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Transdiagnostic Profiles of BOLD Signal Variability in Autism and Schizophrenia Spectrum Disorders: Associations With Cognition and Functioning.

Code ↔ Paper

4 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 4 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods and Materials › Intra‐Regional BOLD Signal Variability Calculation ↔ 01_MSSD_calculation/05_MSSD_scanner_harmonization_neurocombat.R, the whole file · a weak match · score 0.65 · neuroCombat, regional MSSD, scanners, covariate, diagnosis, sex
  2. [2] § Methods and Materials › Statistical Analysis ↔ 03_Network_MSSD_analysis/01_MSSD_network_variability_analysis.R, lines 40–71 · score 0.62 · post hoc, Tukey HSD, ANCOVAs, variability, network
  3. [3] § Methods and Materials › Statistical Analysis › Relationship Between Network MSSD and Behavioral/Cognitive/Clinical Variables ↔ 03_Network_MSSD_analysis/01_MSSD_network_variability_analysis.R, lines 73–99 · score 0.60 · post hoc, FDR correction, covariates, sex, network variability, Age
  4. [4] § Results › Effect of Global Signal Regression on BOLD Signal Variability ↔ 03_Network_MSSD_analysis/02_Network_Diffs_visualization.ipynb, lines 9–78 · score 0.58 · posterior multimodal, cingulo opercular, dorsal attention, subcortical, somatomotor, auditory

Paper

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The authors' code

R · 99 lines · 3.8 KB · no license · 2 matches

  1. # Load necessary packages
  2. library(car)
  3. library(dplyr)
  4. library(broom)
  5. library(pbapply)
  7. #Calculating network variability effect sizes
  8. #First for EA task
  9. mssd_val_network <- read.csv("/projects/tsecara/SPINS_ASD_Project2/data/combined/network_MSSD/EA_MSSD_NETWORK_agereduced.csv")
  10. data <- read.csv("/projects/tsecara/SPINS_ASD_Project2/data/combined/demographics/demo_EA_agereduced.csv")
  11. networked_merged <- merge(data, mssd_val_network, by = 'record_id')
  12. networked_merged <- networked_merged[networked_merged$age <= 35, ]
  13. networked_merged$avg_fd <- as.numeric(networked_merged$avg_fd)
  15. #For resting state
  16. data <- read.csv("/projects/tsecara/SPINS_ASD_Project2/data/combined/demographics/demo_RS_agereduced.csv")
  17. mssd_val_network <- read.csv("/projects/tsecara/SPINS_ASD_Project2/data/combined/network_MSSD/RS_MSSD_NETWORK_agereduced.csv")
  18. networked_merged <- merge(data, mssd_val_network, by = 'record_id')
  19. networked_merged <- networked_merged[networked_merged$age <= 35, ]
  20. networked_merged$avg_fd <- as.numeric(networked_merged$avg_fd)
  22. # Function to compute network group differences & Cohen's d between two groups
  23. compute_cohens_d <- function(df, response_col, group1, group2, group_col = "group") {
  24. g1_vals <- df[[response_col]][df[[group_col]] == group1]
  25. g2_vals <- df[[response_col]][df[[group_col]] == group2]
  27. g1_vals <- g1_vals[!is.na(g1_vals)]
  28. g2_vals <- g2_vals[!is.na(g2_vals)]
  30. n1 <- length(g1_vals)
  31. n2 <- length(g2_vals)
  32. sd1 <- sd(g1_vals)
  33. sd2 <- sd(g2_vals)
  35. pooled_sd <- sqrt(((n1 - 1) * sd1^2 + (n2 - 1) * sd2^2) / (n1 + n2 - 2))
  36. d <- (mean(g1_vals) - mean(g2_vals)) / pooled_sd
  37. return(d)
  38. }
  40. # Function to perform ANCOVA and post hoc TukeyHSD
  41. perform_anova <- function(df, response_col, predictor_cols, tukey_if_significant = TRUE) {
  42. # Build formula and run model
  43. formula <- as.formula(paste(response_col, '~', paste(predictor_cols, collapse = ' + ')))
  44. model <- aov(formula, data = df)
  46. # Get ANOVA results
  47. anova_results <- car::Anova(model, type = 'II')
  48. group_significant <- anova_results$'Pr(>F)'[rownames(anova_results) == 'group'] < 0.05
  50. # Run Tukey HSD if group is significant
  51. if (group_significant & tukey_if_significant) {
  52. tukey_raw <- TukeyHSD(aov(as.formula(paste(response_col, '~ group')), data = df))
  53. tukey_results <- tidy(tukey_raw) %>%
  54. mutate(response_col = response_col) %>%
  55. mutate(comparison = rownames(tukey_raw$group)) %>%
  56. rowwise() %>%
  57. mutate(
  58. group1 = strsplit(comparison, "-")[[1]][1],
  59. group2 = strsplit(comparison, "-")[[1]][2],
  60. cohens_d = compute_cohens_d(df, response_col, group1, group2)
  61. )
  62. } else {
  63. tukey_results <- NULL
  64. }
  66. # Return tidy ANOVA and Tukey tables
  67. list(
  68. anova_results = tidy(anova_results) %>% mutate(response_col = response_col),
  69. tukey_results = tukey_results
  70. )
  71. }
  73. # ----------------------------
  74. # Run full analysis
  75. # Replace with your actual dataframe names:
  76. # - mssd_val_network: includes record_id + network columns
  77. # - networked_merged: includes merged network + group/covariates
  79. # Define predictors
  80. predictor_cols <- c('group', 'age', 'sex', 'avg_fd')
  82. # Run analysis for each network column (skip record_id)
  83. results_list <- pblapply(names(mssd_val_network)[-1], function(network_col) {
  84. perform_anova(networked_merged, network_col, predictor_cols)
  85. })
  87. # Combine ANOVA and post hoc results
  88. anova_df <- bind_rows(lapply(results_list, `[[`, 'anova_results'))
  89. tukey_df <- bind_rows(lapply(results_list, `[[`, 'tukey_results'))
  91. # Apply FDR correction to post hoc p-values
  92. tukey_df$fdr_adj_p.value <- p.adjust(tukey_df$adj.p.value, method = "fdr")
  94. # Filter significant post hoc results
  95. filtered_tukey_df <- tukey_df %>% filter(fdr_adj_p.value < 0.05)
  97. # Print results
  98. print(anova_df)
  99. print(filtered_tukey_df) #this contains signficant networks with effect size

01_MSSD_network_variability_analysis.R at commit e39e67b, no license · at the source

Overview

Authors: Maria T Secara1,2, Zara Khan3, Ayesha Rashidi1,2, Lindsay D Oliver1,4, Ju‐Chi Yu1, George Foussias1,2,4, Erin W Dickie1,4, Peter Szatmari1,4,5, Pushpal Desarkar2,4,6,7, Meng‐Chuan Lai1,2,4,5,7,8,9,10, Giulia Baracchini11, Anil K Malhotra12,13,14, Robert W Buchanan15, Aristotle N Voineskos1,2,4, Stephanie H Ameis1,2,4,5, Colin Hawco1,2,4
ORCID iDs: Maria T Secara, Ju‐Chi Yu, Meng‐Chuan Lai
15 affiliations
  1. Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
  2. Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
  3. Integrated Biomedical Engineering and Health Sciences, McMaster University, Hamilton, Ontario, Canada
  4. Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
  5. Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada
  6. Temerty Centre for Therapeutic Brain Intervention, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
  7. Azrieli Adult Neurodevelopmental Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
  8. Department of Psychology, Faculty of Arts and Science, University of Toronto, Toronto, Ontario, Canada
  9. Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
  10. Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
  11. Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
  12. Division of Psychiatry Research, the Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, New York, USA
  13. The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Department of Psychiatry, Hempstead, New York, USA
  14. Center for Psychiatric Neuroscience, the Feinstein Institute for Medical Research, Manhasset, New York, USA
  15. Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
Institutions: Centre for Addiction and Mental Health (Canada); University of Toronto (Canada); Campbell Family Mental Health Research Institute (Canada); McMaster University (Canada); Hospital for Sick Children (Canada); The University of Sydney (Australia); Northwell Health (United States); Feinstein Institute for Medical Research (United States); Hofstra University (United States); Donald & Barbara Zucker School of Medicine at Hofstra/Northwell (United States); Zucker Hillside Hospital (United States); University of Maryland, Baltimore (United States)
Journal: Human brain mapping, volume 47, issue 5, article e70496
Dates: received 13 August 2025; accepted 27 February 2026; published online 16 April 2026; in print April 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1002/hbm.70496 · PMID 41987679 · PMCID PMC13084261 · OpenAlex W7154630223
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), autism (population), schizophrenia / psychosis (population), cognitive (subfield)
Methods: Statistics, fMRI & imaging
Keywords: autism, BOLD signal variability, empathic accuracy, heterogeneity, neurocognition, resting state, schizophrenia spectrum disorders, social cognition
MeSH: Autism Spectrum Disorder*, Empathy*, Nerve Net*, Schizophrenia*, Social Cognition*, Social Perception*, Adolescent, Adult, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIMH NIH HHS (3/3R01MH102318-01, R01MH114879, 1/3R01MH102324-01, 2/3R01MH102313-01); National Institute of Mental Health (1/3R01MH102324‐01, R01MH114879, 3/3R01MH102318‐01, 2/3R01MH102313‐01)
Citations: not cited yet (Europe PMC); 131 references in the paper

Abstract

Autism spectrum disorder (autism) and schizophrenia spectrum disorders (schizophrenia) exhibit overlapping social and neurocognitive impairment and considerable neurobiological heterogeneity. Blood‐oxygen‐level‐dependent (BOLD) signal variability captures the brain's moment‐to‐moment fluctuations, offering a dynamic marker of neural flexibility that is sensitive to cognitive capacity. This study aimed to examine intra‐regional BOLD signal variability during rest and task across schizophrenia, autism, and typically developing controls (TDC) to explore transdiagnostic patterns of brain signal variability and their relationship with cognitive and functional outcomes. Intra‐regional BOLD variability, measured by mean squared successive difference (MSSD), was obtained from resting‐state and empathic accuracy task fMRI in 176 SSD, 89 autism, and 149 TDC participants. ANCOVAs, controlling for age, sex, and motion, assessed group differences in intra‐regional and network‐level BOLD variability and dimensional associations with social cognition, neurocognition, social functioning, and symptom severity. Both autism and schizophrenia exhibited lower BOLD signal variability than TDC across rest and task, with reduced variability observed in somatomotor, visual, and auditory networks (pFDR < 0.01). Greater network variability was positively associated with better social cognitive, neurocognitive, and functional scores across the sample. Resting‐state variability showed stronger group‐based differences and cognitive associations than task‐based variability. BOLD signal variability is positively associated with social cognition, neurocognition, and social functioning across groups, suggesting that variability impacts cognitive efficiency and behavior. Reduced variability in autism and schizophrenia may indicate similar patterns of neural rigidity among these related conditions, positioning BOLD variability as a potential biomarker for neural flexibility and a valuable target for future transdiagnostic clinical interventions.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

tsecara/BOLD_signal_variability_Autism_Schizophrenia_TDC

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: e39e67ba80fe5095e488d367f884398e13cf4a64, 24 June 2025
Languages: Jupyter (7), R (3)
Size: 11 files, 10 scripts
Software Heritage: not archived
Found in: “Code Sharing”
Holds: README, 7 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (7 files), NumPy (7 files), pandas (7 files), SciPy (7 files), scikit-learn (3 files), seaborn (3 files), statsmodels (3 files), broom (2 files), car (2 files), tidyverse (2 files), neuroCombat (1 file), statannotations (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
11 files
At the source: github.com/tsecara/BOLD_signal_variability_Autism_Schizophrenia_TDC

Code Sharing

Code used in the analysis of this dataset has been made available (https://github.com/tsecara/BOLD_signal_variability_Autism_Schizophrenia_TDC).

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

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;
  • 10 scripts, each with its path and the digest of its content;
  • 4 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 code used in these analyses is publicly available on GitHub (https://github.com/tsecara/BOLD_signal_variability_Autism_Schizophrenia_TDC). The SPINS dataset is available through the NIMH Data Archive (https://nda.nih.gov/) under grant numbers R01MH102324, R01MH102313, and R01MH102318. SPIN‐ASD data can be requested from the corresponding author, subject to a data use agreement with the authors’ institutions.

Reproduced under the paper's license (CC BY-NC), 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, issue, pages, dates, 16 authors, 8 keywords, 13 MeSH terms, 2 funders, 118 references.

Cite

This paper

Secara, M. T., Khan, Z., Rashidi, A., Oliver, L. D., Yu, J., Foussias, G., Dickie, E. W., Szatmari, P., Desarkar, P., Lai, M., Baracchini, G., Malhotra, A. K., Buchanan, R. W., Voineskos, A. N., Ameis, S. H., & Hawco, C. (2026). Transdiagnostic Profiles of BOLD Signal Variability in Autism and Schizophrenia Spectrum Disorders: Associations With Cognition and Functioning. Human brain mapping, 47(5), e70496. https://doi.org/10.1002/hbm.70496

BibTeX

@article{secara2026transdiagnostic,
author = {Secara, Maria T and Khan, Zara and Rashidi, Ayesha and Oliver, Lindsay D and Yu, Ju‐Chi and Foussias, George and Dickie, Erin W and Szatmari, Peter and Desarkar, Pushpal and Lai, Meng‐Chuan and Baracchini, Giulia and Malhotra, Anil K and Buchanan, Robert W and Voineskos, Aristotle N and Ameis, Stephanie H and Hawco, Colin},
title = {{Transdiagnostic Profiles of BOLD Signal Variability in Autism and Schizophrenia Spectrum Disorders: Associations With Cognition and Functioning}},
journal = {Human brain mapping},
year = {2026},
month = apr,
volume = {47},
number = {5},
pages = {e70496},
publisher = {Wiley},
issn = {1065-9471},
doi = {10.1002/hbm.70496},
url = {https://doi.org/10.1002/hbm.70496},
pmid = {41987679},
pmcid = {PMC13084261}
}

RIS

TY - JOUR
AU - Secara, Maria T
AU - Khan, Zara
AU - Rashidi, Ayesha
AU - Oliver, Lindsay D
AU - Yu, Ju‐Chi
AU - Foussias, George
AU - Dickie, Erin W
AU - Szatmari, Peter
AU - Desarkar, Pushpal
AU - Lai, Meng‐Chuan
AU - Baracchini, Giulia
AU - Malhotra, Anil K
AU - Buchanan, Robert W
AU - Voineskos, Aristotle N
AU - Ameis, Stephanie H
AU - Hawco, Colin
TI - Transdiagnostic Profiles of BOLD Signal Variability in Autism and Schizophrenia Spectrum Disorders: Associations With Cognition and Functioning
T2 - Human brain mapping
J2 - Hum Brain Mapp
PY - 2026
DA - 2026/04/01
VL - 47
IS - 5
SP - e70496
SN - 1065-9471
PB - Wiley
DO - 10.1002/hbm.70496
UR - https://doi.org/10.1002/hbm.70496
LA - en
ER -

CSL-JSON

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