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Understanding variability in full-term newborns' fNIRS data: the impact of birth weight and gestational age on infants' speech perception abilities.

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  1. [1] § Materials and Methods › Data Analysis › Extraction of individual gestational age and birth weight data ↔ Code/ROIavg_Cond2vs1_HbR.Rmd, lines 163–269 · score 0.63 · 255–298 days, 2235–4535, 255 days, Gestational ages, birth weight
  2. [2] § Materials and Methods › Data Analysis › Extraction of individual gestational age and birth weight data ↔ Code/SingleChannels_Cond2vs1_HbR.Rmd, lines 261–367 · score 0.63 · 255–298 days, 2235–4535, 255 days, Gestational ages, birth weight

Paper

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The authors' code

R Markdown · 270 lines · 7.1 KB · no license · 1 match

  1. ---
  2. title: "Analysis: ROI-based - Condition 2 vs 1 - HbR"
  3. author: "Jessica Gemignani"
  4. date: "`r Sys.Date()`"
  5. output:
  6. html_document: default
  7. pdf_document: default
  8. ---
  10. ## *Understanding variability in full-term newborns' NIRS data: the impact of birth weight and gestational age on infants’ speech perception abilities*
  11. ### Neurophotonics (XXX bibliographic details to be added)
  13. Authors:
  14. Noémi Szeberényi (a,d), Judit Gervain (a,b,c), Jessica Gemignani (a,b) *
  16. a University of Padua, Department of Developmental Psychology and Socialization, Padua, Italy
  18. b Padova Neuroscience Center, Padua, Italy
  20. c Université Paris Cité & CNRS, Integrative Neuroscience and Cognition Center, Paris, France
  22. d Budapest University of Technology and Economics, Department of Cognitive Neuroscience, Budapest, Hungary
  25. *Correspondence: jessica.gemignani at unipd.it
  27. These scripts reproduce statistical analyses reported in the manuscript.
  30. ```{r setup, include=FALSE}
  32. knitr::opts_chunk$set(echo = TRUE)
  34. #libraries
  35. #libraries
  36. library(plyr)
  37. library(dplyr)
  38. library(lme4)
  39. library(psych)
  40. library(broom)
  41. library(car)
  42. library(ggplot2)
  43. #library(qqplotr)
  44. library(emmeans)
  45. library(leaps)
  46. library(lmerTest)
  47. library(magrittr)
  48. library(Matrix)
  49. library(olsrr)
  50. library(here)
  51. library(interactions)
  52. library(ggpubr)
  53. library(here)
  54. library(MuMIn)
  56. Final_Activations= read.csv(here::here('Full final data manuscript', 'Effects_RN_HbR_Final.csv'), sep = ",")
  57. # I'll call them again AvgAct only so I can recycle the script, but this time these are actually effect sizes (yi)
  58. Final_Activations$AvgAct = Final_Activations$yi;
  60. ```
  62. # Preparation
  63. Define categorical and numerical variables. Rename columns.
  65. ```{r, include=TRUE}
  66. # Define categorical variables (not numericals)
  67. Final_Activations$ROI= as.factor(Final_Activations$ROI)
  68. Final_Activations$Hemisphere= as.factor(Final_Activations$Hemisphere)
  69. Final_Activations$SubjectID= as.factor(Final_Activations$SubjectID)
  70. Final_Activations$ExperimentName= as.factor(Final_Activations$ExperimentName)
  71. Final_Activations$Age.Test= as.numeric(Final_Activations$Age.Test)
  72. Final_Activations$Birth.Weight= as.numeric(Final_Activations$Birth.Weight)
  73. Final_Activations$GA.in.days= as.numeric(Final_Activations$GA.in.days)
  76. names(Final_Activations)[names(Final_Activations) == "GA.in.days"]= "GA"
  77. names(Final_Activations)[names(Final_Activations) == "Birth.Weight"]= "BW"
  78. names(Final_Activations)[names(Final_Activations) == "Age.Test"]= "Days"
  82. ```
  84. # Model selection: random effects
  85. ```{r, include=TRUE}
  87. # Remove rows displaying NaN in any of these cells, otherwise it won't be able to compare model
  89. vars <- c("AvgAct", "BW", "GA", "ROI", "Hemisphere", "SubjectID", "Days")
  90. data_filt <- na.omit(Final_Activations[, vars])
  92. # Compare these null_models in terms of AIC and explained variance
  93. null_model1 = lmer(AvgAct~(1|SubjectID),data= data_filt,REML = FALSE)
  94. null_model2= lmer(AvgAct~(1|SubjectID)+ (1|SubjectID:ROI) ,data= data_filt,REML = FALSE)
  96. AIC(null_model1, null_model2)
  98. # Null_model2 selected: (1|SubjectID)+ (1|SubjectID:ROI)
  100. ```
  103. # Model selection: fixed effects
  104. Note: run only once because it takes forever, then extract best_model and paste it in the next chunk (var: "selected_full").
  106. ```{r, include=TRUE}
  108. # global_model <- lmer(
  109. # AvgAct ~ BW + GA + Days + ROI + Hemisphere +
  110. # BW:ROI + GA:ROI + Days:ROI +
  111. # BW:Hemisphere + GA:Hemisphere + Days:Hemisphere + ROI:Hemisphere +
  112. # BW:ROI:Hemisphere + GA:ROI:Hemisphere + Days:ROI:Hemisphere +
  113. # (1|SubjectID) + (1|SubjectID:ROI) ,
  114. # data = data_filt,
  115. # REML = FALSE,
  116. # control = lmerControl(check.scaleX = "ignore")
  117. # )
  118. #
  119. # options(na.action = "na.fail")
  120. # dd <- dredge(global_model) # these are all possible models, returned in terms of ascending AIC
  121. #
  122. # # Select the top N models displaying very similar AIC
  123. # top_models <- get.models(dd, subset = delta < 3)
  124. #
  125. # # Keep only models with at least 1 fixed effect (otherwise it will frequently return the random only one)
  126. #
  127. # top_models <- Filter(function(m) {
  128. # length(lme4::fixef(m)) > 1 # >1 because intercept is always included
  129. # }, top_models)
  130. #
  131. # # Among the top N models, select the least complex:
  132. #
  133. # get_complexity <- function(fm) {
  134. # length(lme4::fixef(fm)) + length(lme4::VarCorr(fm))
  135. # }
  136. #
  137. # complexity <- sapply(top_models, get_complexity)
  138. #
  139. # # Best model:
  140. # best_model <- top_models[[which.min(complexity)]]
  141. #
  142. # selected_full= best_model
  144. ```
  146. # Refit best model with REML + anova
  148. ```{r, include=TRUE}
  150. options(na.action = "na.omit")
  151. selected_full= lmer( AvgAct ~ BW + GA + ROI + BW:ROI + GA:ROI + (1 | SubjectID) + (1 | SubjectID:ROI), data= data_filt, REML = TRUE)
  153. anova(selected_full)
  155. ```
  157. # Main effect of ROI
  159. ```{r, include=TRUE}
  160. emmeans(selected_full, pairwise ~ ROI)
  161. ```
  163. # Interaction GA x ROI and BW x ROI
  165. ```{r, include=TRUE}
  167. ####
  168. # Slopes GA x ROI:
  170. slopesGAxROI= emtrends(selected_full, pairwise ~ ROI, var = "GA")
  172. slopesGAxROI
  174. slopesBWxROI= emtrends(selected_full, pairwise ~ ROI, var = "BW")
  176. slopesBWxROI
  179. # Only to create plot:
  180. grid <- emmeans(selected_full, ~ GA * ROI,
  181. at = list(GA = seq(255, 298, length.out = 100)))
  183. grid_df <- as.data.frame(grid)
  185. # plot
  186. p_ga <- ggplot(grid_df, aes(x = GA, y = emmean, color = ROI)) +
  188. geom_line(linewidth = 1) + # <- ONLY solid lines here
  189. geom_ribbon(aes(ymin = lower.CL, ymax = upper.CL, fill = ROI),
  190. alpha = 0.15, color = NA) +
  192. scale_color_manual(values = c("#00BFC4", "#F8766D")) +
  193. scale_fill_manual(values = c("#00BFC4", "#F8766D")) +
  195. theme_bw() +
  196. theme(
  197. legend.position = "bottom",
  198. text = element_text(size = 25)
  199. ) +
  201. labs(
  202. x = "Gestational Age (days)",
  203. y = "Linear prediction (mmol x mm)"
  204. ) +
  206. geom_hline(yintercept = 0, linetype = "dashed") +
  207. coord_cartesian(ylim = c(-0.6, 0.6),
  208. xlim = c(255, 298)) +
  209. theme(plot.margin = margin(t = 10, r = 20, b = 10, l = 10, "pt")) # Add bottom margin
  212. # show
  213. p_ga
  215. # save file
  216. ggsave("ROIavg_RvsN_HbR_GAxROI.png",
  217. plot = p_ga,
  218. width = 25,
  219. height = 20,
  220. units = "cm",
  221. dpi = 300)
  223. # #####
  224. # #BW:
  225. #
  226. # Only to create plot:
  227. grid <- emmeans(selected_full, ~ BW * ROI,
  228. at = list(BW = seq(2235, 4535, length.out = 100)))
  230. grid_df <- as.data.frame(grid)
  232. # plot
  233. p_bw <- ggplot(grid_df, aes(x = BW, y = emmean, color = ROI)) +
  235. geom_line(linewidth = 1) + # <- ONLY solid lines here
  236. geom_ribbon(aes(ymin = lower.CL, ymax = upper.CL, fill = ROI),
  237. alpha = 0.15, color = NA) +
  239. scale_color_manual(values = c("#00BFC4", "#F8766D")) +
  240. scale_fill_manual(values = c("#00BFC4", "#F8766D")) +
  242. theme_bw() +
  243. theme(
  244. legend.position = "bottom",
  245. text = element_text(size = 25)
  246. ) +
  248. labs(
  249. x = "Birth weight (gr)",
  250. y = "Linear prediction (mmol x mm)"
  251. ) +
  253. geom_hline(yintercept = 0, linetype = "dashed") +
  254. coord_cartesian(ylim = c(-0.6, 0.6),
  255. xlim = c(2235, 4535)) +
  256. theme(plot.margin = margin(t = 10, r = 20, b = 10, l = 10, "pt")) # Add bottom margin
  258. # show
  259. p_bw
  261. # save file
  262. ggsave("ROIavg_RvsN_HbR_BWxROI.png",
  263. plot = p_bw,
  264. width = 25,
  265. height = 20,
  266. units = "cm",
  267. dpi = 300)
  269. ```

ROIavg_Cond2vs1_HbR.Rmd, no license · at the source

Overview

Authors: Noémi Petra Szeberényi1,2, Judit Gervain1,3,4, Jessica Gemignani1,3
ORCID iDs: Noémi Petra Szeberényi, Judit Gervain, Jessica Gemignani
  1. University of Padua, Department of Developmental Psychology and Socialisation, Padua, Italy
  2. Budapest University of Technology and Economics, Department of Cognitive Science, Budapest, Hungary
  3. Padova Neuroscience Center, Padua, Italy
  4. Université Paris Cité & CNRS, Integrative Neuroscience and Cognition Center, Paris, France
Institutions: University of Padua (Italy)
Journal: Neurophotonics, volume 13, issue 3, article 035001
Dates: received 31 July 2025; accepted 26 May 2026; published online 6 July 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1117/1.nph.13.3.035001 · PMID 42441174 · PMCID PMC13336347 · OpenAlex W7167516473
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fNIRS (modality), human (organism), developmental (subfield)
Methods: Spectral & time-frequency, Statistics, fMRI & imaging
Keywords: newborns, perinatal physiological measures, prenatal language experience, hemodynamic response, near-infrared spectroscopy
Topic: Infant Development and Preterm Care (Pediatrics, Perinatology and Child Health, Medicine), according to OpenAlex
Funding: National Research Development and Innovation Fund of the Ministry of Culture and Innovation and the Budapest University of Technology and Economics, Doctoral Excellence Fellowship Programme (DCEP) (DKÖP-25-1-BME-15); European Commission (101031716); Ecos-Sud (C20S02); HORIZON EUROPE European Research Council (773202); Agence Nationale de la Recherche (ANR-10-LABX-0083); MUR (R204MPRHKE, PRIN-2022WX3FM5); European Union−Next Generation EU (NRRP M6C2-SYNPHONIA)
Citations: not cited yet (Europe PMC); 56 references in the paper

Abstract

Significance: Language acquisition is a complex process already influenced by prenatal neural development and auditory experiences. From the onset of the third trimester, fetuses perceive sounds already influencing the fetal brain.

Aim: The study investigates how the length of intrauterine language exposure, indexed by gestational age (GA), and overall maturation, indexed by birth weight (BW), affect full-term newborns’ brain responses to linguistic stimuli.

Approach: Data from 14 near-infrared spectroscopy studies testing responses to different auditory sound patterns in 192 0- to 7-day-old newborns were pooled together and analyzed to assess the impact of GA and BW on changes in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR).

Results: Results showed that, for HbO, activations in both considered conditions were larger in the temporal than in the frontal areas, irrespective of BW or GA. A similar spatial pattern was observed for HbR, with stronger responses in the temporal compared with frontal regions across conditions. In contrast, when considering effect sizes and reflecting discrimination abilities, these were more strongly associated with BW in the bilateral frontal regions, whereas in the bilateral temporal regions, they were more strongly associated with GA.

Conclusions: The findings suggest a differential impact of BW and GA on neural measures of linguistic sensitivity in newborns, reflecting their roles in biological maturation and auditory experience, respectively. Overall, the study suggests that both the length of prenatal experience and maturation play significant roles in shaping newborns’ hemodynamic responses.

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

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License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
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Size: 24 files, 12 scripts
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Found in: “Code and Data Availability”
Holds: 12 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: broom (12 files), car (12 files), emmeans (12 files), ggplot2 (12 files), lme4 (12 files), lmerTest (12 files), psych (12 files), tidyverse (12 files), ggpubr (11 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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At the source: osf.io/6mzqp

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

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Code and Data Availability

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

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Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 7 funders, 54 references.

Cite

This paper

Szeberényi, N. P., Gervain, J., & Gemignani, J. (2026). Understanding variability in full-term newborns' fNIRS data: the impact of birth weight and gestational age on infants' speech perception abilities. Neurophotonics, 13(3), 035001. https://doi.org/10.1117/1.nph.13.3.035001

BibTeX

@article{szeberenyi2026understanding,
author = {Szeberényi, Noémi Petra and Gervain, Judit and Gemignani, Jessica},
title = {{Understanding variability in full-term newborns' fNIRS data: the impact of birth weight and gestational age on infants' speech perception abilities}},
journal = {Neurophotonics},
year = {2026},
month = jul,
volume = {13},
number = {3},
pages = {035001},
publisher = {Society of Photo-Optical Instrumentation Engineers},
issn = {2329-423X},
doi = {10.1117/1.nph.13.3.035001},
url = {https://doi.org/10.1117/1.nph.13.3.035001},
pmid = {42441174},
pmcid = {PMC13336347}
}

RIS

TY - JOUR
AU - Szeberényi, Noémi Petra
AU - Gervain, Judit
AU - Gemignani, Jessica
TI - Understanding variability in full-term newborns' fNIRS data: the impact of birth weight and gestational age on infants' speech perception abilities
T2 - Neurophotonics
J2 - Neurophotonics
PY - 2026
DA - 2026/07/06
VL - 13
IS - 3
SP - 035001
SN - 2329-423X
PB - Society of Photo-Optical Instrumentation Engineers
DO - 10.1117/1.nph.13.3.035001
UR - https://doi.org/10.1117/1.nph.13.3.035001
LA - en
ER -

CSL-JSON

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"PMCID": "PMC13336347",
"ISSN": "2329-423X",
"publisher": "Society of Photo-Optical Instrumentation Engineers",
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