OSCR

Cortical Iron and Mesostriatal Dopamine Function in Schizophrenia: A Positron Emission Tomography and Magnetic Resonance Imaging Study.

Code ↔ Paper

2 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 2 matches
  1. [1] § Methods › NM-MRI Preprocessing ↔ scripts/image_processing_functions.py, lines 212–252 · score 0.69 · noise ratio, NM MRI, NM CNR, midbrain, masks, maps
  2. [2] § Methods › NM-MRI Preprocessing ↔ scripts/main_script.py, lines 13–85 · score 0.63 · midbrain atlas, T1 weighted, NM CNR, maps, MNI, space

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

Python · 300 lines · 12 KB · CC-BY-NC-4.0 · 1 match

  1. import os
  2. import shutil
  3. import nibabel as nb
  4. import numpy as np
  5. import pandas as pd
  6. import ants
  7. import subprocess
  8. from nipype.interfaces.fsl import RobustFOV
  9. from nipype.interfaces.base import CommandLine
  10. from nipype.interfaces.ants import Registration
  11. from scipy.stats import gaussian_kde
  12. from scipy.optimize import minimize
  13. from filelock import FileLock
  15. def apply_robustfov(input_file, output_file):
  16. """
  17. Apply FSL's robustfov to the input file.
  18. """
  19. robustfov = RobustFOV()
  20. robustfov.inputs.in_file = input_file
  21. robustfov.inputs.out_roi = output_file
  22. robustfov.inputs.out_transform = output_file
  23. robustfov.run()
  24. print(f"RobustFOV applied to {input_file}, output saved to {output_file}")
  25. return output_file
  27. def apply_bias_correction(input_file, output_file):
  28. """
  29. Apply FreeSurfer's mri_nu_correct.mni to the input file and remove the log file after processing.
  30. """
  31. bias_correct = CommandLine(
  32. "mri_nu_correct.mni",
  33. args=f"--i {input_file} --o {output_file}",
  34. )
  35. bias_correct.run()
  36. print(f"Bias correction completed, output saved to {output_file}")
  38. log_file_path = os.path.join(os.path.dirname(output_file), "mri_nu_correct.mni.log")
  39. if os.path.exists(log_file_path):
  40. os.remove(log_file_path)
  41. return output_file
  43. def ants_linear_T1w_NM_registration(T1_corrected_path, NM_corrected_path, output_dir, subject_id):
  44. """
  45. Perform ANTs rigid registration to align the NM image to the T1-weighted image using the Nipype ANTs wrapper.
  46. """
  47. # Define output file paths
  48. output_prefix = os.path.join(output_dir, f"{subject_id}_NM_space-T1w")
  49. warped_image_path = f"{output_prefix}_Warped.nii.gz"
  50. inverse_warped_image_path = f"{output_prefix}_InverseWarped.nii.gz"
  51. affine_transform_path = f"{output_prefix}_Affine.mat"
  53. # Create registration object
  54. reg = Registration()
  55. reg.inputs.verbose = True
  56. reg.inputs.float = False
  58. # Set input images
  59. reg.inputs.fixed_image = T1_corrected_path
  60. reg.inputs.moving_image = NM_corrected_path
  62. # Set output transform prefix
  63. reg.inputs.output_transform_prefix = output_prefix
  65. # Define transformations
  66. reg.inputs.transforms = ["Rigid"]
  67. reg.inputs.transform_parameters = [(0.1,)] # Equivalent to --transform Rigid[0.1]
  69. # Convergence parameters
  70. reg.inputs.number_of_iterations = [[100, 70, 50, 20]] # Corresponds to --convergence [100x70x50x20, 1e-6, 10]
  71. reg.inputs.convergence_threshold = [1e-6]
  72. reg.inputs.convergence_window_size = [10]
  74. # Shrink factors and smoothing sigmas
  75. reg.inputs.shrink_factors = [[8, 4, 2, 1]] # Corresponds to --shrink-factors 8x4x2x1
  76. reg.inputs.smoothing_sigmas = [[3, 2, 1, 0]] # Corresponds to --smoothing-sigmas 3x2x1x0vox
  77. reg.inputs.sigma_units = ["vox"]
  79. # Similarity metric
  80. reg.inputs.metric = ["CC"] # Corresponds to --metric CC[T1_corrected_path, NM_corrected_path,1,4]
  81. reg.inputs.metric_weight = [1]
  82. reg.inputs.radius_or_number_of_bins = [4]
  84. # Histogram matching & intensity settings
  85. reg.inputs.use_histogram_matching = [False] # Corresponds to --use-histogram-matching 0
  86. reg.inputs.winsorize_lower_quantile = 0.005 # Corresponds to --winsorize-image-intensities [0.005, 0.995]
  87. reg.inputs.winsorize_upper_quantile = 0.995
  89. # Interpolation method
  90. reg.inputs.interpolation = "Linear" # Corresponds to --interpolation Linear
  92. # Output file settings
  93. reg.inputs.output_warped_image = warped_image_path
  94. reg.inputs.output_inverse_warped_image = inverse_warped_image_path
  96. # Run the registration
  97. print(f"Beginning NM-to-T1 registration")
  98. print(reg.cmdline)
  99. reg.run()
  100. print(f"NM-to-T1 registration saved to {warped_image_path}")
  102. def ants_normalization_to_MNI(T1_corrected_path, MNI_template_path, output_dir, subject_id, num_threads):
  103. """
  104. Perform ANTs SyN-based normalization to align the T1-weighted image to the MNI template.
  106. Parameters:
  107. T1_corrected_path (str): Path to the bias-corrected T1-weighted image.
  108. MNI_template_path (str): Path to the MNI template.
  109. output_dir (str): Directory where output files will be saved.
  110. subject_id (str): Subject ID to name the output files appropriately.
  111. num_threads (int): Number of threads to use for parallel processing.
  112. """
  113. # Define output file paths
  114. output_prefix = os.path.join(output_dir, f"{subject_id}_T1w_space-MNI152NLin2009cSym")
  115. warped_image_path = f"{output_prefix}_Warped.nii.gz"
  116. inverse_warped_image_path = f"{output_prefix}_InverseWarped.nii.gz"
  117. affine_transform_path = f"{output_prefix}_Affine.mat"
  118. warp_transform_path = f"{output_prefix}_Warp.nii.gz"
  119. inverse_warp_transform_path = f"{output_prefix}_InverseWarp.nii.gz"
  121. # Set number of threads for ANTs processing
  122. os.environ["ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS"] = str(num_threads)
  124. # Print thread info
  125. print(f"Using {num_threads} threads for ANTs processing.")
  127. # Create a Registration object
  128. reg = Registration()
  129. reg.inputs.verbose = True # Enable verbose output
  130. reg.inputs.num_threads = num_threads # Set number of threads
  131. reg.inputs.dimension = 3 # Set image dimensionality
  132. reg.inputs.float = False
  134. # Set fixed (MNI template) and moving (T1-weighted) images
  135. reg.inputs.fixed_image = MNI_template_path
  136. reg.inputs.moving_image = T1_corrected_path
  138. # Output transform prefix
  139. reg.inputs.output_transform_prefix = output_prefix
  140. reg.inputs.output_warped_image = warped_image_path
  141. reg.inputs.output_inverse_warped_image = inverse_warped_image_path
  143. # Define transformations
  144. reg.inputs.transforms = ["Rigid", "Affine", "SyN"]
  145. reg.inputs.transform_parameters = [(0.1,), (0.1,), (0.1, 3, 0)]
  146. reg.inputs.initial_moving_transform_com = 1
  147. reg.inputs.initialize_transforms_per_stage = False
  149. # Convergence parameters
  150. reg.inputs.number_of_iterations = [[1000, 500, 250, 100], [1000, 500, 250, 100], [100, 70, 50, 20]]
  151. reg.inputs.convergence_threshold = [1e-6, 1e-6, 1e-6]
  152. reg.inputs.convergence_window_size = [10, 10, 10]
  154. # Shrink factors & smoothing sigmas
  155. reg.inputs.shrink_factors = [[8, 4, 2, 1], [8, 4, 2, 1], [8, 4, 2, 1]]
  156. reg.inputs.smoothing_sigmas = [[3, 2, 1, 0], [3, 2, 1, 0], [3, 2, 1, 0]]
  157. reg.inputs.sigma_units = ["vox", "vox", "vox"]
  159. # Similarity metric
  160. reg.inputs.metric = ["MI", "MI", "CC"]
  161. reg.inputs.metric_weight = [1, 1, 1]
  162. reg.inputs.radius_or_number_of_bins = [32, 32, 4]
  163. reg.inputs.sampling_strategy = ["Regular", "Regular", None]
  164. reg.inputs.sampling_percentage = [0.25, 0.25, None]
  166. # Histogram matching & intensity settings
  167. reg.inputs.use_histogram_matching = [False, False, False]
  168. reg.inputs.winsorize_lower_quantile = 0.005
  169. reg.inputs.winsorize_upper_quantile = 0.995
  171. # Interpolation method
  172. reg.inputs.interpolation = "Linear"
  174. # Run the registration
  175. print(f"Beginning T1-to-MNI normalization for subject {subject_id}")
  176. print(reg.cmdline)
  177. reg.run()
  178. print(f"T1-to-MNI normalization complete. Warped image saved at {warped_image_path}")
  180. def transform_images_to_MNI_and_NM_space(
  181. midbrain_MNI_space_path,
  182. MNI_template_path,
  183. NM_input_path,
  184. warp_T1_to_MNI_path,
  185. inv_warp_T1_to_MNI_path,
  186. affine_T1_to_MNI_path,
  187. transform_NM_to_T1_path,
  188. output_dir,
  189. subject_id,
  190. interpolator
  191. ):
  192. """
  193. Transform images between MNI and NM space.
  194. """
  195. transformed_image = ants.apply_transforms(
  196. fixed=ants.image_read(NM_input_path),
  197. moving=ants.image_read(midbrain_MNI_space_path),
  198. transformlist=[
  199. transform_NM_to_T1_path,
  200. affine_T1_to_MNI_path,
  201. inv_warp_T1_to_MNI_path
  202. ],
  203. whichtoinvert=[True, True, False],
  204. interpolator=interpolator,
  205. )
  207. output_path = os.path.join(output_dir, f"{subject_id}_midbrain_atlas_space-NM.nii.gz")
  208. transformed_image.to_filename(output_path)
  209. print(f"Midbrain atlas transformed into NM-MRI space using {interpolator} saved at {output_path}")
  212. def compute_CNR_map(
  213. NM_input_path, midbrain_NM_space_path, NM_CNR_path, NM_CNR_MNI_space_path,
  214. MNI_template_path, warp_T1_to_MNI_path, affine_T1_to_MNI_path,
  215. transform_NM_to_T1_path, subject_id
  216. ):
  217. """
  218. Compute and save the Contrast-to-Noise Ratio (CNR) map for NM-MRI and move it to MNI space.
  219. """
  220. if not os.path.isfile(NM_input_path) or not os.path.isfile(midbrain_NM_space_path):
  221. print(f"Missing input files: {NM_input_path}, {midbrain_NM_space_path}")
  222. return
  224. NM_nii = nb.load(NM_input_path)
  225. midbrain_nii = nb.load(midbrain_NM_space_path).get_fdata()
  226. NM_data = NM_nii.get_fdata()
  228. CC_mask = (midbrain_nii == 1)
  229. if not CC_mask.any():
  230. print(f"Warning: No mask values found in CC mask for subject {subject_id}")
  231. return
  233. CC_mode = refined_mode_estimation(NM_data[CC_mask])
  234. NM_CNR = (NM_data - CC_mode) / CC_mode
  235. nb.save(nb.Nifti1Image(NM_CNR, affine=NM_nii.affine), NM_CNR_path)
  236. print(f"CNR map saved to {NM_CNR_path}")
  238. # Move NM-CNR image to MNI space
  239. transformed_image = ants.apply_transforms(
  240. fixed=ants.image_read(MNI_template_path),
  241. moving=ants.image_read(NM_CNR_path),
  242. transformlist=[
  243. warp_T1_to_MNI_path,
  244. affine_T1_to_MNI_path,
  245. transform_NM_to_T1_path
  246. ],
  247. whichtoinvert=[False, False, False],
  248. interpolator="linear"
  249. )
  251. transformed_image.to_filename(NM_CNR_MNI_space_path)
  252. print(f"NM-CNR image transformed to MNI space saved at {NM_CNR_MNI_space_path}")
  254. def refined_mode_estimation(array, cut_down=True, bw_method="scott"):
  255. """
  256. Estimate the mode of an array using refined KDE-based optimization.
  257. """
  258. kernel = kde(array, cut_down=cut_down, bw_method=bw_method)
  259. x0 = array[np.argmax(kernel.pdf(array))]
  260. results = minimize(lambda x: -kernel(x)[0], x0=x0, bounds=[[array.min(), array.max()]])
  261. return results.x[0]
  263. def kde(array, cut_down=True, bw_method="scott"):
  264. """
  265. Compute the kernel density estimation (KDE) for a given array.
  266. """
  267. if cut_down:
  268. bins, counts = np.unique(array, return_counts=True)
  269. threshold = bins[counts > counts.mean()]
  270. array = array[(threshold.min() < array) & (array < threshold.max())]
  271. return gaussian_kde(array, bw_method=bw_method)
  273. def save_SNVTA_results(NM_CNR_path, midbrain_NM_space_path, results_dir, subject_id):
  274. """
  275. Save mean and standard deviation of values in SNVTA mask using a file lock.
  276. This prevents race conditions when multiple processes are writing to the same file.
  277. """
  278. os.makedirs(results_dir, exist_ok=True)
  279. results_file = os.path.join(results_dir, "NM_CNR_SNVTA_Results.xlsx")
  280. lock_file = results_file + ".lock" # Lock file to prevent corruption
  282. NM_CNR_img = nb.load(NM_CNR_path).get_fdata()
  283. midbrain_img = nb.load(midbrain_NM_space_path).get_fdata()
  284. mask_values = NM_CNR_img[midbrain_img == 2]
  286. mean_value = np.mean(mask_values)
  287. std_value = np.std(mask_values)
  289. # Use FileLock to ensure only one process writes to the file at a time
  290. with FileLock(lock_file):
  291. results_df = pd.read_excel(results_file) if os.path.exists(results_file) else pd.DataFrame(columns=["Subject ID", "Mean", "Standard Deviation"])
  293. if subject_id in results_df["Subject ID"].values:
  294. results_df.loc[results_df["Subject ID"] == subject_id, ["Mean", "Standard Deviation"]] = [mean_value, std_value]
  295. else:
  296. results_df = pd.concat([results_df, pd.DataFrame([{"Subject ID": subject_id, "Mean": mean_value, "Standard Deviation": std_value}])], ignore_index=True)
  298. results_df.to_excel(results_file, index=False)
  300. print(f"Results saved to {results_file} (processed subject {subject_id})")

image_processing_functions.py at commit d7dd6e8, under CC-BY-NC-4.0 · at the source

Overview

Authors: Luke James Vano1,2,3,4, Jan Sedlacik2,3,5, Stephen John Kaar1,2,3,6, Grazia Rutigliano1,2,3, Richard Carr1, Alaine Berry2,3,5, Ben Statton2,3,5, Amir Fazlollahi7,8, Mattia Veronese9,10, Oliver David Howes1,3, Robert Ali McCutcheon1,11,12
ORCID iDs: Luke James Vano, Mattia Veronese
  1. Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, SE5 8AF, United Kingdom
  2. Psychiatric Imaging Group, MRC Laboratory of Medical Sciences, Hammersmith Hospital, London, W12 0HS, United Kingdom
  3. Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, SW7 5NH, United Kingdom
  4. South London and Maudsley NHS Foundation Trust, London, SE5 8AZ, United Kingdom
  5. Mansfield Centre for Innovation - MR Facility, MRC Laboratory of Medical Sciences, Hammersmith Hospital, London, W12 0HS, United Kingdom
  6. Division of Psychology and Mental Health, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, M13 9PL, United Kingdom
  7. Department of Radiology, Royal Melbourne Hospital, University of Melbourne, Parkville, VIC, 3050, Australia
  8. Faculty of Health, Medicine and Behavioural Sciences, Queensland Brain Institute, The University of Queensland, St Lucia, QLD, 4067, Australia
  9. Department of Neuroimaging, King's College London, London, SE5 8AF, United Kingdom
  10. Department of Information Engineering, University of Padua, 35131, Padova, PD, Italy
  11. Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
  12. Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, OX3 7JX, United Kingdom
Institutions: King's College London (United Kingdom); South London and Maudsley NHS Foundation Trust (United Kingdom); Hammersmith Hospital (United Kingdom); Imperial College London (United Kingdom); Medical Research Council (United Kingdom); University of Manchester (United Kingdom); The Royal Melbourne Hospital (Australia); The University of Queensland (Australia); The University of Melbourne (Australia); University of Padua (Italy); Oxford Health NHS Foundation Trust (United Kingdom); Warneford Hospital (United Kingdom); University of Oxford (United Kingdom)
Journal: Schizophrenia bulletin, volume 52, issue 4, article sbag045
Dates: received 4 August 2025; accepted 9 March 2026; published online 23 July 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/schbul/sbag045 · PMID 42487413 · PMCID PMC13391635 · OpenAlex W7170141545
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), PET / SPECT (modality), human (organism), schizophrenia / psychosis (population), developmental (subfield)
Methods: Statistics, fMRI & imaging, Preprocessing
Keywords: cortex, iron, basal ganglia, dopamine, neuromelanin
MeSH: Cerebral Cortex*, Corpus Striatum*, Dopamine*, Iron*, Schizophrenia*, Adolescent, Adult, Case-Control Studies, Dihydroxyphenylalanine, Dopaminergic Imaging, Female, Humans, Magnetic Resonance Imaging, Male, Melanins, Middle Aged, Positron-Emission Tomography, Young Adult (* major topic)
Topic: Schizophrenia research and treatment (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Wellcome Trust (224625/Z/21/Z, 094849/Z/10/Z); Wellcome Trust Clinical Research Career Development Fellowship (224625/Z/21/Z); National Institute for Health and Care Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London and Sumitomo Pharma America, Inc (NCT04038957); NIHR Oxford Health Biomedical Research Centre; Medical Research Council-UK (MR/W005557/1, MC_U120097115, MR/V013734/1); National Institute for Health and Care Research; National Institute for Health Research (NIHR); Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London and Sumitomo Pharma America, Inc (NCT04038957)
Citations: not cited yet (Europe PMC); 69 references in the paper

Abstract

Background and Hypothesis: Elevated postmortem iron in Brodmann areas 10-11 has recently been linked to schizophrenia. Although in vivo studies have linked subcortical iron abnormalities to disease-related striatal hyperdopaminergia, in vivo cortical iron alterations have not been previously examined. We therefore used neuroimaging to test whether cortical iron is elevated in individuals with schizophrenia and whether this correlated with mesostriatal dopamine function.

Study Design: We acquired quantitative susceptibility mapping magnetic resonance imaging (MRI) to measure magnetic susceptibility (χ), a marker of iron, in 149 participants aged 18-45 (73 with schizophrenia and 76 matched healthy controls). Subsets of patients with schizophrenia underwent [18F]-DOPA PET to estimate striatal dopamine synthesis capacity (n = 39) and neuromelanin-sensitive MRI of the dopaminergic midbrain (n = 68), as neuromelanin is a byproduct of dopamine synthesis.

Study Results: Primary analyses showed no significant case–control differences in χ in the whole cortex (P = .675) or Brodmann areas 10-11 (P = .537). Exploratory analyses examined χ for 360 cortical regions, correcting for multiple comparisons. Two left temporo-parieto-occipital junction regions showed significantly elevated χ in schizophrenia: the posterior temporo-parieto-occipital junction the posterior temporo-parieto-occipital junction (d = 0.752, P < .001) and the superior temporal visual area (d = 0.638, P = .034). Mean χ across these regions inversely correlated with dopamine synthesis capacity in the associative (r = –0.37, P = .048) and limbic (r = –0.34, P = .048) striatum, and with neuromelanin-sensitive MRI values in the dopaminergic midbrain (r = –0.35, P = .005), corrected for multiple comparisons.

Conclusions: This study provides the first in vivo evidence of elevated cortical iron in schizophrenia and links this to mesostriatal dopamine dysfunction.

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 2 matches between paragraphs and lines of code.

lukevano/KCL_Neuromelanin-MRI

License: CC-BY-NC-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d7dd6e8025c281287e76e952567b7371dbcd4b2e, 26 June 2025
Languages: Python (2)
Size: 57 files, 2 scripts
Software Heritage: not archived
Found in: the text, “NM-MRI Preprocessing”
Holds: README, license file, environment (environment.yml, requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: ANTs (1 file), FSL (1 file), NiBabel (1 file), Nipype (1 file), NumPy (1 file), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/lukevano/KCL_Neuromelanin-MRI

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;
  • 2 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 5 keywords, 18 MeSH terms, 8 funders, 66 references.

Cite

This paper

Vano, L. J., Sedlacik, J., Kaar, S. J., Rutigliano, G., Carr, R., Berry, A., Statton, B., Fazlollahi, A., Veronese, M., Howes, O. D., & McCutcheon, R. A. (2026). Cortical Iron and Mesostriatal Dopamine Function in Schizophrenia: A Positron Emission Tomography and Magnetic Resonance Imaging Study. Schizophrenia bulletin, 52(4), sbag045. https://doi.org/10.1093/schbul/sbag045

BibTeX

@article{vano2026cortical,
author = {Vano, Luke James and Sedlacik, Jan and Kaar, Stephen John and Rutigliano, Grazia and Carr, Richard and Berry, Alaine and Statton, Ben and Fazlollahi, Amir and Veronese, Mattia and Howes, Oliver David and McCutcheon, Robert Ali},
title = {{Cortical Iron and Mesostriatal Dopamine Function in Schizophrenia: A Positron Emission Tomography and Magnetic Resonance Imaging Study}},
journal = {Schizophrenia bulletin},
year = {2026},
month = jul,
volume = {52},
number = {4},
pages = {sbag045},
publisher = {Oxford University Press},
issn = {1787-9965},
doi = {10.1093/schbul/sbag045},
url = {https://doi.org/10.1093/schbul/sbag045},
pmid = {42487413},
pmcid = {PMC13391635}
}

RIS

TY - JOUR
AU - Vano, Luke James
AU - Sedlacik, Jan
AU - Kaar, Stephen John
AU - Rutigliano, Grazia
AU - Carr, Richard
AU - Berry, Alaine
AU - Statton, Ben
AU - Fazlollahi, Amir
AU - Veronese, Mattia
AU - Howes, Oliver David
AU - McCutcheon, Robert Ali
TI - Cortical Iron and Mesostriatal Dopamine Function in Schizophrenia: A Positron Emission Tomography and Magnetic Resonance Imaging Study
T2 - Schizophrenia bulletin
J2 - Schizophr Bull
PY - 2026
DA - 2026/07/01
VL - 52
IS - 4
SP - sbag045
SN - 1787-9965
PB - Oxford University Press
DO - 10.1093/schbul/sbag045
UR - https://doi.org/10.1093/schbul/sbag045
LA - en
ER -

CSL-JSON

{
"id": "10.1093/schbul/sbag045",
"type": "article-journal",
"title": "Cortical Iron and Mesostriatal Dopamine Function in Schizophrenia: A Positron Emission Tomography and Magnetic Resonance Imaging Study",
"container-title": "Schizophrenia bulletin",
"author": [
{
"family": "Vano",
"given": "Luke James"
},
{
"family": "Sedlacik",
"given": "Jan"
},
{
"family": "Kaar",
"given": "Stephen John"
},
{
"family": "Rutigliano",
"given": "Grazia"
},
{
"family": "Carr",
"given": "Richard"
},
{
"family": "Berry",
"given": "Alaine"
},
{
"family": "Statton",
"given": "Ben"
},
{
"family": "Fazlollahi",
"given": "Amir"
},
{
"family": "Veronese",
"given": "Mattia"
},
{
"family": "Howes",
"given": "Oliver David"
},
{
"family": "McCutcheon",
"given": "Robert Ali"
}
],
"container-title-short": "Schizophr Bull",
"volume": "52",
"issue": "4",
"page": "sbag045",
"DOI": "10.1093/schbul/sbag045",
"PMID": "42487413",
"PMCID": "PMC13391635",
"ISSN": "1787-9965",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/schbul/sbag045",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
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:10.1038/s41386-026-02426-x [code]
A frontotemporal dementia-like phenotype in schizophrenia: links to striatal dopamine and iron accumulation.
Journal: Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
In common: pandas, SciPy, NumPy, schizophrenia / psychosis, PET / SPECT, structural MRI / diffusion, 21 references, author Luke James Vano
[2] doi:10.1162/imag.a.1276 [code]
High-resolution whole-brain magnetic resonance spectroscopic imaging in youth at risk for psychosis.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: ANTs, FSL, NiBabel, 3 other tools, schizophrenia / psychosis, 4 references
[3] doi:10.1038/s41593-026-02359-0 [code]
The cross-site reproducibility of MRI morphometric phenotypes in psychiatric disorders.
Journal: Nature neuroscience
In common: FSL, NiBabel, pandas, 2 other tools, schizophrenia / psychosis, structural MRI / diffusion, 4 references
[4] doi:10.1002/nbm.70353 [code]
Automated Surface-Based Segmentation of Deep Gray Matter Regions Based on Diffusion Tensor Images Reveals Unique Age Trajectories Over the Healthy Lifespan.
Journal: NMR in biomedicine
In common: ANTs, FSL, NiBabel, 2 other tools, structural MRI / diffusion, 3 references
[5] doi:10.1162/imag.a.1245 [code]
Towards precision EEG connectomics: Evaluating the benefits of dense sampling.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: Nipype, ANTs, FSL, 4 other tools, 1 reference
[6] doi:10.1038/s41398-026-04157-5 [code]
Association of glymphatic function with 40-Hz neural oscillations, systemic metabolic markers, and cognitive performance in healthy aging adults: An EEG and MRI study.
Journal: Translational psychiatry
In common: Nipype, ANTs, FSL, 4 other tools, structural MRI / diffusion
[7] doi:10.1002/epi.70296 [code]
Fully automated three-dimensional deep learning-based magnetic resonance imaging segmentation of brain cavities in epilepsy surgery.
Journal: Epilepsia
In common: Nipype, ANTs, FSL, 4 other tools, structural MRI / diffusion
[8] doi:10.3389/fnins.2026.1841093 [code]
Brain protein burden is related to intravoxel incoherent motion: PET-MR imaging study.
Journal: Frontiers in neuroscience
In common: ANTs, FSL, NiBabel, 3 other tools, PET / SPECT, structural MRI / diffusion, 1 reference
[9] doi:10.1162/imag.a.1347 [code]
Neural and behavioural correlates of theory of mind reasoning in five-year-old children born preterm.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: Nipype, ANTs, FSL, 4 other tools
[10] doi:10.1016/j.dcn.2026.101765 [code]
Fusiform face area development correlates with development in higher-order social brain regions.
Journal: Developmental cognitive neuroscience
In common: Nipype, ANTs, FSL, 4 other tools

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.