OSCR

Functional brain network alterations in a rat model of dental malocclusion.

Code ↔ Paper

4 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 4 matches
  1. [1] § Materials and methods › MRI data preprocessing ↔ rabies/boilerplate.py, lines 10–140 · score 0.96 · volumetric EPI images, susceptibility distortions, head motion parameters, open source RABIES, EPI frame, Temporal spikes
  2. [2] § Materials and methods › MRI data preprocessing ↔ rabies/preprocess_pkg/resampling.py, lines 7–98 · score 0.86 · susceptibility distortions, single resampling, native space, fMRI, volumetric, concatenated
  3. [3] § Materials and methods › MRI data preprocessing ↔ rabies/preprocess_pkg/main_wf.py, lines 13–95 · score 0.72 · rigid body motion, Confound regression, common space, linear, preprocessing, resampled
  4. [4] § Materials and methods › MRI data preprocessing ↔ rabies/confound_correction_pkg/confound_correction.py, lines 137–172 · score 0.67 · Confound regression, spatial smoothing, Gaussian, artifacts, detrending, minimize

Paper

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The authors' code

Python · 329 lines · 18 KB · other · 1 match

  1. def define_registration(string):
  2. if string=='SyN':
  3. return 'nonlinear'
  4. elif string=='Affine':
  5. return 'affine'
  6. elif string=='Rigid':
  7. return 'rigid'
  10. def preprocess_boilerplate(opts):
  11. methods=''
  12. references={}
  13. i=1
  15. # define references
  16. rabies_ref='Desrosiers-Gregoire, G., Devenyi, G. A., Grandjean, J., & Chakravarty, M. M. (2023). Rodent Automated Bold Improvement of EPI Sequences (RABIES): A standardized image processing and data quality platform for rodent fMRI. bioRxiv.'
  17. afni='Cox, R. W. (1996). AFNI: software for analysis and visualization of functional \
  18. magnetic resonance neuroimages. Computers and Biomedical research, 29(3), 162-173.'
  19. avants_2011='Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2011). A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage, 54(3), 2033–2044.'
  20. sdc_nonlinear='Wang, S. et al. Evaluation of field map and nonlinear registration methods for correction of susceptibility artifacts in diffusion MRI. Front.Neuroinform. 11, 17 (2017).'
  21. fmriprep='Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., Oya, H., Ghosh, S. S., Wright, J., Durnez, J., Poldrack, R. A., & Gorgolewski, K. J. (2019). fMRIPrep: a robust preprocessing pipeline for functional MRI. Nature Methods, 16(1), 111–116.'
  23. # citing RABIES
  24. references[rabies_ref]=i
  25. i+=1
  26. methods+=f" The preprocessing of fMRI images was conducted using the open-source RABIES software (https://github.com/CoBrALab/RABIES)[{references[rabies_ref]}]. "
  28. # Applying autobox
  29. if opts.bold_autobox or opts.anat_autobox:
  30. references[afni]=i
  31. i+=1
  32. autobox=f"extra-space around the brain was automatically cropped (3dAutobox, AFNI)[{references[afni]}]. "
  33. if opts.bold_autobox and opts.anat_autobox:
  34. autobox="For both the anatomical and functional images, "+autobox
  35. elif opts.bold_autobox and not opts.anat_autobox:
  36. autobox="For the functional images, "+autobox
  37. elif not opts.bold_autobox and opts.anat_autobox:
  38. autobox="For the anatomical images, "+autobox
  39. methods+=autobox
  41. # Despiking
  42. if opts.apply_despiking:
  43. if not afni in references.keys():
  44. references[afni]=i
  45. i+=1
  47. methods+=f"Temporal spikes were corrected for at each voxel (3dDespike, AFNI)[{references[afni]}]. "
  49. # Detect dummy
  50. if opts.detect_dummy:
  51. methods+="Dummy scans were automatically detected and removed from each EPI. "
  52. detect_dummy="If dummy scans are detected, the median of these volumes provides a volumetric EPI image as reference, given their \
  53. higher anatomical contrast. Otherwise, a"
  54. else:
  55. detect_dummy="A"
  57. # Generate 3D EPI ref
  58. methods+=f"{detect_dummy} volumetric EPI image was derived using a trimmed mean across the EPI frames, after an initial motion realignment step. "
  60. # HMC
  61. methods+="Using this volumetric EPI as a target, the head motion parameters are estimated by realigning each EPI frame to the target using a rigid registration. "
  63. # common space alignment
  64. if not opts.bold_only:
  65. reg_image='structural images'
  66. else:
  67. reg_image='volumetric EPI scans'
  68. methods+=f"To derive an alignment to common space, {reg_image} are initially corrected for \
  69. inhomogeneities and "
  70. if not opts.commonspace_reg['fast_commonspace']:
  71. references[avants_2011]=i
  72. i+=1
  73. methods+=f"then registered together to allow the alignment of different acquisition sessions. This is done by generating \
  74. an unbiased data-driven template through the iterative nonlinear registration of each image to a dataset average, where the average \
  75. is improved at each iteration \
  76. (https://github.com/CoBrALab/optimized_antsMultivariateTemplateConstruction) [{references[avants_2011]}]. After aligning the acquisition \
  77. sessions, this newly-generated unbiased template is then itself registered, using a {define_registration(opts.commonspace_reg['template_registration'])} registration, to an external reference atlas."
  78. else:
  79. # Fast commonspace alignment
  80. methods+=f"then aligned individually to an external reference atlas using a {define_registration(opts.commonspace_reg['template_registration'])} registration. "
  82. # STC
  83. if opts.apply_STC:
  84. if not afni in references.keys():
  85. references[afni]=i
  86. i+=1
  87. stc_str=f"slice timing correction was applied to the timeseries (3dTshift)[{references[afni]}], then "
  88. else:
  89. stc_str=""
  91. # Resampling
  92. resampling_str=f"Finally, after calculating the transformations required to correct for head motion and susceptibility distortions, {stc_str}transforms were \
  93. concatenated into a single resampling operation (avoiding multiple resampling) which is applied at each EPI frame, "
  95. if not opts.nativespace_resampling == 'inputs_defined':
  96. nativespace_resampling = f", resampled at a voxel resolution of {opts.nativespace_resampling}mm"
  97. else:
  98. nativespace_resampling = ""
  99. if not opts.commonspace_resampling == 'inputs_defined':
  100. commonspace_resampling = f", at a voxel resolution of {opts.commonspace_resampling}mm"
  101. else:
  102. commonspace_resampling = ""
  104. if not opts.bold_only:
  105. if opts.bold2anat_coreg['registration']=='SyN':
  106. # Cross-modal alignment
  107. references[sdc_nonlinear]=i
  108. i+=1
  109. sdc_str=f"To correct for EPI susceptibility distortions, the volumetric EPI is also subjected to inhomogeneity correction, and then registered \
  110. using a {define_registration(opts.bold2anat_coreg['registration'])} registration to the anatomical scan from the same MRI session, which allows to calculate the required \
  111. geometrical transforms for recovering brain anatomy [{references[sdc_nonlinear]}]. "
  112. else:
  113. sdc_str="[NO SUSCEPTIBILITY DISTORTION CORRECTION CONDUCTED] "
  115. references[fmriprep]=i
  116. i+=1
  117. resampling_str+=f"generating the preprocessed EPI timeseries in native space [{references[fmriprep]}]{nativespace_resampling}. \
  118. Preprocessed timeseries in common space are also generated by further concatenating the transforms allowing resampling to the reference atlas{commonspace_resampling}. "
  120. if opts.bold_only:
  121. if opts.commonspace_reg['template_registration']=='SyN':
  122. references[sdc_nonlinear]=i
  123. i+=1
  124. sdc_str=f"The nonlinear registration to common space allows to calculate the required geometrical transforms for recovering brain anatomy, and \
  125. is thus used to correct susceptibility distortions of the EPI[{references[sdc_nonlinear]}]. "
  126. else:
  127. sdc_str="[NO SUSCEPTIBILITY DISTORTION CORRECTION CONDUCTED]"
  129. references[fmriprep]=i
  130. i+=1
  131. resampling_str+=f"generating the preprocessed EPI timeseries [{references[fmriprep]}]. Since susceptibility distortion correction is completed with \
  132. the registration to common space, the preprocessed timeseries are resampled to common space{commonspace_resampling}. "
  134. methods+=sdc_str
  135. methods+=resampling_str
  137. ref_string='\n'
  138. for key in references.keys():
  139. ref_string+=f"[{references[key]}] {key} \n"
  140. return methods,ref_string
  143. def confound_correction_boilerplate(opts):
  144. methods=''
  145. references={}
  146. i=1
  148. # define references
  149. rabies_ref='Desrosiers-Gregoire, G., Devenyi, G. A., Grandjean, J., & Chakravarty, M. M. (2023). Rodent Automated Bold Improvement of EPI Sequences (RABIES): A standardized image processing and data quality platform for rodent fMRI. bioRxiv.'
  150. aroma='Pruim, R. H., Mennes, M., van Rooij, D., Llera, A., Buitelaar, J. K., & Beckmann, C. F. (2015). ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage, 112, 267-277.'
  151. Lindquist='Lindquist, M. A., Geuter, S., Wager, T. D., & Caffo, B. S. (2019). Modular preprocessing pipelines can reintroduce artifacts into fMRI data. Human Brain Mapping, 40(8), 2358–2376.'
  152. mathias='Mathias, A., Grond, F., Guardans, R., Seese, D., Canela, M., & Diebner, H. H. (2004). Algorithms for spectral analysis of irregularly sampled time series. Journal of Statistical Software, 11(1), 1–27.'
  153. melodic='Beckmann, C. F., & Smith, S. M. (2004). Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE transactions on medical imaging, 23(2), 137-152.'
  154. nilearn='Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., ... & Varoquaux, G. (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in neuroinformatics, 8, 14.'
  155. power2012='Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage, 59(3), 2142-2154.'
  156. power2014='Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84, 320–341.'
  157. friston24='Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S., & Turner, R. (1996). Movement‐related effects in fMRI time‐series. Magnetic resonance in medicine, 35(3), 346-355.'
  158. aCompCor='Muschelli, J., Nebel, M. B., Caffo, B. S., Barber, A. D., Pekar, J. J., & Mostofsky, S. H. (2014). Reduction of motion-related artifacts in resting state fMRI using aCompCor. Neuroimage, 96, 22-35.'
  160. # Commonspace VS native space
  161. if opts.nativespace_analysis:
  162. EPI_space=f"native space EPI timeseries"
  163. else:
  164. EPI_space=f"EPI timeseries resampled to commonspace"
  166. # citing RABIES
  167. references[rabies_ref]=i
  168. i+=1
  169. methods+=f" Confound correction was executed using the RABIES software (https://github.com/CoBrALab/RABIES)[{references[rabies_ref]}] on {EPI_space}. "
  171. # Frame censoring
  172. if opts.frame_censoring['FD_censoring'] or opts.frame_censoring['DVARS_censoring']:
  173. references[power2012]=i
  174. i+=1
  175. methods+=f"First, frames with prominent corruption were censored (i.e. scrubbing [{references[power2012]}]). "
  177. if opts.frame_censoring['FD_censoring']:
  178. methods+=f"Framewise displacement [{references[power2012]}] was measured across time and each frame surpassing {opts.frame_censoring['FD_threshold']}mm of motion, together with 1 backward and 2 forward frames, were removed. "
  180. if opts.frame_censoring['DVARS_censoring']:
  181. if not power2012 in references.keys():
  182. references[power2012]=i
  183. i+=1
  184. methods+=f"Using the DVARS measure[{references[power2012]}] of temporal fluctuations in global signal, timepoints presenting outlier DVARS values, characteristic of confounds, were removed. \
  185. This was conducted by iteratively removing frames which present outlier DVARS values above or below 2.5 standard deviations until no more outliers are detected. "
  186. if opts.match_number_timepoints:
  187. methods+=f"Following censoring, only a total of {opts.match_number_timepoints} frames was retained in each scan through random selection among the remaining frames. This was done \
  188. to enforce equal degrees of freedom across scans despite an inconsistent number of frames removed through censoring. "
  190. methods+="Next, "
  191. else:
  192. methods+="First, "
  194. # Detrending
  195. order = opts.detrending['order']
  196. time_interval=opts.detrending['time_interval']
  197. methods+=f"voxelwise detrending was computed over {time_interval} timepoints and applied to remove {order} drifts and the average image from whole timeseries. "
  199. # ICA-AROMA
  200. if opts.ica_aroma['apply']:
  201. references[aroma]=i
  202. i+=1
  203. methods+=f"Then, motion sources were then automatically removed using a modified version of the ICA-AROMA classifier[{references[aroma]}], \
  204. where classifier parameters and anatomical masks are instead adapted for rodent images. "
  205. if not opts.ica_aroma['dim']==0:
  206. references[melodic]=i
  207. i+=1
  208. methods+=f"Specifically {opts.ica_aroma['dim']} components were derived for each image independently using MELODIC-ICA algorithm[{melodic}] \
  209. before classification. "
  211. # High/lowpass filtering
  212. highpass=opts.highpass is not None
  213. lowpass=opts.lowpass is not None
  214. if highpass or lowpass:
  215. references[nilearn]=i
  216. i+=1
  217. if highpass and lowpass:
  218. filter='bandpass'
  219. freq=f"{opts.highpass}-{opts.lowpass}"
  220. elif highpass:
  221. filter='highpass'
  222. freq=opts.highpass
  223. elif lowpass:
  224. filter='lowpass'
  225. freq=opts.lowpass
  226. if opts.frame_censoring['FD_censoring'] or opts.frame_censoring['DVARS_censoring']:
  227. references[power2014]=i
  228. i+=1
  229. references[mathias]=i
  230. i+=1
  231. methods+=f"To apply frequency filters despite missing data points (i.e. following the frame censoring step), \
  232. missing data was simulated based on a method introduced by Power and colleagues, where data is simulated \
  233. using the Lomb-Scargle periodogram to preserve the frequency profile of the timeseries before applying \
  234. the filters[{references[power2014]},{references[mathias]}]. "
  236. methods+=f"Following the simulation, "
  237. else:
  238. methods+=f"Next, "
  240. if not power2014 in references.keys():
  241. references[power2014]=i
  242. i+=1
  243. methods+=f"{filter} filtering({freq}Hz) was applied using a 3rd-order Butterworth filter, and {opts.edge_cutoff} seconds \
  244. is removed at each edge of the timeseries to account for edge artefacts following filtering [{references[power2014]}]. "
  246. if len(opts.nuisance_regressors) > 0:
  247. references[Lindquist]=i
  248. i+=1
  249. methods+=f"The nuisance regressors are also filtered to ensure orthogonality between the frequency filters and subsequent confound regression, which can otherwise re-introduce removed confounds [{references[Lindquist]}]."
  251. if opts.frame_censoring['FD_censoring'] or opts.frame_censoring['DVARS_censoring']:
  252. methods+=f"After frequency filtering, the temporal masks are re-applied to remove the simulated timepoints. "
  254. # Confound Regression
  255. if len(opts.nuisance_regressors)>0:
  256. str_list=[]
  257. if 'mot_6' in opts.nuisance_regressors:
  258. str_list.append('the 6 rigid motion parameters')
  259. if 'mot_24' in opts.nuisance_regressors:
  260. references[friston24]=i
  261. i+=1
  262. str_list.append(f'24 motion parameters (the 6 rigid motion parameters, their temporal derivative, together with all 12 parameters squared[{references[friston24]}])')
  263. if 'aCompCor_5' in opts.nuisance_regressors:
  264. references[aCompCor]=i
  265. i+=1
  266. str_list.append(f'the timecourses from the first 5 principal components derived from the voxels combining the WM and CSF masks (aCompCor)[{references[aCompCor]}]')
  267. if 'aCompCor_percent' in opts.nuisance_regressors:
  268. references[aCompCor]=i
  269. i+=1
  270. str_list.append(f'the timecourses from principal components explaining 50% of the variance among the voxels from the combined WM and CSF masks (aCompCor)[{references[aCompCor]}]')
  271. if 'WM_signal' in opts.nuisance_regressors or 'CSF_signal' in opts.nuisance_regressors or 'vascular_signal' in opts.nuisance_regressors:
  272. mask_str='the mean signal from the '
  274. masks=[]
  275. if 'WM_signal' in opts.nuisance_regressors:
  276. masks.append('WM')
  277. if 'CSF_signal' in opts.nuisance_regressors:
  278. masks.append('CSF')
  279. if 'vascular_signal' in opts.nuisance_regressors:
  280. masks.append('vascular')
  282. mask_str+=masks[0]
  283. for str_ in masks[1:-1]:
  284. mask_str+=', '+str_
  285. if len(masks)>1:
  286. mask_str+=f' and {masks[-1]} masks'
  287. else:
  288. mask_str+=' mask'
  290. str_list.append(mask_str)
  292. if 'global_signal' in opts.nuisance_regressors:
  293. str_list.append(f'the global signal')
  295. nuisance_regressors_str=str_list[0]
  296. for str_ in str_list[1:-1]:
  297. nuisance_regressors_str+=', '+str_
  298. if len(str_list)>1:
  299. nuisance_regressors_str+=f' and {str_list[-1]}'
  301. methods+=f"Selected nuisance regressors were then used for confound regression. More specifically, using ordinary least square regression, \
  302. {nuisance_regressors_str} were modelled at each voxel and regressed from the data. "
  304. # variance standardization
  305. if opts.image_scaling=="grand_mean_scaling":
  306. methods+=f"Timeseries were converted to percent BOLD fluctuations by dividing by the grand mean signal across voxels, \
  307. and multiplying by 100. "
  308. elif opts.image_scaling=="voxelwise_mean":
  309. methods+=f"Each voxel was individually converted to percent BOLD fluctuations by dividing by the temporal mean and multiplying by 100. "
  310. elif opts.image_scaling=="global_variance":
  311. methods+=f"To normalize variance, each image was seperately scaled according to its total variance. "
  312. elif opts.image_scaling=="voxelwise_standardization":
  313. methods+=f"To normalize variance, each voxel was individually standardized to unit variance. "
  314. if opts.scale_variance_voxelwise:
  315. methods+=f"Additionally, each voxel was set to equal temporal variance while preserving the total variance of the entire image. \
  316. This allows to obtain a spatially-homogeneous variance distribution, and mitigate influence from confounds [{references[rabies_ref]}]. "
  318. # Spatial smoothing
  319. if opts.smoothing_filter is not None:
  320. if not nilearn in references.keys():
  321. references[nilearn]=i
  322. i+=1
  323. methods+=f"Finally, a spatial Gaussian smoothing filter(nilearn.image.smooth_img)[{references[nilearn]}] was applied at {opts.smoothing_filter}mm full-width at half maximum (FWHM). "
  325. methods+='\n'
  326. ref_string='\n'
  327. for key in references.keys():
  328. ref_string+=f"[{references[key]}] {key} \n"
  329. return methods,ref_string

boilerplate.py at commit 3350a51, under other · at the source

Overview

Authors: Qian Lv1, Jiali Xu2, Yuejiao Zhang2, Shuang Chen3, Meiqing Wang2,4
ORCID iDs: Qian Lv
  1. State Key Laboratory of Brain Function and Disorders and MOE Frontiers Center for Brain Science and the Institutes of Brain Science, Fudan University, Shanghai, China
  2. Department of Oral Anatomy and Physiology, Shanghai Stomatological Hospital and School of Stomatology, Fudan University, Shanghai, China
  3. Department of Prosthodontics, Shanghai Stomatological Hospital and School of Stomatology, Fudan University, Shanghai, China
  4. Department of Oral Anatomy and Physiology, College of Stomatology, The Fourth Military Medical University, Xi’an, China
Institutions: Fudan University (China); Frontiers Center for Brain Science of the Ministry of Education (China); Shanghai Stomatological Hospital (China); Air Force Medical University (China)
Journal: Frontiers in neuroscience, volume 20, article 1873417
Dates: received 6 May 2026; accepted 13 July 2026; published online 28 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fnins.2026.1873417 · PMID 42582224 · PMCID PMC13457461 · OpenAlex W7171485143
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), rat (organism), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Preprocessing, Graphs, fMRI & imaging
Keywords: amygdala, anxiety, brain network, dental malocclusion, functional MRI, orofacial proprioceptive pathway
Topic: Temporomandibular Joint Disorders (Complementary and Manual Therapy, Health Professions), according to OpenAlex
Citations: not cited yet (Europe PMC); 43 references in the paper

Abstract

Background: Malocclusion has been associated with alterations in central nervous system function; however, the effects of persistent abnormal occlusal input on whole-brain functional organization remain poorly understood. This study aimed to investigate functional brain network reorganization associated with unilateral anterior crossbite (UAC) in rats.

Methods: Fourteen female rats were used, seven of which were treated with left-sided UAC at 6 weeks old and the other seven served as sham-operation controls. Resting-state functional magnetic resonance imaging data were acquired at 26 weeks old. Regional activity in the proprioceptive pathway was assessed, followed by graph theoretical analysis of whole-brain functional networks.

Results: UAC rats exhibited altered regional activity in the right ventral posteromedial thalamus (p = 0.023) and primary somatosensory cortex (p < 0.001), contralateral to the modeling side. Network analysis revealed significantly higher normalized clustering coefficient (p = 0.014) and small-worldness (p = 0.006) in the UAC group compared with controls. Furthermore, UAC rats showed enhanced functional connectivity within a subnetwork (p = 0.023) in which the right amygdala exhibited the greatest number of altered connections. In addition, abnormal regional activity was also detected in the right amygdala of UAC rats (p = 0.039).

Conclusion: Long-term UAC in rats is associated with alterations in whole-brain functional network organization. These findings provide new insights into the central neural responses associated with abnormal occlusal input.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

CoBrALab/RABIES

License: other
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 3350a51efc89ba93940ef101dd156edf172c4511, 23 September 2026
Languages: Python (47), Shell (5)
Size: 139 files, 52 scripts
Software Heritage: archived
Found in: the end of the paper
Holds: README, license file, CITATION.cff, environment (Dockerfile, setup.py, docs/requirements.txt, rabies/confound_correction_pkg/mod_ICA_AROMA/Dockerfile), continuous integration, documentation
Not found: tests
Tools: SimpleITK (23 files), Nipype (22 files), NumPy (21 files), pandas (13 files), Matplotlib (9 files), ANTs (4 files), SciPy (4 files), FSL (3 files), PyBIDS (3 files), NiBabel (2 files), scikit-learn (2 files), scikit-image (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
54 files
At the source: github.com/CoBrALab/RABIES

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Data

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Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

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

Versions

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Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 43 references.

Cite

This paper

Lv, Q., Xu, J., Zhang, Y., Chen, S., & Wang, M. (2026). Functional brain network alterations in a rat model of dental malocclusion. Frontiers in neuroscience, 20, 1873417. https://doi.org/10.3389/fnins.2026.1873417

BibTeX

@article{lv2026functional,
author = {Lv, Qian and Xu, Jiali and Zhang, Yuejiao and Chen, Shuang and Wang, Meiqing},
title = {{Functional brain network alterations in a rat model of dental malocclusion}},
journal = {Frontiers in neuroscience},
year = {2026},
month = jul,
volume = {20},
pages = {1873417},
publisher = {Frontiers Media SA},
issn = {1662-4548},
doi = {10.3389/fnins.2026.1873417},
url = {https://doi.org/10.3389/fnins.2026.1873417},
pmid = {42582224},
pmcid = {PMC13457461}
}

RIS

TY - JOUR
AU - Lv, Qian
AU - Xu, Jiali
AU - Zhang, Yuejiao
AU - Chen, Shuang
AU - Wang, Meiqing
TI - Functional brain network alterations in a rat model of dental malocclusion
T2 - Frontiers in neuroscience
J2 - Front Neurosci
PY - 2026
DA - 2026/07/28
VL - 20
SP - 1873417
SN - 1662-4548
PB - Frontiers Media SA
DO - 10.3389/fnins.2026.1873417
UR - https://doi.org/10.3389/fnins.2026.1873417
LA - en
ER -

CSL-JSON

{
"id": "10.3389/fnins.2026.1873417",
"type": "article-journal",
"title": "Functional brain network alterations in a rat model of dental malocclusion",
"container-title": "Frontiers in neuroscience",
"author": [
{
"family": "Lv",
"given": "Qian"
},
{
"family": "Xu",
"given": "Jiali"
},
{
"family": "Zhang",
"given": "Yuejiao"
},
{
"family": "Chen",
"given": "Shuang"
},
{
"family": "Wang",
"given": "Meiqing"
}
],
"container-title-short": "Front Neurosci",
"volume": "20",
"page": "1873417",
"DOI": "10.3389/fnins.2026.1873417",
"PMID": "42582224",
"PMCID": "PMC13457461",
"ISSN": "1662-4548",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fnins.2026.1873417",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
28
]
]
}
}

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