OSCR

Pulvinar-posterior superior temporal sulcus connectivity contributes to non-conscious emotion processing in affective blindsight.

Code ↔ Paper

7 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 7 matches
  1. [1] § Materials and methods › Neuroimaging protocols › Bayesian statistical Modeling ↔ Codes/Bayesian_regression_ROI_analysis_version_8_LOO_PPC.r, lines 78–160 · score 0.94 · posterior predictive checks, adapt_delta, max_treedepth, brms, amygdala pathway, Full Model
  2. [2] § Materials and methods › Neuroimaging protocols › Bayesian statistical Modeling ↔ Logistic regression with SHAP/Bayesian_regression_ROI_analysis_version_7_LOO.r, lines 75–119 · score 0.92 · adapt_delta, max_treedepth, brms, amygdala pathway, Full Model, logistic regression
  3. [3] § Results › Bayesian model comparison reveals preservation of right-lateralized subcortical pathways supports affective blindsight ↔ Logistic regression with SHAP/Bayesian_regression_ROI_analysis_version_7_LOO.r, lines 165–213 · score 0.62 · pairwise Bayes factor, logistic regression, Model comparisons, bridge, heatmap, BF
  4. [4] § Materials and methods › Behavioral data analysis ↔ Codes/Binomial test_FWE_correction.py, lines 9–20 · score 0.61 · Static Emotion Recognition, Dynamic Emotion Recognition, PENN, AFC, binomial
  5. [5] § Results › Bayesian model comparison reveals preservation of right-lateralized subcortical pathways supports affective blindsight ↔ Logistic regression with SHAP/Bayesian_regression_ROI_analysis_version_7_LOO.r, lines 1–63 · score 0.61 · posterior predictive checks, logistic regression, model comparison, Bayesian, affective blindsight, pathways
  6. [6] § Materials and methods › Neuroimaging protocols › Region and tract of interests analysis ↔ Codes/Logistic Regression with Regularization Permuatation testing.py, lines 52–58 · score 0.60 · logistic regression, L1, Lasso, L2, Ridge, model
  7. [7] § Results › Bayesian model comparison reveals preservation of right-lateralized subcortical pathways supports affective blindsight ↔ Codes/Bayesian_regression_ROI_analysis_version_8_LOO_PPC.r, lines 78–160 · score 0.58 · posterior predictive checks, amygdala pathways, Full Model, affective blindsight, Bayesian, regression

Paper

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The authors' code

R · 213 lines · 9.8 KB · no license · 3 matches

  1. # Bayesian Model Comparison for Affective Blindsight Pathways
  2. # Version 6.0: Added posterior predictive checks and 95% credible intervals for all parameters
  4. # 1. Load Required Packages ---------------------------------------------------
  5. library(tidyverse) # Data manipulation
  6. library(brms) # Bayesian regression
  7. library(bridgesampling) # Marginal likelihood estimation
  8. library(pheatmap) # Heatmap visualization
  9. library(readxl) # Excel file reading
  10. library(bayesplot) # MCMC diagnostics
  11. library(cowplot) # Plot grids
  13. # Set plotting theme
  14. color_scheme_set("blue")
  15. theme_set(theme_minimal() + theme(
  16. panel.background = element_rect(fill = "white", colour = NA),
  17. plot.background = element_rect(fill = "white", colour = NA),
  18. legend.background = element_rect(fill = "white", colour = NA),
  19. panel.grid.major = element_line(colour = "lightgray"),
  20. panel.grid.minor = element_line(colour = "lightgray")
  21. ))
  23. # 2. Data Loading and Preparation ---------------------------------------------
  24. data_path <- "D:/PhD/Blindsight/ROI analysis in R/Blindsight_manual_Tract_TOI_Analysis_data_15N_july_2025.xlsx"
  25. data <- read_excel(data_path) %>%
  26. mutate(
  27. Affective_blindsight = factor(Affective_blindsight, levels = c("0", "1")),
  28. Calcrine = as.numeric(scale(Calcrine)),
  29. across(c(R_Pul_Amyg, L_Pul_Amyg, R_Pul_STS, L_Pul_STS), as.factor)
  30. )
  32. # Check predictor variance and factor levels
  33. predictor_vars <- c("Calcrine", "R_Pul_Amyg", "L_Pul_Amyg", "R_Pul_STS", "L_Pul_STS")
  34. for (var in predictor_vars) {
  35. if (is.numeric(data[[var]])) {
  36. var_val <- var(data[[var]], na.rm = TRUE)
  37. if (var_val < .Machine$double.eps) warning(sprintf("WARNING: Variable '%s' has near-zero variance.", var))
  38. } else {
  39. level_counts <- table(data[[var]])
  40. if (nlevels(data[[var]]) < 2 || any(level_counts < 1)) warning(sprintf("WARNING: Factor '%s' has issues with levels.", var))
  41. }
  42. }
  44. # 3. Set Up Output Directory --------------------------------------------------
  45. output_dir <- file.path(dirname(data_path), "Blindsight_Model_Comparison_Outputs-V7_PPC_CI")
  46. if (dir.exists(output_dir)) unlink(output_dir, recursive = TRUE) # Clear previous output directory
  47. dir.create(output_dir, showWarnings = FALSE)
  49. # 4. Define Priors ------------------------------------------------------------
  50. prior_params <- list(
  51. b_Intercept = list(mu = 0, sigma = 2.5),
  52. b_Calcrine = list(mu = 0, sigma = 2.5),
  53. b_R_Pul_Amyg1 = list(mu = 0, sigma = 2.5),
  54. b_L_Pul_Amyg1 = list(mu = 0, sigma = 2.5),
  55. b_R_Pul_STS1 = list(mu = 0, sigma = 2.5),
  56. b_L_Pul_STS1 = list(mu = 0, sigma = 2.5),
  57. `b_R_Pul_Amyg1:R_Pul_STS1` = list(mu = 0, sigma = 1.5),
  58. `b_L_Pul_Amyg1:L_Pul_STS1` = list(mu = 0, sigma = 1.5),
  59. `b_R_Pul_STS1:L_Pul_STS1` = list(mu = 0, sigma = 1.5),
  60. `b_R_Pul_Amyg1:L_Pul_Amyg1` = list(mu = 0, sigma = 1.5),
  61. `b_R_Pul_Amyg1:L_Pul_STS1` = list(mu = 0, sigma = 1.5),
  62. `b_L_Pul_Amyg1:R_Pul_STS1` = list(mu = 0, sigma = 1.5)
  63. )
  65. sample_prior_distribution <- function(param_name) {
  66. prior_info <- prior_params[[param_name]]
  67. if (!is.null(prior_info)) {
  68. rnorm(10000, prior_info$mu, prior_info$sigma)
  69. } else {
  70. warning(sprintf("No prior defined for %s", param_name))
  71. NULL
  72. }
  73. }
  75. # 5. Common brms Arguments ----------------------------------------------------
  76. n_iter <- 8000
  77. warmup <- 2000
  78. common_brm_args <- list(
  79. data = data,
  80. family = bernoulli(),
  81. chains = 4,
  82. iter = n_iter,
  83. warmup = warmup,
  84. cores = parallel::detectCores(),
  85. seed = 1234,
  86. control = list(adapt_delta = 0.95, max_treedepth = 12),
  87. file_refit = "always"
  88. )
  90. # 6. Model Specifications ----------------------------------------------------
  91. model_specs <- list(
  92. Model_0_Null = list(formula = Affective_blindsight ~ 1, file = "null_model",
  93. params = c("b_Intercept")),
  94. Model_Base_Covariate = list(formula = Affective_blindsight ~ Calcrine, file = "covar_model",
  95. params = c("b_Intercept", "b_Calcrine")),
  96. Model_Right_Sided = list(formula = Affective_blindsight ~ Calcrine + R_Pul_Amyg * R_Pul_STS,
  97. file = "right_model",
  98. params = c("b_Intercept", "b_Calcrine", "b_R_Pul_Amyg1", "b_R_Pul_STS1",
  99. "b_R_Pul_Amyg1:R_Pul_STS1")),
  100. Model_Left_Sided = list(formula = Affective_blindsight ~ Calcrine + L_Pul_Amyg * L_Pul_STS,
  101. file = "left_model",
  102. params = c("b_Intercept", "b_Calcrine", "b_L_Pul_Amyg1", "b_L_Pul_STS1",
  103. "b_L_Pul_Amyg1:L_Pul_STS1")),
  104. Model_STS_Pathways = list(formula = Affective_blindsight ~ Calcrine + R_Pul_STS * L_Pul_STS,
  105. file = "sts_model",
  106. params = c("b_Intercept", "b_Calcrine", "b_R_Pul_STS1", "b_L_Pul_STS1",
  107. "b_R_Pul_STS1:L_Pul_STS1")),
  108. Model_Amygdala_Pathways = list(formula = Affective_blindsight ~ Calcrine + R_Pul_Amyg * L_Pul_Amyg,
  109. file = "amyg_model",
  110. params = c("b_Intercept", "b_Calcrine", "b_R_Pul_Amyg1", "b_L_Pul_Amyg1",
  111. "b_R_Pul_Amyg1:L_Pul_Amyg1")),
  112. Model_Full_Model = list(formula = Affective_blindsight ~ Calcrine + (R_Pul_Amyg + L_Pul_Amyg + R_Pul_STS + L_Pul_STS)^2,
  113. file = "full_model",
  114. params = c("b_Intercept", "b_Calcrine", "b_R_Pul_Amyg1", "b_L_Pul_Amyg1",
  115. "b_R_Pul_STS1", "b_L_Pul_STS1", "b_R_Pul_Amyg1:R_Pul_STS1",
  116. "b_L_Pul_Amyg1:L_Pul_STS1", "b_R_Pul_STS1:L_Pul_STS1",
  117. "b_R_Pul_Amyg1:L_Pul_Amyg1", "b_R_Pul_Amyg1:L_Pul_STS1",
  118. "b_L_Pul_Amyg1:R_Pul_STS1"))
  119. )
  121. # 7. Fit Models --------------------------------------------------------------
  122. models <- list()
  123. for (model_name in names(model_specs)) {
  124. spec <- model_specs[[model_name]]
  126. priors <- lapply(spec$params, function(param) {
  127. prior_info <- prior_params[[param]]
  128. if (param == "b_Intercept") {
  129. set_prior(sprintf("student_t(%s, %s, %s)", 3, prior_info$mu, prior_info$sigma), class = "Intercept")
  130. } else {
  131. set_prior(sprintf("normal(%s, %s)", prior_info$mu, prior_info$sigma), class = "b", coef = gsub("b_", "", param))
  132. }
  133. })
  135. model <- do.call(brm, c(list(
  136. formula = spec$formula,
  137. file = file.path(output_dir, spec$file),
  138. prior = do.call(c, priors)
  139. ), common_brm_args))
  141. models[[model_name]] <- model
  143. # Save fixed effects summary with 95% credible intervals
  144. summary_df <- as.data.frame(posterior_summary(model, probs = c(0.025, 0.975))) %>%
  145. rownames_to_column("Parameter") %>%
  146. mutate(Model = model_name, .before = 1)
  148. write_csv(summary_df, file.path(output_dir, sprintf("%s_fixed_effects_with_CI.csv", model_name)))
  150. # Save model summary text
  151. sink(file.path(output_dir, sprintf("%s_summary.txt", model_name)))
  152. print(summary(model))
  153. sink()
  154. }
  156. # 8. Posterior Predictive Checks ---------------------------------------------
  157. for (model_name in names(models)) {
  158. model <- models[[model_name]]
  159. pp_hist <- pp_check(model, type = "hist") + ggtitle(paste(model_name, "PPC Histogram"))
  160. pp_dens <- pp_check(model, type = "dens_overlay") + ggtitle(paste(model_name, "PPC Density Overlay"))
  161. ggsave(file.path(output_dir, sprintf("%s_PPC_hist.png", model_name)), pp_hist, width = 6, height = 4, dpi = 300)
  162. ggsave(file.path(output_dir, sprintf("%s_PPC_density.png", model_name)), pp_dens, width = 6, height = 4, dpi = 300)
  163. }
  165. # 9. Compute Bayes Factors and Posterior Probabilities ------------------------
  166. log_marg_liks <- lapply(models, function(model) {
  167. tryCatch({
  168. bridge_sampler(model, silent = TRUE, maxiter = 1000)$logml
  169. }, error = function(e) NA)
  170. })
  172. valid_log_marg_liks <- log_marg_liks[!is.na(log_marg_liks)]
  173. log_marg_liks_vec <- unlist(valid_log_marg_liks)
  174. null_log_ml <- log_marg_liks_vec["Model_0_Null"]
  176. bf_results_df <- tibble(
  177. Model = names(log_marg_liks_vec),
  178. logML = log_marg_liks_vec,
  179. logBF = ifelse(Model == "Model_0_Null", 0, logML - null_log_ml),
  180. BF = exp(logBF)
  181. ) %>% arrange(desc(BF))
  183. posterior_probs <- bf_results_df %>%
  184. mutate(Posterior_Prob = BF / sum(BF)) %>%
  185. select(Model, BF, logBF, Posterior_Prob)
  187. write_csv(posterior_probs, file.path(output_dir, "bayes_factors_and_probs.csv"))
  189. # 10. Pairwise Bayes Factor Heatmap ------------------------------------------
  190. n_models <- length(log_marg_liks_vec)
  191. pairwise_log10_bf_matrix <- outer(log_marg_liks_vec, log_marg_liks_vec, FUN = function(x, y) (x - y)/log(10))
  192. pheatmap(pairwise_log10_bf_matrix,
  193. cluster_rows = FALSE,
  194. cluster_cols = FALSE,
  195. display_numbers = TRUE,
  196. number_format = "%.2f",
  197. main = "Pairwise log10 Bayes Factor",
  198. filename = file.path(output_dir, "pairwise_log10_bayes_factor_heatmap.png"),
  199. color = colorRampPalette(c("#4575b4", "white", "#d73027"))(100))
  201. # 11. Save All Results --------------------------------------------------------
  202. save(models, posterior_probs, bf_results_df, log_marg_liks_vec, data,
  203. file = file.path(output_dir, "blindsight_model_comparison_results.RData"))
  205. # 12. Completion Message ------------------------------------------------------
  206. cat("\nANALYSIS COMPLETE\n")
  207. cat(sprintf("All outputs saved to: %s\n", output_dir))
  208. cat("\nOutputs include:\n")
  209. cat("- Fixed effects summaries with 95% credible intervals (.csv)\n")
  210. cat("- Posterior predictive check plots (.png)\n")
  211. cat("- Bayes Factors and posterior probabilities (.csv)\n")
  212. cat("- Pairwise Bayes Factor heatmap (.png)\n")
  213. cat("- Model summaries (.txt) and RData for all models\n")

Bayesian_regression_ROI_analysis_version_7_LOO.r, no license · at the source

Overview

Authors: Appaswamy Thirumal Prabhakar1,2, Kavitha Margabandhu1, Christilda John Bosco1, Rohan Thomas Jepegnanam1, Thanusha Prasad1, Sowmiya Sampathkumar1, Evelyn Sheena Sunderraj1, John Davis Prasad1, George Abraham Ninan1, Deepti Bal1, Harshad Vanjare3, Anitha Jasper3, Pavithra Mannam3, Allison M McKendrick4,5,6, Olivia Carter2, Marta Isabel Garrido2,7
ORCID iDs: Appaswamy Thirumal Prabhakar, Harshad Vanjare, Olivia Carter
  1. Cognitive Neuroscience and Clinical Phenomenology Laboratory, Department of Neurological Sciences, University of Melbourne and Christian Medical College Vellore, Kilminnal, 632517, Ranipet District, Tamil nadu, India
  2. Melbourne School of Psychological Sciences, Redmond Barry Building, The University of Melbourne, Spencer Road, Parkville VIC 3010, Melbourne, Victoria, Australia
  3. Department of Radiology, Christian Medical College Vellore, Ida Scudder Road, 632004, Vellore, Tamil Nadu, India
  4. Department of Optometry and Vision Science, School of Allied Health, University of Western Australia, 2/39 Fairway, Crawley, WA 6009, Perth, Western Australia, Australia
  5. Lions Eye Institute, 2 Verdun St, Nedlands, WA 6009, Perth, Western Australia, Australia
  6. Department of Optometry & Vision Sciences, University of Melbourne, Grattan Street, Parkville, VIC 3010, Melbourne, Victoria, Australia
  7. Graeme Clark Institute for Biomedical Engineering, Grattan Street, Parkville, VIC 3010, Melbourne, Victoria, Australia
Institutions: The University of Melbourne (Australia); Christian Medical College, Vellore (India); Lions Eye Institute (Australia); The University of Western Australia (Australia)
Journal: Cerebral cortex (New York, N.Y. : 1991), volume 36, issue 3, article bhag032
Dates: received 26 November 2025; accepted 23 February 2026; published online 28 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/cercor/bhag032 · PMID 41902577 · PMCID PMC13168818 · OpenAlex W7141980916
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), other condition (population), cognitive (subfield)
Methods: Statistics, Machine learning, Connectivity, fMRI & imaging
Keywords: affective blindsight, amygdala, pSTS, pulvinar, third visual pathway
MeSH: Blindness*, Emotions*, Pulvinar*, Temporal Lobe*, Adult, Aged, Amygdala, Brain Mapping, Diffusion Tensor Imaging, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neural Pathways, Visual Pathways (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: University of Melbourne PhD Scholarship
Citations: not cited yet (Europe PMC); 78 references in the paper

Abstract

Affective blindsight, the capacity to discriminate emotional stimuli despite bilateral damage to the primary visual cortex (V1) and without conscious awareness, offers a unique model of non-conscious visual processing. Subcortical pathways involving the pulvinar and amygdala have been proposed, but putative cortical contributions remain unclear. We examined 182 patients, including 31 with bilateral V1 lesions. Among these, 15 had cortical visual loss and seven showed affective blindsight. Using behavioral testing, lesion symptom mapping, and tractography, we found that preserved pulvinar connectivity with both the posterior superior temporal sulcus (pSTS) and the amygdala is necessary for affective blindsight. These findings provide causal evidence for a multi-route architecture, identifying the pulvinar–pSTS pathway, alongside the pulvinar–amygdala pathway, as a critical substrate for non-conscious affective processing.

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

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cogneuro-rgb

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Size: 93 files, 14 scripts
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Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: brms (6 files), pandas (6 files), tidyverse (6 files), cowplot (5 files), NumPy (5 files), pheatmap (5 files), Matplotlib (4 files), SciPy (2 files), statsmodels (2 files), ggplot2 (1 file), reshape2 (1 file), scikit-learn (1 file), seaborn (1 file)
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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 16 authors, 5 keywords, 16 MeSH terms, 1 funder, 71 references.

Cite

This paper

Prabhakar, A. T., Margabandhu, K., Bosco, C. J., Jepegnanam, R. T., Prasad, T., Sampathkumar, S., Sunderraj, E. S., Prasad, J. D., Abraham Ninan, G., Bal, D., Vanjare, H., Jasper, A., Mannam, P., M McKendrick, A., Carter, O., & Garrido, M. I. (2026). Pulvinar-posterior superior temporal sulcus connectivity contributes to non-conscious emotion processing in affective blindsight. Cerebral cortex (New York, N.Y. : 1991), 36(3), bhag032. https://doi.org/10.1093/cercor/bhag032

BibTeX

@article{prabhakar2026pulvinar,
author = {Prabhakar, Appaswamy Thirumal and Margabandhu, Kavitha and Bosco, Christilda John and Jepegnanam, Rohan Thomas and Prasad, Thanusha and Sampathkumar, Sowmiya and Sunderraj, Evelyn Sheena and Prasad, John Davis and Abraham Ninan, George and Bal, Deepti and Vanjare, Harshad and Jasper, Anitha and Mannam, Pavithra and M McKendrick, Allison and Carter, Olivia and Garrido, Marta Isabel},
title = {{Pulvinar-posterior superior temporal sulcus connectivity contributes to non-conscious emotion processing in affective blindsight}},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
year = {2026},
month = mar,
volume = {36},
number = {3},
pages = {bhag032},
publisher = {Oxford University Press},
issn = {1047-3211},
doi = {10.1093/cercor/bhag032},
url = {https://doi.org/10.1093/cercor/bhag032},
pmid = {41902577},
pmcid = {PMC13168818}
}

RIS

TY - JOUR
AU - Prabhakar, Appaswamy Thirumal
AU - Margabandhu, Kavitha
AU - Bosco, Christilda John
AU - Jepegnanam, Rohan Thomas
AU - Prasad, Thanusha
AU - Sampathkumar, Sowmiya
AU - Sunderraj, Evelyn Sheena
AU - Prasad, John Davis
AU - Abraham Ninan, George
AU - Bal, Deepti
AU - Vanjare, Harshad
AU - Jasper, Anitha
AU - Mannam, Pavithra
AU - M McKendrick, Allison
AU - Carter, Olivia
AU - Garrido, Marta Isabel
TI - Pulvinar-posterior superior temporal sulcus connectivity contributes to non-conscious emotion processing in affective blindsight
T2 - Cerebral cortex (New York, N.Y. : 1991)
J2 - Cereb Cortex
PY - 2026
DA - 2026/03/01
VL - 36
IS - 3
SP - bhag032
SN - 1047-3211
PB - Oxford University Press
DO - 10.1093/cercor/bhag032
UR - https://doi.org/10.1093/cercor/bhag032
LA - en
ER -

CSL-JSON

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}

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