OSCR

Time-resolved aperiodic dynamics in event segmentation in attention-deficit/hyperactivity disorder.

Code ↔ Paper

9 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 9 matches
  1. [1] § Materials and methods › Time-resolved aperiodic signal analysis ↔ 02_Codes_EEG.zip/C1_time-resolved aperiodic signal.ipynb, lines 4–139 · score 0.95 · peak width limits, resolved aperiodic signal, 4–40 Hz, peak threshold, aperiodic mode, FieldTrip
  2. [2] § Materials and methods › EEG recording and preprocessing ↔ 02_Codes_EEG.zip/A1_step01_detect_flat_channels.m, lines 92–128 · score 0.83 · flat channels, BrainAmp, removed channels, Algorithm, amplified, Automagic
  3. [3] § Materials and methods › Statistical analyses ↔ 01_Codes_Behavior.zip/B2_LogisticRegression_perGroup_Plotting.R, lines 43–83 · score 0.73 · logistic regression, Random intercepts, Odds ratios, binomial, glmer, coefficients
  4. [4] § Materials and methods › Statistical analyses ↔ 01_Codes_Behavior.zip/B1_LogisticRegression_FOOOF.R, lines 85–157 · score 0.71 · Odds ratios, logistic regression, binary, multicollinearity, variance, VIF
  5. [5] § Results › Behaviour ↔ 01_Codes_Behavior.zip/B2_LogisticRegression_perGroup_Plotting.R, lines 86–133 · score 0.60 · logistic regression, small space, large space, scene, model, segmentation
  6. [6] § Results › Behaviour ↔ 01_Codes_Behavior.zip/B1_LogisticRegression_FOOOF.R, lines 85–157 · score 0.57 · large space, Odds ratios, confidence interval, logistic regression, model, Behavioural
  7. [7] § Results › Behaviour ↔ 01_Codes_Behavior.zip/B2_LogisticRegression_perGroup_Plotting.R, lines 135–175 · score 0.56 · large space, Odds ratios, confidence interval, logistic regression, model, Behavioural
  8. [8] § Materials and methods › Statistical analyses ↔ 02_Codes_EEG.zip/C1_time-resolved aperiodic signal.ipynb, lines 4–139 · score 0.54 · aperiodic model, FieldTrip, threshold, fit, error, exponent
  9. [9] § Results › Aperiodic activity ↔ 02_Codes_EEG.zip/C2_Statistical_Analysis_3sTW.m, lines 631–723 · score 0.51 · interaction clusters, negative clusters, SEM, bars, axis, electrodes

Paper

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The authors' code

R · 228 lines · 11 KB · no license · 3 matches

  1. #######################################################################################################################################
  2. ##################################### PLOTTING ########################################################################################
  3. #######################################################################################################################################
  5. library(Matrix)
  6. library(tidyverse)
  7. library(lme4)
  8. library(ggplot2)
  9. library(dplyr)
  10. library(carData)
  11. library(car)
  13. # load data
  14. input_HC <- read.csv("PATH/For_Rlanguage_Response_Change_HC.csv")
  15. input_ADHD <- read.csv("PATH/For_Rlanguage_Response_Change_ADHD.csv")
  17. ################ REGRESSION BASED ON NUMBER OF CHANGES ############################################
  18. ### HC ###
  19. input_HC_sum <- data.frame()
  20. input_HC_sum <- cbind(Subs = input_HC[,1], Resp = input_HC[,2], SumChange = input_HC[,3])
  21. input_HC_sum <- data.frame(input_HC_sum)
  22. input_HC_sum$SumChange <- factor(input_HC_sum$SumChange,levels = 0:5)
  23. input_HC_sum <- na.omit(input_HC_sum)
  24. input_HC_sum$SumChange <- as.numeric(input_HC_sum$SumChange)
  25. input_HC_sum$SumChange <- input_HC_sum$SumChange - 1
  27. # build GLMM between response and total number of changes, with random intercepts on subjects level
  28. Model_Sum_HC <- glmer(Resp~ SumChange + (1| Subs),
  29. data = input_HC_sum, family = binomial,
  30. control = glmerControl(optimizer = "bobyqa"), nAGQ = 10)
  31. summary(Model_Sum_HC)
  33. # To get confidence intervals (CIs)
  34. se_Model_Sum_HC <- sqrt(diag(vcov(Model_Sum_HC)))
  35. # table of estimates with 95% CI
  36. (tab_Model_Sum_HC <- cbind(Est = fixef(Model_Sum_HC), LL = fixef(Model_Sum_HC) - 1.96 * se_Model_Sum_HC,
  37. UL = fixef(Model_Sum_HC) + 1.96 * se_Model_Sum_HC))
  39. # To get odds ratios and order all resuls by Change names
  40. OddsRatio_Model_Sum_HC <- exp(tab_Model_Sum_HC)
  43. # get coefficients for fixed effect
  44. CofFix_HC = fixef(Model_Sum_HC)
  45. # calculate estimated probability by logistic regression formula
  46. EstimateY <- numeric()
  47. EstimateY[1] <- 1/(1+exp(-(CofFix_HC[1])))
  48. EstimateY[2] <- 1/(1+exp(-(CofFix_HC[1] + CofFix_HC[2]* 1 )))
  49. EstimateY[3] <- 1/(1+exp(-(CofFix_HC[1] + CofFix_HC[2]* 2 )))
  50. EstimateY[4] <- 1/(1+exp(-(CofFix_HC[1] + CofFix_HC[2]* 3 )))
  51. EstimateY[5] <- 1/(1+exp(-(CofFix_HC[1] + CofFix_HC[2]* 4 )))
  52. EstimateY[6] <- 1/(1+exp(-(CofFix_HC[1] + CofFix_HC[2]* 5 )))
  54. # construct another dataframe for ploting, then plot it
  55. ChangeNum <- c(0,1,2,3,4,5)
  57. Plot_table_HC <- cbind(ChangeNum,EstimateY)
  58. Plot_table_HC <- data.frame(Plot_table_HC)
  60. ### ADHD ###
  61. input_ADHD_sum <- data.frame()
  62. input_ADHD_sum <- cbind(Subs = input_ADHD[,1], Resp = input_ADHD[,2], SumChange = input_ADHD[,3])
  63. input_ADHD_sum <- data.frame(input_ADHD_sum)
  64. input_ADHD_sum$SumChange <- factor(input_ADHD_sum$SumChange,levels = 0:5)
  65. input_ADHD_sum <- na.omit(input_ADHD_sum)
  66. input_ADHD_sum$SumChange <- as.numeric(input_ADHD_sum$SumChange)
  67. input_ADHD_sum$SumChange <- input_ADHD_sum$SumChange - 1
  69. # build GLMM between response and total number of changes, with random intercepts on subjects level
  70. Model_Sum_ADHD <- glmer(Resp~ SumChange + (1| Subs),
  71. data = input_ADHD_sum, family = binomial,
  72. control = glmerControl(optimizer = "bobyqa"), nAGQ = 10)
  74. summary(Model_Sum_ADHD)
  76. # To get confidence intervals (CIs)
  77. se_Model_Sum_ADHD <- sqrt(diag(vcov(Model_Sum_ADHD)))
  78. # table of estimates with 95% CI
  79. (tab_Model_Sum_ADHD <- cbind(Est = fixef(Model_Sum_ADHD), LL = fixef(Model_Sum_ADHD) - 1.96 * se_Model_Sum_ADHD,
  80. UL = fixef(Model_Sum_ADHD) + 1.96 * se_Model_Sum_ADHD))
  82. # To get odds ratios and order all resuls by Change names
  83. OddsRatio_Model_Sum_ADHD <- exp(tab_Model_Sum_ADHD)
  86. # get coefficients for fixed effect
  87. CofFix_ADHD = fixef(Model_Sum_ADHD)
  88. # calculate estimated probability by logistic regression formula
  89. EstimateY <- numeric()
  90. EstimateY[1] <- 1/(1+exp(-(CofFix_ADHD[1])))
  91. EstimateY[2] <- 1/(1+exp(-(CofFix_ADHD[1] + CofFix_ADHD[2]* 1 )))
  92. EstimateY[3] <- 1/(1+exp(-(CofFix_ADHD[1] + CofFix_ADHD[2]* 2 )))
  93. EstimateY[4] <- 1/(1+exp(-(CofFix_ADHD[1] + CofFix_ADHD[2]* 3 )))
  94. EstimateY[5] <- 1/(1+exp(-(CofFix_ADHD[1] + CofFix_ADHD[2]* 4 )))
  95. EstimateY[6] <- 1/(1+exp(-(CofFix_ADHD[1] + CofFix_ADHD[2]* 5 )))
  97. # construct another dataframe for ploting, then plot it
  98. ChangeNum <- c(0,1,2,3,4,5)
  100. Plot_table_ADHD <- cbind(ChangeNum,EstimateY)
  101. Plot_table_ADHD <- data.frame(Plot_table_ADHD)
  104. ######### PLOT GROUPS TOGETHER #######
  105. Pic_P_all <- ggplot(NULL, aes(x = ChangeNum, y = EstimateY)) +
  106. theme(panel.grid = element_blank()) +
  107. geom_point(data = Plot_table_HC, shape=21,size=3, color = "#7BD389") +
  108. geom_line(data = Plot_table_HC, linetype=3) +
  109. geom_point(data = Plot_table_ADHD, shape=21,size=3, color = "#F17CB0") +
  110. geom_line(data = Plot_table_ADHD, linetype=3) +
  111. xlab(" Number of Changes in 2s Interval ") +
  112. ylab(" Estimated Probability of Segmentation ") +
  113. ylim(0,0.2) +
  114. ggtitle("light green - HC, pink - ADHD")
  116. # change background to white
  117. theme_set(theme_bw())
  118. Pic_P_all +
  119. geom_point(data = Plot_table_HC,color = "#7BD389")+
  120. geom_point(data = Plot_table_ADHD,color = "#F17CB0") +
  121. theme(legend.position = "top")
  124. ################ REGRESSION BASED ON TYPES OF CHANGE ############################################
  125. ### HC ###
  126. input_HC_each <- data.frame()
  127. input_HC_each <- cbind('Subs' = input_HC[,1], Resp = input_HC[,2],
  128. 'Character' = input_HC[,4], 'CharChar' = input_HC[,5],
  129. 'CharObj' = input_HC[,6], 'Temporal' = input_HC[,7],
  130. 'LargeSpace' = input_HC[,8], 'SmallSpace' = input_HC[,9],
  131. 'Cause' = input_HC[,10], 'Goal' = input_HC[,11], 'Scene' = input_HC[,12])
  133. input_HC_each <- as.data.frame(input_HC_each)
  135. # model between response and each type of situational change
  136. Model_Each_HC <- glmer(Resp~ Character + CharChar + CharObj + Temporal + LargeSpace +
  137. SmallSpace + Cause + Goal + Scene + (1 | Subs),
  138. data = input_HC_each, family = binomial, control = glmerControl(optimizer = "bobyqa"), nAGQ = 10)
  140. # evaluate if there are correlations between 9 independent variables
  141. vif(Model_Each_HC,merge_coef = TRUE) # all vif results are smaller than 4, so we can still use these independent variables
  142. summary(Model_Each_HC)
  144. # To get confidence intervals (CIs)
  145. se_Model_Each_HC <- sqrt(diag(vcov(Model_Each_HC)))
  146. # table of estimates with 95% CI
  147. (tab_Model_Each_HC <- cbind(Est = fixef(Model_Each_HC), LL = fixef(Model_Each_HC) - 1.96 * se_Model_Each_HC,
  148. UL = fixef(Model_Each_HC) + 1.96 * se_Model_Each_HC))
  150. # To get odds ratios and order all resuls by Change names
  151. OddsRatio_Model_Each_HC <- exp(tab_Model_Each_HC)
  152. ChanName <-factor(c("Intercept","Character","CharChar","CharObj","Temporal","LargeSpace","SmallSpace","Cause","Goal","Scene"),
  153. levels = c("Scene","Goal","Cause","SmallSpace","LargeSpace","Temporal","CharObj","CharChar","Character","Intercept"))
  154. Order <- c(1.8,1.6,1.4,1.2,1.0,0.8,0.6,0.4,0.2,0)
  155. Order <- as.character(Order)
  156. # OddsRatio <- cbind(ChanName = ChanName, OddsRatio)
  157. OddsRatio_Model_Each_HC <- data.frame(ChanName,OddsRatio_Model_Each_HC)
  158. OddsRatio_Model_Each_HC <- data.frame(Order,OddsRatio_Model_Each_HC)
  159. OddsRatio_Model_Each_HC <- OddsRatio_Model_Each_HC[2:10,] # remove intercept line
  160. OddsRatio_Model_Each_HC <- OddsRatio_Model_Each_HC[order(OddsRatio_Model_Each_HC$Order),]
  162. ### ADHD ###
  163. input_ADHD_each <- data.frame()
  164. input_ADHD_each <- cbind('Subs' = input_ADHD[,1], Resp = input_ADHD[,2],
  165. 'Character' = input_ADHD[,4], 'CharChar' = input_ADHD[,5],
  166. 'CharObj' = input_ADHD[,6], 'Temporal' = input_ADHD[,7],
  167. 'LargeSpace' = input_ADHD[,8], 'SmallSpace' = input_ADHD[,9],
  168. 'Cause' = input_ADHD[,10], 'Goal' = input_ADHD[,11], 'Scene' = input_ADHD[,12])
  170. input_ADHD_each <- as.data.frame(input_ADHD_each)
  172. # model between response and each type of situational change
  173. Model_Each_ADHD <- glmer(Resp~ Character + CharChar + CharObj + Temporal + LargeSpace +
  174. SmallSpace + Cause + Goal + Scene + (1 | Subs),
  175. data = input_ADHD_each, family = binomial, control = glmerControl(optimizer = "bobyqa"), nAGQ = 10)
  177. # evaluate if there are correlations between 9 independent variables
  178. vif(Model_Each_ADHD,merge_coef = TRUE) # all vif results are smaller than 4, so we can still use these independent variables
  179. summary(Model_Each_ADHD)
  181. # To get confidence intervals (CIs)
  182. se_Model_Each_ADHD <- sqrt(diag(vcov(Model_Each_ADHD)))
  183. # table of estimates with 95% CI
  184. (tab_Model_Each_ADHD <- cbind(Est = fixef(Model_Each_ADHD), LL = fixef(Model_Each_ADHD) - 1.96 * se_Model_Each_ADHD,
  185. UL = fixef(Model_Each_ADHD) + 1.96 * se_Model_Each_ADHD))
  187. # To get odds ratios and order all resuls by Change names
  188. OddsRatio_Model_Each_ADHD <- exp(tab_Model_Each_ADHD)
  189. ChanName <-factor(c("Intercept","Character","CharChar","CharObj","Temporal","LargeSpace","SmallSpace","Cause","Goal","Scene"),
  190. levels = c("Scene","Goal","Cause","SmallSpace","LargeSpace","Temporal","CharObj","CharChar","Character","Intercept"))
  191. Order <- c(1.8,1.6,1.4,1.2,1.0,0.8,0.6,0.4,0.2,0)
  192. Order <- as.character(Order)
  193. # OddsRatio <- cbind(ChanName = ChanName, OddsRatio)
  194. OddsRatio_Model_Each_ADHD <- data.frame(ChanName,OddsRatio_Model_Each_ADHD)
  195. OddsRatio_Model_Each_ADHD <- data.frame(Order,OddsRatio_Model_Each_ADHD)
  196. OddsRatio_Model_Each_ADHD <- OddsRatio_Model_Each_ADHD[2:10,] # remove intercept line
  197. OddsRatio_Model_Each_ADHD <- OddsRatio_Model_Each_ADHD[order(OddsRatio_Model_Each_ADHD$Order),]
  200. ######### plot all groups together #####
  201. # plot Odds ratio
  202. Pic_ORs_all <- ggplot(NULL, aes(x = Est, y = Order)) +
  203. theme(panel.grid = element_blank()) +
  204. geom_vline(xintercept = 1,linetype = "dashed") +
  205. geom_point(data = OddsRatio_Model_Each_HC, shape=21,size=3,color = "#7BD389") +
  206. geom_errorbar(data = OddsRatio_Model_Each_HC, aes(xmin = LL,xmax = UL), size = 0.25, width = 0.16,color = "#7BD389") +
  207. geom_point(data = OddsRatio_Model_Each_ADHD, shape=21,size=3,color = "#F17CB0") +
  208. geom_errorbar(data = OddsRatio_Model_Each_ADHD, aes(xmin = LL,xmax = UL), size = 0.25, width = 0.16,color = "#F17CB0") +
  209. xlab(" Odds Ratio") +
  210. ylab(NULL) +
  211. xlim(0,3) +
  212. ggtitle("light green - HC, pink - ADHD, green - Afree")
  214. # change background to white
  215. theme_set(theme_bw())
  216. Pic_ORs_all +
  217. geom_point(data = OddsRatio_Model_Each_HC,color = "#7BD389") +
  218. geom_point(data = OddsRatio_Model_Each_ADHD,color = "#F17CB0") +
  219. geom_hline(yintercept = 2.5) +
  220. geom_hline(yintercept = 4.5) +
  221. geom_hline(yintercept = 6.5) +
  222. geom_hline(yintercept = 8.5) +
  223. geom_hline(yintercept = 10.5) +
  224. geom_hline(yintercept = 12.5) +
  225. geom_hline(yintercept = 14.5) +
  226. geom_hline(yintercept = 16.5) +
  227. theme(axis.ticks.y = element_blank()) + ## delete all ticks in y axis
  228. theme(axis.text.y = element_blank())

B2_LogisticRegression_perGroup_Plotting.R, no license · at the source

Overview

Authors: Xianzhen Zhou1, Foroogh Ghorbani1, Bernhard Hommel2, Veit Roessner1,3, Christian Beste1,3, Astrid Prochnow1
ORCID iDs: Bernhard Hommel, Christian Beste, Astrid Prochnow
  1. Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden 01307, Germany
  2. School of Psychology, Shandong Normal University, Jinan 250014, China
  3. German Center for Child and Adolescent Health (DZKJ), Dresden 01307, Germany
Institutions: Technische Universität Dresden (Germany); Shandong Normal University (China)
Journal: Brain communications, volume 8, issue 5, article fcag351
Dates: received 9 February 2026; accepted 29 August 2026; published online 12 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag351 · PMID 42765003 · PMCID PMC13590235 · OpenAlex W7212398961
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), ADHD (population), cognitive (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Spectral & time-frequency, Physiology & signal measures, Connectivity
Keywords: ADHD, event segmentation, aperiodic activity, EEG
Topic: Attention Deficit Hyperactivity Disorder (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Bundesministerium für Bildung und Forschung (01GL2405B)
Citations: not cited yet (Europe PMC); 54 references in the paper

Abstract

Attention-deficit/hyperactivity disorder (ADHD) is associated with difficulties in organizing behaviour over time, yet the neurophysiological mechanisms underlying these temporal control deficits remain poorly understood. The brain continuously segments ongoing experience into discrete events to guide prediction and adaptive action. In our study, we examined whether adolescents with ADHD differ from neurotypical (NT) peers in how they behaviourally and neurally structure continuous experience. Seventy-three adolescents with ADHD and 73 adolescent NT controls viewed a movie while indicating perceived event boundaries. Concurrent EEG recordings were analysed using time-resolved spectral parameterization to track moment-to-moment changes in aperiodic neural activity. Both groups detected meaningful transitions, but NT adolescents segmented more sensitively to situational changes, particularly social and spatial cues. Across the entire recording window, ADHD participants exhibited steeper aperiodic exponents than NT participants across boundary and no-boundary intervals. Time-resolved analyses revealed that, around event boundaries, ADHD participants showed exaggerated pre-boundary steepening and attenuated post-boundary flattening of the aperiodic exponent—signifying over-stabilization before, and insufficient re-engagement after, event transitions. In ADHD only, behavioural segmentation sensitivity correlated with post-boundary aperiodic modulation. Although counterintuitive given common assumptions about increased neural ‘noise’ in ADHD, this pattern indicates that ADHD is characterized by an imbalance between neural stabilization and re-engagement when parsing continuous experience. Steeper aperiodic activity reflects a restricted neural state space that limits adaptive updating, leading to less context-sensitive segmentation. These findings link everyday temporal disorganization in ADHD to alterations in aperiodic neural dynamics and impaired temporal cognition in ADHD.

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 9 matches between paragraphs and lines of code.

OSF wexfp

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Size: 3 files
Software Heritage: not checked
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (4 files), EEGLAB (3 files), FieldTrip (3 files), ggplot2 (3 files), lme4 (3 files), car (2 files), broom (1 file), easystats (1 file), ICLabel (1 file), Image Processing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file), NumPy (1 file), pandas (1 file), SciPy (1 file), specparam (formerly FOOOF) (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
12 files
At the source: osf.io/wexfp/overview?view_only=8d4bb5053689428896a947efc633ad5d

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;
  • 12 scripts, each with its path and the digest of its content;
  • 9 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

The data are not publicly available due to ethical regulation. The data that support the findings of this study are available from the corresponding author upon reasonable request. The analysis code used for the behavioural and EEG analyses is available on OSF: https://osf.io/wexfp/overview?view_only=8d4bb5053689428896a947efc633ad5d.

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 3, 28 September 2026

  • Funding: added Bundesministerium für Bildung und Forschung: 01GL2405B

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 4 keywords, 50 references.

Cite

This paper

Zhou, X., Ghorbani, F., Hommel, B., Roessner, V., Beste, C., & Prochnow, A. (2026). Time-resolved aperiodic dynamics in event segmentation in attention-deficit/hyperactivity disorder. Brain communications, 8(5), fcag351. https://doi.org/10.1093/braincomms/fcag351

BibTeX

@article{zhou2026time,
author = {Zhou, Xianzhen and Ghorbani, Foroogh and Hommel, Bernhard and Roessner, Veit and Beste, Christian and Prochnow, Astrid},
title = {{Time-resolved aperiodic dynamics in event segmentation in attention-deficit/hyperactivity disorder}},
journal = {Brain communications},
year = {2026},
month = sep,
volume = {8},
number = {5},
pages = {fcag351},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag351},
url = {https://doi.org/10.1093/braincomms/fcag351},
pmid = {42765003},
pmcid = {PMC13590235}
}

RIS

TY - JOUR
AU - Zhou, Xianzhen
AU - Ghorbani, Foroogh
AU - Hommel, Bernhard
AU - Roessner, Veit
AU - Beste, Christian
AU - Prochnow, Astrid
TI - Time-resolved aperiodic dynamics in event segmentation in attention-deficit/hyperactivity disorder
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/09/12
VL - 8
IS - 5
SP - fcag351
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag351
UR - https://doi.org/10.1093/braincomms/fcag351
LA - en
ER -

CSL-JSON

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"title": "Time-resolved aperiodic dynamics in event segmentation in attention-deficit/hyperactivity disorder",
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"author": [
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"container-title-short": "Brain Commun",
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"page": "fcag351",
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"PMID": "42765003",
"PMCID": "PMC13590235",
"ISSN": "2632-1297",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/braincomms/fcag351",
"language": "en",
"issued": {
"date-parts": [
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]
]
}
}

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[6] doi:10.1016/j.isci.2026.116936 [code]
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[7] doi:10.1016/j.neuroimage.2026.122115 [code]
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[8] doi:10.1016/j.celrep.2026.117646 [code]
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App-based mindfulness meditation training enhances cognitive flexibility and modulates ACC and medial frontal gyrus activation during task switching in adolescent OCD.
Journal: Neuroimage. Reports
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