]
The constrained brain in multiple sclerosis: Cognitive impairment is related to network-specific coupling of structural and functional connectivity.
Journal: Multiple sclerosis journal - experimental, translational and clinical
In common: fMRI, author Frederik Barkhof

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.