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Gray matter volume alterations in adolescents with ADHD are associated with cell type-specific transcriptional signatures.

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  1. [1] § Methods › Gene expression data preprocessing ↔ alleninf/scripts.py, lines 33–107 · score 0.74 · MNI coordinates, Allen Human Brain, available gene expression, radius, probes, discarding

Paper

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The authors' code

Python · 111 lines · 5.5 KB · BSD-3-Clause · 1 match

  1. #!/usr/bin/env python
  2. import argparse
  3. import os
  4. import numpy as np
  5. import pandas as pd
  6. import nibabel as nb
  8. from alleninf.api import get_probes_from_genes,\
  9. get_expression_values_from_probe_ids, get_mni_coordinates_from_wells
  10. from alleninf.data import get_values_at_locations, combine_expression_values
  11. from alleninf.analysis import fixed_effects, approximate_random_effects,\
  12. bayesian_random_effects
  15. def nifti_file(string):
  16. if not os.path.exists(string):
  17. msg = "%r does not exist" % string
  18. raise argparse.ArgumentTypeError(msg)
  19. try:
  20. nii = nb.load(string)
  21. except IOError as e:
  22. raise argparse.ArgumentTypeError(str(e))
  23. except:
  24. msg = "%r is not a nifti file" % string
  25. raise argparse.ArgumentTypeError(msg)
  26. else:
  27. if len(nii.shape) == 4 and nii.shape[3] > 1:
  28. msg = "%r is four dimensional" % string
  29. raise argparse.ArgumentTypeError(msg)
  30. return string
  33. def main():
  34. parser = argparse.ArgumentParser(
  35. description="Compare a statistical map with gene expression patterns from Allen Human Brain Atlas.")
  36. parser.add_argument(
  37. "stat_map", help="Unthresholded statistical map in the form of a 3D NIFTI file (.nii or .nii.gz) in MNI space.", type=nifti_file)
  38. parser.add_argument("gene_name", help="Name of the gene you want to compare your map with. For list of all available genes see: "
  39. "http://help.brain-map.org/download/attachments/2818165/HBA_ISH_GeneList.pdf?version=1&modificationDate=1348783035873.",
  40. type=str)
  41. parser.add_argument("--inference_method", help="Which model to use: fixed - fixed effects, approximate_random - approximate random effects (default), "
  42. "bayesian_random - Bayesian hierarchical model (requires PyMC3).",
  43. default="approximate_random")
  44. parser.add_argument("--n_samples", help="(Bayesian hierarchical model) Number of samples for MCMC model estimation (default 2000).",
  45. default=2000, type=int)
  46. parser.add_argument("--n_burnin", help="(Bayesian hierarchical model) How many of the first samples to discard (default 500).",
  47. default=500, type=float)
  48. parser.add_argument("--probes_reduction_method", help="How to combine multiple probes: average (default) or pca - use first principal component (requires scikit-learn).",
  49. default="average")
  50. parser.add_argument("--mask", help="Explicit mask for the analysis in the form of a 3D NIFTI file (.nii or .nii.gz) in the same space and "
  51. "dimensionality as the stat_map. If not specified an implicit mask (non zero and non NaN voxels) will be used.",
  52. type=nifti_file)
  53. parser.add_argument("--radius", help="Radius in mm of of the sphere used to average statistical values at the location of each probe (default: 4mm).",
  54. default=4, type=float)
  55. parser.add_argument("--probe_exclusion_keyword", help="If the probe name includes this string the probe will not be used.",
  56. type=str)
  58. args = parser.parse_args()
  60. print "Fetching probe ids for gene %s" % args.gene_name
  61. probes_dict = get_probes_from_genes(args.gene_name)
  62. print "Found %s probes: %s" % (len(probes_dict), ", ".join(probes_dict.values()))
  64. if args.probe_exclusion_keyword:
  65. probes_dict = {probe_id: probe_name for (probe_id, probe_name) in probes_dict.iteritems() if not args.probe_exclusion_keyword in probe_name}
  66. print "Probes after applying exclusion cryterion: %s" % (", ".join(probes_dict.values()))
  68. print "Fetching expression values for probes %s" % (", ".join(probes_dict.values()))
  69. expression_values, well_ids, donor_names = get_expression_values_from_probe_ids(
  70. probes_dict.keys())
  71. print "Found data from %s wells sampled across %s donors" % (len(well_ids), len(set(donor_names)))
  73. print "Combining information from selected probes"
  74. combined_expression_values = combine_expression_values(
  75. expression_values, method=args.probes_reduction_method)
  77. print "Translating locations of the wells to MNI space"
  78. mni_coordinates = get_mni_coordinates_from_wells(well_ids)
  80. print "Checking values of the provided NIFTI file at well locations"
  81. nifti_values = get_values_at_locations(
  82. args.stat_map, mni_coordinates, mask_file=args.mask, radius=args.radius, verbose=True)
  84. # preparing the data frame
  85. names = ["NIFTI values", "%s expression" % args.gene_name, "donor ID"]
  86. data = pd.DataFrame(np.array(
  87. [nifti_values, combined_expression_values, donor_names]).T, columns=names)
  88. data = data.convert_objects(convert_numeric=True)
  89. len_before = len(data)
  90. data.dropna(axis=0, inplace=True)
  91. nans = len_before - len(data)
  92. if nans > 0:
  93. print "%s wells fall outside of the mask" % nans
  95. if args.inference_method == "fixed":
  96. print "Performing fixed effect analysis"
  97. fixed_effects(data, ["NIFTI values", "%s expression" % args.gene_name])
  99. if args.inference_method == "approximate_random":
  100. print "Performing approximate random effect analysis"
  101. approximate_random_effects(
  102. data, ["NIFTI values", "%s expression" % args.gene_name], "donor ID")
  104. if args.inference_method == "bayesian_random":
  105. print "Fitting Bayesian hierarchical model"
  106. bayesian_random_effects(
  107. data, ["NIFTI values", "%s expression" % args.gene_name], "donor ID", args.n_samples, args.n_burnin)
  110. if __name__ == '__main__':
  111. main()

scripts.py at commit bc6c8f4, under BSD-3-Clause · at the source

Overview

Authors: Meng Chen1, Renhao Zhang2, Zhongtian Chen3, Kai Wu4, Hui Wang3, Haitao Yu3
  1. Department of Medical lmaging, Luoyang Maternal and Child Health Hospital, Luoyang, China
  2. Department of Neurosurgery, First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China
  3. Department of Pediatric Surgery, Luoyang Maternal and Child Health Hospital, Luoyang, China
  4. Department of Pediatrics, Huangshan City People's Hospital, Huangshan City, China
Institutions: Liuyang City Maternal and Child Health Hospital (China); First Affiliated Hospital of Bengbu Medical College (China)
Journal: Frontiers in neurology, volume 17, article 1815799
Dates: received 23 February 2026; accepted 7 May 2026; published online 29 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fneur.2026.1815799 · PMID 42293076 · PMCID PMC13260793 · OpenAlex W7162778401
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), ADHD (population)
Methods: Statistics, Machine learning, Preprocessing, fMRI & imaging, Smoothing, state filtering, decompositions
Keywords: attention-deficit/hyperactivity disorder, enrichment analyses, gray matter volume, machine learning, transcriptional
Topic: Attention Deficit Hyperactivity Disorder (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: University of Pennsylvania
Citations: not cited yet (Europe PMC); 30 references in the paper

Abstract

Objective: Attention-deficit/hyperactivity disorder (ADHD) is characterized by atypical brain development, yet the molecular and cellular mechanisms underlying its characteristic gray matter volume (GMV) alterations remain poorly understood. This study aimed to integrate neuroimaging and transcriptomic data to identify cell-type-specific transcriptional signatures associated with GMV changes in adolescents with ADHD.

Methods: Voxel-based morphometry was performed on structural MRI data from 27 adolescents with ADHD and 34 typically developing (TD) controls to map regional GMV differences. The spatial pattern of these alterations was then correlated with whole-brain gene expression profiles from the Allen Human Brain Atlas using partial least squares (PLS) regression. Functional and cell-type enrichment analyses were conducted on significant gene sets. Finally, three machine-learning models (support vector machine, random forest, and decision tree) were developed to evaluate the diagnostic utility of GMV changes.

Results: Increased GMV was observed in the bilateral precuneus, and decreased GMV was found in the left middle occipital gyrus and orbital part of the right inferior frontal gyrus in ADHD compared to TD. The spatial distribution of these GMV changes was significantly correlated with a specific gene expression pattern. Functional enrichment analysis revealed that positively correlated genes were involved in fundamental cellular processes, while negatively correlated genes were associated with synaptic organization and brain development. Cell-type analysis demonstrated significant enrichment of positively correlated genes in microglia, and negatively correlated genes in excitatory and inhibitory neurons. Random Forest achieved the highest accuracy in distinguishing between ADHD and TD (AUC = 0.871 ± 0.029).

Conclusion: In summary, this study provides unique insights into the brain structural development of attention-deficit/hyperactivity disorder (ADHD) and offers new perspectives for the future diagnosis and treatment of ADHD.

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 1 match between paragraphs and lines of code.

chrisfilo/alleninf

License: BSD-3-Clause
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: bc6c8f41f84e420b2d0009ae207eced0f5324017, 7 September 2018
Languages: Python (8)
Size: 15 files, 8 scripts
Software Heritage: archived
Found in: the text, “Gene expression data preprocessing”
Holds: README, license file, environment (requirements.txt, setup.py)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (7 files), pandas (5 files), NiBabel (4 files), scikit-learn (3 files), SciPy (3 files), Matplotlib (2 files), seaborn (2 files), PyMC (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
10 files
At the source: github.com/chrisfilo/alleninf

The paper's code and data availability statement is in the Data section.

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 8 scripts, each with its path and the digest of its content;
  • 1 match 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

Datasets cited

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: The data from the Penn Longitudinal Executive Functioning in Adolescent Development (Penn LEAD) study used in this research are publicly available on OpenNeuro (https://openneuro.org/datasets/ds007116). All supporting data (e.g., statistical/analytic code) are available upon reasonable request.

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

Versions

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Version 2, 28 September 2026

  • Funding: added University of Pennsylvania

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 5 keywords, 30 references.

Cite

This paper

Chen, M., Zhang, R., Chen, Z., Wu, K., Wang, H., & Yu, H. (2026). Gray matter volume alterations in adolescents with ADHD are associated with cell type-specific transcriptional signatures. Frontiers in neurology, 17, 1815799. https://doi.org/10.3389/fneur.2026.1815799

BibTeX

@article{chen2026gray,
author = {Chen, Meng and Zhang, Renhao and Chen, Zhongtian and Wu, Kai and Wang, Hui and Yu, Haitao},
title = {{Gray matter volume alterations in adolescents with ADHD are associated with cell type-specific transcriptional signatures}},
journal = {Frontiers in neurology},
year = {2026},
month = may,
volume = {17},
pages = {1815799},
publisher = {Frontiers Media SA},
issn = {1664-2295},
doi = {10.3389/fneur.2026.1815799},
url = {https://doi.org/10.3389/fneur.2026.1815799},
pmid = {42293076},
pmcid = {PMC13260793}
}

RIS

TY - JOUR
AU - Chen, Meng
AU - Zhang, Renhao
AU - Chen, Zhongtian
AU - Wu, Kai
AU - Wang, Hui
AU - Yu, Haitao
TI - Gray matter volume alterations in adolescents with ADHD are associated with cell type-specific transcriptional signatures
T2 - Frontiers in neurology
J2 - Front Neurol
PY - 2026
DA - 2026/05/29
VL - 17
SP - 1815799
SN - 1664-2295
PB - Frontiers Media SA
DO - 10.3389/fneur.2026.1815799
UR - https://doi.org/10.3389/fneur.2026.1815799
LA - en
ER -

CSL-JSON

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