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Sleep spindles are linked to white matter connectivity differentially in older adults with and without cognitive impairment.

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  1. [1] § Materials and methods › Participants ↔ DWI_EEG_osf.Rmd, lines 157–199 · score 0.74 · cholinesterase inhibitors, mood stabilizers, PSG, hypnotics, sedatives, MMSE
  2. [2] § Materials and methods › Overnight polysomnography ↔ DWI_EEG_osf.Rmd, lines 157–199 · score 0.58 · sleep onset, sleep efficiency, scoring, wake, events, architecture

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The authors' code

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  1. ---
  2. title: "Sleep microarchitecture and DWI"
  3. author: "Aaron Lam"
  4. date: "01/11/2024"
  5. output: word_document
  6. ---
  8. ```{r setup, include=FALSE}
  9. knitr::opts_chunk$set(echo = TRUE)
  11. library(haven)
  12. library(dplyr)
  13. library(tidyr)
  14. library(ggplot2)
  15. library(interactions)
  16. library(effectsize)
  17. library(rsq)
  18. library(readxl)
  19. library(lubridate)
  21. ```
  23. ## R Markdown
  25. This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.
  27. When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
  29. ```{r load-data, include = FALSE}
  30. df <- read.csv("/Users/alam2002/Library/CloudStorage/OneDrive-TheUniversityofSydney(Staff)/9. PAPERS/DWI_EEG/1. Analysis/Data/Shawn_DWI_EEG dataset.csv")
  32. sleep_df <- read_sav("/Users/alam2002/Library/CloudStorage/OneDrive-TheUniversityofSydney(Staff)/9. PAPERS/DWI_EEG/1. Analysis/Data/HBA_Sleep Satellite_Master Data File_20231222_AL.sav")
  34. redcap_df <- read.csv("/Users/alam2002/Library/CloudStorage/OneDrive-TheUniversityofSydney(Staff)/9. PAPERS/DWI_EEG/1. Analysis/Data/NEWDiscoveringNovelB_DATA_2026-02-28_0910.csv")
  36. morediagnoses_df <- read.csv("/Users/alam2002/Library/CloudStorage/OneDrive-TheUniversityofSydney(Staff)/9. PAPERS/DWI_EEG/1. Analysis/Data/ABC_ID_and_clinical_diagnosis.csv")
  38. ```
  41. ```{r data cleaning, include = FALSE}
  43. # Define timepoints, derivations, and metrics
  44. timepoints <- c("BL", "LTFU1", "LTFU2")
  45. derivations <- c("C3", "C4", "Cz", "F3", "F4", "Fz")
  46. metrics <- c(
  47. "Absolute_NREM_Delta", "Absolute_NREM_Theta", "Absolute_NREM_Alpha",
  48. "Absolute_NREM_Sigma", "Absolute_NREM_Beta", "Absolute_NREM_0.25_to_1.0Hz",
  49. "Absolute_NREM_11.0_to_13.0Hz", "Absolute_NREM_13.0_to_15.0Hz",
  50. "NREM_Spl_pmin", "NREM_Spl_fast_pmin", "NREM_Spl_slow_pmin", "NREM_MeanMaxHeight"
  51. )
  53. # Create a list of column names by iterating over each combination
  54. sleep_columns <- c("ABC_ID") # Start with IDs for merging
  56. for (tp in timepoints) {
  57. for (deriv in derivations) {
  58. for (metric in metrics) {
  59. sleep_columns <- c(sleep_columns, paste(tp, deriv, metric, sep = "_"))
  60. }
  61. }
  62. }
  64. # Keep only Sleep Micro-columns
  65. sleep_df_filtered <- sleep_df %>%
  66. select(all_of(sleep_columns)) %>%
  67. as.data.frame()
  70. # Convert from wide to long format, extracting timepoint, derivation, and metric from column names
  71. sleep_data_long <- sleep_df_filtered %>%
  72. pivot_longer(
  73. -ABC_ID,
  74. names_to = c("Timepoint", ".value"),
  75. names_pattern = "(BL|LTFU1|LTFU2)_(.*)"
  76. )
  78. # Extract ABC_ID and Timepoint from XNAT_scan_id in df
  79. df <- df %>%
  80. mutate(
  81. ABC_ID = substr(XNAT_scan_id, 1, 8),
  82. Timepoint = sub(".*_(\\w+)$", "\\1", XNAT_scan_id)
  83. )
  85. merged_data <- df %>%
  86. left_join(sleep_data_long, by = c("ABC_ID" = "ABC_ID", "Timepoint" = "Timepoint"))
  89. # Calculate relative delta power for each region
  90. merged_data <- merged_data %>%
  91. mutate(
  92. F3_Relative_Delta = F3_Absolute_NREM_Delta / (
  93. F3_Absolute_NREM_Delta + F3_Absolute_NREM_Theta + F3_Absolute_NREM_Alpha +
  94. F3_Absolute_NREM_Sigma + F3_Absolute_NREM_Beta),
  95. F4_Relative_Delta = F4_Absolute_NREM_Delta / (
  96. F4_Absolute_NREM_Delta + F4_Absolute_NREM_Theta + F4_Absolute_NREM_Alpha +
  97. F4_Absolute_NREM_Sigma + F4_Absolute_NREM_Beta),
  98. Fz_Relative_Delta = Fz_Absolute_NREM_Delta / (
  99. Fz_Absolute_NREM_Delta + Fz_Absolute_NREM_Theta + Fz_Absolute_NREM_Alpha +
  100. Fz_Absolute_NREM_Sigma + Fz_Absolute_NREM_Beta),
  101. C3_Relative_Delta = C3_Absolute_NREM_Delta / (
  102. C3_Absolute_NREM_Delta + C3_Absolute_NREM_Theta + C3_Absolute_NREM_Alpha +
  103. C3_Absolute_NREM_Sigma + C3_Absolute_NREM_Beta),
  104. C4_Relative_Delta = C4_Absolute_NREM_Delta / (
  105. C4_Absolute_NREM_Delta + C4_Absolute_NREM_Theta + C4_Absolute_NREM_Alpha +
  106. C4_Absolute_NREM_Sigma + C4_Absolute_NREM_Beta),
  107. Cz_Relative_Delta = Cz_Absolute_NREM_Delta / (
  108. Cz_Absolute_NREM_Delta + Cz_Absolute_NREM_Theta + Cz_Absolute_NREM_Alpha +
  109. Cz_Absolute_NREM_Sigma + Cz_Absolute_NREM_Beta)
  110. )
  112. # Calculate frontal and central averages
  113. merged_data <- merged_data %>%
  114. mutate(
  115. Frontal_Avg_Relative_Delta = rowMeans(select(., F3_Relative_Delta, F4_Relative_Delta, Fz_Relative_Delta), na.rm = TRUE),
  116. Central_Avg_Relative_Delta = rowMeans(select(., C3_Relative_Delta, C4_Relative_Delta, Cz_Relative_Delta), na.rm = TRUE)
  117. )
  119. head(merged_data)
  121. # Average C3, C4, F3, C4
  122. merged_data$central_NREM_Spl_pmin <- rowMeans(merged_data[, c("C3_NREM_Spl_pmin", "C4_NREM_Spl_pmin",
  123. "Cz_NREM_Spl_pmin")],
  124. na.rm = TRUE)
  127. merged_data$frontal_NREM_Spl_pmin <- rowMeans(merged_data[, c("F3_NREM_Spl_pmin",
  128. "F4_NREM_Spl_pmin", "Fz_NREM_Spl_pmin")],
  129. na.rm = TRUE)
  132. merged_data$central_NREM_Spl_fast_pmin <- rowMeans(merged_data[, c("C3_NREM_Spl_fast_pmin", "C4_NREM_Spl_fast_pmin",
  133. "Cz_NREM_Spl_fast_pmin" )],
  134. na.rm = TRUE)
  136. merged_data$frontal_NREM_Spl_fast_pmin <- rowMeans(merged_data[, c( "F3_NREM_Spl_fast_pmin",
  137. "F4_NREM_Spl_fast_pmin", "Fz_NREM_Spl_fast_pmin")] ,
  138. na.rm = TRUE)
  140. merged_data$central_NREM_Spl_slow_pmin <- rowMeans(merged_data[, c("C3_NREM_Spl_slow_pmin", "C4_NREM_Spl_slow_pmin",
  141. "Cz_NREM_Spl_slow_pmin")],
  142. na.rm = TRUE)
  144. merged_data$frontal_NREM_Spl_slow_pmin <- rowMeans(merged_data[, c( "F3_NREM_Spl_slow_pmin",
  145. "F4_NREM_Spl_slow_pmin", "Fz_NREM_Spl_slow_pmin")],
  146. na.rm = TRUE)
  148. merged_data$central_NREM_MeanMaxHeight <- rowMeans(merged_data[, c( "C3_NREM_MeanMaxHeight",
  149. "C4_NREM_MeanMaxHeight", "Cz_NREM_MeanMaxHeight")],
  150. na.rm = TRUE)
  152. merged_data$frontal_NREM_MeanMaxHeight <- rowMeans(merged_data[, c( "F3_NREM_MeanMaxHeight",
  153. "F4_NREM_MeanMaxHeight", "Fz_NREM_MeanMaxHeight")],
  154. na.rm = TRUE)
  157. # SLOW OSCILLATIONS
  158. merged_data$central_Absolute_NREM_0.25_to_1.0Hz <- rowMeans(merged_data[, c("C3_Absolute_NREM_0.25_to_1.0Hz", "C4_Absolute_NREM_0.25_to_1.0Hz",
  159. "Cz_Absolute_NREM_0.25_to_1.0Hz")],
  160. na.rm = TRUE)
  163. merged_data$frontal_Absolute_NREM_0.25_to_1.0Hz <- rowMeans(merged_data[, c("F3_Absolute_NREM_0.25_to_1.0Hz",
  164. "F4_Absolute_NREM_0.25_to_1.0Hz", "Fz_Absolute_NREM_0.25_to_1.0Hz")],
  165. na.rm = TRUE)
  167. # Remove rows where there is no micro architecture
  169. merged_data <- merged_data %>%
  170. filter(!is.na(central_NREM_Spl_pmin) | !is.na(frontal_Absolute_NREM_0.25_to_1.0Hz))
  173. # Convert redcap_event_name values in redcap_df to match timepoint labels
  174. redcap_df <- redcap_df %>%
  175. mutate(Timepoint = case_when(
  176. redcap_event_name == "baseline_clinical_arm_1" ~ "BL",
  177. redcap_event_name == "ltfu1_clinical_ass_arm_1" ~ "LTFU1",
  178. redcap_event_name == "ltfu2_clinical_ass_arm_1" ~ "LTFU2",
  179. TRUE ~ NA_character_ # Set unmatched values to NA if there are any
  180. )) %>%
  181. select(abc_id, Timepoint, cognitive_classification, sex_spss, age_spss, gds_total_spss, vascular_disease_spss, bmi_spss, mmse_spss, cirs_total_score_spss, antidep_yn_spss, psqi_global_spss, apnea_hypopnea_index_tst, o2_desat3percent_index_tst,
  182. total_sleep_time_min, wake_after_sleep_onset_min, sleep_efficiency_percent_tib, nrem_min, rem_min, antidep_yn, t0_ct_2_1,t0_ct_3_1,t0_ct_4_1,t0_ct_5_1,t0_ct_6_1, psg_study_date, hba_mri_date) %>%
  183. mutate(across(everything(), ~ replace(.x, .x %in% c(777, 888, 999), NA)))%>%
  184. mutate(abc_id = paste0("ABC_", abc_id))
  185. ### List of Meds and RedCap codes
  186. # antidep_yn
  187. # hypnotics or sedatives - t0_ct_2_1
  188. # mood stabilisers/antiepileptic - t0_ct_3_1
  189. # antipsychotics - t0_ct_4_1
  190. # Benzo - t0_ct_5_1
  191. # Cholinesterase inhibitirs - t0_ct_6_1
  194. final_merged_data <- left_join(merged_data, redcap_df, by = c("ABC_ID" = "abc_id", "Timepoint" = "Timepoint"))
  196. morediagnoses_df <- morediagnoses_df %>%
  197. filter(ABC_ID != "")
  199. final_merged_data <- left_join(final_merged_data, morediagnoses_df, by = c("ABC_ID" = "ABC_ID", "Timepoint" = "Timepoint"))
  201. # Code below was used for identifying missing clinical diagnoses
  203. #missing_data <- final_merged_data %>%
  204. # filter(is.na(SCIvsMCI)) %>%
  205. # select(ABC_ID, Timepoint, clinical_diagnosis)
  207. # Save the selected columns to a CSV file
  208. #write.csv(missing_data, "C:/Users/Aaron Lam/OneDrive - The University of Sydney (Staff)/9. PAPERS/1. #Co-authors/2024/SK_FBA_Sleepmicro/Analysis/ABC_ID_and_clinical_diagnosis.csv", row.names = FALSE)
  210. #clean up diagnoses - # 0 = SCI, 1=MCI
  212. final_merged_data <- final_merged_data %>%
  213. mutate(SCIvsMCI = ifelse(cognitive_classification == 1, 1,
  214. ifelse(cognitive_classification == 2, 0,
  215. ifelse(cognitive_classification == 3,1,
  216. ifelse(cognitive_classification == 4, 1,
  217. ifelse(cognitive_classification == 5, 1,
  218. ifelse(cognitive_classification == 6, 1, NA)))))))
  220. # Obtaining more diagnoses
  221. final_merged_data <- final_merged_data %>%
  222. mutate(SCIvsMCI = ifelse(is.na(SCIvsMCI),
  223. case_when(
  224. clinical_diagnosis == 1 ~ 0,
  225. clinical_diagnosis == 2 ~ 0,
  226. clinical_diagnosis == 3 ~ 1,
  227. clinical_diagnosis == 4 ~ 1,
  228. clinical_diagnosis == 5 ~ 1,
  229. clinical_diagnosis == 6 ~ 1,
  230. clinical_diagnosis == 7 ~ 1,
  231. clinical_diagnosis == 8 ~ 1
  232. ),
  233. SCIvsMCI))
  236. final_merged_data$SCIvsMCI <- as.factor(final_merged_data$SCIvsMCI)
  237. final_merged_data <- final_merged_data %>%
  238. filter(!is.na(SCIvsMCI))
  240. table(final_merged_data$SCIvsMCI)
  242. final_merged_data <- final_merged_data %>%
  243. mutate(frontal_Absolute_NREM_0.25_to_1.0Hz = ifelse(frontal_Absolute_NREM_0.25_to_1.0Hz >= 800, NA, frontal_Absolute_NREM_0.25_to_1.0Hz)) %>%
  244. mutate(central_Absolute_NREM_0.25_to_1.0Hz = ifelse(central_Absolute_NREM_0.25_to_1.0Hz >= 800, NA, central_Absolute_NREM_0.25_to_1.0Hz))
  247. dvs <- c("central_NREM_Spl_pmin",
  248. "frontal_NREM_Spl_pmin",
  249. "central_NREM_Spl_fast_pmin",
  250. "frontal_NREM_Spl_fast_pmin",
  251. "central_NREM_Spl_slow_pmin",
  252. "frontal_NREM_Spl_slow_pmin",
  253. "frontal_NREM_MeanMaxHeight",
  254. "central_NREM_MeanMaxHeight",
  255. "frontal_Absolute_NREM_0.25_to_1.0Hz",
  256. "central_Absolute_NREM_0.25_to_1.0Hz")
  258. final_merged_data[paste0("log_", dvs)] <- log(final_merged_data[dvs])
  260. final_merged_data$ATR_total_fbc<- rowMeans(
  261. final_merged_data[, c("ATR_left_fbc", "ATR_right_fbc")],
  262. na.rm = TRUE
  263. )
  265. final_merged_data$SLF_III_total_fbc <- rowMeans(
  266. final_merged_data[, c("SLF_III_left_fbc", "SLF_III_right_fbc")],
  267. na.rm = TRUE
  268. )
  270. final_merged_data$SLF_II_total_fbc <- rowMeans(
  271. final_merged_data[, c("SLF_II_left_fbc", "SLF_II_right_fbc")],
  272. na.rm = TRUE
  273. )
  275. ```
  277. ```{r model functions, include = false}
  278. # Define the function
  279. run_moderation_analysis <- function(data, dvs, ivs, moderator, output_dir = "results") {
  280. # Create output directory if it doesn't exist
  281. if (!dir.exists(output_dir)) dir.create(output_dir)
  283. # Loop through each combination of DV and IV
  284. for (dv in dvs) {
  285. for (iv in ivs) {
  286. # Subset the data to remove rows with NA in the relevant variables
  287. temp_data <- data[complete.cases(data[, c(dv, iv, moderator)]), ]
  289. # Construct the formula
  290. formula <- as.formula(paste(scale(dv)), "~", scale(iv), "*", scale(moderator))
  292. # Fit the model
  293. model <- lm(formula, data = temp_data)
  295. # Save the model summary to a text file
  296. summary_file <- file.path(output_dir, paste0("model_", dv, "_", iv, "_summary.txt"))
  297. capture.output(summary(model), file = summary_file)
  299. # Create interaction plot
  300. temp_data$predicted <- predict(model)
  302. gg <- ggplot(temp_data, aes_string(x = iv, y = dv, color = moderator)) +
  303. geom_point(alpha = 0.6) +
  304. geom_smooth(method = "lm", se = FALSE) +
  305. labs(title = paste("Interaction Effect:", iv, "and", moderator, "on", dv),
  306. x = iv, y = dv, color = moderator) +
  307. theme_minimal()
  309. # Save the plot
  310. plot_file <- file.path(output_dir, paste0("plot_", dv, "_", iv, ".png"))
  311. ggsave(plot_file, plot = gg)
  312. }
  313. }
  314. }
  317. # Define the function
  318. run_moderation_analysis_withcovariates <- function(data, dvs, ivs, moderator, covariates = NULL, output_dir = "results") {
  319. # Create output directory if it doesn't exist
  320. if (!dir.exists(output_dir)) dir.create(output_dir)
  322. # Standardize function (only for numeric variables)
  323. standardize <- function(x) {
  324. if (is.numeric(x)) {
  325. return((x - mean(x, na.rm = TRUE)) / sd(x, na.rm = TRUE))
  326. } else {
  327. return(x) # Return non-numeric variables unchanged
  328. }
  329. }
  331. # Loop through each combination of DV and IV
  332. for (dv in dvs) {
  333. for (iv in ivs) {
  334. # Subset the data to remove rows with NA in the relevant variables
  335. vars_to_check <- c(dv, iv, moderator, covariates)
  336. temp_data <- data[complete.cases(data[, vars_to_check]), ]
  338. # Standardize only numeric variables
  339. temp_data[[dv]] <- standardize(temp_data[[dv]])
  340. temp_data[[iv]] <- standardize(temp_data[[iv]])
  341. temp_data[[moderator]] <- standardize(temp_data[[moderator]])
  342. if (!is.null(covariates)) {
  343. numeric_covariates <- covariates[sapply(temp_data[covariates], is.numeric)]
  344. temp_data[numeric_covariates] <- lapply(temp_data[numeric_covariates], standardize)
  345. }
  347. # Construct the formula with covariates
  348. covariate_terms <- if (!is.null(covariates)) paste(covariates, collapse = " + ") else ""
  349. formula <- if (covariate_terms != "") {
  350. as.formula(paste(dv, "~", iv, "*", moderator, "+", covariate_terms))
  351. } else {
  352. as.formula(paste(dv, "~", iv, "*", moderator))
  353. }
  355. # Fit the model
  356. model <- lm(formula, data = temp_data)
  358. # Save the model summary to a text file
  359. summary_file <- file.path(output_dir, paste0("model_", dv, "_", iv, "_summary.txt"))
  360. capture.output(summary(model), file = summary_file)
  362. # Create interaction plot
  363. temp_data$predicted <- predict(model)
  365. gg <- ggplot(temp_data, aes_string(x = iv, y = dv, color = moderator)) +
  366. geom_point(alpha = 0.6) +
  367. geom_smooth(method = "lm", se = FALSE) +
  368. labs(title = paste("Interaction Effect:", iv, "and", moderator, "on", dv),
  369. x = iv, y = dv, color = moderator) +
  370. theme_minimal()
  372. # Save the plot
  373. plot_file <- file.path(output_dir, paste0("plot_", dv, "_", iv, ".png"))
  374. ggsave(plot_file, plot = gg)
  375. }
  376. }
  377. }
  379. # t-test function
  380. analyze_group_differences <- function(data, binary_var, variables, output_dir = "results") {
  381. # Create output directory if it doesn't exist
  382. if (!dir.exists(output_dir)) dir.create(output_dir)
  384. # Ensure the binary variable is a factor
  385. data[[binary_var]] <- as.factor(data[[binary_var]])
  387. # Loop through the variables
  388. for (var in variables) {
  389. # Remove rows with NA in the relevant columns
  390. temp_data <- data[complete.cases(data[, c(binary_var, var)]), ]
  392. # Perform a t-test
  393. t_test <- t.test(as.formula(paste(var, "~", binary_var)), data = temp_data)
  395. # Save the t-test results to a text file
  396. summary_file <- file.path(output_dir, paste0("ttest_", var, "_by_", binary_var, ".txt"))
  397. capture.output(print(t_test), file = summary_file)
  399. # Calculate mean and standard error for bar graph
  400. summary_stats <- temp_data %>%
  401. group_by(!!sym(binary_var)) %>%
  402. summarise(
  403. Mean = mean(!!sym(var), na.rm = TRUE),
  404. SE = sd(!!sym(var), na.rm = TRUE) / sqrt(n())
  405. )
  407. # Create bar graph of means with error bars
  408. gg <- ggplot(summary_stats, aes_string(x = binary_var, y = "Mean", fill = binary_var)) +
  409. geom_bar(stat = "identity", position = position_dodge(), alpha = 0.7) +
  410. geom_errorbar(aes(ymin = Mean - SE, ymax = Mean + SE), width = 0.2, position = position_dodge(0.9)) +
  411. labs(
  412. title = paste("Mean of", var, "by", binary_var),
  413. x = binary_var, y = paste("Mean of", var), fill = binary_var
  414. ) +
  415. theme_minimal() +
  416. theme(legend.position = "none")
  418. # Save the plot
  419. plot_file <- file.path(output_dir, paste0("barchart_", var, "_by_", binary_var, ".png"))
  420. ggsave(plot_file, plot = gg)
  421. }
  422. }
  424. ```
  427. ```{r Table 1, echo=FALSE}
  428. table(final_merged_data$SCIvsMCI)
  429. table(final_merged_data$SCIvsMCI, final_merged_data$sex_spss)
  430. chisq.test(final_merged_data$SCIvsMCI, final_merged_data$sex_spss)
  431. table(final_merged_data$SCIvsMCI, final_merged_data$antidep_yn_spss)
  432. chisq.test(final_merged_data$SCIvsMCI, final_merged_data$antidep_yn_spss)
  433. # Mean (SD) within groups - t-test for Age, MMSE, GDS15, BMI, CIRS-G, PSQI-total score, Vascular risk index.
  434. hist(final_merged_data$age_spss)
  435. t.test(age_spss ~ SCIvsMCI, var.equal = TRUE, data = final_merged_data)
  436. tapply(final_merged_data$age_spss, final_merged_data$SCIvsMCI, sd, na.rm = TRUE)
  438. wilcox.test(mmse_spss ~ SCIvsMCI, data = final_merged_data, exact = FALSE)
  439. tapply(final_merged_data$mmse_spss, final_merged_data$SCIvsMCI, median, na.rm = TRUE)
  440. tapply(final_merged_data$mmse_spss, final_merged_data$SCIvsMCI==1, IQR, na.rm = TRUE)
  442. hist(final_merged_data$gds_total_spss)
  443. t.test(log1p(final_merged_data$gds_total_spss) ~ SCIvsMCI, var.equal = TRUE,data = final_merged_data)
  444. tapply(final_merged_data$gds_total_spss, final_merged_data$SCIvsMCI, mean, na.rm = TRUE)
  445. tapply(final_merged_data$gds_total_spss, final_merged_data$SCIvsMCI, sd, na.rm = TRUE)
  447. t.test(bmi_spss ~ SCIvsMCI, var.equal = TRUE, data = final_merged_data)
  448. tapply(final_merged_data$bmi_spss, final_merged_data$SCIvsMCI, sd, na.rm = TRUE)
  450. t.test(log1p(cirs_total_score_spss) ~ SCIvsMCI, var.equal = TRUE,data = final_merged_data)
  451. tapply(final_merged_data$cirs_total_score_spss, final_merged_data$SCIvsMCI, median, na.rm = TRUE)
  452. tapply(final_merged_data$cirs_total_score_spss, final_merged_data$SCIvsMCI==1, IQR, na.rm = TRUE)
  454. hist(final_merged_data$psqi_global_spss)
  455. t.test(sqrt(psqi_global_spss) ~ SCIvsMCI,var.equal = TRUE, data = final_merged_data)
  456. tapply(final_merged_data$psqi_global_spss, final_merged_data$SCIvsMCI, mean, na.rm = TRUE)
  457. tapply(final_merged_data$psqi_global_spss, final_merged_data$SCIvsMCI, sd, na.rm = TRUE)
  459. #t.test(as.numeric(vascular_disease_spss) ~ SCIvsMCI, data = final_merged_data) # issue with the data
  460. #tapply(final_merged_data$vascular_disease_spss, final_merged_data$SCIvsMCI, sd, na.rm = TRUE)
  462. ```
  464. ```{r Table 2, echo=FALSE}
  465. hist(final_merged_data$total_sleep_time_min)
  466. t.test(total_sleep_time_min ~ SCIvsMCI, var.equal = TRUE, data = final_merged_data)
  467. tapply(final_merged_data$total_sleep_time_min, final_merged_data$SCIvsMCI, sd, na.rm = TRUE)
  469. hist(final_merged_data$nrem_min)
  470. t.test(nrem_min ~ SCIvsMCI, var.equal = TRUE, data = final_merged_data)
  471. tapply(final_merged_data$nrem_min, final_merged_data$SCIvsMCI, sd, na.rm = TRUE)
  473. hist(final_merged_data$rem_min)
  474. t.test(rem_min ~ SCIvsMCI, var.equal = TRUE, data = final_merged_data)
  475. tapply(final_merged_data$rem_min, final_merged_data$SCIvsMCI, sd, na.rm = TRUE)
  477. hist(log(final_merged_data$wake_after_sleep_onset_min))
  478. t.test(log(wake_after_sleep_onset_min) ~ SCIvsMCI, var.equal = TRUE, data = final_merged_data)
  479. tapply(final_merged_data$wake_after_sleep_onset_min, final_merged_data$SCIvsMCI==0, median, na.rm = TRUE)
  482. hist(final_merged_data$sleep_efficiency_percent_tib^2)
  483. t.test(sleep_efficiency_percent_tib^2 ~ SCIvsMCI, var.equal = TRUE, data = final_merged_data)
  484. tapply(final_merged_data$sleep_efficiency_percent_tib, final_merged_data$SCIvsMCI, median, na.rm = TRUE)
  485. tapply(final_merged_data$sleep_efficiency_percent_tib, final_merged_data$SCIvsMCI==0, IQR, na.rm = TRUE)
  487. hist(log(final_merged_data$apnea_hypopnea_index_tst))
  488. t.test(log1p(apnea_hypopnea_index_tst) ~ SCIvsMCI, var.equal = TRUE, data = final_merged_data)
  489. tapply(final_merged_data$apnea_hypopnea_index_tst, final_merged_data$SCIvsMCI, median, na.rm = TRUE)
  490. tapply(final_merged_data$apnea_hypopnea_index_tst, final_merged_data$SCIvsMCI==0, IQR, na.rm = TRUE)
  492. hist(log(final_merged_data$o2_desat3percent_index_tst))
  493. t.test(log1p(o2_desat3percent_index_tst) ~ SCIvsMCI, var.equal = TRUE, data = final_merged_data)
  494. tapply(final_merged_data$o2_desat3percent_index_tst, final_merged_data$SCIvsMCI, median, na.rm = TRUE)
  495. tapply(final_merged_data$o2_desat3percent_index_tst, final_merged_data$SCIvsMCI==0, IQR, na.rm = TRUE)
  498. # SO
  499. hist(log(final_merged_data$frontal_Absolute_NREM_0.25_to_1.0Hz))
  500. t.test(log(frontal_Absolute_NREM_0.25_to_1.0Hz) ~ SCIvsMCI, var.equal = TRUE, data = final_merged_data)
  501. tapply(final_merged_data$frontal_Absolute_NREM_0.25_to_1.0Hz, final_merged_data$SCIvsMCI, median, na.rm = TRUE)
  502. tapply(final_merged_data$frontal_Absolute_NREM_0.25_to_1.0Hz, final_merged_data$SCIvsMCI==1, IQR, na.rm = TRUE)
  505. t.test(log(central_Absolute_NREM_0.25_to_1.0Hz) ~ SCIvsMCI,var.equal = TRUE, data = final_merged_data)
  506. tapply(final_merged_data$central_Absolute_NREM_0.25_to_1.0Hz, final_merged_data$SCIvsMCI, median, na.rm = TRUE)
  507. tapply(final_merged_data$central_Absolute_NREM_0.25_to_1.0Hz, final_merged_data$SCIvsMCI==1, IQR, na.rm = TRUE)
  510. #Spindles
  511. t.test(log(frontal_NREM_Spl_pmin) ~ SCIvsMCI, var.equal = TRUE, data = final_merged_data)
  512. tapply(final_merged_data$frontal_NREM_Spl_pmin, final_merged_data$SCIvsMCI, median, na.rm = TRUE)
  513. tapply(final_merged_data$frontal_NREM_Spl_pmin, final_merged_data$SCIvsMCI==1, IQR, na.rm = TRUE)
  515. t.test(log(central_NREM_Spl_pmin) ~ SCIvsMCI, var.equal = TRUE, data = final_merged_data)
  516. tapply(final_merged_data$central_NREM_Spl_pmin, final_merged_data$SCIvsMCI, median, na.rm = TRUE)
  517. tapply(final_merged_data$central_NREM_Spl_pmin, final_merged_data$SCIvsMCI==1, IQR, na.rm = TRUE)
  519. t.test(log(frontal_NREM_Spl_fast_pmin) ~ SCIvsMCI, var.equal = TRUE, data = final_merged_data)
  520. tapply(final_merged_data$frontal_NREM_Spl_fast_pmin, final_merged_data$SCIvsMCI, median, na.rm = TRUE)
  521. tapply(final_merged_data$frontal_NREM_Spl_fast_pmin, final_merged_data$SCIvsMCI==1, IQR, na.rm = TRUE)
  523. t.test(log(central_NREM_Spl_fast_pmin) ~ SCIvsMCI, var.equal = TRUE, data = final_merged_data)
  524. tapply(final_merged_data$central_NREM_Spl_fast_pmin, final_merged_data$SCIvsMCI, median, na.rm = TRUE)
  525. tapply(final_merged_data$central_NREM_Spl_fast_pmin, final_merged_data$SCIvsMCI==1, IQR, na.rm = TRUE)
  527. t.test(log(frontal_NREM_MeanMaxHeight) ~ SCIvsMCI, var.equal = TRUE, data = final_merged_data)
  528. tapply(final_merged_data$frontal_NREM_MeanMaxHeight, final_merged_data$SCIvsMCI, median, na.rm = TRUE)
  529. tapply(final_merged_data$frontal_NREM_MeanMaxHeight, final_merged_data$SCIvsMCI==1, IQR, na.rm = TRUE)
  531. t.test(log(central_NREM_MeanMaxHeight) ~ SCIvsMCI, var.equal = TRUE, data = final_merged_data)
  532. tapply(final_merged_data$central_NREM_MeanMaxHeight, final_merged_data$SCIvsMCI, median, na.rm = TRUE)
  533. tapply(final_merged_data$central_NREM_MeanMaxHeight, final_merged_data$SCIvsMCI==1, IQR, na.rm = TRUE)
  534. ```
  536. ```{r Supplementary Table 1, echo=FALSE}
  537. hist(final_merged_data$ATR_left_fbc)
  538. hist(final_merged_data$ATR_right_fbc)
  539. hist(final_merged_data$SLF_II_left_fbc)
  540. hist(final_merged_data$SLF_II_right_fbc)
  541. hist(final_merged_data$SLF_III_left_fbc)
  542. hist(final_merged_data$SLF_III_right_fbc)
  544. wilcox.test(ATR_total_fbc ~ SCIvsMCI, data=final_merged_data)
  545. tapply(final_merged_data$ATR_total_fbc, final_merged_data$SCIvsMCI, median, na.rm = TRUE)
  546. tapply(final_merged_data$ATR_total_fbc, final_merged_data$SCIvsMCI==1, IQR, na.rm = TRUE)
  548. wilcox.test(ATR_right_fbc ~ SCIvsMCI, data=final_merged_data)
  549. tapply(final_merged_data$ATR_right_fbc, final_merged_data$SCIvsMCI, median, na.rm = TRUE)
  550. tapply(final_merged_data$ATR_right_fbc, final_merged_data$SCIvsMCI==1, IQR, na.rm = TRUE)
  552. wilcox.test(SLF_II_total_fbc ~ SCIvsMCI, data=final_merged_data)
  553. tapply(final_merged_data$SLF_II_total_fbc, final_merged_data$SCIvsMCI, median, na.rm = TRUE)
  554. tapply(final_merged_data$SLF_II_total_fbc, final_merged_data$SCIvsMCI==1, IQR, na.rm = TRUE)
  556. wilcox.test(SLF_II_right_fbc ~ SCIvsMCI, data=final_merged_data)
  557. tapply(final_merged_data$SLF_II_right_fbc, final_merged_data$SCIvsMCI, median, na.rm = TRUE)
  558. tapply(final_merged_data$SLF_II_right_fbc, final_merged_data$SCIvsMCI==1, IQR, na.rm = TRUE)
  560. wilcox.test(SLF_III_total_fbc ~ SCIvsMCI, data=final_merged_data)
  561. tapply(final_merged_data$SLF_III_total_fbc, final_merged_data$SCIvsMCI, median, na.rm = TRUE)
  562. tapply(final_merged_data$SLF_III_total_fbc, final_merged_data$SCIvsMCI==1, IQR, na.rm = TRUE)
  564. wilcox.test(SLF_III_right_fbc ~ SCIvsMCI, data=final_merged_data)
  565. tapply(final_merged_data$SLF_III_right_fbc, final_merged_data$SCIvsMCI, median, na.rm = TRUE)
  566. tapply(final_merged_data$SLF_III_right_fbc, final_merged_data$SCIvsMCI==1, IQR, na.rm = TRUE)
  569. ```
  570. # Averaged tract analysis
  572. ```{r AVERAGED TRACTS ANALYSIS}
  573. # Setup your variables of interest
  574. dvs <- c("log_central_NREM_Spl_pmin",
  575. "log_frontal_NREM_Spl_pmin",
  576. "log_central_NREM_Spl_fast_pmin",
  577. "log_frontal_NREM_Spl_fast_pmin",
  578. "log_central_NREM_Spl_slow_pmin",
  579. "log_frontal_NREM_Spl_slow_pmin",
  580. "log_frontal_NREM_MeanMaxHeight",
  581. "log_central_NREM_MeanMaxHeight")
  583. ivs <- c("ATR_total_fbc",
  584. "SLF_III_total_fbc",
  585. "SLF_II_total_fbc")
  587. moderator <- "SCIvsMCI"
  589. covariates <- c("sex_spss", "age_spss")
  591. # Run the function
  592. run_moderation_analysis_withcovariates(
  593. data = final_merged_data,
  594. dvs = dvs,
  595. ivs = ivs,
  596. moderator = moderator,
  597. covariates = covariates,
  598. output_dir = "/Users/alam2002/Library/CloudStorage/OneDrive-TheUniversityofSydney(Staff)/9. PAPERS/DWI_EEG/1. Analysis/Revision1_Results/Total_tract_log_spl_Corrected_moderationAnalyses/"
  599. )
  600. ```
  603. ```{r AVERAGED TRACTS ANALYSIS}
  604. # Setup your variables of interest
  605. dvs <- c("log_frontal_Absolute_NREM_0.25_to_1.0Hz",
  606. "log_central_Absolute_NREM_0.25_to_1.0Hz")
  608. ivs <- c("ATR_total_fbc",
  609. "SLF_III_total_fbc",
  610. "SLF_II_total_fbc")
  612. moderator <- "SCIvsMCI"
  614. covariates <- c("sex_spss", "age_spss")
  616. # Run the function
  617. run_moderation_analysis_withcovariates(
  618. data = final_merged_data,
  619. dvs = dvs,
  620. ivs = ivs,
  621. moderator = moderator,
  622. covariates = covariates,
  623. output_dir = "/Users/alam2002/Library/CloudStorage/OneDrive-TheUniversityofSydney(Staff)/9. PAPERS/DWI_EEG/1. Analysis/Revision1_Results/Total_tract_log_SOpower_Corrected_moderationAnalyses/"
  624. )
  625. ```
  627. ## Table 3 effect sizes and FDR corrections
  628. ```{r}
  630. # frontal
  631. fiti <- lm(log_frontal_NREM_Spl_slow_pmin ~ ATR_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  632. rsq::rsq.partial(fiti)
  634. fiti <- lm(log_frontal_NREM_Spl_slow_pmin ~ SLF_II_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  635. rsq::rsq.partial(fiti)
  637. fiti <- lm(log_frontal_NREM_Spl_slow_pmin ~ SLF_III_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  638. rsq::rsq.partial(fiti)
  641. #central
  642. fiti <- lm(log_central_NREM_Spl_slow_pmin ~ ATR_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  643. rsq::rsq.partial(fiti)
  646. fiti <- lm(log_central_NREM_Spl_slow_pmin ~ SLF_II_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  647. rsq::rsq.partial(fiti)
  649. fiti <- lm(log_central_NREM_Spl_slow_pmin ~ SLF_III_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  650. rsq::rsq.partial(fiti)
  653. # Benjamini-Hochberg procedure
  654. # Main
  655. pvals <- c(0.291,0.419,0.629,0.202,0.961,0.213)
  656. qvals <- p.adjust(pvals, method = "BH")
  657. qvals
  659. # Interactions
  660. pvals <- c(0.408,0.186,0.883,0.190,0.501,0.381)
  661. qvals <- p.adjust(pvals, method = "BH")
  662. qvals
  664. ```
  667. # Fast spindle density - moderation
  668. ```{r simple slopes}
  669. fiti <- lm(log_central_NREM_Spl_fast_pmin ~ SLF_III_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  670. summary(fiti)
  671. sim_slopes(fiti, pred = SLF_III_total_fbc, modx = SCIvsMCI, jnplot = TRUE)
  672. # Interaction plot with confidence intervals
  673. interact_plot(fiti, plot.points=TRUE, pred = SLF_III_total_fbc, x.label = "Total SLF-III FBC", y.label = "Central fast spindle denstiy",modx.labels = c("SCD","MCI"), modx = SCIvsMCI, line.thickness = 2, point.size = 2,interval = TRUE) +
  674. theme(
  675. axis.title.x = element_text(size = 20, colour = "black"),
  676. axis.title.y = element_text(size = 20, colour = "black"),
  677. axis.text = element_text(size = 20, face="bold", colour = "black")
  678. )
  681. # Effect size of fast spindle density
  682. fiti <- lm(log_central_NREM_Spl_fast_pmin ~ SLF_III_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 0))
  683. rsq::rsq.partial(fiti)
  685. fiti <- lm(log_central_NREM_Spl_fast_pmin ~ SLF_III_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 1))
  686. rsq::rsq.partial(fiti)
  688. ```
  691. ## Table 4 effect sizes and FDR corrections
  692. ```{r}
  694. # frontal
  695. fiti <- lm(log_frontal_NREM_Spl_fast_pmin ~ ATR_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  696. rsq::rsq.partial(fiti)
  698. fiti <- lm(log_frontal_NREM_Spl_fast_pmin ~ SLF_II_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  699. rsq::rsq.partial(fiti)
  701. fiti <- lm(log_frontal_NREM_Spl_fast_pmin ~ SLF_III_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  702. rsq::rsq.partial(fiti)
  705. #central
  706. fiti <- lm(log_central_NREM_Spl_fast_pmin ~ ATR_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  707. rsq::rsq.partial(fiti)
  709. fiti <- lm(log_central_NREM_Spl_fast_pmin ~ SLF_II_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  710. rsq::rsq.partial(fiti)
  712. fiti <- lm(log_central_NREM_Spl_fast_pmin ~ SLF_III_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  713. rsq::rsq.partial(fiti)
  716. # Benjamini-Hochberg procedure
  717. # Main
  718. pvals <- c(0.236,0.696,0.745,0.809,0.149,0.028)
  719. qvals <- p.adjust(pvals, method = "BH")
  720. qvals
  722. # Interactions
  723. pvals <- c(0.490,0.642,0.904,0.659,0.052,0.028)
  724. qvals <- p.adjust(pvals, method = "BH")
  725. qvals
  729. ```
  731. ## Table 5 effect sizes and FDR corrections
  732. ```{r}
  734. # frontal
  735. fiti <- lm(log_frontal_Absolute_NREM_0.25_to_1.0Hz ~ ATR_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  736. rsq::rsq.partial(fiti)
  739. fiti <- lm(log_frontal_Absolute_NREM_0.25_to_1.0Hz ~ SLF_II_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  740. rsq::rsq.partial(fiti)
  741. fiti <- lm(log_frontal_Absolute_NREM_0.25_to_1.0Hz ~ SLF_III_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  742. rsq::rsq.partial(fiti)
  744. #central
  745. fiti <- lm(log_central_Absolute_NREM_0.25_to_1.0Hz ~ ATR_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  746. rsq::rsq.partial(fiti)
  748. fiti <- lm(log_central_Absolute_NREM_0.25_to_1.0Hz ~ SLF_II_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  749. rsq::rsq.partial(fiti)
  751. fiti <- lm(log_central_Absolute_NREM_0.25_to_1.0Hz ~ SLF_III_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  752. rsq::rsq.partial(fiti)
  755. # Benjamini-Hochberg procedure
  756. # Main
  757. pvals <- c(0.751,0.774,0.432,0.771,0.324,0.158)
  758. qvals <- p.adjust(pvals, method = "BH")
  759. qvals
  761. # Interactions
  762. pvals <- c(0.928,0.322,0.073,0.771,0.324,0.158)
  763. qvals <- p.adjust(pvals, method = "BH")
  764. qvals
  767. ```
  769. ## Table 6 effect sizes and FDR corrections
  770. ```{r}
  772. # frontal
  773. fiti <- lm(log_frontal_NREM_MeanMaxHeight ~ ATR_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  774. rsq::rsq.partial(fiti)
  776. fiti <- lm(log_frontal_NREM_MeanMaxHeight ~ SLF_II_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  777. rsq::rsq.partial(fiti)
  779. fiti <- lm(log_frontal_NREM_MeanMaxHeight ~ SLF_III_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  780. rsq::rsq.partial(fiti)
  783. #central
  784. fiti <- lm(log_central_NREM_MeanMaxHeight ~ ATR_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  785. rsq::rsq.partial(fiti)
  788. fiti <- lm(log_central_NREM_MeanMaxHeight ~ SLF_II_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  789. rsq::rsq.partial(fiti)
  791. fiti <- lm(log_central_NREM_MeanMaxHeight ~ SLF_III_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  792. rsq::rsq.partial(fiti)
  795. # Benjamini-Hochberg procedure
  796. pvals <- c(0.871,0.872,0.929,0.813,0.937,0.989)
  798. qvals <- p.adjust(pvals, method = "BH")
  800. qvals
  802. # Interactions
  803. pvals <- c(0.555,0.026,0.461,0.554,0.009,0.351)
  804. qvals <- p.adjust(pvals, method = "BH")
  806. qvals
  809. ```
  811. # Spindle amplitude simple slopes with FDR at the end
  812. ``` {r simple slopes}
  813. fiti <- lm(log_central_NREM_MeanMaxHeight ~ SLF_II_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  814. x <- sim_slopes(fiti, pred = SLF_II_total_fbc, modx = SCIvsMCI, jnplot = TRUE)
  815. x$slopes
  816. # Interaction plot with confidence intervals
  817. interact_plot(fiti, plot.points=TRUE, pred = SLF_II_total_fbc, x.label = "Total SLF-II FBC", y.label = "Central overall spindle amplitude", modx.labels = c("SCD","MCI"), modx = SCIvsMCI, line.thickness = 2, point.size = 2,interval = TRUE) +
  818. theme(
  819. axis.title.x = element_text(size = 20, colour = "black"),
  820. axis.title.y = element_text(size = 20, colour = "black"),
  821. axis.text = element_text(size = 20, face="bold", colour = "black")
  822. )
  825. # remove outliers and repeat analysis
  826. outliers <- order(final_merged_data$log_central_NREM_MeanMaxHeight,
  827. decreasing = TRUE)[1:2]
  829. # Create new variable
  830. final_merged_data$log_central_NREM_MeanMaxHeight_outlierremoved <-
  831. final_merged_data$log_central_NREM_MeanMaxHeight
  833. final_merged_data$log_central_NREM_MeanMaxHeight_outlierremoved[outliers] <- NA
  836. fiti <- lm(log_central_NREM_MeanMaxHeight_outlierremoved ~ SLF_II_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  837. sim_slopes(fiti, pred = SLF_II_total_fbc, modx = SCIvsMCI, jnplot = TRUE)
  838. # Interaction plot with confidence intervals
  839. interact_plot(fiti, plot.points=TRUE, pred = SLF_II_total_fbc, x.label = "Total SLF-II FBC", y.label = "Central overall spindle amplitude",modx.labels = c("SCD","MCI"), modx = SCIvsMCI, interval = TRUE)
  842. # frontal repeat
  843. fiti <- lm(log_frontal_NREM_MeanMaxHeight ~ SLF_II_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  844. sim_slopes(fiti, pred = SLF_II_total_fbc, modx = SCIvsMCI, jnplot = TRUE)
  845. # Interaction plot with confidence intervals
  846. interact_plot(fiti, plot.points=TRUE, pred = SLF_II_total_fbc, x.label = "Total SLF-II FBC", y.label = "Frontal overall spindle amplitude",modx.labels = c("SCD","MCI"), modx = SCIvsMCI, interval = TRUE)
  848. # remove frontal outliers
  849. outliers <- order(final_merged_data$log_frontal_NREM_MeanMaxHeight,
  850. decreasing = TRUE)[1:4]
  852. # Create new variable
  853. final_merged_data$log_frontal_NREM_MeanMaxHeight_outlierremoved <-
  854. final_merged_data$log_frontal_NREM_MeanMaxHeight
  856. final_merged_data$log_frontal_NREM_MeanMaxHeight_outlierremoved[outliers] <- NA
  858. # model
  859. fiti <- lm(log_frontal_NREM_MeanMaxHeight_outlierremoved ~ SLF_II_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  860. summary(fiti)
  861. sim_slopes(fiti, pred = SLF_II_total_fbc, modx = SCIvsMCI, jnplot = TRUE)
  862. # Interaction plot with confidence intervals
  863. interact_plot(fiti, plot.points=TRUE, pred = SLF_II_total_fbc, x.label = "Total SLF-II FBC", y.label = "Central overall spindle amplitude",modx.labels = c("SCD","MCI"), modx = SCIvsMCI, interval = TRUE)
  867. ### FDR corrections
  868. fiti <- lm(log_central_NREM_MeanMaxHeight ~ SLF_II_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  869. x <- sim_slopes(fiti, pred = SLF_II_total_fbc, modx = SCIvsMCI, jnplot = TRUE)
  870. x$slopes
  872. fiti <- lm(log_frontal_NREM_MeanMaxHeight ~ SLF_II_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  873. x <- sim_slopes(fiti, pred = SLF_II_total_fbc, modx = SCIvsMCI, jnplot = TRUE)
  874. x$slopes
  876. pvals <- c(0.937,0.00001,0.873,0.000010)
  877. qvals <- p.adjust(pvals, method = "BH")
  879. qvals
  882. ```
  884. # Correlation of fast and slow spindles
  885. ```{r}
  886. cor.test(final_merged_data$frontal_NREM_Spl_fast_pmin,final_merged_data$frontal_NREM_Spl_slow_pmin)
  888. cor.test(final_merged_data$central_NREM_Spl_fast_pmin,final_merged_data$central_NREM_Spl_slow_pmin)
  889. ```
  892. # removing outliers in unilateral tracts (left vs. right)
  893. ```{r}
  894. ### FRONTAL ####
  895. # remove outliers and repeat analysis
  896. outliers <- order(final_merged_data$log_frontal_NREM_MeanMaxHeight,
  897. decreasing = TRUE)[1:4]
  899. # Create new variable
  900. final_merged_data$log_frontal_NREM_MeanMaxHeight_outlierremoved <-
  901. final_merged_data$log_frontal_NREM_MeanMaxHeight
  903. final_merged_data$log_frontal_NREM_MeanMaxHeight_outlierremoved[outliers] <- NA
  905. fiti <- lm(log_frontal_NREM_MeanMaxHeight_outlierremoved ~ SLF_II_left_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  906. summary(fiti)
  907. sim_slopes(fiti, pred = SLF_II_left_fbc, modx = SCIvsMCI, jnplot = TRUE)
  908. # Interaction plot with confidence intervals
  909. interact_plot(fiti, plot.points=TRUE, pred = SLF_II_left_fbc, x.label = "Frontal overall spindle amplitude", y.label = "Total SLF-II FBC",modx.labels = c("SCD","MCI"), modx = SCIvsMCI, interval = TRUE)
  911. fiti <- lm(log_frontal_NREM_MeanMaxHeight_outlierremoved ~ SLF_II_right_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  912. summary(fiti)
  913. sim_slopes(fiti, pred = SLF_II_right_fbc, modx = SCIvsMCI, jnplot = TRUE)
  914. # Interaction plot with confidence intervals
  915. interact_plot(fiti, plot.points=TRUE, pred = SLF_II_right_fbc, x.label = "Frontal overall spindle amplitude", y.label = "Total SLF-II FBC",modx.labels = c("SCD","MCI"), modx = SCIvsMCI, interval = TRUE)
  917. ### CENTRAL ####
  918. # remove outliers and repeat analysis
  919. outliers <- order(final_merged_data$log_central_NREM_MeanMaxHeight,
  920. decreasing = TRUE)[1:2]
  922. # Create new variable
  923. final_merged_data$log_central_NREM_MeanMaxHeight_outlierremoved <-
  924. final_merged_data$log_central_NREM_MeanMaxHeight
  926. final_merged_data$log_central_NREM_MeanMaxHeight_outlierremoved[outliers] <- NA
  928. fiti <- lm(log_central_NREM_MeanMaxHeight_outlierremoved ~ SLF_II_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  929. summary(fiti)
  930. sim_slopes(fiti, pred = SLF_II_total_fbc, modx = SCIvsMCI, jnplot = TRUE)
  931. # Interaction plot with confidence intervals
  932. interact_plot(fiti, plot.points=TRUE, pred = SLF_II_left_fbc, x.label = "Central overall spindle amplitude", y.label = "Total SLF-II FBC",modx.labels = c("SCD","MCI"), modx = SCIvsMCI, interval = TRUE)
  934. fiti <- lm(log_central_NREM_MeanMaxHeight_outlierremoved ~ SLF_II_right_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  935. summary(fiti)
  936. sim_slopes(fiti, pred = SLF_II_right_fbc, modx = SCIvsMCI, jnplot = TRUE)
  937. # Interaction plot with confidence intervals
  938. interact_plot(fiti, plot.points=TRUE, pred = SLF_II_right_fbc, x.label = "Central overall spindle amplitude", y.label = "Total SLF-II FBC",modx.labels = c("SCD","MCI"), modx = SCIvsMCI, interval = TRUE)
  941. ```
  944. # Correlation of tracts
  945. ```{r}
  946. library(Hmisc)
  947. tract_data <- final_merged_data[, c("ATR_total_fbc",
  948. "SLF_II_total_fbc",
  949. "SLF_III_total_fbc")]
  951. cor_results <- rcorr(as.matrix(tract_data))
  953. cor_results$r # correlation matrix
  954. cor_results$P # p-value matrix
  955. ```
  958. # Effect size of combined tracts - whole sample
  959. ```{r}
  961. ### FRONTAL ###
  962. fiti <- lm(log_frontal_NREM_MeanMaxHeight ~ ATR_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 0))
  963. eta_squared(fiti, partial = TRUE)
  965. fiti <- lm(log_frontal_NREM_MeanMaxHeight ~ ATR_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 1))
  966. eta_squared(fiti, partial = TRUE)
  969. fiti <- lm(log_frontal_NREM_MeanMaxHeight ~ SLF_II_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 0))
  970. eta_squared(fiti, partial = TRUE)
  972. fiti <- lm(log_frontal_NREM_MeanMaxHeight ~ SLF_II_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 1))
  973. eta_squared(fiti, partial = TRUE)
  976. fiti <- lm(log_frontal_NREM_MeanMaxHeight ~ SLF_III_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 0))
  977. eta_squared(fiti, partial = TRUE)
  979. fiti <- lm(log_frontal_NREM_MeanMaxHeight ~ SLF_III_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 1))
  980. eta_squared(fiti, partial = TRUE)
  985. #### CENTRAL ####
  986. fiti <- lm(log_central_NREM_MeanMaxHeight ~ ATR_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 0))
  987. eta_squared(fiti, partial = TRUE)
  989. fiti <- lm(log_central_NREM_MeanMaxHeight ~ ATR_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 1))
  990. eta_squared(fiti, partial = TRUE)
  993. fiti <- lm(log_central_NREM_MeanMaxHeight ~ SLF_II_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 0))
  994. eta_squared(fiti, partial = TRUE)
  996. fiti <- lm(log_central_NREM_MeanMaxHeight_outlierremoved ~ SLF_II_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 1))
  997. summary(fiti)
  998. eta_squared(fiti, partial = TRUE)
  1001. fiti <- lm(log_central_NREM_MeanMaxHeight ~ SLF_III_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 0))
  1002. eta_squared(fiti, partial = TRUE)
  1004. fiti <- lm(log_central_NREM_MeanMaxHeight ~ SLF_III_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 1))
  1005. eta_squared(fiti, partial = TRUE)
  1007. ```
  1010. # Effect size of combined tracts -REPEAT IN OUTLIERS REMOVED
  1011. ```{r}
  1012. ### CHOOSE HOW MANY OUTLIERS TO REMOVE
  1013. # remove outliers and repeat analysis
  1014. # Frontal
  1015. # remove outliers and repeat analysis
  1016. outliers <- order(final_merged_data$log_frontal_NREM_MeanMaxHeight,
  1017. decreasing = TRUE)[1:2]
  1019. # Create new variable
  1020. final_merged_data$log_frontal_NREM_MeanMaxHeight_outlierremoved <-
  1021. final_merged_data$log_frontal_NREM_MeanMaxHeight
  1023. final_merged_data$log_frontal_NREM_MeanMaxHeight_outlierremoved[outliers] <- NA
  1026. # Central
  1027. outliers <- order(final_merged_data$log_central_NREM_MeanMaxHeight,
  1028. decreasing = TRUE)[1:2]
  1030. # Create new variable
  1031. final_merged_data$log_central_NREM_MeanMaxHeight_outlierremoved <-
  1032. final_merged_data$log_central_NREM_MeanMaxHeight
  1034. final_merged_data$log_central_NREM_MeanMaxHeight_outlierremoved[outliers] <- NA
  1037. ### FRONTAL ###
  1038. fiti <- lm(log_frontal_NREM_MeanMaxHeight_outlierremoved ~ ATR_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 0))
  1039. summary(fiti)
  1040. eta_squared(fiti, partial = TRUE)
  1042. fiti <- lm(log_frontal_NREM_MeanMaxHeight_outlierremoved ~ ATR_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 1))
  1043. summary(fiti)
  1044. eta_squared(fiti, partial = TRUE)
  1047. fiti <- lm(log_frontal_NREM_MeanMaxHeight_outlierremoved ~ SLF_II_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 0))
  1048. summary(fiti)
  1049. eta_squared(fiti, partial = TRUE)
  1051. fiti <- lm(log_frontal_NREM_MeanMaxHeight_outlierremoved ~ SLF_II_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 1))
  1052. summary(fiti)
  1053. eta_squared(fiti, partial = TRUE)
  1056. fiti <- lm(log_frontal_NREM_MeanMaxHeight_outlierremoved ~ SLF_III_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 0))
  1057. eta_squared(fiti, partial = TRUE)
  1059. fiti <- lm(log_frontal_NREM_MeanMaxHeight_outlierremoved ~ SLF_III_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 1))
  1060. eta_squared(fiti, partial = TRUE)
  1065. #### CENTRAL ####
  1066. fiti <- lm(log_central_NREM_MeanMaxHeight_outlierremoved ~ ATR_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 0))
  1067. eta_squared(fiti, partial = TRUE)
  1069. fiti <- lm(log_central_NREM_MeanMaxHeight_outlierremoved ~ ATR_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 1))
  1070. eta_squared(fiti, partial = TRUE)
  1073. fiti <- lm(log_central_NREM_MeanMaxHeight_outlierremoved ~ SLF_II_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 0))
  1074. summary(fiti)
  1075. eta_squared(fiti, partial = TRUE)
  1078. fiti <- lm(log_central_NREM_MeanMaxHeight_outlierremoved ~ SLF_II_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 1))
  1079. summary(fiti)
  1080. eta_squared(fiti, partial = TRUE)
  1083. fiti <- lm(log_central_NREM_MeanMaxHeight_outlierremoved ~ SLF_III_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 0))
  1084. eta_squared(fiti, partial = TRUE)
  1086. fiti <- lm(log_central_NREM_MeanMaxHeight_outlierremoved ~ SLF_III_total_fbc + sex_spss + age_spss, data = subset(final_merged_data, SCIvsMCI == 1))
  1087. eta_squared(fiti, partial = TRUE)
  1089. ```
  1092. ## Supplementary Table 2 effect sizes and FDR correction
  1093. ```{r}
  1095. # frontal
  1096. fiti <- lm(log_frontal_NREM_Spl_pmin ~ ATR_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  1097. rsq::rsq.partial(fiti)
  1099. fiti <- lm(log_frontal_NREM_Spl_pmin ~ SLF_II_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  1100. rsq::rsq.partial(fiti)
  1102. fiti <- lm(log_frontal_NREM_Spl_pmin ~ SLF_III_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  1103. rsq::rsq.partial(fiti)
  1106. #central
  1107. fiti <- lm(log_central_NREM_Spl_pmin ~ ATR_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  1108. rsq::rsq.partial(fiti)
  1111. fiti <- lm(log_central_NREM_Spl_pmin ~ SLF_II_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  1112. rsq::rsq.partial(fiti)
  1114. fiti <- lm(log_central_NREM_Spl_pmin ~ SLF_III_total_fbc * SCIvsMCI + sex_spss + age_spss, data = final_merged_data)
  1115. rsq::rsq.partial(fiti)
  1118. # Benjamini-Hochberg procedure
  1119. # Main
  1120. pvals <- c(0.639,0.655,0.620,0.664,0.652,0.644)
  1121. qvals <- p.adjust(pvals, method = "BH")
  1122. qvals
  1124. # Interactions
  1125. pvals <- c(0.755,0.285,0.819,0.841,0.181,0.594)
  1126. qvals <- p.adjust(pvals, method = "BH")
  1127. qvals
  1129. ```
  1132. ```{r bimodal distribution of spindles}
  1135. path <- "/Users/alam2002/Library/CloudStorage/OneDrive-TheUniversityofSydney(Staff)/9. PAPERS/DWI_EEG/1. Analysis/Data/IndividualSpindles/ABC_1169_spindles.xls" # Two random subjects with high number of spindles ABC0378
  1137. spindles <- read_excel(path, sheet = "C3") #F3 and C3
  1139. freq_df <- spindles %>%
  1140. select(`Frequency (Hz)`) %>%
  1141. filter(!is.na(`Frequency (Hz)`))
  1143. hist(freq_df$`Frequency (Hz)`, breaks = 40,
  1144. main = "Central spindle frequency distribution",
  1145. xlab = "Frequency (Hz)")
  1146. abline(v = 13, lty = 2)
  1149. ```
  1150. ## Medications
  1151. ```{r}
  1152. # antidep_yn
  1154. table(final_merged_data$antidep_yn)
  1155. # first antidep medication
  1156. table(final_merged_data$t0_ct_1_1)
  1158. # hypnotics or sedatives - t0_ct_2_1
  1159. sum(!is.na(final_merged_data$t0_ct_2_1))
  1161. # mood stabilisers/antiepileptic - t0_ct_3_1
  1162. sum(!is.na(final_merged_data$t0_ct_3_1))
  1164. # antipsychotics - t0_ct_4_1
  1165. sum(!is.na(final_merged_data$t0_ct_4_1))
  1167. # Benzo - t0_ct_5_1
  1168. sum(!is.na(final_merged_data$t0_ct_5_1))
  1169. # Cholinesterase inhibitirs - t0_ct_6_1
  1170. sum(!is.na(final_merged_data$t0_ct_6_1))
  1172. ```
  1174. ## MRI date difference
  1175. ```{r}
  1176. final_merged_data <- final_merged_data %>%
  1177. mutate(
  1178. hba_mri_date = as.Date(hba_mri_date)
  1179. )
  1180. final_merged_data <- final_merged_data %>%
  1181. mutate(
  1182. psg_study_date = parse_date_time(
  1183. psg_study_date,
  1184. orders = c("mdy", "dmy")
  1185. ) %>% as.Date()
  1186. )
  1187. final_merged_data$hba_mri_date
  1188. final_merged_data$psg_study_date
  1190. date_diff <- difftime(as.Date(final_merged_data$hba_mri_date),
  1191. as.Date(final_merged_data$psg_study_date),
  1192. units = "days")
  1194. mean(abs(as.numeric(date_diff)), na.rm = TRUE)
  1195. sd(abs(as.numeric(date_diff)), na.rm = TRUE)
  1196. ```

DWI_EEG_osf.Rmd, no license · at the source

Overview

Authors: Aaron Lam1,2,3, Shawn Dexiao Kong1,2, Jake R Palmer1,2, Fernando Calamante4, Hannes Almgren4, Angela L D’Rozario1,3,5, Sharon L Naismith1,2
ORCID iDs: Aaron Lam, Shawn Dexiao Kong, Jake R Palmer, Angela L D’Rozario, Sharon L Naismith
  1. School of Psychology, The University of Sydney, Sydney, NSW 2050, Australia
  2. Brain and Mind Centre and Charles Perkins Centre, The University of Sydney, Sydney 2050, NSW, Australia
  3. CIRUS, Sleep and Circadian Research Group, Woolcock Institute of Medical Research, Macquarie University, Sydney 2113, NSW, Australia
  4. Sydney Imaging and School of Biomedical Engineering, The University of Sydney, Sydney 2050, NSW, Australia
  5. School of Psychological Sciences, Macquarie University, Sydney 2113, NSW, Australia
Institutions: The University of Sydney (Australia); Woolcock Institute of Medical Research (Australia); Macquarie University (Australia)
Journal: Brain communications, volume 8, issue 4, article fcag303
Dates: received 18 December 2025; accepted 29 July 2026; published online 10 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag303 · PMID 42582630 · PMCID PMC13458153 · OpenAlex W7202139527
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), EEG (modality), human (organism), Alzheimer's / dementia (population), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Preprocessing, fMRI & imaging
Keywords: dementia, ageing, memory, neuroimaging, slow oscillations
Topic: Sleep and Wakefulness Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Health and Medical Research Council (GNT2008001); Centre of Research Excellence to Optimise Sleep in Brain Ageing and Neurodegeneration (GNT1152945); Synergise, Integrate and Enhance Sleep Research to Transform Brain Ageing; SIESTA (GNT2018668, GNT1135639)
Citations: not cited yet (Europe PMC); 69 references in the paper

Abstract

While sleep disturbance increases with age and neurodegeneration, the relationship between sleep micro-architecture and changes in key white matter tracts is unknown. We aimed to determine how sleep spindles and slow oscillations relate to structural connectivity within selected white matter tracts in older adults at risk for dementia. In this cross-sectional study, 67 participants (mean age = 68.1; 47 females) with subjective cognitive decline (n = 21) or mild cognitive impairment (n = 46) underwent neuropsychological assessment, magnetic resonance imaging and polysomnography. White matter connectivity of anterior thalamic radiation and superior longitudinal fasciculus subdivisions II and III was quantified using whole-brain tractography, with left and right tracts combined. Primary sleep micro-architecture outcomes included slow (11–13 Hz) and fast (13–16 Hz) spindle density and slow oscillation (0.25–1 Hz) power. Secondary outcomes included: overall spindle density (11–16 Hz) and amplitude. Linear regressions assessed associations between sleep micro-architecture and structural connectivity, adjusting for age and sex, with cognitive classification as a moderator. Multiple comparisons were controlled for using the Benjamini–Hochberg false discovery rate procedure, with q < 0.1 considered statistically significant. The mild cognitive impairment group exhibited lower frontal and central fast spindle density compared to the subjective cognitive impairment group (P < 0.05), with no other group differences in sleep macro-architecture or slow oscillatory activity. After false discovery rate correction, there were no significant associations between slow spindle density, fast spindle density or slow oscillation power and tract connectivity. An interaction between central fast spindle density and superior longitudinal fasciculus III connectivity was observed prior to false discovery rate correction, explaining 8% of unique variance, but did not remain significant after correction (q > 0.10). Exploratory analyses reveal a significant interaction between superior longitudinal fasciculus II connectivity and cognitive classification on spindle amplitude, with greater connectivity associated with higher spindle amplitude in individuals with mild cognitive impairment (P = 0.001), but not in those with subjective cognitive decline (P > 0.05). Overall, white matter connectivity of the anterior thalamic radiation and superior longitudinal fasciculus was not robustly associated with sleep spindle density or slow oscillatory power in this at-risk cohort. However, superior longitudinal fasciculus II connectivity may differentially relate to spindle amplitude in mild cognitive impairment, potentially reflecting altered thalamocortical network engagement in early neurodegeneration.

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

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OSF uy7jk

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Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 5 keywords, 4 funders, 63 references.

Cite

This paper

Lam, A., Kong, S. D., Palmer, J. R., Calamante, F., Almgren, H., D’Rozario, A. L., & Naismith, S. L. (2026). Sleep spindles are linked to white matter connectivity differentially in older adults with and without cognitive impairment. Brain communications, 8(4), fcag303. https://doi.org/10.1093/braincomms/fcag303

BibTeX

@article{lam2026sleep,
author = {Lam, Aaron and Kong, Shawn Dexiao and Palmer, Jake R and Calamante, Fernando and Almgren, Hannes and D’Rozario, Angela L and Naismith, Sharon L},
title = {{Sleep spindles are linked to white matter connectivity differentially in older adults with and without cognitive impairment}},
journal = {Brain communications},
year = {2026},
month = aug,
volume = {8},
number = {4},
pages = {fcag303},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag303},
url = {https://doi.org/10.1093/braincomms/fcag303},
pmid = {42582630},
pmcid = {PMC13458153}
}

RIS

TY - JOUR
AU - Lam, Aaron
AU - Kong, Shawn Dexiao
AU - Palmer, Jake R
AU - Calamante, Fernando
AU - Almgren, Hannes
AU - D’Rozario, Angela L
AU - Naismith, Sharon L
TI - Sleep spindles are linked to white matter connectivity differentially in older adults with and without cognitive impairment
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/08/11
VL - 8
IS - 4
SP - fcag303
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag303
UR - https://doi.org/10.1093/braincomms/fcag303
LA - en
ER -

CSL-JSON

{
"id": "10.1093/braincomms/fcag303",
"type": "article-journal",
"title": "Sleep spindles are linked to white matter connectivity differentially in older adults with and without cognitive impairment",
"container-title": "Brain communications",
"author": [
{
"family": "Lam",
"given": "Aaron"
},
{
"family": "Kong",
"given": "Shawn Dexiao"
},
{
"family": "Palmer",
"given": "Jake R"
},
{
"family": "Calamante",
"given": "Fernando"
},
{
"family": "Almgren",
"given": "Hannes"
},
{
"family": "D’Rozario",
"given": "Angela L"
},
{
"family": "Naismith",
"given": "Sharon L"
}
],
"container-title-short": "Brain Commun",
"volume": "8",
"issue": "4",
"page": "fcag303",
"DOI": "10.1093/braincomms/fcag303",
"PMID": "42582630",
"PMCID": "PMC13458153",
"ISSN": "2632-1297",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/braincomms/fcag303",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
11
]
]
}
}

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