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The neurophysiology of healthy and pathological aging: a comprehensive systematic review

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  1. #The neurophysiology of healthy and pathological aging: A comprehensive systematic review (2024)
  3. #Gemma Fernández-Rubio ([email hidden])
  4. #Center for Music in the Brain, Aarhus University, Aarhus (Denmark)
  5. #05-02-2024
  7. #LIBRARIES, WORKING DIRECTORY AND DATA ====
  8. library(readxl)
  9. library(ggplot2)
  10. library(dplyr)
  11. library(tidyr)
  12. library(stringr)
  13. library(maps)
  14. library(RColorBrewer)
  15. library(writexl)
  17. setwd('Working_Directory')
  18. srdata <- read_excel('data.xlsx')
  20. #PUBLICATIONS PER YEAR (Fig. 2a) ====
  22. #total frequency and percentage
  23. year <- data.frame(year = sort(unique(srdata$year[1:942])),
  24. frequency = as.vector(table(srdata$year[1:942])),
  25. percentage = as.vector(prop.table(table(srdata$year[1:942]))*100))
  27. #bar plot
  28. ggplot(year, aes(x = year, y = frequency)) +
  29. geom_col(color = 'black', fill = '#FBB4AE', width = 1, size = 0.25) +
  30. scale_x_continuous(expand = c(0,0.5), breaks = seq(1979,2023,2), name = 'Publication year') +
  31. scale_y_continuous(expand = c(0,0,0.05,0),breaks = seq(0,160,10),name = 'Number of articles') +
  32. theme_bw() +
  33. theme(text = element_text(size = 12, family = 'sans'),
  34. axis.title = element_text(face = 'bold'))
  36. ggsave('Year.pdf')
  38. #PUBLICATIONS PER COUNTRY (Fig. 2b) ====
  40. #total frequency and percentage
  41. country <- data.frame(country = sort(unique(srdata$country[1:942])),
  42. frequency = as.vector(table(srdata$country[1:942])),
  43. percentage = as.vector(prop.table(table(srdata$country[1:942]))*100))
  45. #world map plot
  46. world_map <- map_data('world') #load world map
  47. country_freq <- data.frame(table(srdata$country[1:942])) #calculate frequency of each country
  48. names(country_freq)[names(country_freq) == 'Var1'] <- 'region' #rename variable
  49. worldSubset <- left_join(world_map, country_freq, by = 'region') #join world map and country data
  50. ggplot(data = worldSubset, mapping = aes(x = long, y = lat, group = group)) +
  51. coord_fixed(1.3) +
  52. geom_polygon(aes(fill = Freq)) +
  53. scale_fill_distiller(name = 'Number of articles',
  54. palette = 'Reds',
  55. direction = 1,
  56. guide = guide_colorsteps(even.steps = TRUE,
  57. show.limits = TRUE,
  58. barwidth = 10,
  59. barheight = 1,
  60. frame.colour = 'gray10',
  61. ticks.colour = 'gray10')) +
  62. theme(text = element_text(size = 12, family= 'sans'),
  63. axis.line = element_blank(),
  64. axis.text = element_blank(),
  65. axis.title = element_blank(),
  66. axis.ticks = element_blank(),
  67. panel.grid = element_blank(),
  68. panel.background = element_rect(fill = 'white'),
  69. plot.title = element_text(hjust = 0.5),
  70. legend.position = 'bottom',
  71. legend.title.position = 'top')
  73. ggsave('Country.pdf')
  75. #STUDY DESIGN (Fig. 3a, 3b, & 3c) ====
  77. #total frequency and percentage
  78. design <- data.frame(design = unique(srdata$design[1:942]),
  79. frequency = as.vector(table(srdata$design[1:942])),
  80. percentage = as.vector(prop.table(table(srdata$design[1:942]))*100))
  82. #pie chart
  83. ggplot(design, aes(x = '', y = percentage, fill = design)) +
  84. geom_bar(stat = 'identity', color = 'black') +
  85. coord_polar('y', start = 0) +
  86. geom_text(aes(label = paste0(c(round(percentage, digits = 2)), '%')),
  87. position = position_stack(vjust = 0.5), size = 3) +
  88. scale_fill_brewer(palette = 'Pastel1') +
  89. theme_classic() +
  90. theme(text = element_text(size = 15, family = 'sans'),
  91. axis.title = element_blank(),
  92. axis.line = element_blank(),
  93. axis.text = element_blank(),
  94. axis.ticks = element_blank(),
  95. legend.position = 'bottom',
  96. legend.title = element_blank())
  98. ggsave('Design.pdf')
  100. #bar plot - design & year
  101. dum <- data.frame(table(srdata$year[1:942],srdata$design[1:942])) #frequency of study design per year
  102. dum <- rbind(dum[1:3, ], data.frame(Var1 = '1984', Var2 = 'Cross-sectional', Freq = 0), dum[4:126, ]) #add missing year ('1984')
  103. dum$Var1 <- factor(dum$Var1, levels = unique(dum$Var1)) #reorder year ('1984' between '1983' and '1985')
  104. dum <- dum[complete.cases(dum), ] #remove empty rows
  106. ggplot(dum, aes(x = Var1, y = Freq, fill = Var2)) +
  107. geom_col(color = 'black', width = 0.75, size = 0.25) +
  108. scale_x_discrete(expand = c(0,0.5), breaks = seq(1979,2023,2), name = 'Publication year') +
  109. scale_y_continuous(expand = c(0,0,0.05,0),breaks = seq(0,160,5),name = 'Number of articles') +
  110. scale_fill_brewer(palette = 'Pastel1') +
  111. theme_bw() +
  112. theme(text = element_text(size = 10, family = 'sans'),
  113. axis.title = element_text(size = 12, face = 'bold'),
  114. legend.position = c(0.086,0.94),
  115. legend.background = element_rect(colour = 'black', fill = 'white', linetype='solid', linewidth = 0.25),
  116. legend.title = element_blank())
  118. ggsave('Design_Year.pdf')
  120. #ratio of study design to publications per year
  121. ratio <- data.frame(year = srdata$year[1:942], var = srdata$design[1:942])
  122. ratio <- ratio %>%
  123. group_by(year) %>%
  124. summarise(total_studies = n(), var1 = sum(var == 'Cross-sectional'), var2 = sum(var == 'Longitudinal'),
  125. ratio_var1 = var1 / total_studies, ratio_var2 = var2 / total_studies)
  127. ratio <- ratio %>%
  128. pivot_longer(cols = starts_with('var'), names_to = 'var', values_to = 'value') %>%
  129. mutate(ratio = ifelse(var == "var1", ratio_var1, ratio_var2)) %>%
  130. select(year, ratio, var)
  132. ggplot(ratio, aes(x = year, y = ratio, group = var)) +
  133. geom_line(aes(color = var)) +
  134. geom_point(aes(color = var)) +
  135. scale_x_continuous(expand = c(0,0.5), breaks = seq(1979,2023,2), name = 'Year') +
  136. scale_y_continuous(expand = c(0.05,0,0.05,0),breaks = seq(0,1,0.1),name = 'Experimental design ratio') +
  137. scale_color_brewer(palette = 'Pastel1', labels = c('Cross-sectional', 'Longitudinal')) +
  138. theme_bw() +
  139. theme(text = element_text(size = 10, family = 'sans'),
  140. axis.title = element_text(size = 12, face = 'bold'),
  141. legend.position = c(0.9,0.5),
  142. legend.background = element_rect(colour = 'black', fill = 'white', linetype='solid', linewidth = 0.25),
  143. legend.title = element_blank())
  145. ggsave('Design_Ratio.pdf')
  147. #STUDY DURATION (Fig. 3d) ====
  149. #total frequency and percentage
  150. duration <- data.frame(duration = sort(unique(srdata$duration[1:72])),
  151. frequency = as.vector(table(srdata$duration[1:72])),
  152. percentage = as.vector(prop.table(table(srdata$duration[1:72]))*100))
  154. #bar plot
  155. ggplot(duration, aes(x = duration, y = frequency)) +
  156. geom_col(color = 'black', fill = '#FBB4AE', width = 0.75, size = 0.25) +
  157. scale_x_discrete(name = 'Average duration', limits = c('< 1 year','1 - 2 years','2 - 3 years','3 - 4 years','> 4 years')) +
  158. scale_y_continuous(expand = c(0,0,0.05,0), breaks = seq(0,20,1),name = 'Number of longitudinal articles') +
  159. theme_bw() +
  160. theme(text = element_text(size = 12, family = 'sans'),
  161. axis.title = element_text(face = 'bold'))
  163. ggsave('Duration.pdf')
  165. #NEUROIMAGING TECHNIQUES (Fig. 3e, 3f, & 3g) ====
  167. #total frequency and percentage
  168. neuro <- data.frame(neuro = sort(unique(srdata$neuroimaging[1:942])),
  169. frequency = as.vector(table(srdata$neuroimaging[1:942])),
  170. percentage = as.vector(prop.table(table(srdata$neuroimaging[1:942]))*100))
  172. #pie chart
  173. ggplot(neuro, aes(x = '', y = percentage, fill = reorder(neuro, -percentage))) +
  174. geom_bar(stat = 'identity', color = 'black') +
  175. coord_polar('y', start = 0) +
  176. geom_text(aes(label = paste0(c(round(percentage, digits = 2)), '%')),
  177. position = position_stack(vjust = 0.5), size = 3) +
  178. scale_fill_manual(values = brewer.pal(9, 'Pastel1')[c(1,2,5)]) +
  179. theme_classic() +
  180. theme(text = element_text(size = 15, family = 'sans'),
  181. axis.title = element_blank(),
  182. axis.line = element_blank(),
  183. axis.text = element_blank(),
  184. axis.ticks = element_blank(),
  185. legend.position = 'bottom',
  186. legend.title = element_blank())
  188. ggsave('Neuroimaging.pdf')
  190. #bar plot - design & year
  191. dum <- data.frame(table(srdata$year[1:942],srdata$neuroimaging[1:942])) #frequency of neuroimaging technique per year
  192. dum <- rbind(dum[1:3, ], data.frame(Var1 = '1984', Var2 = 'MEG', Freq = 0), dum[4:126, ]) #add missing year ('1984')
  193. dum$Var1 <- factor(dum$Var1, levels = unique(dum$Var1)) #reorder year ('1984' between '1983' and '1985')
  194. dum$Var2 <- factor(dum$Var2, levels = c('EEG','MEG','M/EEG')) #reorder neuroimaging techniques (highest to lowest frequency)
  196. ggplot(dum, aes(x = Var1, y = Freq, fill = Var2)) +
  197. geom_col(color = 'black', width = 0.75, size = 0.25) +
  198. scale_x_discrete(expand = c(0,0.5), breaks = seq(1979,2023,2), name = 'Publication year') +
  199. scale_y_continuous(expand = c(0,0,0.05,0),breaks = seq(0,160,5),name = 'Number of articles') +
  200. scale_fill_manual(values = brewer.pal(9, 'Pastel1')[c(1,2,5)]) +
  201. theme_bw() +
  202. theme(text = element_text(size = 10, family = 'sans'),
  203. axis.title = element_text(size = 12, face = 'bold'),
  204. legend.position = c(0.08,0.92),
  205. legend.background = element_rect(colour = 'black', fill = 'white', linetype='solid', linewidth = 0.25),
  206. legend.title = element_blank())
  208. ggsave('Neuroimaging_Year.pdf')
  210. #ratio of neuroimaging technique to publications per year
  211. ratio <- data.frame(year = srdata$year[1:942], var = srdata$neuroimaging[1:942])
  212. ratio <- ratio %>%
  213. group_by(year) %>%
  214. summarise(total_studies = n(), var1 = sum(var == 'EEG'), var2 = sum(var == 'MEG'), var3 = sum(var == 'M/EEG'),
  215. ratio_var1 = var1 / total_studies, ratio_var2 = var2 / total_studies, ratio_var3 = var3 / total_studies)
  217. ratio <- ratio %>%
  218. pivot_longer(cols = starts_with('var'), names_to = 'var', values_to = 'value') %>%
  219. mutate(ratio = case_when(var == 'var1' ~ ratio_var1, var == 'var2' ~ ratio_var2, var == 'var3' ~ ratio_var3)) %>%
  220. select(year, ratio, var)
  222. ggplot(ratio, aes(x = year, y = ratio, group = var)) +
  223. geom_line(aes(color = var)) +
  224. geom_point(aes(color = var)) +
  225. scale_x_continuous(expand = c(0,0.5), breaks = seq(1979,2023,2), name = 'Publication year') +
  226. scale_y_continuous(expand = c(0.05,0,0.05,0),breaks = seq(0,1,0.1),name = 'Neuroimaging technique ratio') +
  227. scale_color_manual(values = brewer.pal(9, 'Pastel1')[c(1,2,5)], labels = c('EEG','MEG','M/EEG')) +
  228. theme_bw() +
  229. theme(text = element_text(size = 10, family = 'sans'),
  230. axis.title = element_text(size = 12, face = 'bold'),
  231. legend.position = c(0.94,0.93),
  232. legend.background = element_rect(colour = 'black', fill = 'white', linetype='solid', linewidth = 0.25),
  233. legend.title = element_blank())
  235. ggsave('Neuroimaging_Ratio.pdf')
  237. #EXPERIMENTAL PARADIGM (Fig. 3h, 3i, & 3j) ====
  239. #total frequency and percentage
  240. paradigm <- data.frame(paradigm = sort(unique(srdata$paradigm[1:942])),
  241. frequency = as.vector(table(srdata$paradigm[1:942])),
  242. percentage = as.vector(prop.table(table(srdata$paradigm[1:942]))*100))
  244. #pie chart
  245. ggplot(paradigm, aes(x = '', y = percentage, fill = reorder(paradigm, -percentage))) +
  246. geom_bar(stat = 'identity', color = 'black') +
  247. coord_polar('y', start = 0) +
  248. geom_text(aes(label = paste0(c(round(percentage, digits = 2)), '%')),
  249. position = position_stack(vjust = 0.5), size = 3) +
  250. scale_fill_manual(values = brewer.pal(9, 'Pastel1')[c(1,2,5)]) +
  251. theme_classic() +
  252. theme(text = element_text(size = 15, family = 'sans'),
  253. axis.title = element_blank(),
  254. axis.line = element_blank(),
  255. axis.text = element_blank(),
  256. axis.ticks = element_blank(),
  257. legend.position = 'bottom',
  258. legend.title = element_blank())
  260. ggsave('paradigm.pdf')
  262. #bar plot - rs/task & year
  263. dum <- data.frame(table(srdata$year[1:942],srdata$paradigm[1:942])) #frequency of neuroimaging technique per year
  264. dum <- rbind(dum[1:3, ], data.frame(Var1 = '1984', Var2 = 'RS', Freq = 0), dum[4:126, ]) #add missing year ('1984')
  265. dum$Var1 <- factor(dum$Var1, levels = unique(dum$Var1)) #reorder year ('1984' between '1983' and '1985')
  266. dum$Var2 <- factor(dum$Var2, levels = c('RS','Task','Both')) #reorder neuroimaging techniques (highest to lowest frequency)
  268. ggplot(dum, aes(x = Var1, y = Freq, fill = Var2)) +
  269. geom_col(color = 'black', width = 0.75, size = 0.25) +
  270. scale_x_discrete(expand = c(0,0.5), breaks = seq(1979,2023,2), name = 'Publication year') +
  271. scale_y_continuous(expand = c(0,0,0.05,0),breaks = seq(0,160,5),name = 'Number of articles') +
  272. scale_fill_manual(values = brewer.pal(9, 'Pastel1')[c(1,2,5)]) +
  273. theme_bw() +
  274. theme(text = element_text(size = 10, family = 'sans'),
  275. axis.title = element_text(size = 12, face = 'bold'),
  276. legend.position = c(0.08,0.89),
  277. legend.background = element_rect(colour = 'black', fill = 'white', linetype='solid', linewidth = 0.25),
  278. legend.title = element_blank())
  280. ggsave('paradigm_Year.pdf')
  282. #ratio of paradigm to publications per year
  283. ratio <- data.frame(year = srdata$year[1:942], var = srdata$paradigm[1:942])
  284. ratio <- ratio %>%
  285. group_by(year) %>%
  286. summarise(total_studies = n(), var1 = sum(var == 'RS'), var2 = sum(var == 'Task'), var3 = sum(var == 'Both'),
  287. ratio_var1 = var1 / total_studies, ratio_var2 = var2 / total_studies, ratio_var3 = var3 / total_studies)
  289. ratio <- ratio %>%
  290. pivot_longer(cols = starts_with('var'), names_to = 'var', values_to = 'value') %>%
  291. mutate(ratio = case_when(var == 'var1' ~ ratio_var1, var == 'var2' ~ ratio_var2, var == 'var3' ~ ratio_var3)) %>%
  292. select(year, ratio, var)
  294. ggplot(ratio, aes(x = year, y = ratio, group = var)) +
  295. geom_line(aes(color = var)) +
  296. geom_point(aes(color = var)) +
  297. scale_x_continuous(expand = c(0,0.5), breaks = seq(1979,2023,2), name = 'Publication year') +
  298. scale_y_continuous(expand = c(0.05,0,0.05,0),breaks = seq(0,1,0.1),name = 'paradigm ratio') +
  299. scale_color_manual(values = brewer.pal(9, 'Pastel1')[c(1,2,5)], labels = c('RS','Task','Both')) +
  300. theme_bw() +
  301. theme(text = element_text(size = 10, family = 'sans'),
  302. axis.title = element_text(size = 12, face = 'bold'),
  303. legend.position = c(0.93,0.89),
  304. legend.background = element_rect(colour = 'black', fill = 'white', linetype='solid', linewidth = 0.25),
  305. legend.title = element_blank())
  307. ggsave('paradigm_Ratio.pdf')
  309. #RESTING-STATE (Fig. 3k) ====
  311. #total frequency and percentage
  312. rs <- na.omit(srdata$paradigm_spec) #remove empty rows
  313. rs <- rs[rs %in% c('EO','EC','EO/EC','N/A')] #only keep values related to resting-state
  314. rs <- data.frame(rs = sort(unique(rs)),
  315. frequency = as.vector(table(rs)),
  316. percentage = as.vector(prop.table(table(rs))*100))
  318. #pie chart
  319. ggplot(rs, aes(x = '', y = percentage, fill = reorder(rs, -percentage))) +
  320. geom_bar(stat = 'identity', color = 'black') +
  321. coord_polar('y', start = 0) +
  322. geom_text(aes(label = paste0(c(round(percentage, digits = 2)), '%')),
  323. position = position_stack(vjust = 0.5), size = 3) +
  324. scale_fill_manual(values = brewer.pal(9, 'Pastel1')[c(1,2,5,4)]) +
  325. theme_classic() +
  326. theme(text = element_text(size = 15, family = 'sans'),
  327. axis.title = element_blank(),
  328. axis.line = element_blank(),
  329. axis.text = element_blank(),
  330. axis.ticks = element_blank(),
  331. legend.position = 'bottom',
  332. legend.title = element_blank())
  334. ggsave('RestingState.pdf')
  336. #EXPERIMENTAL TASK (Supp. Table 3) ====
  338. #total frequency and percentage
  339. task <- na.omit(srdata$paradigm_spec) #remove empty rows
  340. task <- task[!(task %in% c('EO','EC','EO/EC','N/A'))] #remove values related to resting-state
  341. frequency <- table(task) #calculate frequency
  343. #write table with tasks, frequency, and percentage
  344. task <- data.frame(task = as.factor(names(frequency)),
  345. frequency = as.vector(frequency),
  346. percentage = as.vector(prop.table(frequency)*100))
  348. write_xlsx(task,'Experimental_Task.xlsx')
  350. #POPULATION (Fig. 4a, 4b, & 4c) ====
  352. #re-organize aging, MCI, and dementia columns
  353. aging <- ifelse(is.na(srdata$aging[1:942]), 0, 'Healthy aging') #remove empty rows
  354. mci <- ifelse(is.na(srdata$mci[1:942]), 0, 'MCI') #remove empty rows
  355. dementia <- ifelse(is.na(srdata$dementia[1:942]), 0, 'Dementia') #remove empty rows
  356. pall <- apply(cbind(aging, mci, dementia), 1, function(x) max(x[x != '0']))
  358. #total frequency and percentage
  359. frequency <- table(pall)
  360. all <- data.frame(all = as.factor(names(frequency)), #total frequency and percentage
  361. frequency = as.vector(frequency),
  362. percentage = as.vector(prop.table(frequency)*100))
  364. #pie chart
  365. ggplot(all, aes(x = '', y = percentage, fill = reorder(all, -percentage))) +
  366. geom_bar(stat = 'identity', color = 'black') +
  367. coord_polar('y', start = 0) +
  368. geom_text(aes(label = paste0(c(round(percentage, digits = 2)), '%')),
  369. position = position_stack(vjust = 0.5), size = 3) +
  370. scale_fill_manual(values = brewer.pal(9, 'Pastel1')[c(1,2,5)]) +
  371. theme_classic() +
  372. theme(text = element_text(size = 15, family = 'sans'),
  373. axis.title = element_blank(),
  374. axis.line = element_blank(),
  375. axis.text = element_blank(),
  376. axis.ticks = element_blank(),
  377. legend.position = 'bottom',
  378. legend.title = element_blank())
  380. ggsave('Population.pdf')
  382. #bar plot - population & year
  383. dum <- data.frame(table(srdata$year[1:942],Var2 = pall)) #frequency per year
  384. dum <- rbind(dum[1:3, ], data.frame(Var1 = '1984', Var2 = 'Dementia', Freq = 0), dum[4:126, ]) #add missing year ('1984')
  385. dum$Var1 <- factor(dum$Var1, levels = unique(dum$Var1)) #reorder year
  387. ggplot(dum, aes(x = Var1, y = Freq, fill = Var2)) +
  388. geom_col(color = 'black', width = 0.75, size = 0.25) +
  389. scale_x_discrete(expand = c(0,0.75), breaks = seq(1979,2023,2), name = 'Publication year') +
  390. scale_y_continuous(expand = c(0,0,0.05,0),breaks = seq(0,160,5),name = 'Number of articles') +
  391. scale_fill_manual(values = brewer.pal(9, 'Pastel1')[c(1,2,5)]) +
  392. theme_bw() +
  393. theme(text = element_text(size = 10, family = 'sans'),
  394. axis.title = element_text(size = 12, face = 'bold'),
  395. legend.position = c(0.11,0.89),
  396. legend.background = element_rect(colour = 'black', fill = 'white', linetype='solid', linewidth = 0.25),
  397. legend.title = element_blank())
  399. ggsave('Population_Year.pdf')
  401. #ratio of population to publications per year
  402. ratio <- data.frame(year = srdata$year[1:942], var = pall)
  403. ratio <- ratio %>%
  404. group_by(year) %>%
  405. summarise(total_studies = n(), var1 = sum(var == 'Dementia'), var2 = sum(var == 'MCI'), var3 = sum(var == 'Healthy aging'),
  406. ratio_var1 = var1 / total_studies, ratio_var2 = var2 / total_studies, ratio_var3 = var3 / total_studies)
  408. ratio <- ratio %>%
  409. pivot_longer(cols = starts_with('var'), names_to = 'var', values_to = 'value') %>%
  410. mutate(ratio = case_when(var == 'var1' ~ ratio_var1, var == 'var2' ~ ratio_var2, var == 'var3' ~ ratio_var3)) %>%
  411. select(year, ratio, var)
  413. ggplot(ratio, aes(x = year, y = ratio, group = var)) +
  414. geom_line(aes(color = var)) +
  415. geom_point(aes(color = var)) +
  416. scale_x_continuous(expand = c(0,0.5), breaks = seq(1979,2023,2), name = 'Publication year') +
  417. scale_y_continuous(expand = c(0.05,0,0.05,0),breaks = seq(0,1,0.1),name = 'Population ratio') +
  418. scale_color_manual(values = brewer.pal(9, 'Pastel1')[c(1,2,5)], labels = c('Dementia', 'MCI','Healthy aging')) +
  419. theme_bw() +
  420. theme(text = element_text(size = 10, family = 'sans'),
  421. axis.title = element_text(size = 12, face = 'bold'),
  422. legend.position = c(0.89,0.89),
  423. legend.background = element_rect(colour = 'black', fill = 'white', linetype='solid', linewidth = 0.25),
  424. legend.title = element_blank())
  426. ggsave('Population_Ratio.pdf')
  428. #DEMENTIA (Fig. 4d & 4e) ====
  430. #total frequency and percentage
  431. dementia <- data.frame(aging = sort(unique(srdata$dementia[1:942])),
  432. frequency = as.vector(table(srdata$dementia[1:942])),
  433. percentage = as.vector(prop.table(table(srdata$dementia[1:942]))*100))
  435. #bar plot
  436. popu <- data.frame(table(srdata$neuroimaging,srdata$dementia)) #number of EEG + MEG studies
  437. popu <- popu[popu$Freq >= 10, ] #remove single studies
  438. total_frequency <- aggregate(Freq ~ Var2, data = popu, FUN = sum)
  439. ordered_groups <- total_frequency$Var2[order(total_frequency$Freq, decreasing = TRUE)]
  440. popu$Var2 <- factor(popu$Var2, levels = ordered_groups)
  442. ggplot(popu, aes(x = Var2, y = Freq, fill = Var1)) +
  443. geom_col(stat = 'identity', color = 'black', width = 0.75, size = 0.25) +
  444. scale_x_discrete(expand = c(0,0.5), name = 'Dementia',
  445. labels = c('ADD vs HC', 'ADD vs MCI vs HC', 'ADD', 'ADD vs YHC vs OHC', 'ADD vs aMCI vs HC', 'ADD vs VD vs HC')) +
  446. scale_y_continuous(expand = c(0,0,0.05,0), breaks = seq(0,200,5), name = 'Number of articles') +
  447. scale_fill_brewer(palette = 'Pastel1') +
  448. theme_bw() +
  449. theme(text = element_text(size = 10, family = 'sans'),
  450. axis.title = element_text(size = 12, face = 'bold'),
  451. legend.position = c(0.93,0.94),
  452. legend.background = element_rect(colour = 'black', fill = 'white', linetype='solid', linewidth = 0.25),
  453. legend.title = element_blank())
  455. ggsave('Dementia.pdf')
  457. #bar plot - types of dementia
  458. type <- data.frame(group = na.omit(srdata$dementia)) #all groups
  459. type$add <- ifelse(grepl('ADD', type$group), 'ADD', NA) #Alzheimer's disease
  460. type$pdd <- ifelse(grepl('PDD', type$group), 'PDD', NA) #Parkinson's disease
  461. type$ftd <- ifelse(grepl('FTD', type$group), 'FTD', NA) #Frontotemporal dementia
  462. type$dlb <- ifelse(grepl('DLB', type$group), 'DLB', NA) #Dementia with Lewy bodies
  463. type$vd <- ifelse(grepl('VD', type$group), 'VD', NA) #Vascular dementia
  464. type$deu <- ifelse(grepl('DEM', type$group), 'DEM', NA) #Dementia (unknown etiology)
  465. type$other <- ifelse(!grepl('[ADD|PDD|FTD|DLB|VD|DEM]', type$group), 'OTHER', NA) #other types
  467. type_freq <- data.frame(dem = c('ADD','PDD','FTD','DLB','VD','DEU'),
  468. frequency = c(table(type$add),table(type$pdd),table(type$ftd),table(type$dlb),table(type$vd),table(type$deu)))
  470. ggplot(type_freq, aes(x = reorder(dem, -frequency), y = frequency, fill = total)) +
  471. geom_col(color = 'black', fill = '#FBB4AE', width = 0.75, size = 0.25) +
  472. scale_x_discrete(expand = c(0,0.5), name = 'Type') +
  473. scale_y_continuous(expand = c(0,0,0.05,0),breaks = seq(0,500,30),name = 'Number of publications') +
  474. theme_bw() +
  475. theme(text = element_text(size = 12, family = 'sans'),
  476. axis.title = element_text(face = 'bold'))
  478. ggsave('Dementia_Type.pdf')
  480. #HEALTHY AGING (Fig. 4f) ====
  482. #total frequency and percentage
  483. aging <- data.frame(aging = sort(unique(srdata$aging[1:942])),
  484. frequency = as.vector(table(srdata$aging[1:942])),
  485. percentage = as.vector(prop.table(table(srdata$aging[1:942]))*100))
  487. #bar plot
  488. popu <- data.frame(table(srdata$neuroimaging,srdata$aging)) #number of EEG + MEG studies
  489. popu <- popu[popu$Freq >= 2, ] #remove single studies
  490. total_frequency <- aggregate(Freq ~ Var2, data = popu, FUN = sum)
  491. ordered_groups <- total_frequency$Var2[order(total_frequency$Freq, decreasing = TRUE)]
  492. popu$Var2 <- factor(popu$Var2, levels = ordered_groups)
  494. ggplot(popu, aes(x = Var2, y = Freq, fill = Var1)) +
  495. geom_bar(stat = 'identity', color = 'black', width = 0.75, size = 0.25) +
  496. scale_x_discrete(expand = c(0,0.5), name = 'Healthy aging',
  497. labels = c('Y vs O', 'O', 'Lifespan', 'Y vs M vs O', 'M vs O')) +
  498. scale_y_continuous(expand = c(0,0,0.05,0), breaks = seq(0,160,5), name = 'Number of articles') +
  499. scale_fill_manual(values = brewer.pal(9, 'Pastel1')[c(1,2,5)], labels = c('EEG', 'MEG', 'M/EEG')) +
  500. theme_bw() +
  501. theme(text = element_text(size = 10, family = 'sans'),
  502. axis.title = element_text(size = 12, face = 'bold'),
  503. legend.position = c(0.92,0.92),
  504. legend.background = element_rect(colour = 'black', fill = 'white', linetype='solid', linewidth = 0.25),
  505. legend.title = element_blank())
  507. ggsave('HealthyAging.pdf')
  509. #MILD COGNITIVE IMPAIRMENT (Fig. 4g) ====
  511. #total frequency and percentage
  512. mci <- data.frame(aging = sort(unique(srdata$mci[1:942])),
  513. frequency = as.vector(table(srdata$mci[1:942])),
  514. percentage = as.vector(prop.table(table(srdata$mci[1:942]))*100))
  515. #bar plot
  516. popu <- data.frame(table(srdata$neuroimaging,srdata$mci)) #number of EEG + MEG studies
  517. popu <- popu[popu$Freq >= 5, ] #remove single studies
  518. total_frequency <- aggregate(Freq ~ Var2, data = popu, FUN = sum)
  519. ordered_groups <- total_frequency$Var2[order(total_frequency$Freq, decreasing = TRUE)]
  520. popu$Var2 <- factor(popu$Var2, levels = ordered_groups)
  522. ggplot(popu, aes(x = Var2, y = Freq, fill = Var1)) +
  523. geom_col(stat = 'identity', color = 'black', width = 0.75, size = 0.25) +
  524. scale_x_discrete(expand = c(0,0.5), name = 'Mild cognitive impairment',
  525. labels = c('MCI vs HC', 'aMCI vs HC', 'ADMCI vs HC', 'MCI vs YHC vs OHC','Stable vs Progressive MCI', 'MCI vs SCD vs HC')) +
  526. scale_y_continuous(expand = c(0,0,0.05,0), breaks = seq(0,200,5), name = 'Number of articles') +
  527. scale_fill_brewer(palette = 'Pastel1') +
  528. theme_bw() +
  529. theme(text = element_text(size = 10, family = 'sans'),
  530. axis.title = element_text(size = 12, face = 'bold'),
  531. legend.position = c(0.93,0.94),
  532. legend.background = element_rect(colour = 'black', fill = 'white', linetype='solid', linewidth = 0.25),
  533. legend.title = element_blank())
  535. ggsave('MCI.pdf')
  537. #bar plot - types of mci
  538. type <- data.frame(comp = na.omit(srdata$mci))
  539. type$mci <- ifelse(!grepl('[AD|PD|a|STABLE]', type$comp), 'x', NA) #MCI
  540. type$admci <- ifelse(grepl('ADMCI', type$comp), 'x', NA) #ADMCI
  541. type$pdmci <- ifelse(grepl('PDMCI', type$comp), 'x', NA) #PDMCI
  542. type$amci <- ifelse(grepl('aMCI', type$comp), 'x', NA) #aMCI
  543. type$promci <- ifelse(grepl('PROGREMCI', type$comp), 'x', NA) #Progressive MCI
  545. type_freq <- data.frame(mci = c('MCI','ADMCI','PDMCI','aMCI','PROMCI'),
  546. frequency = c(table(type$mci),table(type$admci),table(type$pdmci),
  547. table(type$amci),table(type$promci)))
  549. ggplot(type_freq, aes(x = reorder(mci, -frequency), y = frequency)) +
  550. geom_col(color = 'black', fill = '#FBB4AE', width = 0.75, size = 0.25) +
  551. scale_x_discrete(expand = c(0,0.5), name = 'Type') +
  552. scale_y_continuous(expand = c(0,0,0.05,0),breaks = seq(0,500,5),name = 'Number of publications') +
  553. theme_bw() +
  554. theme(text = element_text(size = 12, family = 'sans'),
  555. axis.title = element_text(face = 'bold'))
  557. ggsave('Dementia_Type.pdf')
  559. #ANALYSIS (Fig. 5a, 5b, & 5c) ====
  561. #total frequency and percentage
  562. analysis <- data.frame(analysis = sort(unique(srdata$analysis[1:942])),
  563. frequency = as.vector(table(srdata$analysis[1:942])),
  564. percentage = as.vector(prop.table(table(srdata$analysis[1:942]))*100))
  566. #pie chart
  567. ggplot(analysis, aes(x = '', y = percentage, fill = reorder(analysis, -percentage))) +
  568. geom_bar(stat = 'identity', color = 'black') +
  569. coord_polar('y', start = 0) +
  570. geom_text(aes(label = paste0(c(round(percentage, digits = 2)), '%')),
  571. position = position_stack(vjust = 0.5), size = 3) +
  572. scale_fill_manual(values = brewer.pal(9, 'Pastel1')[c(1,2,5,4,7,9)]) +
  573. theme_classic() +
  574. theme(text = element_text(size = 15, family = 'sans'),
  575. axis.title = element_blank(),
  576. axis.line = element_blank(),
  577. axis.text = element_blank(),
  578. axis.ticks = element_blank(),
  579. legend.position = 'bottom',
  580. legend.title = element_blank())
  582. ggsave('Analysis.pdf')
  584. #bar plot - analysis & year
  585. dum <- data.frame(table(srdata$year[1:942],srdata$analysis[1:942]))
  586. dum <- rbind(dum[1:3, ], data.frame(Var1 = '1984', Var2 = 'Complexity', Freq = 0), dum[4:252, ]) #add missing year ('1984')
  587. dum$Var1 <- factor(dum$Var1, levels = unique(dum$Var1)) #reorder year ('1984' between '1983' and '1985')
  588. dum$Var2 <- factor(dum$Var2, levels = c('Spectral','Event-related','Functional connectivity',
  589. 'Mixed','Complexity','Other')) #reorder analysis (highest to lowest frequency)
  590. ggplot(dum, aes(x = Var1, y = Freq, fill = Var2)) +
  591. geom_col(color = 'black', width = 0.75, size = 0.25) +
  592. scale_x_discrete(expand = c(0,0.5), breaks = seq(1979,2023,2), name = 'Publication year') +
  593. scale_y_continuous(expand = c(0,0,0.05,0), breaks = seq(0,800,10), name = 'Number of articles') +
  594. scale_fill_manual(values = brewer.pal(9, 'Pastel1')[c(1,2,5,4,7,9)]) +
  595. theme_bw() +
  596. theme(text = element_text(size = 10, family = 'sans'),
  597. axis.title = element_text(size = 12, face = 'bold'),
  598. legend.position = c(0.15,0.81),
  599. legend.background = element_rect(colour = 'black', fill = 'white', linetype='solid', linewidth = 0.25),
  600. legend.title = element_blank())
  602. ggsave('Analysis_Year.pdf')
  604. #ratio of analysis to publications per year
  605. ratio <- data.frame(year = srdata$year[1:942], var = srdata$analysis[1:942])
  606. ratio <- ratio %>%
  607. group_by(year) %>%
  608. summarise(total_studies = n(), var1 = sum(var == 'Spectral'), var2 = sum(var == 'Event-related'), var3 = sum(var == 'Functional connectivity'), var4 = sum(var == 'Mixed'), var5 = sum(var == 'Complexity'), var6 = sum(var == 'Other'),
  609. ratio_var1 = var1 / total_studies, ratio_var2 = var2 / total_studies, ratio_var3 = var3 / total_studies, ratio_var4 = var4 / total_studies, ratio_var5 = var5 / total_studies, ratio_var6 = var6 / total_studies)
  611. ratio <- ratio %>%
  612. pivot_longer(cols = starts_with('var'), names_to = 'var', values_to = 'value') %>%
  613. mutate(ratio = case_when(var == 'var1' ~ ratio_var1, var == 'var2' ~ ratio_var2, var == 'var3' ~ ratio_var3, var == 'var4' ~ ratio_var4, var == 'var5' ~ ratio_var5, var == 'var6' ~ ratio_var6)) %>%
  614. select(year, ratio, var)
  616. ggplot(ratio, aes(x = year, y = ratio, group = var)) +
  617. geom_line(aes(color = var)) +
  618. geom_point(aes(color = var)) +
  619. scale_x_continuous(expand = c(0,0.5), breaks = seq(1979,2023,2), name = 'Publication year') +
  620. scale_y_continuous(expand = c(0.05,0,0.05,0),breaks = seq(0,1,0.1),name = 'Analysis ratio') +
  621. scale_color_manual(values = brewer.pal(9, 'Pastel1')[c(1,2,5,4,7,9)], labels = c('Spectral','Event-related','Functional connectivity',
  622. 'Mixed','Complexity','Other')) +
  623. theme_bw() +
  624. theme(text = element_text(size = 10, family = 'sans'),
  625. axis.title = element_text(size = 12, face = 'bold'),
  626. legend.position = c(0.85,0.81),
  627. legend.background = element_rect(colour = 'black', fill = 'white', linetype='solid', linewidth = 0.25),
  628. legend.title = element_blank())
  630. ggsave('Analysis_Ratio.pdf')
  632. #FEATURES (Fig. 5d & Supp. Table 4) ====
  634. #total frequency and percentage - spectral
  635. spectral <- data.frame(spectral = sort(unique(srdata$spectral)), #total frequency and percentage
  636. frequency = as.vector(table(srdata$spectral)),
  637. percentage = as.vector(prop.table(table(srdata$spectral))*100))
  639. write_xlsx(spectral,'Spectral.xlsx')
  641. #total frequency and percentage - complexity
  642. complexity <- data.frame(complexity = sort(unique(srdata$complexity)), #total frequency and percentage
  643. frequency = as.vector(table(srdata$complexity)),
  644. percentage = as.vector(prop.table(table(srdata$complexity))*100))
  646. write_xlsx(complexity,'Complexity.xlsx')
  648. #total frequency and percentage - connectivity
  649. connectivity <- data.frame(connectivity = sort(unique(srdata$connectivity)), #total frequency and percentage
  650. frequency = as.vector(table(srdata$connectivity)),
  651. percentage = as.vector(prop.table(table(srdata$connectivity))*100))
  653. write_xlsx(connectivity,'Connectivity.xlsx')
  655. #total frequency and percentage - connectivity
  656. event_related <- data.frame(event_related = sort(unique(srdata$event_related)), #total frequency and percentage
  657. frequency = as.vector(table(srdata$event_related)),
  658. percentage = as.vector(prop.table(table(srdata$event_related))*100))
  660. write_xlsx(event_related,'Event_Related.xlsx')
  662. #total frequency and percentage - other
  663. other <- data.frame(other = sort(unique(srdata$other)), #total frequency and percentage
  664. frequency = as.vector(table(srdata$other)),
  665. percentage = as.vector(prop.table(table(srdata$other))*100))
  667. write_xlsx(other,'Other.xlsx')
  669. #select 3 most used features
  670. spectral <- spectral[order(spectral$frequency,decreasing = TRUE),]
  671. spectral <- spectral[1:3,]
  672. complexity <- complexity[order(complexity$frequency,decreasing = TRUE),]
  673. complexity <- complexity[1:3,]
  674. connectivity <- connectivity[order(connectivity$frequency,decreasing = TRUE),]
  675. connectivity <- connectivity[1:3,]
  676. event_related <- event_related[order(event_related$frequency,decreasing = TRUE),]
  677. event_related <- event_related[1:3,]
  678. other <- other[order(other$frequency,decreasing = TRUE),]
  679. other <- other[1:3,]
  681. #bar plot
  682. features <- data.frame(feature = c(spectral$spectral,complexity$complexity,connectivity$connectivity,event_related$event_related,other$other),
  683. frequency = c(spectral$frequency,complexity$frequency,connectivity$frequency,event_related$frequency,other$frequency),
  684. type = c('Spectral','Spectral','Spectral','Complexity','Complexity','Complexity','Functional connectivity','Functional connectivity','Functional connectivity','Event-related','Event-related','Event-related','Other','Other','Other'))
  685. ggplot(features, aes(x = reorder(feature,-frequency), y = frequency, fill = type)) +
  686. geom_bar(stat = 'identity', color = 'black', width = 0.75, size = 0.25) +
  687. scale_x_discrete(expand = c(0,0.5), name = 'Feature') +
  688. scale_y_continuous(expand = c(0,0,0.05,0), breaks = seq(0,300,10), name = 'Number of articles') +
  689. scale_fill_manual(values = brewer.pal(9, 'Pastel1')[c(1,2,5,4,9)],
  690. labels = c('Spectral','Event-related','Functional connectivity','Complexity','Other')) +
  691. theme_bw() +
  692. theme(text = element_text(size = 10, family = 'sans'),
  693. axis.title = element_text(size = 12, face = 'bold'),
  694. legend.position = c(0.88,0.88),
  695. legend.background = element_rect(colour = 'black', fill = 'white', linetype='solid', linewidth = 0.25),
  696. legend.title = element_blank())
  698. ggsave('Features.pdf')
  700. #MACHINE LEARNING (Fig. 5e, 5f, & 5g) ====
  702. #total frequency and percentage
  703. machine <- data.frame(machine = unique(srdata$machine_learning[1:942]),
  704. frequency = as.vector(table(srdata$machine_learning)),
  705. percentage = as.vector(prop.table(table(srdata$machine_learning))*100))
  707. #pie chart
  708. ggplot(machine, aes(x = '', y = percentage, fill = machine)) +
  709. geom_bar(stat = 'identity', color = 'black') +
  710. coord_polar('y', start = 0) +
  711. geom_text(aes(label = paste0(c(round(percentage, digits = 2)), '%')),
  712. position = position_stack(vjust = 0.5), size = 3) +
  713. scale_fill_brewer(palette = 'Pastel1') +
  714. theme_classic() +
  715. theme(text = element_text(size = 15, family = 'sans'),
  716. axis.title = element_blank(),
  717. axis.line = element_blank(),
  718. axis.text = element_blank(),
  719. axis.ticks = element_blank(),
  720. legend.position = 'bottom',
  721. legend.title = element_blank())
  723. ggsave('MachineLearning.pdf')
  725. #bar plot - machine learning & year
  726. dum <- data.frame(table(srdata$year[1:942],srdata$machine_learning[1:942])) #frequency per year
  727. dum <- rbind(dum[1:3, ], data.frame(Var1 = '1984', Var2 = 'Yes', Freq = 0), dum[4:126, ]) #add missing year ('1984')
  728. dum$Var1 <- factor(dum$Var1, levels = unique(dum$Var1)) #reorder year
  729. dum <- dum[complete.cases(dum), ] #remove empty rows
  731. ggplot(dum, aes(x = Var1, y = Freq, fill = Var2)) +
  732. geom_col(color = 'black', width = 0.75, size = 0.25) +
  733. scale_x_discrete(expand = c(0,0.5), breaks = seq(1979,2023,2), name = 'Year') +
  734. scale_y_continuous(expand = c(0,0,0.05,0), breaks = seq(0,800,10), name = 'Number of publications') +
  735. scale_fill_brewer(palette = 'Pastel1', direction = 1, labels = c('No ML','ML')) +
  736. theme_bw() +
  737. theme(text = element_text(size = 10, family = 'sans'),
  738. axis.title = element_text(size = 12, face = 'bold'),
  739. legend.position = c(0.07,0.94),
  740. legend.background = element_rect(colour = 'black', fill = 'white', linetype='solid', linewidth = 0.25),
  741. legend.title = element_blank())
  743. ggsave('MachineLearning_Year.pdf')
  745. #ratio of machine learning studies to publications per year
  746. ratio <- data.frame(year = srdata$year[1:942], var = srdata$machine_learning[1:942])
  747. ratio <- ratio %>%
  748. group_by(year) %>%
  749. summarise(total_studies = n(), var1 = sum(var == 'No'), var2 = sum(var == 'Yes'),
  750. ratio_var1 = var1 / total_studies, ratio_var2 = var2 / total_studies)
  752. ratio <- ratio %>%
  753. pivot_longer(cols = starts_with('var'), names_to = 'var', values_to = 'value') %>%
  754. mutate(ratio = ifelse(var == 'var1', ratio_var1, ratio_var2)) %>%
  755. select(year, ratio, var)
  757. ggplot(ratio, aes(x = year, y = ratio, group = var)) +
  758. geom_line(aes(color = var)) +
  759. geom_point(aes(color = var)) +
  760. scale_x_continuous(expand = c(0,0.5), breaks = seq(1979,2023,2), name = 'Publication year') +
  761. scale_y_continuous(expand = c(0.05,0,0.05,0),breaks = seq(0,1,0.1),name = 'Machine learning ratio') +
  762. scale_color_brewer(palette = 'Pastel1', labels = c('No ML', 'ML')) +
  763. theme_bw() +
  764. theme(text = element_text(size = 10, family = 'sans'),
  765. axis.title = element_text(size = 12, face = 'bold'),
  766. legend.position = c(0.9,0.5),
  767. legend.background = element_rect(colour = 'black', fill = 'white', linetype='solid', linewidth = 0.25),
  768. legend.title = element_blank())
  770. ggsave('MachineLearning_Ratio.pdf')
  772. #SOURCE RECONSTRUCTION (Fig. 5h, 5i, & 5j) ====
  774. #total frequency and percentage
  775. source <- data.frame(source = unique(srdata$source_reconstruction[1:942]),
  776. frequency = as.vector(table(srdata$source_reconstruction)),
  777. percentage = as.vector(prop.table(table(srdata$source_reconstruction))*100))
  779. #pie chart
  780. ggplot(source, aes(x = '', y = percentage, fill = source)) +
  781. geom_bar(stat = 'identity', color = 'black') +
  782. coord_polar('y', start = 0) +
  783. geom_text(aes(label = paste0(c(round(percentage, digits = 2)), '%')),
  784. position = position_stack(vjust = 0.5), size = 3) +
  785. scale_fill_brewer(palette = 'Pastel1') +
  786. theme_classic() +
  787. theme(text = element_text(size = 15, family = 'sans'),
  788. axis.title = element_blank(),
  789. axis.line = element_blank(),
  790. axis.text = element_blank(),
  791. axis.ticks = element_blank(),
  792. legend.position = 'bottom',
  793. legend.title = element_blank())
  795. ggsave('SourceReconstruction.pdf')
  797. #bar plot - neuroimaging & source reconstruction
  798. dum <- data.frame(table(srdata$neuroimaging[1:942],srdata$source_reconstruction[1:942]))
  799. ggplot(dum, aes(x = Var1, y = Freq, fill = Var2)) +
  800. geom_col(color = 'black', width = 0.75, size = 0.25) +
  801. scale_x_discrete(limits = c('EEG','MEG','M/EEG'), expand = c(0,0.5), name = 'Neuroimaging technique') +
  802. scale_y_continuous(expand = c(0,0,0.05,0), breaks = seq(0,800,40), name = 'Number of articles') +
  803. scale_fill_brewer(palette = 'Pastel1', labels = c('No SR', 'SR')) +
  804. theme_bw() +
  805. theme(text = element_text(size = 10, family = 'sans'),
  806. axis.title = element_text(size = 12, face = 'bold'),
  807. legend.position = c(0.9,0.9),
  808. legend.background = element_rect(colour = 'black', fill = 'white', linetype='solid', linewidth = 0.25),
  809. legend.title = element_blank())
  811. ggsave('SourceReconstruction_Year.pdf')
  813. #ratio of source reconstruction studies to year
  814. ratio <- data.frame(year = srdata$year[1:942], var = srdata$source_reconstruction[1:942])
  815. ratio <- ratio %>%
  816. group_by(year) %>%
  817. summarise(total_studies = n(), var1 = sum(var == 'No'), var2 = sum(var == 'Yes'),
  818. ratio_var1 = var1 / total_studies, ratio_var2 = var2 / total_studies)
  820. ratio <- ratio %>%
  821. pivot_longer(cols = starts_with('var'), names_to = 'var', values_to = 'value') %>%
  822. mutate(ratio = ifelse(var == 'var1', ratio_var1, ratio_var2)) %>%
  823. select(year, ratio, var)
  825. ggplot(ratio, aes(x = year, y = ratio, group = var)) +
  826. geom_line(aes(color = var)) +
  827. geom_point(aes(color = var)) +
  828. scale_x_continuous(expand = c(0,0.5), breaks = seq(1979,2023,2), name = 'Publication year') +
  829. scale_y_continuous(expand = c(0.05,0,0.05,0),breaks = seq(0,1,0.1),name = 'Source reconstruction ratio') +
  830. scale_color_brewer(palette = 'Pastel1', labels = c('No SR', 'SR')) +
  831. theme_bw() +
  832. theme(text = element_text(size = 10, family = 'sans'),
  833. axis.title = element_text(size = 12, face = 'bold'),
  834. legend.position = c(0.9,0.5),
  835. legend.background = element_rect(colour = 'black', fill = 'white', linetype='solid', linewidth = 0.25),
  836. legend.title = element_blank())
  838. ggsave('SourceReconstruction_Ratio.pdf')

code.r at commit c0f73c0, no license · at the source

Overview

Authors: Gemma Fernández-Rubio1, Peter Vuust1, Morten L. Kringelbach1,2,3, Leonardo Bonetti1,2,3
ORCID iDs: Gemma Fernández-Rubio, Leonardo Bonetti
  1. Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, & The Royal Academy of Music, Aarhus/Aalborg,Aarhus, Denmark
  2. Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford,Oxford, UK
  3. Department of Psychiatry, University of Oxford,Oxford, UK
Institutions: Aarhus University (Denmark); University of Oxford (United Kingdom)
Journal: n/a, volume 230, issue 8, article 146
Dates: received 19 May 2025; accepted 4 September 2025; published online 24 September 2025; in print 2025
Type: Research article · Language: English
License: none stated
Identifiers: DOI 10.1007/s00429-025-03012-5 · PMCID PMC12460461 · OpenAlex W4414472522
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), MEG (modality), human (organism), Alzheimer's / dementia (population), clinical / translational (subfield)
Keywords: Dementia, Mild cognitive impairment (MCI), Healthy aging, Electroencephalography (EEG), Magnetoencephalography (MEG)
MeSH: Aging*, Brain*, Cognitive Dysfunction*, Dementia*, Healthy Aging*, Aged, Electroencephalography, Humans, Magnetoencephalography (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Aarhus Universitet
Citations: cited by 9 papers (Europe PMC); 119 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (none stated) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

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gemmaferu/systematic-review-neurophysiology-aging

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: c0f73c077e339416fd099c9c1b749759cbed20ca, 13 September 2024
Languages: R (1)
Size: 2 files, 1 script
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
2 files
At the source: github.com/gemmaferu/systematic-review-neurophysiology-aging.git

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

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Data

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Read it in the paper: doi.org/10.1007/s00429-025-03012-5.

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Version 3, 28 September 2026

  • Publisher: n/a → Springer Science+Business Media

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 9 MeSH terms, 1 funder, 111 references.

Cite

This paper

Fernández-Rubio, G., Vuust, P., Kringelbach, M. L., & Bonetti, L. (2025). The neurophysiology of healthy and pathological aging: a comprehensive systematic review. Brain Structure & Function, 230(8), 146. https://doi.org/10.1007/s00429-025-03012-5

BibTeX

@article{fernandezrubio2025neurophysiology,
author = {Fernández-Rubio, Gemma and Vuust, Peter and Kringelbach, Morten L. and Bonetti, Leonardo},
title = {{The neurophysiology of healthy and pathological aging: a comprehensive systematic review}},
journal = {Brain Structure \& Function},
year = {2025},
volume = {230},
number = {8},
pages = {146},
publisher = {Springer Science+Business Media},
issn = {1863-2653},
doi = {10.1007/s00429-025-03012-5},
url = {https://doi.org/10.1007/s00429-025-03012-5},
pmcid = {PMC12460461}
}

RIS

TY - JOUR
AU - Fernández-Rubio, Gemma
AU - Vuust, Peter
AU - Kringelbach, Morten L.
AU - Bonetti, Leonardo
TI - The neurophysiology of healthy and pathological aging: a comprehensive systematic review
T2 - Brain Structure & Function
J2 - Brain Struct Funct
PY - 2025
DA - 2025
VL - 230
IS - 8
SP - 146
SN - 1863-2653
PB - Springer Science+Business Media
DO - 10.1007/s00429-025-03012-5
UR - https://doi.org/10.1007/s00429-025-03012-5
LA - en
ER -

CSL-JSON

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"id": "10.1007/s00429-025-03012-5",
"type": "article-journal",
"title": "The neurophysiology of healthy and pathological aging: a comprehensive systematic review",
"container-title": "Brain Structure & Function",
"author": [
{
"family": "Fernández-Rubio",
"given": "Gemma"
},
{
"family": "Vuust",
"given": "Peter"
},
{
"family": "Kringelbach",
"given": "Morten L."
},
{
"family": "Bonetti",
"given": "Leonardo"
}
],
"container-title-short": "Brain Struct Funct",
"volume": "230",
"issue": "8",
"page": "146",
"DOI": "10.1007/s00429-025-03012-5",
"PMCID": "PMC12460461",
"ISSN": "1863-2653",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.1007/s00429-025-03012-5",
"language": "en",
"issued": {
"date-parts": [
[
2025
]
]
}
}

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