OSCR

Dynamic Functional Connectivity Changes in Cortical and Cortico-Striatal Strokes in Mice.

Code ↔ Paper

5 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 5 matches
  1. [1] § Methods › MRI Acquisition and Processing ↔ bin/3.1_T2Processing/getIncidenceSize_par.py, lines 89–231 · score 0.68 · atlas space, lesion mask, stroke masks, Incidence, binary, overlaying
  2. [2] § Methods › MRI Acquisition and Processing ↔ bin/3.1_T2Processing/getIncidenceSize.py, lines 89–231 · score 0.67 · atlas space, lesion mask, stroke masks, Incidence, binary, overlaying
  3. [3] § Methods › MRI Acquisition and Processing ↔ bin/3.1_T2Processing/getIncidenceSize_par.py, lines 89–231 · score 0.59 · Reference Atlas, lesion mask, zero, parental, Allen, voxel
  4. [4] § Methods › MRI Acquisition and Processing ↔ bin/5.1_ROI_analysis/02_apply_xfm_process.py, lines 1–42 · score 0.57 · ParaVision, fMRI, positioned, T2w, scan, map
  5. [5] § Methods › MRI Acquisition and Processing ↔ bin/2.1_T2PreProcessing/registration_T2.py, lines 23–156 · score 0.53 · lesion mask, alignment, parental, field, Allen, transformed

Paper

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The authors' code

Python · 296 lines · 14 KB · GPL-3.0 · 2 matches

  2. """
  3. Created on 10/08/2017
  5. @author: Niklas Pallast
  6. Neuroimaging & Neuroengineering
  7. Department of Neurology
  8. University Hospital Cologne
  11. """
  14. import csv
  15. import os,sys
  16. import nibabel as nii
  17. import glob
  18. import numpy as np
  19. import scipy.io as sc
  21. sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir)))
  22. from common.artifact_manifest import start_output_tracking
  23. from common.script_logging import setup_script_logging
  25. REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, os.pardir))
  28. def voxel_volume_mm3(nifti_img):
  29. # Use the full affine so rotated/sheared registered images still get a valid voxel volume.
  30. voxel_volume = abs(np.linalg.det(nifti_img.affine[:3, :3]))
  31. if voxel_volume == 0:
  32. voxel_volume = np.prod(nifti_img.header.get_zooms()[:3])
  33. return voxel_volume
  36. def ensure_same_grid(reference_img, moving_img, moving_description):
  37. if reference_img.shape != moving_img.shape:
  38. sys.exit("Error: Stroke mask and %s have different dimensions." % (moving_description,))
  39. if not np.allclose(reference_img.affine, moving_img.affine, atol=1e-4):
  40. sys.exit("Error: Stroke mask and %s have different affine matrices." % (moving_description,))
  43. def nifti_name_without_extension(file_path):
  44. file_name = os.path.basename(file_path)
  45. if file_name.endswith('.nii.gz'):
  46. return file_name[:-len('.nii.gz')]
  47. if file_name.endswith('.nii'):
  48. return file_name[:-len('.nii')]
  49. sys.exit("Error: '%s' is not a NIfTI file." % (file_path,))
  52. def find_brkraw_nifti(input_folder):
  53. brkraw_folder = os.path.join(input_folder, 'brkraw')
  54. matches = sorted(glob.glob(os.path.join(brkraw_folder, '*.nii')) +
  55. glob.glob(os.path.join(brkraw_folder, '*.nii.gz')))
  56. if len(matches) == 0:
  57. matches = sorted(glob.glob(os.path.join(input_folder, '**', 'brkraw', '*.nii'), recursive=True) +
  58. glob.glob(os.path.join(input_folder, '**', 'brkraw', '*.nii.gz'), recursive=True))
  59. if len(matches) == 0:
  60. sys.exit("Error: No NIfTI file found in a brkraw folder under '%s'." % (input_folder,))
  61. if len(matches) > 1:
  62. sys.exit("Error: Multiple NIfTI files found in brkraw folders under '%s': %s" %
  63. (input_folder, ', '.join(matches),))
  64. return matches[0]
  67. def load_label_table(label_file):
  68. label_ids = []
  69. label_names_by_id = {}
  70. label_names = []
  72. with open(label_file, 'r') as label_handle:
  73. for line in label_handle:
  74. line = line.strip()
  75. if not line:
  76. continue
  77. parts = line.split('\t', 1)
  78. if len(parts) != 2:
  79. sys.exit("Error: Invalid label line in '%s': %s" % (label_file, line,))
  80. label_id = int(parts[0])
  81. label_name = parts[1]
  82. label_ids.append(label_id)
  83. label_names_by_id[label_id] = label_name
  84. label_names.append(label_name)
  86. return np.array(label_ids, dtype=int), label_names_by_id, label_names
  89. def calculate_parental_stroke_overlap(brain_file, parental_annotation_file, ara_template_file, stroke_mask_file,
  90. incidence_lesion_mask_file, output_folder, label_file):
  92. # Load the reference Allen/ARA template used for the affected-regions overlay.
  93. ara_template_img = nii.load(ara_template_file)
  94. ara_template_labels = ara_template_img.get_fdata()
  95. affected_template_labels = np.zeros([np.size(ara_template_labels, 0), np.size(ara_template_labels, 1), np.size(ara_template_labels, 2)])
  97. # Load label IDs and names from the left/right-separated parental rsfMRI label table.
  98. all_label_ids, label_names_by_id, labelNames = load_label_table(label_file)
  99. parental_label_ids = all_label_ids.copy()
  101. # Save the template-space IncidenceData lesion mask labelled by the parental rsfMRI atlas.
  102. incidence_lesion_mask_img = nii.load(incidence_lesion_mask_file)
  103. incidence_lesion_mask = incidence_lesion_mask_img.get_fdata()
  104. incidence_lesion_mask[incidence_lesion_mask > 0.0] = 1.0
  105. incidence_lesion_mask[incidence_lesion_mask <= 0.0] = 0.0
  107. ensure_same_grid(incidence_lesion_mask_img, ara_template_img, "parental incidence atlas")
  108. parental_incidence_atlas = np.round(ara_template_labels)
  110. labelled_incidence_lesion = parental_incidence_atlas*incidence_lesion_mask
  111. labelled_incidence_lesion_img = nii.Nifti1Image(labelled_incidence_lesion, incidence_lesion_mask_img.affine)
  112. labelled_incidence_lesion_img.header.set_xyzt_units('mm')
  113. incidence_data_dir = os.path.dirname(incidence_lesion_mask_file)
  114. incidence_data_name = os.path.basename(incidence_lesion_mask_file)
  115. incidence_input_suffix = 'IncidenceData_Lesion_mask.nii.gz'
  116. if not incidence_data_name.endswith(incidence_input_suffix):
  117. sys.exit("Error: Incidence lesion mask filename must end with '%s'." % (incidence_input_suffix,))
  118. output_name = incidence_data_name[:-len('.nii.gz')] + '_Anno_parental.nii.gz'
  119. output_file = os.path.join(incidence_data_dir, output_name)
  120. nii.save(labelled_incidence_lesion_img, output_file)
  122. # Load and binarize the externally provided stroke mask.
  123. stroke_mask_img = nii.load(stroke_mask_file)
  124. stroke_mask = stroke_mask_img.get_fdata()
  125. stroke_mask[stroke_mask > 0.0] = 1.0
  126. stroke_mask[stroke_mask <= 0.0] = 0.0
  128. # Load subject-space parental annotation and brain image.
  129. parental_annotation_img = nii.load(parental_annotation_file)
  130. parental_annotation = np.round(parental_annotation_img.get_fdata())
  131. brain_img = nii.load(brain_file)
  132. brain_volume = brain_img.get_fdata()
  134. ensure_same_grid(stroke_mask_img, parental_annotation_img, "parental annotation")
  135. ensure_same_grid(stroke_mask_img, brain_img, "brain image")
  137. # Keep only parental atlas labels that overlap with the subject-space stroke mask.
  138. labelled_stroke_overlap = parental_annotation * stroke_mask
  140. # Extract the unique non-zero parental labels affected by the stroke.
  141. affected_labels = np.unique(labelled_stroke_overlap)
  142. affected_labels = affected_labels[affected_labels > 0.0]
  144. # Calculate how much of each affected parental region is covered by the stroke.
  145. region_percent_by_label = {}
  146. region_affected_voxels_by_label = {}
  147. for label_id in affected_labels:
  148. # Percentage of the parental region covered by stroke voxels.
  149. affected_voxels = np.sum(labelled_stroke_overlap == label_id)
  150. total_region_voxels = np.sum(parental_annotation == label_id)
  151. region_percent = (affected_voxels / total_region_voxels) * 100
  152. region_percent_by_label[int(label_id)] = min(region_percent, 100)
  153. region_affected_voxels_by_label[int(label_id)] = affected_voxels
  155. # Keep only label IDs that are actually affected by stroke (in the affected-label list)
  156. affected_label_ids = np.array(sorted(region_percent_by_label))
  157. affected_label_mask = np.isin(parental_label_ids, affected_label_ids)
  158. parental_label_ids = parental_label_ids[affected_label_mask]
  159. parental_label_id_set = set(int(label_id) for label_id in parental_label_ids)
  161. # Create an affected-region overlay in the reference atlas space.
  162. affected_template_mask = np.isin(ara_template_labels, affected_label_ids)
  163. affected_template_labels[affected_template_mask] = ara_template_labels[affected_template_mask]
  165. # Save the affected parental regions as a NIfTI overlay.
  166. affected_regions_img = nii.Nifti1Image(affected_template_labels, ara_template_img.affine)
  167. affected_regions_img.header.set_xyzt_units('mm')
  168. brkraw_nifti_file = find_brkraw_nifti(output_folder)
  169. affected_regions_prefix = '%s_%s_' % (nifti_name_without_extension(brkraw_nifti_file),
  170. nifti_name_without_extension(ara_template_file))
  171. #Create affected Regions folder
  172. affected_regions_dir = os.path.join(output_folder, 'affected_Regions')
  173. os.makedirs(affected_regions_dir, exist_ok=True)
  174. output_file = os.path.join(affected_regions_dir, affected_regions_prefix + 'affectedRegions_Parental.nii.gz')
  175. nii.save(affected_regions_img, output_file)
  177. # Stroke volume calculation
  178. brain_mask = brain_volume.copy()
  179. brain_mask[brain_mask > 0.0] = 1.0
  180. brain_mask[brain_mask <= 0.0] = 0.0
  182. # Estimate volumes from binary voxel count times each image's voxel volume.
  183. strokeVoxelVolumeMM3 = voxel_volume_mm3(stroke_mask_img)
  184. brainVoxelVolumeMM3 = voxel_volume_mm3(brain_img)
  185. strokeVolumeInCubicMM = np.sum(stroke_mask > 0) * strokeVoxelVolumeMM3
  186. brainVolumeInCubicMM = np.sum(brain_mask > 0) * brainVoxelVolumeMM3
  187. if brainVolumeInCubicMM == 0:
  188. sys.exit("Error: Brain mask volume is zero.")
  190. # Write the CSV summary of affected parental regions.
  191. csv_file = open(os.path.join(affected_regions_dir, affected_regions_prefix + 'affectedRegions_Parental.csv'), 'w', newline='')
  192. csv_writer = csv.writer(csv_file)
  193. strokePercentOfBrain = (strokeVolumeInCubicMM / brainVolumeInCubicMM) * 100
  194. csv_writer.writerow(['Label_id', 'Brain region', 'Affected region percentage [%]', 'Region stroke volume [mm3]', '',
  195. 'Stroke percent of brain [%]', 'Total stroke volume [mm3]'])
  196. affected_label_names_by_id = {}
  197. wrote_total_stroke_values = False
  198. for label_id in all_label_ids:
  199. label_name = label_names_by_id[label_id]
  200. # Only write rows for labels that are actually affected by stroke and are in the parental label set.
  201. if label_id in region_percent_by_label and label_id in parental_label_id_set:
  202. # Write label ID, label name, affected percentage, and absolute affected volume for each matched region.
  203. region_stroke_volume_mm3 = region_affected_voxels_by_label[label_id] * strokeVoxelVolumeMM3
  204. if wrote_total_stroke_values:
  205. total_stroke_percent = ''
  206. total_stroke_volume = ''
  207. else:
  208. total_stroke_percent = "%0.2f" % strokePercentOfBrain
  209. total_stroke_volume = "%0.4f" % strokeVolumeInCubicMM
  210. wrote_total_stroke_values = True
  211. csv_writer.writerow([label_id, label_name, "%0.2f" % region_percent_by_label[label_id],
  212. "%0.4f" % region_stroke_volume_mm3, '', total_stroke_percent,
  213. total_stroke_volume])
  214. affected_label_names_by_id[label_id] = label_name
  216. csv_file.close()
  218. # Store the same region statistics in a MATLAB file for downstream workflows.
  219. regionAffectPercent = np.array([region_percent_by_label[int(label_id)] for label_id in parental_label_ids])
  220. regionStrokeVolumeMM3 = np.array([region_affected_voxels_by_label[int(label_id)] * strokeVoxelVolumeMM3
  221. for label_id in parental_label_ids])
  222. affected_label_names = [affected_label_names_by_id.get(int(label_id), "") for label_id in parental_label_ids]
  223. parental_label_ids = np.column_stack((parental_label_ids, regionAffectPercent))
  224. label_mat = {}
  225. label_mat['ABLAbelsIDsParental'] = parental_label_ids
  226. label_mat['ABANamesPar'] = affected_label_names
  227. label_mat['ABAlabels'] = labelNames
  228. label_mat['regionStrokeVolumeMM3'] = regionStrokeVolumeMM3
  229. label_mat['volumePer'] = (strokeVolumeInCubicMM / brainVolumeInCubicMM) * 100
  230. label_mat['volumeMM'] = strokeVolumeInCubicMM
  231. sc.savemat(os.path.join(affected_regions_dir, affected_regions_prefix + 'labelCount_par.mat'), label_mat)
  238. def find_files(input_folder, pattern):
  239. # Search the input folder recursively and return deterministic file ordering.
  240. return sorted(glob.glob(os.path.join(input_folder, '**', pattern), recursive=True))
  243. def find_single_file(input_folder, pattern, description):
  244. matches = find_files(input_folder, pattern)
  245. if len(matches) == 0:
  246. sys.exit("Error: No %s found in '%s'." % (description, input_folder,))
  247. if len(matches) > 1:
  248. sys.exit("Error: Multiple %s found in '%s': %s" % (description, input_folder, ', '.join(matches),))
  249. return matches[0]
  251. if __name__ == "__main__":
  252. import argparse
  254. parser = argparse.ArgumentParser(description='Calculate incidence sizes of parental regions. You do not need to enter single files, but the path to the .../anat folder which includes the T2 data')
  255. requiredNamed = parser.add_argument_group('Required named arguments')
  256. requiredNamed.add_argument('-i', '--inputFolder', help='.../anat', required=True)
  258. parser.add_argument('-a', '--allenBrain_anno', help='File: left/right-separated parental rsfMRI annotation template', nargs='?', type=str,
  259. default=os.path.join(REPO_ROOT, 'lib', 'annoVolume+2000_rsfMRI.nii.gz'))
  261. input_folder = None
  262. allen_template_file = None
  263. output_folder = None
  265. args = parser.parse_args()
  267. # Use the input folder as both source folder and output folder.
  268. if args.inputFolder is not None:
  269. input_folder = args.inputFolder
  270. output_folder = args.inputFolder
  271. if not os.path.exists(input_folder):
  272. sys.exit("Error: '%s' is not an existing directory." % (input_folder,))
  273. start_output_tracking(output_folder, "anat", "processing")
  274. setup_script_logging(output_folder, "process_par.log")
  277. if args.allenBrain_anno is not None:
  278. allen_template_file = args.allenBrain_anno
  279. if not os.path.isfile(allen_template_file):
  280. sys.exit("Error: '%s' is not an existing file." % (allen_template_file,))
  282. # Resolve static label resources from the repository lib folder.
  283. label_file = os.path.join(REPO_ROOT, 'lib', 'annoVolume+2000_rsfMRI.nii.txt')
  284. ara_template_file = allen_template_file
  286. # Collect exactly one required subject file from the input folder.
  287. stroke_mask_file = find_single_file(input_folder, '*Stroke_mask.nii.gz', 'stroke mask')
  288. brain_file = find_single_file(input_folder, '*Bet.nii.gz', 'BET image')
  289. parental_annotation_file = find_single_file(input_folder, '*_AnnoSplit_parental.nii.gz', 'parental annotation')
  290. incidence_lesion_mask_file = find_single_file(input_folder, '*IncidenceData_Lesion_mask.nii.gz', 'incidence lesion mask')
  292. print("1 folder will be processed...")
  294. # Calculate parental-region lesion overlap and write NIfTI, TXT, and MAT outputs.
  295. calculate_parental_stroke_overlap(brain_file, parental_annotation_file, ara_template_file, stroke_mask_file,
  296. incidence_lesion_mask_file, output_folder, label_file)

getIncidenceSize_par.py at commit e12eb82, under GPL-3.0 · at the source

Overview

Authors: Fatemeh S N Mahani1,2,3, Michael Diedenhofen4, Claudia Green4, Dirk Wiedermann4, Gereon R Fink1,2, Mathias Hoehn1, Markus Aswendt2,3
ORCID iDs: Fatemeh S N Mahani, Gereon R Fink, Markus Aswendt
  1. Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM‐3), Research Centre Juelich, Juelich, Germany
  2. Department of Neurology, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany
  3. Department of Neurology, Experimental Neurology Section, Goethe University Frankfurt and University Hospital, Frankfurt am Main, Germany
  4. In‐Vivo‐NMR Laboratory, Max Planck Institute for Metabolism Research, Cologne, Germany
Institutions: Goethe University Frankfurt (Germany); Forschungszentrum Jülich (Germany); University of Cologne (Germany); University Hospital Frankfurt (Germany); University Hospital Cologne (Germany); Max Planck Institute for Metabolism Research (Germany)
Journal: CNS neuroscience & therapeutics, volume 32, issue 8, article e71114
Dates: received 2 April 2026; accepted 19 August 2026; published online 27 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/cns.71114 · PMID 42661330 · PMCID PMC13522393 · OpenAlex W7162802708
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), mouse (organism), stroke (population), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, fMRI & imaging
Keywords: functional brain network analysis, interhemispheric connectivity, MCAO, photothrombosis, resting‐state fMRI, rodents, seed strength, sensorimotor network
MeSH: Cerebral Cortex*, Corpus Striatum*, Stroke*, Animals, Disease Models, Animal, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Mice, Mice, Inbred C57BL, Neural Pathways (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (431549029-SFB 1451)
Citations: not cited yet (Europe PMC); 58 references in the paper

Abstract

Aims: To determine how lesion size and location shape longitudinal functional connectivity (FC) changes after stroke.

Methods: Adult male mice underwent atlas‐based resting‐state fMRI at baseline and 1, 2, and 4 weeks after either a small photothrombotic cortical stroke (N = 25) or a larger transient cortico‐striatal MCAO stroke (N = 6). FC was quantified across 98 atlas regions, focusing on sensorimotor cortex, striatum, and thalamus, including intra‐ and inter‐hemispheric connectivity matrices and regional seed strength.

Results: Cortical stroke caused widespread hyperconnectivity at Weeks 1–2, with about 90% of connections increased, followed by partial normalization by Week 4. This effect declined most strongly in the ischemic hemisphere and remained more sustained contralesionally. In contrast, cortico‐striatal stroke induced global hypoconnectivity at Week 1, with more than 90% of connections decreased, a modest and heterogeneous shift toward baseline at Week 2, and widespread decreases persisting at Week 4. A subset of sensorimotor connections showing opposite changes in the two models robustly separated groups at all post‐stroke time points. Regional lesion involvement scaled with the magnitude of baseline‐referenced FC alterations.

Conclusion: Lesion topography drives distinct longitudinal FC trajectories after stroke and may help define network biomarkers and optimal windows for targeted interventions.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

Aswendt-Lab/AIDAmri

License: GPL-3.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: e12eb8271bf9b1eca6069808d9d1ba08e421c362, 25 September 2026
Languages: Python (92), MATLAB (11), Shell (4), Jupyter (1)
Size: 192 files, 108 scripts
Software Heritage: not archived
Found in: the text, “MRI Acquisition and Processing”
Holds: README, license file, environment (constraints.txt, Dockerfile, requirements.txt), continuous integration, documentation, 1 notebook
Not found: CITATION.cff, tests
Tools: NumPy (72 files), NiBabel (43 files), SciPy (17 files), Matplotlib (13 files), FSL (12 files), Nipype (10 files), ANTs (3 files), OpenCV (3 files), pandas (3 files), seaborn (2 files), AllenSDK (1 file), DIPY (1 file), LMFIT (1 file), Tools for NIfTI and ANALYZE image (MATLAB) (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
110 files
At the source: github.com/Aswendt-Lab/AIDAmri

doi:10.12751/g-node.pmvtz1

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.12751/g-node.pmvtz1

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 108 scripts, each with its path and the digest of its content;
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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 Statement

The dataset including all raw and processed MRI data, custom scripts, and code is available at DOI: 10.12751/g‐node.pmvtz1 (https://doi.org/10.12751/g-node.pmvtz1) (CC BY‐NC‐SA 4.0 license) according to the scheme explained in Ref. [58].

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 8 keywords, 11 MeSH terms, 1 funder, 58 references.

Cite

This paper

Mahani, F. S. N., Diedenhofen, M., Green, C., Wiedermann, D., Fink, G. R., Hoehn, M., & Aswendt, M. (2026). Dynamic Functional Connectivity Changes in Cortical and Cortico-Striatal Strokes in Mice. CNS neuroscience & therapeutics, 32(8), e71114. https://doi.org/10.1002/cns.71114

BibTeX

@article{mahani2026dynamic,
author = {Mahani, Fatemeh S N and Diedenhofen, Michael and Green, Claudia and Wiedermann, Dirk and Fink, Gereon R and Hoehn, Mathias and Aswendt, Markus},
title = {{Dynamic Functional Connectivity Changes in Cortical and Cortico-Striatal Strokes in Mice}},
journal = {CNS neuroscience \& therapeutics},
year = {2026},
month = aug,
volume = {32},
number = {8},
pages = {e71114},
publisher = {Wiley},
issn = {1755-5930},
doi = {10.1002/cns.71114},
url = {https://doi.org/10.1002/cns.71114},
pmid = {42661330},
pmcid = {PMC13522393}
}

RIS

TY - JOUR
AU - Mahani, Fatemeh S N
AU - Diedenhofen, Michael
AU - Green, Claudia
AU - Wiedermann, Dirk
AU - Fink, Gereon R
AU - Hoehn, Mathias
AU - Aswendt, Markus
TI - Dynamic Functional Connectivity Changes in Cortical and Cortico-Striatal Strokes in Mice
T2 - CNS neuroscience & therapeutics
J2 - CNS Neurosci Ther
PY - 2026
DA - 2026/08/01
VL - 32
IS - 8
SP - e71114
SN - 1755-5930
PB - Wiley
DO - 10.1002/cns.71114
UR - https://doi.org/10.1002/cns.71114
LA - en
ER -

CSL-JSON

{
"id": "10.1002/cns.71114",
"type": "article-journal",
"title": "Dynamic Functional Connectivity Changes in Cortical and Cortico-Striatal Strokes in Mice",
"container-title": "CNS neuroscience & therapeutics",
"author": [
{
"family": "Mahani",
"given": "Fatemeh S N"
},
{
"family": "Diedenhofen",
"given": "Michael"
},
{
"family": "Green",
"given": "Claudia"
},
{
"family": "Wiedermann",
"given": "Dirk"
},
{
"family": "Fink",
"given": "Gereon R"
},
{
"family": "Hoehn",
"given": "Mathias"
},
{
"family": "Aswendt",
"given": "Markus"
}
],
"container-title-short": "CNS Neurosci Ther",
"volume": "32",
"issue": "8",
"page": "e71114",
"DOI": "10.1002/cns.71114",
"PMID": "42661330",
"PMCID": "PMC13522393",
"ISSN": "1755-5930",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/cns.71114",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
1
]
]
}
}

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