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Effective Connectivity Identifies Divergent Cerebro-Cerebellar Network Organization in Schizophrenia.

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The authors' code

R Markdown · 162 lines · 5.9 KB · no license

  1. ---
  2. title: "GIMME_ALFF Cerebellum + Striatum"
  3. author: "Kami Pearson"
  4. output: pdf_document
  5. date: "2025-04-16"
  6. ---
  8. ```{r}
  9. # load required libraries
  10. library(gimme)
  11. library(dplyr)
  12. library(car)
  13. ```
  15. ```{r}
  16. # pull in Schaefer data
  17. # change file path by user
  18. sch <- read.csv("C:/path/to/CorrMat_schaefer2011CombinedDseg_label_dlPFC.csv",
  19. header = TRUE)
  20. ```
  22. ----------
  23. # averaging across regions (not done here as averaging was done prior and saved in CorrMat csv)
  24. # EXAMPLE
  25. sch$Left.mPFC <- rowMeans((sch[,162:166]), na.rm = TRUE)
  26. sch$Right.mPFC <- rowMeans((sch[,368:372]), na.rm = TRUE)
  27. ----------
  29. ```{r}
  30. # create a vector of subjects with time series data
  31. vec <- unique(sch$file)
  32. # pull in demographics file for final sample
  33. demos <- read.csv("C:/path/to/Demos_HCSZ.csv",
  34. header = TRUE)
  35. # pull in PANSS assessment data for final sample
  36. PANSS <- read.csv("C:/path/to/COBRE_PANSS.csv",
  37. header = TRUE)
  38. # merge separate files
  39. complete <- merge(PANSS, demos, by = 'file')
  40. ```
  42. ```{r}
  43. # create new df with subject ID and group
  44. # group column needs to be a binary variable for HC v SZ
  45. csub <- demos[, c("file","subgroup")]
  46. # create a subset vector for only schizophrenia pts
  47. scz <- csub[csub$subgroup==2,]
  48. sz <- scz$file
  49. # trim time series file with new name for only schizophrenia pts
  50. pts <- sch[sch$file %in% sz,]
  51. ```
  53. ```{r}
  54. # next, need a data frame with 2 columns for supervised SZ based on negative, positive, and composite scores
  55. # group column needs to be a binary variable for SZ PP and PN
  56. # the diagnostic group column for PANSS Negative score labeled "NGroup"
  57. # create new df with subject ID and group
  58. csubNeg <- data.frame(PANSS$file, PANSS$Ngroup)
  59. # the diagnostic group column for PANSS Positive score is labeled "PGroup"
  60. csubPos <- data.frame(PANSS$file, PANSS$Pgroup)
  61. # the diagnostic group column for PANSS Composite score is labeled called "BalGroup"
  62. csubBal <- data.frame(PANSS$file, PANSS$BalGroup)
  63. # rename labels to match existing dfs
  64. csubSZ_names <- c("file","subgroup")
  65. colnames(csubNeg) <- csubSZ_names
  66. colnames(csubPos) <- csubSZ_names
  67. colnames(csubBal) <- csubSZ_names
  68. ```
  70. ```{r}
  71. # prepare time series for gimme
  72. # split each person into their own slice of the list
  73. ts <- split(sch, sch$file)
  74. # check on sample size
  75. # length will print the number of subjects to be entered into gimme
  76. length(ts)
  77. # create a duplicate so that one of them can be manipulated for selected variables
  78. fmri <- ts
  79. ```
  81. ```{r}
  82. # prepare schizophrenia time series for gimme
  83. # split each person into their own slice of the list
  84. schiz <- split(pts, pts$file)
  85. length(schiz)
  86. # create a duplicate
  87. rsfmri <- schiz
  88. ```
  90. ```{r}
  91. # piping in only the variables to include in gimme
  92. # select V regions of interest (check naming to ensure correct ROIs are pulled)
  93. for(i in 1:length(ts))
  94. fmri[[i]] <- fmri[[i]] %>%
  95. select(LH_dlPFC,LH_dlPFC_Sup,RH_dlPFC,RH_dlPFC_Sup,Left.Accumbens.area,Left.Putamen,Left.Caudate,Right.Accumbens.area,Right.Putamen,Right.Caudate,Cerebellum.8,Cerebellum.9,Cerebellum.13,Cerebellum.17)
  96. # ensure only complete ts
  97. # recall time series is 150 brain volumes, so length of complete cases = 150
  98. rs_150 <- fmri[lapply(1:length(ts), function(x)
  99. length(complete.cases(ts[[x]])))==150]
  100. ```
  102. ```{r}
  103. # piping in only the variables to include in gimme
  104. # select V regions of interest (check naming to ensure correct ROIs are pulled)
  105. # block is coded for SZ only
  106. for(i in 1:length(schiz))
  107. rsfmri[[i]] <- rsfmri[[i]] %>%
  108. select(LH_dlPFC,LH_dlPFC_Sup,RH_dlPFC,RH_dlPFC_Sup,Left.Accumbens.area,Left.Putamen,Left.Caudate,Right.Accumbens.area,Right.Putamen,Right.Caudate,Cerebellum.8,Cerebellum.9,Cerebellum.13,Cerebellum.17)
  109. # ensure only complete ts
  110. # recall time series is 150 brain volumes, so length of complete cases = 150
  111. rsz_150 <- rsfmri[lapply(1:length(schiz), function(x)
  112. length(complete.cases(schiz[[x]])))==150]
  113. ```
  115. ```{r}
  116. # run gimme with confirmatory clustering, i.e., known diagnostic groups
  117. # gimme will create an output directory wherever file path is indicated
  118. # csub is called from above and references the SubjID to index who is who
  119. # clustering on diagnostic groups, i.e., confirmatory
  120. csgimme <- gimme(data = rs_150,
  121. out = "C:/path/to/working/directory/HCSZ_S",
  122. subgroup = TRUE,
  123. confirm_subgroup = csub,
  124. standardize = TRUE)
  125. ```
  127. ```{r}
  128. # run gimme with confirmatory clustering, i.e., PANSS Negative
  129. # gimme will create an output directory wherever file path is indicated
  130. # csub is called from above and references the SubjID to index who is who
  131. # clustering on diagnostic groups, i.e., confirmatory
  132. sgimmeNeg <- gimme(data = rsz_150,
  133. out = "C:/path/to/working/directory/SZ_SNeg",
  134. subgroup = TRUE,
  135. confirm_subgroup = csubNeg,
  136. standardize = TRUE)
  137. ```
  139. ```{r}
  140. # run gimme with confirmatory clustering, i.e., PANSS Positive
  141. # gimme will create an output directory wherever file path is indicated
  142. # csub is called from above and references the SubjID to index who is who
  143. # clustering on diagnostic groups, i.e., confirmatory
  144. sgimmePos <- gimme(data = rsz_150,
  145. out = "C:/path/to/working/directory/SZ_SPos",
  146. subgroup = TRUE,
  147. confirm_subgroup = csubPos,
  148. standardize = TRUE)
  149. ```
  151. ```{r}
  152. # run gimme with confirmatory clustering, i.e., PANSS Composite
  153. # gimme will create an output directory wherever file path is indicated
  154. # csub is called from above and references the SubjID to index who is who
  155. # clustering on diagnostic groups, i.e., confirmatory
  156. sgimmeBal <- gimme(data = rsz_150,
  157. out = "C:/path/to/working/directory/SZ_SBal",
  158. subgroup = TRUE,
  159. confirm_subgroup = csubBal,
  160. standardize = TRUE)
  161. ```

COBRE_GIMME_Cerebellum.Rmd, no license · at the source

Overview

Authors: Kami Pearson1,2, Katrina Aberizk1, Cindy An1,3, Grace Hodges1, Theo G.M. van Erp4,5, Vince D. Calhoun6, Jessica A. Turner1
  1. Department of Psychiatry and Behavioral Health, The Ohio State University Wexner Medical Center,Columbus, OH USA
  2. Neuroscience Graduate Program, The Ohio State University,Columbus, OH USA
  3. Department of Psychology, The Ohio State University,Columbus, OH USA
  4. Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine,Irvine, CA USA
  5. Center for the Neurobiology of Learning and Memory, University of California Irvine,309 Qureshey Research Lab, Irvine, CA USA
  6. Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University,Atlanta, GA USA
Institutions: The Ohio State University Wexner Medical Center (United States); The Ohio State University (United States); University of California, Irvine (United States); Center for Translational Research in Neuroimaging and Data Science (United States)
Journal: Cerebellum (London, England), volume 25, issue 2, article 38
Dates: received 6 October 2025; accepted 13 March 2026; published online 23 March 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s12311-026-01983-7 · PMID 41866416 · PMCID PMC13006459 · OpenAlex W7140032612
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: fMRI (modality), human (organism), schizophrenia / psychosis (population), systems (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, fMRI & imaging
MeSH: Cerebellum*, Cerebral Cortex*, Nerve Net*, Schizophrenia*, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neural Pathways, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) (R01MH121246)
Citations: not cited yet (Europe PMC); 65 references in the paper

Abstract

Introduction functional impairments in schizophrenia may arise from disruptions in large-scale brain networks. Emerging evidence highlights the cerebellum’s role in cognitive and affective regulations, yet its directional influence remains poorly understood. This study examines effective connectivity (EC) within cortico-striato-cerebellar networks in schizophrenia and healthy adults. Methods resting-state functional magnetic resonance imaging data from the Centers of Biomedical Research Excellence (COBRE), including people with schizophrenia and healthy controls (n = 134), were used to analyze intrinsic activity and effective connectivity. Cerebellar clusters showing reduced amplitude of low frequency fluctuations (ALFF) and fractional ALFF in schizophrenia were mapped to the Buckner 17-network atlas to define regions of interest (ROIs). Along with prefrontal and striatal ROIs defined a priori, these served as nodes in Group Iterative Multiple Model Estimation (GIMME). In addition to the best-fitting model of the full sample, schizophrenia participants were stratified by symptomology according to the positive and negative syndrome scale. Results four cerebellar voxel clusters in the posterolateral and anteromedial regions showed lower ALFF/fALFF in schizophrenia compared to controls. GIMME revealed distinct intra-cerebellar and cerebello-prefrontal EC patterns in schizophrenia that were absent in controls, including novel directed paths involving the cerebellar control network representation, consistent with its emergence as a central hub. These patterns persisted across symptom-defined subgroups, suggesting core network reorganization. Conclusions findings support the cerebellum’s involvement in disrupted network dynamics in schizophrenia, particularly its directional influence over prefrontal targets. EC analyses uncovered cerebellar reorganization that may underlie affective deficits in schizophrenia, offering novel targets for circuit-level interventions.

Supplementary Information: The online version contains supplementary material available at 10.1007/s12311-026-01983-7.

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

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Its files are read in the Code ↔ Paper reader above.

OSF g3bkf

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: R (2)
Size: 6 files, 2 scripts
Software Heritage: not checked
Found in: “Data Availability”
Holds: 2 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: car (2 files), tidyverse (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
2 files
At the source: osf.io/g3bkf/?view_only=e7b73749a45c4b69940e68c642254533

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.

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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;
  • 2 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

Imaging, assessment, and demographic data that support the findings of this study (obtained from Centers of Biomedical Research Excellence, Phase I study from the Mind Research Network in Albuquerque, NM) can be accessed at the following link: https://coins.trendscenter.org/#exchangepost-processed time series data, cleaned demographic and assessment data, and associated code can be accessed here: https://osf.io/g3bkf/?view_only=e7b73749a45c4b69940e68c642254533.

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 13 MeSH terms, 1 funder, 62 references.

Cite

This paper

Pearson, K., Aberizk, K., An, C., Hodges, G., van Erp, T. G., Calhoun, V. D., & Turner, J. A. (2026). Effective Connectivity Identifies Divergent Cerebro-Cerebellar Network Organization in Schizophrenia. Cerebellum (London, England), 25(2), 38. https://doi.org/10.1007/s12311-026-01983-7

BibTeX

@article{pearson2026effective,
author = {Pearson, Kami and Aberizk, Katrina and An, Cindy and Hodges, Grace and van Erp, Theo G.M. and Calhoun, Vince D. and Turner, Jessica A.},
title = {{Effective Connectivity Identifies Divergent Cerebro-Cerebellar Network Organization in Schizophrenia}},
journal = {Cerebellum (London, England)},
year = {2026},
month = mar,
volume = {25},
number = {2},
pages = {38},
publisher = {Springer Science+Business Media},
issn = {1473-4222},
doi = {10.1007/s12311-026-01983-7},
url = {https://doi.org/10.1007/s12311-026-01983-7},
pmid = {41866416},
pmcid = {PMC13006459}
}

RIS

TY - JOUR
AU - Pearson, Kami
AU - Aberizk, Katrina
AU - An, Cindy
AU - Hodges, Grace
AU - van Erp, Theo G.M.
AU - Calhoun, Vince D.
AU - Turner, Jessica A.
TI - Effective Connectivity Identifies Divergent Cerebro-Cerebellar Network Organization in Schizophrenia
T2 - Cerebellum (London, England)
J2 - Cerebellum
PY - 2026
DA - 2026/03/23
VL - 25
IS - 2
SP - 38
SN - 1473-4222
PB - Springer Science+Business Media
DO - 10.1007/s12311-026-01983-7
UR - https://doi.org/10.1007/s12311-026-01983-7
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Effective Connectivity Identifies Divergent Cerebro-Cerebellar Network Organization in Schizophrenia",
"container-title": "Cerebellum (London, England)",
"author": [
{
"family": "Pearson",
"given": "Kami"
},
{
"family": "Aberizk",
"given": "Katrina"
},
{
"family": "An",
"given": "Cindy"
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{
"family": "Hodges",
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{
"family": "van Erp",
"given": "Theo G.M."
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{
"family": "Calhoun",
"given": "Vince D."
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{
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"given": "Jessica A."
}
],
"container-title-short": "Cerebellum",
"volume": "25",
"issue": "2",
"page": "38",
"DOI": "10.1007/s12311-026-01983-7",
"PMID": "41866416",
"PMCID": "PMC13006459",
"ISSN": "1473-4222",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.1007/s12311-026-01983-7",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
23
]
]
}
}

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