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Neural dynamics of mindfulness training: A longitudinal EEG network analysis of focused attention and open monitoring meditation.

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  1. [1] § METHODS › Statistical Analysis ↔ Code/Modeling_Public.R, lines 189–268 · score 0.55 · InStrength, OutStrength, PDC, temporal network, closeness, node

Paper

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The authors' code

R · 372 lines · 9.4 KB · no license · 1 match

  1. library(qgraph)
  2. library(tidyverse)
  3. library(kableExtra)
  4. library(here)
  5. library(psychonetrics)
  6. library(bootnet)
  7. rm(list = ls())
  8. source("Code/Test2/Helpers.R")
  9. Axu_names <- c("Subject", "Session_Number", "Induction")
  10. DV_names <- c("SMS_Mind", "SMS_Body")
  11. IV_names <- c(
  12. "Temp_Theta",
  13. "Front_Theta",
  14. "Post_Theta",
  15. "Front_Alpha",
  16. "Temp_Alpha",
  17. "Post_Alpha"
  18. )
  20. dat <- readRDS(here("Clean_Data", "Test2_dat_clean.rds")) |>
  21. mutate(
  22. Session_Number = factor(Session_Number, levels = 1:max(Session_Number))
  23. ) |>
  24. mutate(Subject = factor(Subject, levels = unique(Subject))) |>
  25. ungroup()
  26. unique(dat$Session_Number)
  28. labels <- dat |> select(IV_names, DV_names) |> colnames()
  29. labels_SMS <- c(IV_names, "SMS")
  31. dat_FA_Network <- dat |>
  32. filter(Induction == "FA") |>
  33. select(Subject, Session_Number, IV_names, DV_names)
  35. dat_OM_Network <- dat |>
  36. filter(Induction == "OM") |>
  37. select(Subject, Session_Number, IV_names, DV_names)
  39. dat_FA_Network_SMS <- dat_FA_Network |>
  40. mutate(SMS = SMS_Body + SMS_Mind) |>
  41. select(-SMS_Body, -SMS_Mind)
  42. dat_OM_Network_SMS <- dat_OM_Network |>
  43. mutate(SMS = SMS_Body + SMS_Mind) |>
  44. select(-SMS_Body, -SMS_Mind)
  46. N_FA <- nrow(dat_FA_Network)
  47. N_OM <- nrow(dat_OM_Network)
  49. mod1_FA_org_con <- gvar(
  50. data = dat_FA_Network,
  51. vars = labels,
  52. idvar = "Subject",
  53. estimator = "FIML",
  54. standardize = "z",
  55. storedata = TRUE
  56. ) |>
  57. fixpar(matrix = "beta", row = 1:6, col = 7, value = 0) |>
  58. fixpar(matrix = "beta", row = 1:6, col = 8, value = 0) |>
  59. runmodel()
  61. mod1_FA_org_con_SMS <- gvar(
  62. data = dat_FA_Network_SMS,
  63. vars = labels_SMS,
  64. idvar = "Subject",
  65. estimator = "FIML",
  66. standardize = "z",
  67. storedata = TRUE
  68. ) |>
  69. fixpar(matrix = "beta", row = 1:6, col = 7, value = 0) |>
  70. runmodel()
  72. FA_temporal_network_str_full <- getmatrix(
  73. mod1_FA_org_con,
  74. matrix = "PDC"
  75. ) |>
  76. matrix_rename(labels)
  78. FA_temporal_network_str <- getmatrix(
  79. mod1_FA_org_con,
  80. matrix = "PDC",
  81. threshold = TRUE,
  82. alpha = .05
  83. ) |>
  84. matrix_rename(labels)
  86. FA_temporal_network_str_SMS <- getmatrix(
  87. mod1_FA_org_con_SMS,
  88. matrix = "PDC",
  89. threshold = TRUE,
  90. alpha = .05
  91. ) |>
  92. matrix_rename(labels_SMS)
  94. num_non0(FA_temporal_network_str_full)
  95. num_non0(FA_temporal_network_str)
  97. qgraph(FA_temporal_network_str_full, labels = labels)
  98. qgraph(
  99. FA_temporal_network_str,
  100. labels = labels,
  101. filename = here("Figure", "FA_temporal_network_str"),
  102. filetype = "jpg"
  103. )
  105. FA_temporal_layout <- qgraph(
  106. FA_temporal_network_str,
  107. labels = labels
  108. )$layout
  110. qgraph(
  111. FA_temporal_network_str_SMS,
  112. labels = labels_SMS,
  113. filename = here("Figure", "FA_temporal_network_str_SMS"),
  114. filetype = "jpg"
  115. )
  117. FA_contem_network_str_full <- getmatrix(mod1_FA_org_con, matrix = "omega_zeta")
  118. FA_contem_network_str <- getmatrix(
  119. mod1_FA_org_con,
  120. matrix = "omega_zeta",
  121. threshold = TRUE,
  122. alpha = .05
  123. ) |>
  124. matrix_rename(labels)
  126. qgraph(
  127. FA_contem_network_str,
  128. labels = labels,
  129. filename = here("Figure", "FA_contem_network_str"),
  130. filetype = "jpg"
  131. )
  133. FA_contem_network_str_SMS <- getmatrix(
  134. mod1_FA_org_con_SMS,
  135. matrix = "omega_zeta",
  136. threshold = TRUE,
  137. alpha = .05
  138. ) |>
  139. matrix_rename(labels_SMS)
  140. qgraph(
  141. FA_contem_network_str_SMS,
  142. labels = labels_SMS,
  143. filename = here("Figure", "FA_contem_network_str_SMS"),
  144. filetype = "jpg"
  145. )
  147. centrality_temporal_fa <- qgraph::centralityTable(FA_temporal_network_str) |>
  148. filter(
  149. measure %in% c("InStrength", "OutStrength", "Betweenness", "Closeness")
  150. )
  152. centrality_temporal_fa_tbl <- centrality_temporal_fa |>
  153. select(node, measure, value) |>
  154. pivot_wider(names_from = measure, values_from = value) |>
  155. mutate(node_label = labels) |>
  156. dplyr::relocate(node_label, .after = node)
  158. centrality_contem_fa <- qgraph::centralityTable(FA_contem_network_str) |>
  159. filter(measure %in% c("Strength", "Betweenness", "Closeness"))
  161. centrality_contem_fa_tbl <- centrality_contem_fa |>
  162. select(node, measure, value) |>
  163. pivot_wider(names_from = measure, values_from = value) |>
  164. mutate(node_label = labels) |>
  165. dplyr::relocate(node_label, .after = node)
  167. mod1_OM_org_con <- gvar(
  168. data = dat_OM_Network,
  169. vars = labels,
  170. idvar = "Subject",
  171. estimator = "FIML",
  172. standardize = "z"
  173. ) |>
  174. fixpar(matrix = "beta", row = 1:6, col = 7, value = 0) |>
  175. fixpar(matrix = "beta", row = 1:6, col = 8, value = 0) |>
  176. runmodel()
  178. mod1_OM_org_con_SMS <- gvar(
  179. data = dat_OM_Network_SMS,
  180. vars = labels_SMS,
  181. idvar = "Subject",
  182. estimator = "FIML",
  183. standardize = "z"
  184. ) |>
  185. fixpar(matrix = "beta", row = 1:6, col = 7, value = 0) |>
  186. fixpar(matrix = "beta", row = 1:6, col = 8, value = 0) |>
  187. runmodel()
  189. OM_temporal_network_str <- getmatrix(
  190. mod1_OM_org_con,
  191. matrix = "PDC",
  192. threshold = TRUE,
  193. alpha = .05
  194. ) |>
  195. matrix_rename(labels)
  197. qgraph(
  198. OM_temporal_network_str,
  199. labels = labels,
  200. layout = FA_temporal_layout,
  201. filename = here("Figure", "OM_temporal_network_str"),
  202. filetype = "jpg"
  203. )
  205. OM_temporal_network_str_SMS <- getmatrix(
  206. mod1_OM_org_con_SMS,
  207. matrix = "PDC",
  208. threshold = TRUE,
  209. alpha = .05
  210. ) |>
  211. matrix_rename(labels_SMS)
  213. qgraph(
  214. OM_temporal_network_str_SMS,
  215. labels = labels_SMS,
  216. filename = here("Figure", "OM_temporal_network_str_SMS"),
  217. filetype = "jpg"
  218. )
  220. OM_contem_network_str <- getmatrix(
  221. mod1_OM_org_con,
  222. matrix = "omega_zeta",
  223. threshold = TRUE,
  224. alpha = .05
  225. ) |>
  226. matrix_rename(labels)
  227. qgraph(
  228. OM_contem_network_str,
  229. labels = labels,
  230. filename = here("Figure", "OM_contem_network_str"),
  231. filetype = "jpg"
  232. )
  234. OM_contem_network_str_SMS <- getmatrix(
  235. mod1_OM_org_con_SMS,
  236. matrix = "omega_zeta",
  237. threshold = TRUE,
  238. alpha = .05
  239. ) |>
  240. matrix_rename(labels_SMS)
  241. qgraph(
  242. OM_contem_network_str_SMS,
  243. labels = labels_SMS,
  244. filename = here("Figure", "OM_contem_network_str_SMS"),
  245. filetype = "jpg"
  246. )
  248. centrality_temporal_OM <- qgraph::centralityTable(OM_temporal_network_str) |>
  249. filter(
  250. measure %in% c("InStrength", "OutStrength", "Betweenness", "Closeness")
  251. )
  253. centrality_temporal_OM_tbl <- centrality_temporal_OM |>
  254. select(node, measure, value) |>
  255. pivot_wider(names_from = measure, values_from = value) |>
  256. mutate(node_label = labels) |>
  257. dplyr::relocate(node_label, .after = node)
  259. centrality_contem_OM <- qgraph::centralityTable(OM_contem_network_str) |>
  260. filter(measure %in% c("Strength", "Strength", "Betweenness", "Closeness"))
  262. centrality_contem_OM_tbl <- centrality_contem_OM |>
  263. select(node, measure, value) |>
  264. pivot_wider(names_from = measure, values_from = value) |>
  265. mutate(node_label = labels) |>
  266. dplyr::relocate(node_label, .after = node)
  268. load("Clean_Data/Network_Structures_Bootstrapped.RData")
  269. get_non0_edges_info <- function(matrix) {
  270. colnames(matrix) <- rownames(matrix) <- labels
  271. as.data.frame(matrix) |>
  272. rownames_to_column("Source") |>
  273. pivot_longer(-Source, names_to = "Target", values_to = "Beta") |>
  274. filter(Beta != 0)
  275. }
  277. get_correct_sign <- function(original, bootstrap, network) {
  278. if (network == "temporal") {
  279. which_matrix = "beta"
  280. } else if (network == "contem") {
  281. which_matrix = "omega_zeta"
  282. } else if (network == "between") {
  283. which_matrix = "exo_cholesky"
  284. } else {
  285. stop(
  286. "Invalid network type. Choose from 'temporal', 'contem', or 'between'."
  287. )
  288. }
  289. bootstrap_table <-
  290. bootstrap |>
  291. filter(matrix == which_matrix) |>
  292. filter(var1 != var2) |>
  293. select(
  294. Source = var2,
  295. Target = var1,
  296. prop_non0,
  297. prop_non0_pos,
  298. prop_non0_neg
  299. ) |>
  300. mutate(
  301. Source = str_replace(Source, "_lag1", ""),
  302. Target = str_replace(Target, "_lag1", "")
  303. )
  305. get_non0_edges_info(original) |>
  306. left_join(bootstrap_table) |>
  307. filter(!is.na(prop_non0)) |>
  308. mutate(prop_correct_sign = ifelse(Beta > 0, prop_non0_pos, prop_non0_neg))
  309. }
  311. get_correct_sign(
  312. original = FA_temporal_network_str,
  313. bootstrap = FA_bootstrap_networks_agrregate_params,
  314. network = "temporal"
  315. ) |>
  316. summarize(
  317. Mean_prop_correct_sign = mean(prop_correct_sign),
  318. Min_prop_correct_sign = min(prop_correct_sign),
  319. Max_prop_correct_sign = max(prop_correct_sign),
  320. ) -> FA_temporal_prop_correct_sign
  322. get_correct_sign(
  323. original = FA_contem_network_str,
  324. bootstrap = FA_bootstrap_networks_agrregate_params,
  325. network = "contem"
  326. ) |>
  327. summarize(
  328. Mean_prop_correct_sign = mean(prop_correct_sign),
  329. Min_prop_correct_sign = min(prop_correct_sign),
  330. Max_prop_correct_sign = max(prop_correct_sign),
  331. ) -> FA_contem_prop_correct_sign
  333. rbind(
  334. FA_temporal_prop_correct_sign,
  335. FA_contem_prop_correct_sign
  336. ) |>
  337. mutate(Network = c("Temporal", "Contemporaneous")) |>
  338. select(Network, everything()) -> FA_correct_sign_summary
  340. get_correct_sign(
  341. original = OM_temporal_network_str,
  342. bootstrap = OM_bootstrap_networks_agrregate_params,
  343. network = "temporal"
  344. ) |>
  345. summarize(
  346. Mean_prop_correct_sign = mean(prop_correct_sign),
  347. Min_prop_correct_sign = min(prop_correct_sign),
  348. Max_prop_correct_sign = max(prop_correct_sign),
  349. ) -> OM_temporal_prop_correct_sign
  351. get_correct_sign(
  352. original = OM_contem_network_str,
  353. bootstrap = OM_bootstrap_networks_agrregate_params,
  354. network = "contem"
  355. ) |>
  356. summarize(
  357. Mean_prop_correct_sign = mean(prop_correct_sign),
  358. Min_prop_correct_sign = min(prop_correct_sign),
  359. Max_prop_correct_sign = max(prop_correct_sign),
  360. ) -> OM_contem_prop_correct_sign
  362. rbind(
  363. OM_temporal_prop_correct_sign,
  364. OM_contem_prop_correct_sign
  365. ) |>
  366. mutate(Network = c("Temporal", "Contemporaneous")) |>
  367. select(Network, everything()) -> OM_correct_sign_summary
  369. save(
  370. list = ls(all.names = TRUE),
  371. file = "Clean_Data/FA_Network_Structures.RData"
  372. )

Modeling_Public.R, no license · at the source

Overview

Authors: Yanli Lin1, Marne L White2, Jihong Zhang3, Todd S Braver4
ORCID iDs: Yanli Lin
  1. Department of Psychological Science, University of Arkansas, Fayetteville, AR, USA
  2. Department of Psychology, University of Maryland, College Park, MD, USA
  3. Department of Counseling, Leadership, and Research Methods, University of Arkansas, Fayetteville, AR, USA
  4. Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
Institutions: University of Arkansas at Fayetteville (United States); University of Maryland, College Park (United States); Washington University in St. Louis (United States); Washington University Medical Center (United States)
Journal: Network neuroscience (Cambridge, Mass.), volume 10, issue 3, pages 655-682
Dates: received 21 October 2025; accepted 3 March 2026; published online 27 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/netn.a.555 · PMID 42529651 · PMCID PMC13418251 · OpenAlex W7135167199
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Preprocessing, Physiology & signal measures
Keywords: Mindfulness, EEG, Network analysis, Alpha, Theta
Topic: Mindfulness and Compassion Interventions (Clinical Psychology, Psychology), according to OpenAlex
Funding: NIA NIH HHS (F32 AG069499)
Citations: not cited yet (Europe PMC); 75 references in the paper

Abstract

Neural oscillatory activity within the alpha and theta bands have long been considered putative markers of state mindfulness, yet understanding of their functional role has been limited by the challenges of linking objective brain indices with subjective experience. To address this gap, the current study applied longitudinal network analysis to a unique dataset from 16 novices who completed up to 24 laboratory training sessions of both focused attention (FA) and open monitoring (OM) meditation, during which both EEG and self-report measures of state mindfulness quality were collected. This approach enabled the parsimonious characterization of both cross-lagged temporal (across-session) and contemporaneous (within-session) influences of regional spectral power on state mindfulness. The analysis revealed distinguishable neurophenomenological network structures for each practice, providing data-driven support for their theoretical differentiation. These distinctions emerged alongside shared commonalities to both practices, including strong autoregressive effects for state mindfulness, consistent with training-related skill acquisition, and opposing regional influences of frontal versus posterior alpha power. Taken together, these findings challenge monolithic interpretations of meditation-related EEG activity, advancing a more nuanced neurophenomenological approach wherein the functional significance of neural activity is dynamically situated within the specific type and time course of training.

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.

OSF buxah

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (2)
Size: 5 files, 2 scripts
Software Heritage: not checked
Found in: “DATA AVAILABILITY”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
2 files
At the source: osf.io/buxah/

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:

  • 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;
  • 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

No dataset and no data link were found in the paper.

Data availability

All data and analytic code, including full model specifications and additional visualizations, are publicly available on the OSF at https://osf.io/buxah/. For transparency, we note that the longitudinal network approach represents a methodological deviation from our preregistered aims (https://osf.io/gws3q), which specified the use of linear mixed-effects models. This deviation was made based on the theoretical and empirical considerations outlined in the manuscript.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 1 funder, 69 references.

Cite

This paper

Lin, Y., White, M. L., Zhang, J., & Braver, T. S. (2026). Neural dynamics of mindfulness training: A longitudinal EEG network analysis of focused attention and open monitoring meditation. Network neuroscience (Cambridge, Mass.), 10(3), 655-682. https://doi.org/10.1162/netn.a.555

BibTeX

@article{lin2026neural,
author = {Lin, Yanli and White, Marne L and Zhang, Jihong and Braver, Todd S},
title = {{Neural dynamics of mindfulness training: A longitudinal EEG network analysis of focused attention and open monitoring meditation}},
journal = {Network neuroscience (Cambridge, Mass.)},
year = {2026},
month = jul,
volume = {10},
number = {3},
pages = {655--682},
publisher = {MIT Press},
issn = {2472-1751},
doi = {10.1162/netn.a.555},
url = {https://doi.org/10.1162/netn.a.555},
pmid = {42529651},
pmcid = {PMC13418251}
}

RIS

TY - JOUR
AU - Lin, Yanli
AU - White, Marne L
AU - Zhang, Jihong
AU - Braver, Todd S
TI - Neural dynamics of mindfulness training: A longitudinal EEG network analysis of focused attention and open monitoring meditation
T2 - Network neuroscience (Cambridge, Mass.)
J2 - Netw Neurosci
PY - 2026
DA - 2026/07/27
VL - 10
IS - 3
SP - 655
EP - 682
SN - 2472-1751
PB - MIT Press
DO - 10.1162/netn.a.555
UR - https://doi.org/10.1162/netn.a.555
LA - en
ER -

CSL-JSON

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"author": [
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"given": "Yanli"
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"given": "Marne L"
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"given": "Todd S"
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"container-title-short": "Netw Neurosci",
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"page": "655-682",
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"PMID": "42529651",
"PMCID": "PMC13418251",
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2026,
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