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Remoteness sensitive theta network dynamics during early autobiographical memory access.

Code ↔ Paper

8 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 8 matches
  1. [1] § Methods › Data analysis › Behavioral data ↔ behavioural_analysis/behavioural_analysis_logistic_regression.R, lines 1–74 · score 0.92 · Logistic Regression, ROC, caret, hyperparameters, binomial, family
  2. [2] § Methods › Data analysis › Directed connectivity analysis › EEG processing ↔ functional_connectivity_analysis/granger_tfr_cluster.m, lines 54–141 · score 0.82 · improve stationarity, frequency domain, Granger Causality, functional connectivity, bsxfun, ERP
  3. [3] § Methods › Data analysis › Directed connectivity analysis › Directed weighted networks ↔ visualization/figure_5/anterior_posterior_flow.m, lines 4–24 · score 0.64 · 800–1100 ms, 800 ms, anterior, posterior, flow, window
  4. [4] § Methods › Data analysis › EEG data preprocessing ↔ preprocessing/c_raw_ICA.py, lines 19–68 · score 0.64 · ICA, MNE, IIR, Component, phase, filtered
  5. [5] § Methods › Data analysis › Directed connectivity analysis › Directed weighted networks ↔ functional_connectivity_analysis/granger_tfr_cluster.m, lines 54–141 · score 0.63 · frequency domain Granger, sliding windows, ERP, detrended, subtracted, scored
  6. [6] § Results › Behavioural: characterization of recall ↔ behavioural_analysis/behavioural_analysis_logistic_regression.R, lines 1–74 · score 0.59 · logistic regression, Elastic, coefficients, predict, variable, behavioral
  7. [7] § Results › Neural correlates › Functional connectivity ↔ visualization/figure_5/anterior_posterior_flow.m, lines 4–24 · score 0.57 · 800–1100 ms, 800 ms, anterior, posterior, flow, window
  8. [8] § Methods › Data analysis › Cluster-based time-frequency comparison ↔ time_frequency_analysis/b_cluster_based_stats_TFR.m, lines 25–69 · score 0.55 · neighboring, tailed, 4–8 Hz, FieldTrip, cluster, channels

Paper

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The authors' code

R · 98 lines · 3.4 KB · no license · 2 matches

  1. # ==============================================================================
  2. # Logistic Regression Analysis with Elastic Net and Bootstrapping
  3. # ==============================================================================
  5. # Load required libraries
  6. library(glmnet)
  7. library(reshape)
  8. library(ggplot2)
  9. library(GGally)
  10. library(caret)
  11. library(dplyr)
  12. library(boot)
  13. library(lme4)
  14. library(pROC)
  16. # 1. Data Loading and Preprocessing
  17. data_raw <- read.csv("/home/usuario/Documentos/Doc/matlab/resultados/onset/metadata.csv")
  19. data_filtrada <- data_raw %>%
  20. dplyr::select(-tipo_marca, -participante, -Potencia_theta, -orden_preg, -Var1,
  21. -offset_samples, -outliers, -tipo_preg, -numero_preg, -onset_samples,
  22. -event_id, -edad_memoria, -edad_memoria_invertida, -tiempo_subjetivo) %>%
  23. filter(grupo_preg %in% c(0, 1))
  25. data_filtrada$grupo_preg <- factor(data_filtrada$grupo_preg,
  26. levels = c(0, 1),
  27. labels = c("remotos", "recientes"))
  29. # 2. Model Training with Caret (Elastic Net)
  30. set.seed(3)
  31. k_folds <- 5
  32. cv_index <- createFolds(data_filtrada$grupo_preg, k = k_folds, list = TRUE, returnTrain = TRUE)
  34. trctrl <- trainControl(
  35. method = "cv",
  36. number = k_folds,
  37. classProbs = TRUE,
  38. summaryFunction = twoClassSummary,
  39. index = cv_index,
  40. savePredictions = "all"
  41. )
  43. # Hyperparameter grid
  44. log_lambda_grid <- seq(0, -4, length = 100)
  45. lambda_grid <- 10^log_lambda_grid
  46. alpha_grid <- seq(0, 1, length = 10)
  47. tune_grid <- expand.grid(alpha = alpha_grid, lambda = lambda_grid)
  49. # Train model
  50. logFit <- train(grupo_preg ~ ., data = data_filtrada,
  51. method = "glmnet",
  52. trControl = trctrl,
  53. metric = 'ROC',
  54. tuneGrid = tune_grid)
  56. # Extract best parameters
  57. best_lambda <- logFit$bestTune$lambda
  58. best_alpha <- logFit$bestTune$alpha
  60. # 3. Visualization
  61. plot(varImp(logFit), top = 6, main = "Feature Importance")
  63. # Plot coefficients against lambda
  64. Xtrain <- as.matrix(data_filtrada[, -which(names(data_filtrada) == "grupo_preg")])
  65. L1_models <- glmnet(Xtrain, data_filtrada$grupo_preg, alpha = best_alpha, lambda = lambda_grid, family = "binomial")
  66. L1_coef_Df <- data.frame(t(as.matrix(coef(L1_models))))
  67. L1_coef_Df$grid <- log_lambda_grid
  68. L1_melted <- melt(L1_coef_Df, id.vars = "grid")
  70. ggplot(L1_melted, aes(x = grid, y = value, col = variable)) +
  71. geom_line(size = 1) +
  72. xlab('log(lambda)') + ylab('Coefficients') +
  73. theme_minimal() +
  74. geom_vline(xintercept = log10(best_lambda), color = 'black', linetype = "dashed")
  76. # 4. Bootstrapping for Confidence Intervals (99%)
  77. coef_boot_func <- function(data, indices) {
  78. train_boot <- data[indices, ]
  79. # Ensure index -6 corresponds to target correctly
  80. model_boot <- glmnet(as.matrix(train_boot[, -which(names(train_boot) == "grupo_preg")]),
  81. train_boot$grupo_preg,
  82. alpha = best_alpha,
  83. lambda = best_lambda,
  84. family = "binomial")
  85. return(as.vector(coef(model_boot)))
  86. }
  88. boot_results <- boot(data = data_filtrada, statistic = coef_boot_func, R = 1000)
  90. # Calculate 99% CI (percentile method)
  91. alpha_level <- 0.01
  92. ics <- apply(boot_results$t, 2, quantile, probs = c(alpha_level/2, 1 - alpha_level/2))
  93. ics_final <- t(ics)
  95. colnames(ics_final) <- c("IC 0.5%", "IC 99.5%")
  96. rownames(ics_final) <- c("Intercept", colnames(Xtrain))
  98. print(round(ics_final, 4))

behavioural_analysis_logistic_regression.R at commit 55cd160, no license · at the source

Overview

Authors: María Carla Navas1, Ignacio Ferrelli1, María Eugenia Pedreira1, Rodrigo S. Fernández1, Luz Bavassi1,2
  1. Consejo Nacional de Investigaciones Científicas y Técnicas - Universidad de Buenos Aires, Instituto de Fisiología, Biología Molecular y Neurociencia (IFIBYNE),Buenos Aires, Argentina
  2. Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física,Buenos Aires, Argentina
Institutions: Universidad de Buenos Aires (Argentina)
Journal: Scientific reports, volume 16, issue 1, article 28425
Dates: received 15 December 2025; accepted 15 June 2026; published online 22 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-58505-y · PMID 42324345 · PMCID PMC13562634 · OpenAlex W7165496277
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials, Graphs, Physiology & signal measures
Keywords: EEG, Theta band, Granger causality, Autobiographical memory, Neuroscience, Psychology
MeSH: Brain*, Memory, Episodic*, Theta Rhythm*, Adult, Brain Mapping, Electroencephalography, Female, Humans, Male, Mental Recall, Young Adult (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: CONICET; Agencia Nacional de Promoción Científica y Tecnológica; University of Buenos Aires
Citations: not cited yet (Europe PMC); 51 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

Zenodo 20633675

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: MNE-Python (7 files), NumPy (7 files), pandas (5 files), Statistics and Machine Learning Toolbox (4 files), Matplotlib (4 files), FieldTrip (3 files), ggplot2 (2 files), seaborn (2 files), tidyverse (2 files), autoreject (1 file), caret (1 file), glmnet (1 file), lme4 (1 file), NetworkX (1 file), pROC (1 file), reshape2 (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
16 files
At the source: doi.org/10.5281/zenodo.20633675

carla94/Remoteness-sensitive-theta-network-dynamics-during-early-autobiographical-memory-access

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 55cd160557439dde77bc4f3b57f3e2483b3e641f, 10 June 2026
Languages: Python (8), MATLAB (5), R (2)
Size: 17 files, 15 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: MNE-Python (7 files), NumPy (7 files), pandas (5 files), Statistics and Machine Learning Toolbox (4 files), Matplotlib (4 files), FieldTrip (3 files), ggplot2 (2 files), seaborn (2 files), tidyverse (2 files), autoreject (1 file), caret (1 file), glmnet (1 file), lme4 (1 file), NetworkX (1 file), pROC (1 file), reshape2 (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
16 files
At the source: github.com/carla94/Remoteness-sensitive-theta-network-dynamics-during-early-autobiographical-memory-access

The paper's code and data availability statement is in the Data section.

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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Data

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Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41598-026-58505-y.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 6 keywords, 11 MeSH terms, 3 funders, 49 references.

Cite

This paper

Navas, M. C., Ferrelli, I., Pedreira, M. E., Fernández, R. S., & Bavassi, L. (2026). Remoteness sensitive theta network dynamics during early autobiographical memory access. Scientific reports, 16(1), 28425. https://doi.org/10.1038/s41598-026-58505-y

BibTeX

@article{navas2026remoteness,
author = {Navas, María Carla and Ferrelli, Ignacio and Pedreira, María Eugenia and Fernández, Rodrigo S. and Bavassi, Luz},
title = {{Remoteness sensitive theta network dynamics during early autobiographical memory access}},
journal = {Scientific reports},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {28425},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-58505-y},
url = {https://doi.org/10.1038/s41598-026-58505-y},
pmid = {42324345},
pmcid = {PMC13562634}
}

RIS

TY - JOUR
AU - Navas, María Carla
AU - Ferrelli, Ignacio
AU - Pedreira, María Eugenia
AU - Fernández, Rodrigo S.
AU - Bavassi, Luz
TI - Remoteness sensitive theta network dynamics during early autobiographical memory access
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/06/22
VL - 16
IS - 1
SP - 28425
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-58505-y
UR - https://doi.org/10.1038/s41598-026-58505-y
LA - en
ER -

CSL-JSON

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