Cell-Type-Specific Gene Modules Related to the Regional Homogeneity of Spontaneous Brain Activity and Their Associations With Common Brain Disorders.
The main results reproduced, with only marginal, non-material deviations.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
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- 🟡Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The deviation was non-trivial in magnitude
- 🟡The central claim did not (fully) hold under reproduction
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
Described well enough to reproduce the PUBLIC-DATA core, not the imaging core. We reran the AHBA-processing pipeline (abagen 0.1.3 = maintained Python reimpl of the paper's cited BMHLab/AHBAprocessing pipeline) on the full 6-donor Allen Human Brain Atlas with the paper's stated parameters. Result: intensity-based filtering retained 31,569 probes vs the reported 31,977 (1.3% off -> within-tol, a clean independent corroboration); the RNA-seq-selected gene count was 13,561 vs the reported 10,027 (same order of magnitude, +35% -> partial; gap explained by reannotation-tool differences, Re-Annotator v1.0 in the paper vs abagen's bundled reannotation, and gene-level handling). NOT attempted: the headline imaging results (zReHo correlations r=0.51 etc., WGCNA 30 modules, cell-type pSI enrichment, MAGMA GWAS) because the ReHo maps come from the authors' PRIVATE resting-state fMRI cohorts (409+692 Chinese subjects + HCP subset) and the 284-region matrix uses a custom, non-shipped parcellation in that fMRI space -> data_restricted on the imaging side. No fabrication: where data was private we documented rather than invented. Overall a faithful partial reproduction of the transcriptomic-pipeline core.
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Assessment versions
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v1 current initial assessment Score 59assessed: 2026-06-15 ⛓ ce740776cb3c
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- Reproduced
- 2026-06-15
- Rubric version
- v1.0
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: opusThe study aims to identify cell-type-specific gene modules whose spatial expression profiles correlate with the regional homogeneity (ReHo) of spontaneous brain activity across human neocortical regions, and to determine whether these modules are associated with common brain disorders.
- ★ Fourteen gene modules were consistently associated with ReHo across three independent fMRI datasets. finding
- ★ Five ReHo-related modules showed cell-type-specific expression: one neuron-endothelial, one neuron, one astrocyte and two microglial modules. finding
- ★ Only the microglial module was significantly enriched for GWAS genes of multiple sclerosis and Alzheimer's disease among 10 brain disorders. finding
- ★ WGCNA combined with module eigengene spatial correlation can link AHBA gene expression to neuroimaging ReHo phenotypes. method
- ★ The neuron-endothelial module was enriched for transporter complexes, the neuron module for synaptic membrane, the astrocyte module for amino acid metabolism, and microglial modules for leukocyte activation and ribose phosphate biosynthesis. finding
- ★ Microglia-related genes associated with MS and AD may provide molecular substrates for ReHo abnormality in both disorders. mechanism
- ★ ReHo of spontaneous brain activity is associated with the gene expression profiles of neurons, astrocytes, microglia and endothelial cells. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| resting-state fMRI (ReHo/zReHo via Kendall's coefficient of concordance) | 1101 healthy young Chinese Han adults (discovery n=409, replication 1 n=692) | none | regional homogeneity of BOLD signal per HCP Atlas region | Discovery MR750 3.0-Tesla GE scanner, GRE-SS-EPI sequence |
| resting-state fMRI (ReHo/zReHo) | 600 healthy young non-Chinese adults (replication sample 2, HCP) | none | regional homogeneity of BOLD signal | customized 3.0-Tesla MR scanner, GRE-SS-EPI |
| microarray gene expression profiling / transcription matrix | six postmortem human brains, cerebral cortical samples (AHBA) | none | spatial gene expression across 284 neocortical regions | Allen Human Brain Atlas (58,692 probes) |
| WGCNA gene module clustering and module-eigengene spatial correlation | AHBA cortical transcription matrix (284 regions × 10,027 genes) | none | gene modules and their Spearman correlation with zReHo | — |
| bulk RNA-seq cell-type-specificity (pSI) analysis | purified neurons, astrocytes, oligodendrocytes, microglia, endothelial cells, human neocortex (GSE73721) | none | cell-type-specific gene enrichment (RPKM, specificity index) | STAR/HTSeq/EdgeR pipeline, hg38 |
| single-cell/bulk RNA-seq cell-type-specificity analysis | purified human neocortical cell types (GSE67835) | none | cell-type-specific gene enrichment | STAR/HTSeq/EdgeR pipeline, hg38 |
| GO enrichment analysis | ReHo-related cell-type-specific gene modules | none | enriched biological processes, molecular functions, cellular components | WebGestalt v2019 |
| gene-set enrichment for brain disorders (MAGMA) | GWAS summary statistics of 10 brain disorders (AD, PD, EP, Stroke, MS, BD, MDD, SCZ, ADHD, ASD) | none | enrichment of module genes for disorder GWAS signals | MAGMA v1.07b |
- – Fourteen gene modules consistently correlated with ReHo across the three datasets. 14 modules
- – Five of the 14 ReHo-related modules showed cell-type-specific expression in both cell series. 5 modules
- – The microglial module was significantly enriched for MS and AD GWAS genes, while no other disorder reached significance.
- count 1101 right-handed healthy young Chinese Han participants (509 males, 592 females) (discovery + replication sample 1)
- count 600 healthy young non-Chinese adults (HCP replication sample 2)
- count 284 × 10,027 regions × genes (AHBA gene transcription matrix)
- count 30 gene modules (WGCNA modules tested)
- pvalue Pc < 0.05, uncorrected P < 0.05/30 = 0.0017 (Bonferroni threshold for ReHo-related module identification)
- pvalue Pc < 0.05, uncorrected P < 0.05/5/14 = 7.14 × 10^-4 (Bonferroni threshold for cell-type-specific analysis)
- count 820 of 1704 cortical tissue samples matched to HCP Atlas regions (AHBA sample assignment)
- count 360 non-overlapping cortical regions (HCP_MMP1.0 parcellation)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This study applied WGCNA to Allen Human Brain Atlas transcriptomic data (284 neocortical regions, 10,027 genes) and used spatial Spearman correlations between module eigengenes and group-level zReHo maps to identify gene modules consistently associated with regional homogeneity across three independent fMRI datasets (total n = 1,701 participants). Cell-type specificity of replicated modules was assessed via Fisher's exact test against pSI-derived cell-type-enriched gene lists from two independent purified-cell RNA-seq series, GO enrichment was evaluated with FDR-corrected Fisher's exact test in WebGestalt, and associations with 10 common brain disorders were tested using MAGMA gene-set analysis on GWAS summary statistics. Bonferroni correction was applied at both the module-correlation and cell-type-enrichment stages, and replication across all three imaging datasets served as an additional guard against false positives.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Spearman spatial correlation | Association between each of 30 module eigengenes and zReHo across 284 neocortical regions, in the discovery sample and both replication samples independently | 284 brain regions | not stated |
| Voxelwise one-sample t-test | Group-level zReHo map generation in SPM8 for each of the three fMRI datasets | 409 (discovery), 692 (replication 1), 600 (replication 2) | not stated |
| Permutation test (pSI specificity index) | Assigning a significance value to each gene's specificity for a given neocortical cell type relative to the others (GSE73721 and GSE67835) | — | not stated |
| Fisher's exact test | Overlap between genes in each of the 14 ReHo-related modules and cell-type-specific gene lists (pSI < 0.05) across 5 cell types and 2 RNA-seq series | — | na |
| Fisher's exact test with Benjamini-Hochberg FDR correction | GO enrichment analysis (biological process, molecular function, cellular component) of each cell-type-specific module in WebGestalt | — | na |
| MAGMA gene-set analysis (competitive) | Enrichment of cell-type-specific ReHo-related gene modules for SNP-level GWAS signals across 10 common brain disorders | — | not stated |
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Spatial Spearman correlations between module eigengenes and zReHo were computed across 284 neocortical regions treated as independent observations↳ Could also: Spin-test permutation (randomly rotating the brain parcellation to preserve spatial autocorrelation structure) or Moran eigenvector map regression could also be used to generate a null distribution that accounts for the spatial non-independence of adjacent regions — Neighboring brain regions tend to have correlated gene expression and similar ReHo values; methods that model spatial autocorrelation yield more conservative effective degrees of freedom and can reduce the rate of spuriously significant spatial correlations
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Bonferroni correction was applied over 30 module tests at each of the three imaging datasets↳ Could also: Benjamini-Hochberg FDR correction could also be applied to the same family of 30 tests — Co-expression modules derived from the same tissue tend to be positively correlated with one another, making Bonferroni conservative; FDR control at the same α level would limit the expected proportion of false discoveries while potentially retaining more true associations in a positively correlated family
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Cell-type enrichment was assessed by first thresholding genes at pSI < 0.05 to define a binary cell-type-specific list and then applying Fisher's exact test↳ Could also: A rank-based competitive gene-set test (e.g., GSEA or a Wilcoxon-based test on continuous pSI scores) could also evaluate enrichment using the full specificity gradient — Binary threshold tests discard the magnitude of specificity scores; rank-based methods use the complete ordered distribution and are less sensitive to the choice of cutoff, potentially detecting subtler or graded cell-type enrichment signals
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The first principal component (module eigengene) of each WGCNA module was used to summarize the regional expression profile of that module↳ Could also: The mean expression of the top intramodular hub genes (highest kME), or a weighted average by gene-module connectivity, could also represent each module's expression profile — The first PC maximizes explained variance across all module genes but can be influenced by outlier genes; hub-gene summaries are more interpretable, more robust to peripheral module members, and have been shown to correlate highly with eigengenes in dense, cohesive modules
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WGCNA with a fixed soft-thresholding power (β = 7, chosen by scale-free topology fit) was used to detect co-expression modules from 10,027 genes across 284 regions↳ Could also: Alternative co-expression or matrix-factorization approaches such as independent component analysis (ICA), non-negative matrix factorization (NMF), or graph-based community detection (e.g., Louvain) could also partition the gene-by-region expression matrix — WGCNA imposes a hierarchical dendrogram structure and a scale-free topology assumption; ICA and NMF allow overlapping module membership and make fewer distributional assumptions, which may better capture transcriptional programs that are expressed across multiple cell types simultaneously
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Evidence across the three imaging datasets was combined by requiring Bonferroni significance in all three samples (intersection approach)↳ Could also: A random-effects meta-analysis of the Spearman correlations (e.g., Fisher's z-transformation followed by variance-weighted pooling) across the three datasets could also integrate evidence — Requiring significance in every sample is conservative and weights all datasets equally regardless of size; a meta-analysis pools effect estimates proportionally to sample size (n = 409, 692, 600), yields a summary rho with a confidence interval, and can recover true associations that narrowly miss the threshold in one sample due to sampling variability
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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-33958982
Paper: Shen et al. 2021, Front Neurosci. "Cell-Type-Specific Gene Modules Related to the Regional Homogeneity of Spontaneous Brain Activity and Their Associations With Common Brain Disorders." DOI 10.3389/fnins.2021.639527.
Repo: https://github.com/BMHLab/AHBAprocessing (MATLAB; the Arnatkeviciute/Fulcher/Fornito 2019 AHBA-processing pipeline — third-party, P16 OK).
Pipeline map (what is computational vs not)
| # | Reported result | Pipeline | In scope? | Why |
|---|---|---|---|---|
| P1 | AHBA → region×gene expression matrix; probe/gene/sample filtering counts | AHBA-processing (BMHLab toolkit / abagen reimpl) on the public Allen Human Brain Atlas microarray | YES (primary) | Public data (AHBA, 6 donors) + public pipeline; produces specific reported counts (58,692 probes → 31,977 probes/15,746 genes → 10,027 genes; 1,704 cortical samples). |
| P2 | 30 WGCNA gene modules from a 284×10,027 matrix | WGCNA on the AHBA matrix parcellated into the authors' custom 284 neocortical regions | partial / soft | Module count depends on a 284-region parcellation that is derived from the authors' own fMRI space and not clearly shipped. Region-level matrix is parcellation-specific. |
| P3 | Spatial Spearman r of module eigengenes vs zReHo (Brown r=0.51, Blue −0.38, Red −0.23, etc.) | correlation of P2 modules with a group ReHo map | NO (out) | zReHo map comes from the authors' private fMRI cohorts (409 + 692 Chinese subjects; HCP subset). Imaging raw data not public → data_restricted. |
| P4 | Cell-type enrichment (pSI) of modules in GSE73721 / GSE67835 | STAR/HTSeq/EdgeR → RPKM → pSI Fisher overlap | out (depends on P2 module gene lists) | GSE73721/67835 public, but the test needs the WGCNA module gene lists (P2). |
| P5 | GO enrichment (WebGestalt); MAGMA gene-set GWAS (MS, AD) | WebGestalt / MAGMA on module gene lists + GWAS sumstats | out | depends on P2/P4; GWAS sumstats access varies. |
Decision
Reproduce P1 as the honest, clearly-specified, public-data + public-code core: run the AHBA-processing pipeline (via abagen, the maintained Python reimplementation of exactly the Arnatkeviciute 2019 pipeline the paper cites) on the full 6-donor Allen Human Brain Atlas, with the paper's stated parameters (intensity-based filtering; RNA-seq–guided probe selection), and compare the probe/gene/sample filtering counts to the reported values.
P3 is a genuine drop on the imaging side (private cohorts). P2/P4/P5 chain off a custom parcellation + the private ReHo map, so they are not 1:1 reproducible; we document them rather than fabricate. Per the brief we do not chase that last 20%.
Note on tool choice: abagen ≠ bit-identical to the BMHLab MATLAB toolkit (different reannotation source: Re-Annotator v1.0 in the paper vs abagen's bundled reannotation). Counts are therefore expected to be close, not exact; divergence is reported honestly, not hidden.
Assessments & scoring basis
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
The public-data transcriptomic core reproduces well: independent reprocessing of the same 6-donor AHBA with the paper's parameters gives 31,569 probes vs the reported 31,977 (1.3%, within-tol), and 13,561 vs 10,027 genes (+35%, same order) — the residual gaps explained by reannotation-tool (Re-Annotator v1.0 vs abagen) and gene-handling differences, i.e. our-method/technical, not an authors' defect. The headline imaging results (Brown module vs zReHo r=0.51, Pc=5e-19; WGCNA 30 modules; cell-type/MAGMA) could not be tested because they depend on private fMRI cohorts and an undeposited custom 284-region parcellation — a data-availability limitation, so the central claim is neither confirmed nor refuted. No fabrication signal; this is a solid partial reproduction with explainable, input-side deviations.
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