Cortical morphometric inverse divergence in attention-deficit/hyperactivity disorder correlates with cell-type-specific, laminar-specific and developmental transcriptomic signatures.

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] § Methods › Construction of the MIND network ↔ MIND.py, lines 8–49 · score 0.74 · cortical thickness, surface area, MIND network, CT, SA, vertex
[2] § Methods › Validation of reproducibility ↔ MIND.py, lines 8–49 · score 0.57 · cortical thickness, surface area, MIND network, volume, brain

Paper

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The authors' code

Python · 52 lines · 1.9 KB · no license · 2 matches

import sys
import os
import numpy as np
import pandas as pd
from MIND_helpers import calculate_mind_network, is_outlier
from get_vertex_df import get_vertex_df
def compute_MIND(surf_dir, features, parcellation, filter_vertices=False, resample=False, n_samples = 4000):
vertex_data, regions, features_used = get_vertex_df(surf_dir, features, parcellation)
'''
Filter the data, do some QC checks here.
To double check everything, please look at histograms of individual features to make sure everything looks ok.
'''
columns = ['Label'] + features_used
feature_conv_dict = dict(zip(list(features), list(features_used)))
#The filter_vertices parameter determines you want to filter out all the non-biologically feasible vertices (i.e. any of volume, surface area or cortical thickness equalling zero)
if filter_vertices == True:
if 'CT' in features:
vertex_data = vertex_data.drop(vertex_data[vertex_data[feature_conv_dict['CT']] == 0].index)
if 'Vol' in features:
vertex_data = vertex_data.drop(vertex_data[vertex_data[feature_conv_dict['Vol']] == 0].index)
if 'SA' in features:
vertex_data = vertex_data.drop(vertex_data[vertex_data[feature_conv_dict['SA']] == 0].index)
vertex_data = vertex_data[columns]
#standardize across the brain for each feature to get each dimension to roughly the same scale.
for x in features_used:
vertex_data[x] = (vertex_data[x] - vertex_data[x].mean())/vertex_data[x].std()
#Drop outliers. This drops vertices with an MAD score in ANY of the used features. Can be customized.
# z_score_threshhold = 7
# outliers_per_features = np.array([is_outlier(vertex_data[x].values, z_score_threshhold) for x in features_used]).T
# vertex_data = vertex_data.loc[np.sum(outliers_per_features, axis = 1) == 0]
print('Computing MIND...')
#calculate MIND network
MIND = calculate_mind_network(vertex_data, features_used, regions, resample=resample, n_samples = n_samples)
print('Done!')
return MIND

MIND.py at commit 0d33445, no license · at the source

Overview

Authors: Yexian Zeng^1,2,3,4,5, Li Yang^1,2,3,4,5, Zaixu Cui^6,7, Qingjiu Cao^1,2,3,4,5

ORCID iDs: Li Yang

Peking University Sixth Hospital, Beijing, China
Peking University Institute of Mental Health, Beijing, China
NHC Key Laboratory of Mental Health, Peking University, Beijing, China
National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
Beijing Key Laboratory for Big Data Innovative Application of Child and Adolescent Mental Disorders, Beijing, China
Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 102206, China
Chinese Institute for Brain Research, Beijing, 102206, China

Institutions: Peking University (China); Peking University Sixth Hospital (China); Chinese Academy of Medical Sciences & Peking Union Medical College (China); Chinese Institute for Brain Research (China)

Journal: Psychological medicine, volume 56, article e136

Dates: received 18 November 2025; accepted 9 February 2026; published online 11 May 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1017/s003329172610405x · PMID 42112549 · PMCID PMC13161819 · OpenAlex W7160859872

Open access: hybrid, a free copy (OpenAlex)

Status: code verified

Categories: genetics / omics (modality), structural MRI / diffusion (modality), human (organism), ADHD (population), developmental (subfield)

Methods: Connectivity, Statistics, Machine learning, Preprocessing, Graphs, fMRI & imaging, Smoothing, state filtering, decompositions

Keywords: ADHD combined subtype, cell-type-specific signatures, cortical gene expression, cortical layers enrichment developmental stages assessment, morphometric inverse divergence network, transcriptional patterns

MeSH: Attention Deficit Disorder with Hyperactivity*, Cerebral Cortex*, Transcriptome*, Adolescent, Case-Control Studies, Child, Female, Humans, Male (* major topic)

Topic: Attention Deficit Hyperactivity Disorder (Psychiatry and Mental health, Medicine), according to OpenAlex

Funding: National Natural Science Foundation of China (82371548)

Citations: not cited yet (Europe PMC); 75 references in the paper

Abstract

Background: Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition with widespread brain structure alterations. However, the relationship between macroscale cortical organization and microscale molecular mechanisms remains unclear, particularly regarding the neurobiological mechanisms shared between the full ADHD cohort and its combined subtype (ADHD-C).

Methods: We analyzed 176 patients with ADHD (105 ADHD-C, 71 ADHD inattentive subtype) and 176 matched typically developing (TD) controls from the ADHD-200 dataset. Morphometric Inverse Divergence (MIND) networks quantified cortical similarity. Partial least squares (PLS) regression linked case–control MIND differences to cortical gene expression, assessing functional enrichment, cell-type specificity, and developmental trajectories.

Results: Neuroanatomically, the ADHD-C subtype exhibited widespread increases in regional MIND values, particularly in temporal and parietal cortices, reflecting greater inter-regional morphological homogeneity. PLS regression revealed that these MIND alterations were spatially correlated with a specific transcriptomic signature (PLS1+). These PLS1+ genes were significantly enriched in mitochondria-related metabolic pathways and showed distinct cortical layer specificity (notably layer V) and developmental stage specificity (from late fetal to late infancy stages). Regarding cell-type specificity, while PLS1+ genes in the full ADHD cohort were significantly enriched in excitatory and inhibitory neurons, the ADHD-C subtype showed similar but trend-level associations. Importantly, the full ADHD cohort and the ADHD-C group shared numerous PLS1-related genes and broad functional pathway enrichment commonalities.

Conclusions: This study links macroscale cortical abnormalities to microscale transcriptional regulation, with pronounced alterations in ADHD-C. The shared genetic and functional profiles between ADHD and its combined subtype underscore common pathological processes, providing novel insights into the neurodevelopmental mechanisms of ADHD.

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 2 matches between paragraphs and lines of code.

isebenius/MIND

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 0d334454eaac62e49a197801446ce7750256c882, 12 March 2024

Languages: Python (4), Jupyter (2)

Size: 15 files, 6 scripts

Software Heritage: not archived

Found in: “Data and code availability”

Holds: README, environment (requirements.txt)

Not found: license file, CITATION.cff, tests, continuous integration, documentation

Tools: NumPy (5 files), pandas (5 files), NiBabel (3 files), SciPy (3 files), Matplotlib (2 files), AFNI (1 file), FreeSurfer (1 file), Nipype (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

7 files

.ipynb_checkpoints/ABCD-MIND-checkpoint.ipynb, Jupyter, 429 lines
.ipynb_checkpoints/ABCD-MSN-and-raw-features-checkpoint.ipynb, Jupyter, 668 lines
MIND.py, Python, 52 lines, 2 matches
MIND_helpers.py, Python, 149 lines
get_vertex_df.py, Python, 203 lines
register_and_vol2surf.py, Python, 115 lines
README.md, Text, 135 lines

At the source: github.com/isebenius/MIND

SarahMorgan/Morphometric_Similarity_SZ

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 9134abfbc773c57a3a91b96612a7c8fa5969732b, 8 May 2019

Size: 7 files, 0 scripts

Software Heritage: not archived

Found in: “Data and code availability”

Holds: README, license file

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

2 files

LICENSE, License, 21 lines
README.md, Text, 10 lines

At the source: github.com/SarahMorgan/Morphometric_Similarity_SZ

frantisekvasa/rotate_parcellation

License: MIT

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: 65673ea7f47fca36b2982df669fc649b9a4bc5da, 29 June 2023

Languages: MATLAB (3), R (2)

Size: 9 files, 5 scripts

Software Heritage: archived

Found in: “Data and code availability”

Holds: README, license file

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: FreeSurfer (1 file)

Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

7 files

Matlab/centroid_extraction_sphere.m, MATLAB, 33 lines
Matlab/perm_sphere_p.m, MATLAB, 93 lines
Matlab/rotate_parcellation.m, MATLAB, 167 lines
R/perm.sphere.p.R, R, 52 lines
R/rotate.parcellation.R, R, 216 lines
LICENSE, License, 21 lines
README.md, Text, 55 lines

At the source: github.com/frantisekvasa/rotate_parcellation

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:

3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
11 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.

Data availability statement

The data included in this study are available from the corresponding author upon reasonable request.

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

Data Availability Statement

The raw neuroimaging and phenotypic data used in this study are publicly available via the ADHD-200 Consortium (http://fcon_1000.projects.nitrc.org/indi/adhd200/) and OpenNeuro (Accession Numbers: ds002424, ds005899, ds004605).

The transcriptomic data resources are available at the following links: the Allen Human Brain Atlas (https://human.brain-map.org/static/download) and BrainSpan (https://www.brainspan.org/static/download.html).

Regarding the analysis code, this study utilized established open-source pipelines. The code for calculating the MIND networks is available on GitHub (https://github.com/isebenius/MIND). The code for Partial Least Squares (PLS) analysis can be accessed at (https://github.com/SarahMorgan/Morphometric_Similarity_SZ). The scripts for spatial permutation testing (spin tests) are available at (https://github.com/frantisekvasa/rotate_parcellation).

Any additional custom scripts or intermediate data used in this study are available from the corresponding author upon reasonable request.

The data included in this study are available from the corresponding author upon reasonable request.

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

Versions

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

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 6 keywords, 9 MeSH terms, 1 funder, 74 references.

Cite

This paper

Zeng, Y., Yang, L., Cui, Z., & Cao, Q. (2026). Cortical morphometric inverse divergence in attention-deficit/hyperactivity disorder correlates with cell-type-specific, laminar-specific and developmental transcriptomic signatures. Psychological medicine, 56, e136. https://doi.org/10.1017/s003329172610405x

BibTeX

@article{zeng2026cortical, author = {Zeng, Yexian and Yang, Li and Cui, Zaixu and Cao, Qingjiu}, title = {{Cortical morphometric inverse divergence in attention-deficit/hyperactivity disorder correlates with cell-type-specific, laminar-specific and developmental transcriptomic signatures}}, journal = {Psychological medicine}, year = {2026}, month = may, volume = {56}, pages = {e136}, publisher = {Cambridge University Press}, issn = {0033-2917}, doi = {10.1017/s003329172610405x}, url = {https://doi.org/10.1017/s003329172610405x}, pmid = {42112549}, pmcid = {PMC13161819} }

RIS

TY - JOUR AU - Zeng, Yexian AU - Yang, Li AU - Cui, Zaixu AU - Cao, Qingjiu TI - Cortical morphometric inverse divergence in attention-deficit/hyperactivity disorder correlates with cell-type-specific, laminar-specific and developmental transcriptomic signatures T2 - Psychological medicine J2 - Psychol Med PY - 2026 DA - 2026/05/11 VL - 56 SP - e136 SN - 0033-2917 PB - Cambridge University Press DO - 10.1017/s003329172610405x UR - https://doi.org/10.1017/s003329172610405x LA - en ER -

CSL-JSON

{ "id": "10.1017/s003329172610405x", "type": "article-journal", "title": "Cortical morphometric inverse divergence in attention-deficit/hyperactivity disorder correlates with cell-type-specific, laminar-specific and developmental transcriptomic signatures", "container-title": "Psychological medicine", "author": [ { "family": "Zeng", "given": "Yexian" }, { "family": "Yang", "given": "Li" }, { "family": "Cui", "given": "Zaixu" }, { "family": "Cao", "given": "Qingjiu" } ], "container-title-short": "Psychol Med", "volume": "56", "page": "e136", "DOI": "10.1017/s003329172610405x", "PMID": "42112549", "PMCID": "PMC13161819", "ISSN": "0033-2917", "publisher": "Cambridge University Press", "URL": "https://doi.org/10.1017/s003329172610405x", "language": "en", "issued": { "date-parts": [ [ 2026, 5, 11 ] ] } }

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Cortical morphometric inverse divergence in attention-deficit/hyperactivity disorder correlates with cell-type-specific, laminar-specific and developmental transcriptomic signatures.

Code ↔ Paper

Paper

The authors' code

Overview

Abstract

Repositories

isebenius/MIND

SarahMorgan/Morphometric_Similarity_SZ

frantisekvasa/rotate_parcellation

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Data

Data availability statement

Data Availability Statement

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Version 1, 28 September 2026: the first record

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