Motor- and cognitive-dominant functional network adaptations supporting dual-task performance in older adults.

Code ↔ Paper

6 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 6 matches

[1] § Methods › fMRI data analysis › Task-based GLM analysis and ROI selection ↔ analysis_code/ROIs_extraction_from_SPM_activation_results/ROI_extraction_procedures/split_SPM_activation_clusters_into_ROI_HO_atlas.m, lines 1–29 · score 0.58 · Harvard Oxford, Atlas, cluster, masking, voxels, ROI
[2] § Methods › Participants ↔ analysis_code/RGCCA_analysis/Run_RGCCA_analysis.R, lines 1–39 · score 0.58 · Carl von Ossietzky, Oldenburg, cognitive, motor
[3] § Methods › Participants ↔ analysis_code/RGCCA_analysis/Run_RGCCA_analysis.R, lines 1–39 · score 0.58 · Carl von Ossietzky, Oldenburg
[4] § Methods › fMRI data analysis ↔ analysis_code/RGCCA_analysis/Run_RGCCA_analysis.R, lines 168–211 · score 0.55 · component scores, Single Motor FC, Dual Task FC, axis, ellipses, RGCCA
[5] § Methods › Multivariate brain–behavior association analysis ↔ analysis_code/RGCCA_analysis/Run_RGCCA_analysis.R, lines 86–124 · score 0.52 · latent variables, component scores, correlation, RGCCA, blocks, behavioral
[6] § Methods › fMRI data analysis ↔ analysis_code/RGCCA_analysis/Run_RGCCA_analysis.R, lines 302–386 · score 0.51 · Pairwise correlations, Single Task FC, Dual Task FC, density, RGCCA, age

Paper

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The authors' code

R · 500 lines · 17 KB · MIT · 5 matches

# RGCCA analysis pipeline
# This pipeline uses motor network input files
# You can replace the input files with cognitive brain FC for cognitive network analysis
# All brain and behavior data are provided in RGCCA folder
# Author: Yan Deng
#
# Biological Psychology Lab,
# Department of Psychology,
# School of Medicine and Health Sciences,
# Carl von Ossietzky Universität Oldenburg,
# Oldenburg, Germany
# Version 3.0 updated: 2026-02-16
#
# Notes:
# - Edit only the "USER SETTINGS" section when moving machines/folders.
# Load the packages
# set the working directory
# =========================
# Libraries
# =========================
library(dplyr)
# this library is used in data cleaning, exploration, and preparation workflows,
# especially with the pipe operator %>%
# Install mixOmics via Bioconductor
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# Install mixOmics
BiocManager::install("mixOmics")
# Load library mixOmics
library(mixOmics) # Run wrapper.rgcca(), Loaded mixOmics, version: 6.30.0
library(ggplot2)
library(GGally)
library(tibble)
library(qgraph)
library(tidyr)
library(stringr)
# USER SETTINGS
# =========================
project_dir <- "Your Project Directory"
setwd(project_dir)
# Input files (edit if you do cognitive task network analysis)
fc_dual_file <- "Old_Young_SPM_motorNetwork_Go_dual_90connections.csv"
fc_single_file <- "Old_Young_SPM_motorNetwork_motor_single_558connections.csv"
behavior_file <- "Merged_motor_cog_summary_data_imputed.csv"
subjects_file <- "subjects_summary_64_young_old_clean_ID_2Groups.csv"
# Step 1: Prepare data blocks
block1 <- read.csv(fc_dual_file) # Dual-task FC
block2 <- read.csv(fc_single_file) # Single-task FC
# load the behavior data
block3 <- readRDS("data/block3_motor_beh_clean_20250418.rds") # Single and dual behavior
block4 <- readRDS("data/block4_motor_beh_clean_20250418.rds") # Dual behavior only
group_vector <- readRDS("data/group_vector_clean_20250418.rds")
sum(is.na(block1))
sum(is.na(block2))
# subject 08, 14, 28, 29 excluded from brain data analysis
# Step 2: Combine into a list,
# Behavioral measurement includes only the Dual task,
blocks <- list(
"DualTaskFC" = block1,
"SingleMotorFC" = block2,
"DualBehavior" = block4
)
# always make block names character strings, not symbols.
# Step 3: Create the design matrix
# Fully connected design (off-diagonal = 1, diagonal = 0)
# connect every block with the others (no self-links)
design <- matrix(
c(0, 1, 1,
1, 0, 1,
1, 1, 0),
nrow = 3, byrow = TRUE)
colnames(design) <- rownames(design) <- names(blocks)
# Make sure they are character strings, matching the block names.
# print and inspect the design:
design
# DualTaskFC SingleMotorFC DualBehavior
#DualTaskFC 0 1 1
#SingleMotorFC 1 0 1
#DualBehavior 1 1 0
#################################################
#___________________ Step 4: Run wrapper.rgcca()
# Set number of components to extract per block
ncomp <- 2 # ncomp: number of components to extract per block
scheme <-"horst" # other options: "factorial", "centroid" (Default: "horst").
set.seed(123) # set seed
# Run RGCCA (wrapper.rgcca)
res_rgcca_dual <- wrapper.rgcca(
X = blocks,
design = design,
ncomp = ncomp,
scheme = scheme,
scale = TRUE,
all.outputs = TRUE
)
# Check the structure of res_rgcca_dual:
str(res_rgcca_dual, max.level = 2)
# a summary of what's inside (scores, loadings, correlations, tau values, etc.).
# 2. Component scores (aka latent variables / variates)
# # Component scores (variates) per block
res_rgcca_dual$variates
# These are the projections of each subject onto the RGCCA components.
# Read scores for difference block
head(res_rgcca_dual$variates$DualBehavior) # Scores for the Behavior block
# 3. Loadings (aka weight vectors or a coefficients)
# tell how much each variable contributes to the component.
res_rgcca_dual$loadings$DualBehavior
# 4. top 10 features by absolute loading on component 1 for each block
# 5. Inner AVE (average variance explained between blocks)
# This reflects how well the blocks relate to each other.
res_rgcca_dual$AVE$AVE_inner
# Look at the first few component scores
head(res_rgcca_dual$variates$DualTaskFC)
head(res_rgcca_dual$variates$SingleMotorFC)
# 7 Plot individuals
plotIndiv(res_rgcca, legend = TRUE)
# If you have a group variable (e.g., AgeGroup):
plotIndiv(res_rgcca_dual, group = group_vector, legend = TRUE)
# Plot Component Scores for All Three Blocks,
# 1. Combine scores from each block
# Extract component scores
scores_dual_fc <- res_rgcca_dual$variates$DualTaskFC
scores_motor_fc <- res_rgcca_dual$variates$SingleMotorFC
scores_behavior <- res_rgcca_dual$variates$DualBehavior
# Create data frames with block labels
df_dual_fc <- data.frame(Comp1 = scores_dual_fc[, 1],
Comp2 = scores_dual_fc[, 2],
Group = group_vector,
Block = "DualTaskFC")
df_motor_fc <- data.frame(Comp1 = scores_motor_fc[, 1],
Comp2 = scores_motor_fc[, 2],
Group = group_vector,
Block = "SingleMotorFC")
df_behavior <- data.frame(Comp1 = scores_behavior[, 1],
Comp2 = scores_behavior[, 2],
Group = group_vector,
Block = "DualBehavior")
# Combine all into one long dataframe
df_all <- rbind(df_dual_fc, df_motor_fc, df_behavior)
# Set block as a factor with desired order
df_all$Block <- factor(df_all$Block,
levels = c("DualTaskFC", "SingleMotorFC", "DualBehavior"))
# Save the data
saveRDS(df_all, "data/df_all_for_Figure7_B.rds")
# Perform Statistical Tests for group comparison
# Run a t-test on Comp1 for each block
stats_results <- df_all %>%
group_by(Block) %>%
summarise(
p_value = t.test(Comp2 ~ Group)$p.value
) %>%
mutate(
sig_label = case_when(
p_value < 0.001 ~ "***",
p_value < 0.01 ~ "**",
p_value < 0.05 ~ "*",
TRUE ~ "ns"
)
)
print(stats_results)
# 2. Plot all blocks using facetting with fixed axis
df_all <- readRDS("data/df_all_for_Figure7_B.rds")
p <- ggplot(df_all, aes(x = Comp1, y = Comp2, color = Group, shape = Group)) +
geom_point(size = 3, alpha = 0.8) +
stat_ellipse(level = 0.68, type = "norm", linetype = "dashed", size = 0.8) +
facet_wrap(~ Block, scales = "free") +
scale_color_manual(values = c("Young" = "#1f77b4", "Old" = "#ff7f0e")) +
scale_shape_manual(values = c("Young" = 16, "Old" = 17)) +
labs(
#title = "Component Scores Across Blocks",
x = "Component 1",
y = "Component 2",
color = "Group",
shape = "Group"
) +
theme_minimal(base_size = 14) +
theme(legend.position = "bottom")
print(p)
# Save as high-resolution TIFF
ggsave("20250720_motorNetwork_component_scores_3blocks_final.tiff",
plot = p,
dpi = 600,
width = 10,
height = 5,
units = "in",
compression = "lzw")
# added into supplementary figure
#---------------Splitting the plots per block, full control on each plot
library(patchwork) # for side-by-side layout
# Split the data into subsets
df_dual <- df_all %>% filter(Block == "DualTaskFC")
df_motor <- df_all %>% filter(Block == "SingleMotorFC")
df_behav <- df_all %>% filter(Block == "DualBehavior")
# 1. DualTaskFC plot
p1 <- ggplot(df_dual, aes(x = Comp1, y = Comp2, color = Group, shape = Group)) +
geom_point(size = 2, alpha = 0.7) +
stat_ellipse(level = 0.68, linetype = "dashed") +
coord_cartesian(xlim = c(-12, 25), ylim = c(-15, 15)) +
labs(title = "Dual Task FC", x = "Component 1", y = "Component 2") +
theme_minimal(base_size = 14) +
theme(legend.position = "none")
# 2. SingleMotorFC plot
p2 <- ggplot(df_motor, aes(x = Comp1, y = Comp2, color = Group, shape = Group)) +
geom_point(size = 2, alpha = 0.7) +
stat_ellipse(level = 0.68, linetype = "dashed") +
coord_cartesian(xlim = c(-12, 25), ylim = c(-15, 15)) +
labs(title = "Single Motor FC", x = "Component 1", y = NULL) +
theme_minimal(base_size = 14) +
theme(legend.position = "none")
# 3. DualBehavior plot
p3 <- ggplot(df_behav, aes(x = Comp1, y = Comp2, color = Group, shape = Group)) +
geom_point(size = 2, alpha = 0.7) +
stat_ellipse(level = 0.68, linetype = "dashed") +
coord_cartesian(xlim = c(-10, 10), ylim = c(-10, 10)) +
labs(title = "Dual Behavior", x = "Component 1", y = NULL) +
theme_minimal(base_size = 14)
# Combine all three plots horizontally with shared legend
final_plot <- (p1 | p2 | p3) + plot_layout(guides = "collect") & theme(legend.position = "right")
# Print the final combined plot
print(final_plot)
# Save as high-resolution TIFF
ggsave("20250726_motorNetwork_component_scores_uniformaxis_final.tiff",
plot = final_plot,
dpi = 600,
width = 10,
height = 5,
units = "in",
compression = "lzw")
# Correlation summary panel, version 2, with p value
# Step 1: Data with correlation and p-values
cor_data_all <- data.frame(
Component = rep(c("Motor Network", "Cognitive Network"), each = 2),
Block = rep(c("Dual Task FC", "Single Task FC"), times = 2),
Correlation = c(
0.432, 0.476, # Motor
0.402, 0.465 # Cognitive
),
p_value = c(
0.0001, 0.0001, # Motor
0.0001, 0.0001 # Cognitive
)
)
# Step 2: Create significance labels based on p-values
cor_data_all <- cor_data_all %>%
mutate(
stars = case_when(
p_value < 0.001 ~ "***",
p_value < 0.01 ~ "**",
p_value < 0.05 ~ "*",
TRUE ~ ""
),
label = paste0(formatC(Correlation, format = "f", digits = 2), stars)
)
# Step 3: Factor levels for plot order
cor_data_all$Component <- factor(cor_data_all$Component, levels = c("Motor Network", "Cognitive Network"))
cor_data_all$Block <- factor(cor_data_all$Block, levels = c("Dual Task FC", "Single Task FC"))
# Save the data
saveRDS(cor_data_all,"data/cor_data_all.rds")
# Step 4: Plot heatmap with annotated labels
# Read the data
cor_data_all <- readRDS("data/cor_data_all.rds")
p_corr <- ggplot(cor_data_all, aes(x = Block, y = Component, fill = Correlation)) +
geom_tile(color = "white", linewidth = 0.5) +
geom_text(aes(label = label), size = 5) +
scale_fill_gradient2(
low = "#f7fbff", high = "#08306b", mid = "#c6dbef",
midpoint = 0.3, limit = c(0.0, 0.6), name = "Correlation"
) +
theme_minimal(base_size = 14) +
theme(
axis.text = element_text(size = 12),
axis.title = element_blank(),
legend.position = "right",
panel.grid = element_blank()
)
ggsave("20250727_brain_behavior_correlation_final.tiff", plot = p_corr, dpi = 600, width = 10, height = 5, units = "in", compression = "lzw")
#---------------------------------------------------
# Plot multiplots for pairwide correlations_ Figure 8
# a simplified version of plotRGCCAMultiPlot():
plotRGCCAMultiPlot <- function(rgcca_result, group = NULL, block_names = NULL, comp = 1,
lower_fn = "points", upper_fn = "cor", diag_fn = "densityDiag") {
require(GGally) # Uses standard GGally wrappers ("points", "cor", "densityDiag")
require(ggplot2)
# Combine variates from each block
variates_list <- lapply(rgcca_result$variates, function(x) x[, comp])
df <- as.data.frame(variates_list)
colnames(df) <- if (is.null(block_names)) names(rgcca_result$variates) else block_names
# Add group only AFTER selecting columns for ggpairs
color_mapping <- aes() # default
if (!is.null(group)) {
group <- as.factor(group)
df$Group <- group
color_mapping <- aes(color = Group)
cols_to_plot <- setdiff(names(df), "Group")
} else {
cols_to_plot <- names(df)
}
# ggpairs plot
p <-GGally::ggpairs(
df,
columns = cols_to_plot,
mapping = color_mapping,
lower = list(continuous = wrap(lower_fn, alpha = 0.7, size = 2)),
upper = list(continuous = wrap(upper_fn, size = 4)),
diag = list(continuous = wrap(diag_fn, alpha = 0.5))
)
print(p)
return(p)
}
# call the plotting function for pairwise correlations_ Figure 8
myplot <- plotRGCCAMultiPlot(
rgcca_result = res_rgcca_dual,
group = group_vector,
block_names = c("DualTaskFC", "SingleMotorFC", "DualBehavior"),
comp = 1
#diag_fn = diag_no_grid
)
# Save as high-resolution TIFF (good for journals)
ggsave("20250722_motorNetwork_correlationMatrix_comp2_final.tiff", plot = myplot, dpi = 600, width = 10, height = 5, units = "in", compression = "lzw")
# Plot both comp 1 and 2. And comp 1 is much better to distinguish age group.
# - plot loadings of behavior. ------------------------------------
# Reusable Function from rgcca loadings
install.packages("tibble") # If haven’t installed it yet
# Function to plot loadings from RGCCA results: Figure 9_Behavior loading
plot_behavior_loadings <- function(rgcca_result, comp = 1, block_name = "DualBehavior", motor_keywords = c("Motor", "motor")) {
# Extract loadings for the specified block and component
loadings <- rgcca_result$loadings[[block_name]][, comp, drop = FALSE] %>%
as.data.frame() %>%
rownames_to_column(var = "Variable")
colnames(loadings)[2] <- "Loading"
# Add category based on variable name
loadings$Category <- ifelse(
grepl(paste(motor_keywords, collapse = "|"), loadings$Variable),
"Motor",
"Cognitive"
)
# Sort variables by absolute loading
loadings <- loadings %>%
arrange(desc(abs(Loading))) %>%
mutate(Variable = factor(Variable, levels = rev(Variable))) # For proper ggplot ordering
# Plot
p <- ggplot(loadings, aes(x = Variable, y = Loading, fill = Category)) +
geom_bar(stat = "identity", width = 0.7) +
coord_flip() +
scale_fill_manual(values = c("Motor" = "#1f77b4", "Cognitive" = "#2ca02c")) +
theme_minimal(base_size = 14) +
labs(
title = paste("Behavioral Loadings (Component", comp, ")"),
x = NULL,
y = "Loading Weight",
fill = "Task Type"
) +
theme(
panel.grid.major.y = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "top"
)
print(p)
}
# Plot behavior loading
my_plot <- plot_behavior_loadings(res_rgcca_dual, comp = 1)
# Save as high-res TIFF
ggsave("2025_0713_Behavior_Loadings_Comp1_sort.tiff",
plot = my_plot,
width = 8,
height = 5,
dpi = 600,
device = "tiff",
compression = "lzw")
# -------------------------------------------------------
# Pyramid Barplot Function for the daultask FC
plot_pyramid_loadings <- function(rgcca_result, block_name, comp = 2, top_n = 20) {
# Extract and clean loadings
loadings <- rgcca_result$loadings[[block_name]][, comp, drop = FALSE] %>%
as.data.frame()
loadings$FullName <- rownames(loadings)
colnames(loadings)[1] <- "Loading"
pattern_suffix <- if (block_name == "SingleMotorFC") "Motor_single" else "Go_dual"
# Clean variable labels
loadings$Variable <- loadings$FullName %>%
str_extract(paste0("M_ROI_.*?\\.and\\.M_ROI_.*?\\.at\\.", pattern_suffix)) %>%
str_replace_all("M_ROI_", "") %>%
str_replace_all("\\.and\\.", " – ") %>%
str_replace(paste0("\\.at\\.", pattern_suffix), "")
# Keep top N by absolute value
top_loadings <- loadings %>%
slice_max(order_by = abs(Loading), n = top_n) %>%
arrange(Loading) %>%
mutate(Variable = factor(Variable, levels = Variable)) # Keep proper Y ordering
# Plot mirrored barplot
p <- ggplot(top_loadings, aes(x = Loading, y = Variable, fill = Loading > 0)) +
geom_col(width = 0.7) +
scale_fill_manual(
values = c("TRUE" = "#66c2a5", "FALSE" = "#8da0cb"), # softer teal and blue
guide = "none") +
scale_x_continuous(
breaks = scales::pretty_breaks(n = 5),
expand = expansion(mult = c(0.1, 0.1))
) +
labs(
title = paste("Barplot of Loadings -", block_name, "(Component", comp, ")"),
x = "Loading Weight",
y = NULL
) +
theme_minimal(base_size = 12) +
theme(
axis.text.y = element_text(size = 10),
plot.title = element_text(size = 12),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank()
)
print(p)
return(p)
}
my_plot <- plot_pyramid_loadings(res_rgcca_dual, block_name = "DualTaskFC", comp = 1)
my_plot <- plot_pyramid_loadings(res_rgcca_dual, block_name = "SingleMotorFC", comp = 1)
ggsave("20250719_DualTaskFC_loadings_motor_network_comp1_600.tiff",
my_plot, width = 10, height = 6, dpi = 600, compression = "lzw")
# __ End __#

Run_RGCCA_analysis.R at commit d7975c7, under MIT · at the source

Overview

Authors: Yan Deng¹, AmirHussein Abdolalizadeh¹, Kayson Fakhar^2,3, Tina Schmitt⁴, Karsten Witt^5,6, Carsten Gießing^1,6, Jochem W Rieger⁷, Christiane M Thiel^1,6

ORCID iDs: Yan Deng

Institutions: Carl von Ossietzky Universität Oldenburg (Germany); Universität Hamburg (Germany); MRC Cognition and Brain Sciences Unit (United Kingdom); University of Cambridge (United Kingdom); University Medical Center Hamburg-Eppendorf (Germany)

Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1257

Dates: received 31 August 2025; accepted 30 April 2026; published online 29 May 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1162/imag.a.1257 · PMID 42232072 · PMCID PMC13224314 · OpenAlex W7160433290

Open access: diamond, a free copy (OpenAlex)

Status: code verified

Categories: fMRI (modality), human (organism)

Methods: Spectral & time-frequency, Statistics, Machine learning, Preprocessing, fMRI & imaging

Keywords: task-based functional connectivity, cognitive–motor dual-task, brain–behavior association, domain-dominant network, regularized generalized canonical correlation analysis (RGCCA)

Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Deutsche Forschungsgemeinschaft (456732630); German Research Foundation (3T MRI INST 184/152-1 FUGG, INST 184/225-1 FUGG)

Citations: cited by 2 papers (Europe PMC); 112 references in the paper

Research resources: in combination with SPM12 RRID:SCR_007037, RRID:SCR_009550, 2019 RRID:SCR_016216

Abstract

Aging is associated with declines in both motor and cognitive functions, which are often examined using cognitive–motor dual-task paradigms. However, the functional brain network mechanisms supporting dual-task performance across these domains remain incompletely understood. We investigated 40 older adults (50–80 years) and 20 younger adults (20–40 years) who performed a motor single task (pedaling), a cognitive single task (Go/NoGo), and a combined cognitive–motor dual-task during functional magnetic resonance imaging (fMRI) using a custom-built MRI-compatible pedaling device. Behaviorally, older adults showed significant dual-task costs in motor performance, whereas cognitive performance was relatively preserved. At the neural level, task-based functional connectivity revealed distinct patterns of age-related network reorganization. Cognitive-dominant networks showed relatively selective connectivity increases within frontal executive and motor-planning regions, consistent with compensatory recruitment supporting preserved cognitive dual-task performance. In contrast, motor-dominant networks exhibited broader reorganization, characterized by strengthened frontoparietal control circuits but weakened cerebello-parietal and sensorimotor pathways, pointing to reduced automaticity and increased reliance on central cognitive control. Multivariate brain–behavior analyses further revealed age-related differences in latent connectivity–behavior relationships. Motor-dominant networks in older adults showed greater dispersion and stronger coupling with behavioral variability, whereas cognitive-network patterns remained largely stable and overlapping across age groups. Motor response time variability, particularly under dual-task conditions, emerged as the strongest behavioral contributor to this latent brain–behavior dimension and was associated with connectivity in frontoparietal control and motor-planning regions. Together, these findings demonstrate that aging involves an asymmetric reorganization of large-scale networks, in which motor systems become increasingly dependent on cognitive control while cognitive systems remain comparatively resilient. This network-level account identifies motor variability and cognitive–motor interdependence as sensitive markers of aging-related brain changes, with implication for understanding why cognitive–motor abilities remain stable in some individuals while declining in others.

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 6 matches between paragraphs and lines of code.

ydeng2021/dual-task-fmri-aging-RGCCA-analysis

License: MIT

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: d7975c7e994754a92e9df037047a5b3879e64622, 28 February 2026

Languages: MATLAB (3), R (1)

Size: 84 files, 4 scripts

Software Heritage: not archived

Found in: “Data and Code Availability”

Holds: README, license file

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: SPM (3 files), ggplot2 (1 file), patchwork (1 file), tidyverse (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

6 files

analysis_code/RGCCA_analysis/Run_RGCCA_analysis.R, R, 500 lines, 5 matches
analysis_code/ROIs_extraction_from_SPM_activation_results/First-level_GLM_modeling/SPM_Glm_9_contrasts.m, MATLAB, 161 lines
analysis_code/ROIs_extraction_from_SPM_activation_results/ROI_extraction_procedures/split_SPM_activation_clusters_into_ROI_HO_atlas.m, MATLAB, 90 lines, 1 match
analysis_code/ROIs_extraction_from_SPM_activation_results/Second-level_t_test/Second-level_t_test.m, MATLAB, 116 lines
LICENSE, License, 22 lines
README.md, Text, 84 lines

At the source: github.com/ydeng2021/dual-task-fmri-aging-RGCCA-analysis

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

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1 repository 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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6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

osf:pjt2x, at OSF; found in “Data and Code Availability”

Data and Code Availability

The broader research project was preregistered on the Open Science Framework (OSF): https://osf.io/pjt2x/overview. Preprocessed task-based fMRI data are publicly available via OSF: https://osf.io/pjt2x/resources. Analysis code is available on GitHub: https://github.com/ydeng2021/dual-task-fmri-aging-RGCCA-analysis and is distributed under the MIT License. While the preregistration specifies the general study design and hypotheses, the analyses reported here represent a subset of the preregistered framework and include additional methodological extensions.

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

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 5 keywords, 2 funders, 112 references, 3 RRIDs.

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This paper

Deng, Y., Abdolalizadeh, A., Fakhar, K., Schmitt, T., Witt, K., Gießing, C., Rieger, J. W., & Thiel, C. M. (2026). Motor- and cognitive-dominant functional network adaptations supporting dual-task performance in older adults. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1257. https://doi.org/10.1162/imag.a.1257

BibTeX

@article{deng2026motor, author = {Deng, Yan and Abdolalizadeh, AmirHussein and Fakhar, Kayson and Schmitt, Tina and Witt, Karsten and Gießing, Carsten and Rieger, Jochem W and Thiel, Christiane M}, title = {{Motor- and cognitive-dominant functional network adaptations supporting dual-task performance in older adults}}, journal = {Imaging neuroscience (Cambridge, Mass.)}, year = {2026}, month = may, volume = {4}, pages = {IMAG.a.1257}, publisher = {MIT Press}, issn = {2837-6056}, doi = {10.1162/imag.a.1257}, url = {https://doi.org/10.1162/imag.a.1257}, pmid = {42232072}, pmcid = {PMC13224314} }

RIS

TY - JOUR AU - Deng, Yan AU - Abdolalizadeh, AmirHussein AU - Fakhar, Kayson AU - Schmitt, Tina AU - Witt, Karsten AU - Gießing, Carsten AU - Rieger, Jochem W AU - Thiel, Christiane M TI - Motor- and cognitive-dominant functional network adaptations supporting dual-task performance in older adults T2 - Imaging neuroscience (Cambridge, Mass.) J2 - Imaging Neurosci (Camb) PY - 2026 DA - 2026/05/29 VL - 4 SP - IMAG.a.1257 SN - 2837-6056 PB - MIT Press DO - 10.1162/imag.a.1257 UR - https://doi.org/10.1162/imag.a.1257 LA - en ER -

CSL-JSON

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